diff --git a/Mask2Former/configs/ade20k/instance-segmentation/Base-ADE20K-InstanceSegmentation.yaml b/Mask2Former/configs/ade20k/instance-segmentation/Base-ADE20K-InstanceSegmentation.yaml new file mode 100644 index 0000000..50a1c13 --- /dev/null +++ b/Mask2Former/configs/ade20k/instance-segmentation/Base-ADE20K-InstanceSegmentation.yaml @@ -0,0 +1,61 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("ade20k_instance_train",) + TEST: ("ade20k_instance_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 160000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + MAX_SIZE_TRAIN: 2560 + MAX_SIZE_TEST: 2560 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (640, 640) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 640 # used in dataset mapper + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_instance" +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [320, 480, 640, 800, 960, 1120] + MAX_SIZE: 4480 + FLIP: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/ade20k/instance-segmentation/maskformer2_R50_bs16_160k.yaml b/Mask2Former/configs/ade20k/instance-segmentation/maskformer2_R50_bs16_160k.yaml new file mode 100644 index 0000000..e37bcfb --- /dev/null +++ b/Mask2Former/configs/ade20k/instance-segmentation/maskformer2_R50_bs16_160k.yaml @@ -0,0 +1,44 @@ +_BASE_: Base-ADE20K-InstanceSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 100 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: True + PANOPTIC_ON: True + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/configs/ade20k/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml b/Mask2Former/configs/ade20k/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml new file mode 100644 index 0000000..af03d4d --- /dev/null +++ b/Mask2Former/configs/ade20k/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml @@ -0,0 +1,18 @@ +_BASE_: ../maskformer2_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 200 diff --git a/Mask2Former/configs/ade20k/panoptic-segmentation/Base-ADE20K-PanopticSegmentation.yaml b/Mask2Former/configs/ade20k/panoptic-segmentation/Base-ADE20K-PanopticSegmentation.yaml new file mode 100644 index 0000000..559be07 --- /dev/null +++ b/Mask2Former/configs/ade20k/panoptic-segmentation/Base-ADE20K-PanopticSegmentation.yaml @@ -0,0 +1,61 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("ade20k_panoptic_train",) + TEST: ("ade20k_panoptic_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 160000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + MAX_SIZE_TRAIN: 2560 + MAX_SIZE_TEST: 2560 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (640, 640) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 640 # used in dataset mapper + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_panoptic" +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [320, 480, 640, 800, 960, 1120] + MAX_SIZE: 4480 + FLIP: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/ade20k/panoptic-segmentation/maskformer2_R50_bs16_160k.yaml b/Mask2Former/configs/ade20k/panoptic-segmentation/maskformer2_R50_bs16_160k.yaml new file mode 100644 index 0000000..82c0828 --- /dev/null +++ b/Mask2Former/configs/ade20k/panoptic-segmentation/maskformer2_R50_bs16_160k.yaml @@ -0,0 +1,44 @@ +_BASE_: Base-ADE20K-PanopticSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 150 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: True + PANOPTIC_ON: True + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/configs/ade20k/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml b/Mask2Former/configs/ade20k/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml new file mode 100644 index 0000000..af03d4d --- /dev/null +++ b/Mask2Former/configs/ade20k/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml @@ -0,0 +1,18 @@ +_BASE_: ../maskformer2_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 200 diff --git a/Mask2Former/configs/ade20k/semantic-segmentation/Base-ADE20K-SemanticSegmentation.yaml b/Mask2Former/configs/ade20k/semantic-segmentation/Base-ADE20K-SemanticSegmentation.yaml new file mode 100644 index 0000000..dcbba3e --- /dev/null +++ b/Mask2Former/configs/ade20k/semantic-segmentation/Base-ADE20K-SemanticSegmentation.yaml @@ -0,0 +1,61 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("ade20k_sem_seg_train",) + TEST: ("ade20k_sem_seg_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 160000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 512) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 512 + MAX_SIZE_TRAIN: 2048 + MAX_SIZE_TEST: 2048 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (512, 512) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 512 # used in dataset mapper + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [256, 384, 512, 640, 768, 896] + MAX_SIZE: 3584 + FLIP: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/ade20k/semantic-segmentation/maskformer2_R101_bs16_90k.yaml b/Mask2Former/configs/ade20k/semantic-segmentation/maskformer2_R101_bs16_90k.yaml new file mode 100644 index 0000000..49407b2 --- /dev/null +++ b/Mask2Former/configs/ade20k/semantic-segmentation/maskformer2_R101_bs16_90k.yaml @@ -0,0 +1,11 @@ +_BASE_: maskformer2_R50_bs16_160k.yaml +MODEL: + WEIGHTS: "R-101.pkl" + RESNETS: + DEPTH: 101 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used diff --git a/Mask2Former/configs/ade20k/semantic-segmentation/maskformer2_R50_bs16_160k.yaml b/Mask2Former/configs/ade20k/semantic-segmentation/maskformer2_R50_bs16_160k.yaml new file mode 100644 index 0000000..cd6d981 --- /dev/null +++ b/Mask2Former/configs/ade20k/semantic-segmentation/maskformer2_R50_bs16_160k.yaml @@ -0,0 +1,44 @@ +_BASE_: Base-ADE20K-SemanticSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 150 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: False + PANOPTIC_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_384_bs16_160k_res640.yaml b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_384_bs16_160k_res640.yaml new file mode 100644 index 0000000..f2c1964 --- /dev/null +++ b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_384_bs16_160k_res640.yaml @@ -0,0 +1,37 @@ +_BASE_: ../maskformer2_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_base_patch4_window12_384.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + MAX_SIZE_TRAIN: 2560 + MAX_SIZE_TEST: 2560 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (640, 640) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 640 # used in dataset mapper + FORMAT: "RGB" +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [320, 480, 640, 800, 960, 1120] + MAX_SIZE: 4480 + FLIP: True diff --git a/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_160k_res640.yaml b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_160k_res640.yaml new file mode 100644 index 0000000..68d7e83 --- /dev/null +++ b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_160k_res640.yaml @@ -0,0 +1,37 @@ +_BASE_: ../maskformer2_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_base_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + MAX_SIZE_TRAIN: 2560 + MAX_SIZE_TEST: 2560 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (640, 640) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 640 # used in dataset mapper + FORMAT: "RGB" +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [320, 480, 640, 800, 960, 1120] + MAX_SIZE: 4480 + FLIP: True diff --git a/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k_res640.yaml b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k_res640.yaml new file mode 100644 index 0000000..30d7bb0 --- /dev/null +++ b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k_res640.yaml @@ -0,0 +1,37 @@ +_BASE_: ../maskformer2_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 640 + MAX_SIZE_TRAIN: 2560 + MAX_SIZE_TEST: 2560 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (640, 640) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 640 # used in dataset mapper + FORMAT: "RGB" +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [320, 480, 640, 800, 960, 1120] + MAX_SIZE: 4480 + FLIP: True diff --git a/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_small_bs16_160k.yaml b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_small_bs16_160k.yaml new file mode 100644 index 0000000..f75a51e --- /dev/null +++ b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_small_bs16_160k.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_small_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_160k.yaml b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_160k.yaml new file mode 100644 index 0000000..b0bbc38 --- /dev/null +++ b/Mask2Former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_160k.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_160k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 6, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_tiny_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/cityscapes/instance-segmentation/Base-Cityscapes-InstanceSegmentation.yaml b/Mask2Former/configs/cityscapes/instance-segmentation/Base-Cityscapes-InstanceSegmentation.yaml new file mode 100644 index 0000000..28833e7 --- /dev/null +++ b/Mask2Former/configs/cityscapes/instance-segmentation/Base-Cityscapes-InstanceSegmentation.yaml @@ -0,0 +1,61 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + NORM: "SyncBN" # use syncbn for cityscapes dataset + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("cityscapes_fine_instance_seg_train",) + TEST: ("cityscapes_fine_instance_seg_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 90000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1024) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 1024 + MAX_SIZE_TRAIN: 4096 + MAX_SIZE_TEST: 2048 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (512, 1024) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: -1 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_instance" +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792] + MAX_SIZE: 4096 + FLIP: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/cityscapes/instance-segmentation/maskformer2_R101_bs16_90k.yaml b/Mask2Former/configs/cityscapes/instance-segmentation/maskformer2_R101_bs16_90k.yaml new file mode 100644 index 0000000..1eb38da --- /dev/null +++ b/Mask2Former/configs/cityscapes/instance-segmentation/maskformer2_R101_bs16_90k.yaml @@ -0,0 +1,11 @@ +_BASE_: maskformer2_R50_bs16_90k.yaml +MODEL: + WEIGHTS: "R-101.pkl" + RESNETS: + DEPTH: 101 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used diff --git a/Mask2Former/configs/cityscapes/instance-segmentation/maskformer2_R50_bs16_90k.yaml b/Mask2Former/configs/cityscapes/instance-segmentation/maskformer2_R50_bs16_90k.yaml new file mode 100644 index 0000000..16b215b --- /dev/null +++ b/Mask2Former/configs/cityscapes/instance-segmentation/maskformer2_R50_bs16_90k.yaml @@ -0,0 +1,44 @@ +_BASE_: Base-Cityscapes-InstanceSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 8 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: False + INSTANCE_ON: True + PANOPTIC_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml b/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml new file mode 100644 index 0000000..2956571 --- /dev/null +++ b/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml @@ -0,0 +1,16 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_base_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml b/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml new file mode 100644 index 0000000..72860d9 --- /dev/null +++ b/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml @@ -0,0 +1,18 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 200 diff --git a/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml b/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml new file mode 100644 index 0000000..156ef9e --- /dev/null +++ b/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_small_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml b/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml new file mode 100644 index 0000000..0c56e2c --- /dev/null +++ b/Mask2Former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 6, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_tiny_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/cityscapes/panoptic-segmentation/Base-Cityscapes-PanopticSegmentation.yaml b/Mask2Former/configs/cityscapes/panoptic-segmentation/Base-Cityscapes-PanopticSegmentation.yaml new file mode 100644 index 0000000..022567c --- /dev/null +++ b/Mask2Former/configs/cityscapes/panoptic-segmentation/Base-Cityscapes-PanopticSegmentation.yaml @@ -0,0 +1,61 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + NORM: "SyncBN" # use syncbn for cityscapes dataset + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("cityscapes_fine_panoptic_train",) + TEST: ("cityscapes_fine_panoptic_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 90000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1024) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 1024 + MAX_SIZE_TRAIN: 4096 + MAX_SIZE_TEST: 2048 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (512, 1024) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: -1 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_panoptic" +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792] + MAX_SIZE: 4096 + FLIP: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/cityscapes/panoptic-segmentation/maskformer2_R101_bs16_90k.yaml b/Mask2Former/configs/cityscapes/panoptic-segmentation/maskformer2_R101_bs16_90k.yaml new file mode 100644 index 0000000..1eb38da --- /dev/null +++ b/Mask2Former/configs/cityscapes/panoptic-segmentation/maskformer2_R101_bs16_90k.yaml @@ -0,0 +1,11 @@ +_BASE_: maskformer2_R50_bs16_90k.yaml +MODEL: + WEIGHTS: "R-101.pkl" + RESNETS: + DEPTH: 101 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used diff --git a/Mask2Former/configs/cityscapes/panoptic-segmentation/maskformer2_R50_bs16_90k.yaml b/Mask2Former/configs/cityscapes/panoptic-segmentation/maskformer2_R50_bs16_90k.yaml new file mode 100644 index 0000000..3c2d679 --- /dev/null +++ b/Mask2Former/configs/cityscapes/panoptic-segmentation/maskformer2_R50_bs16_90k.yaml @@ -0,0 +1,44 @@ +_BASE_: Base-Cityscapes-PanopticSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 19 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: True + PANOPTIC_ON: True + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml b/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml new file mode 100644 index 0000000..2956571 --- /dev/null +++ b/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml @@ -0,0 +1,16 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_base_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml b/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml new file mode 100644 index 0000000..72860d9 --- /dev/null +++ b/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml @@ -0,0 +1,18 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 200 diff --git a/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml b/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml new file mode 100644 index 0000000..156ef9e --- /dev/null +++ b/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_small_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml b/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml new file mode 100644 index 0000000..0c56e2c --- /dev/null +++ b/Mask2Former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 6, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_tiny_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/cityscapes/semantic-segmentation/Base-Cityscapes-SemanticSegmentation.yaml b/Mask2Former/configs/cityscapes/semantic-segmentation/Base-Cityscapes-SemanticSegmentation.yaml new file mode 100644 index 0000000..ca42fab --- /dev/null +++ b/Mask2Former/configs/cityscapes/semantic-segmentation/Base-Cityscapes-SemanticSegmentation.yaml @@ -0,0 +1,61 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + NORM: "SyncBN" # use syncbn for cityscapes dataset + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("cityscapes_fine_sem_seg_train",) + TEST: ("cityscapes_fine_sem_seg_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 90000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1024) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 1024 + MAX_SIZE_TRAIN: 4096 + MAX_SIZE_TEST: 2048 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (512, 1024) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: -1 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +TEST: + EVAL_PERIOD: 5000 + AUG: + ENABLED: False + MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792] + MAX_SIZE: 4096 + FLIP: True +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/cityscapes/semantic-segmentation/maskformer2_R101_bs16_90k.yaml b/Mask2Former/configs/cityscapes/semantic-segmentation/maskformer2_R101_bs16_90k.yaml new file mode 100644 index 0000000..1eb38da --- /dev/null +++ b/Mask2Former/configs/cityscapes/semantic-segmentation/maskformer2_R101_bs16_90k.yaml @@ -0,0 +1,11 @@ +_BASE_: maskformer2_R50_bs16_90k.yaml +MODEL: + WEIGHTS: "R-101.pkl" + RESNETS: + DEPTH: 101 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used diff --git a/Mask2Former/configs/cityscapes/semantic-segmentation/maskformer2_R50_bs16_90k.yaml b/Mask2Former/configs/cityscapes/semantic-segmentation/maskformer2_R50_bs16_90k.yaml new file mode 100644 index 0000000..d872fcd --- /dev/null +++ b/Mask2Former/configs/cityscapes/semantic-segmentation/maskformer2_R50_bs16_90k.yaml @@ -0,0 +1,44 @@ +_BASE_: Base-Cityscapes-SemanticSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 19 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: False + PANOPTIC_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml b/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml new file mode 100644 index 0000000..2956571 --- /dev/null +++ b/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml @@ -0,0 +1,16 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_base_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml b/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml new file mode 100644 index 0000000..2509717 --- /dev/null +++ b/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml @@ -0,0 +1,18 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 100 diff --git a/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml b/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml new file mode 100644 index 0000000..156ef9e --- /dev/null +++ b/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_small_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml b/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml new file mode 100644 index 0000000..0c56e2c --- /dev/null +++ b/Mask2Former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_90k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 6, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_tiny_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/coco/instance-segmentation/Base-COCO-InstanceSegmentation.yaml b/Mask2Former/configs/coco/instance-segmentation/Base-COCO-InstanceSegmentation.yaml new file mode 100644 index 0000000..98943d9 --- /dev/null +++ b/Mask2Former/configs/coco/instance-segmentation/Base-COCO-InstanceSegmentation.yaml @@ -0,0 +1,47 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("coco_2017_train",) + TEST: ("coco_2017_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + STEPS: (327778, 355092) + MAX_ITER: 368750 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + IMAGE_SIZE: 1024 + MIN_SCALE: 0.1 + MAX_SCALE: 2.0 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "coco_instance_lsj" +TEST: + EVAL_PERIOD: 5000 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/coco/instance-segmentation/maskformer2_R101_bs16_50ep.yaml b/Mask2Former/configs/coco/instance-segmentation/maskformer2_R101_bs16_50ep.yaml new file mode 100644 index 0000000..77defd0 --- /dev/null +++ b/Mask2Former/configs/coco/instance-segmentation/maskformer2_R101_bs16_50ep.yaml @@ -0,0 +1,11 @@ +_BASE_: maskformer2_R50_bs16_50ep.yaml +MODEL: + WEIGHTS: "R-101.pkl" + RESNETS: + DEPTH: 101 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used diff --git a/Mask2Former/configs/coco/instance-segmentation/maskformer2_R50_bs16_50ep.yaml b/Mask2Former/configs/coco/instance-segmentation/maskformer2_R50_bs16_50ep.yaml new file mode 100644 index 0000000..4b9e76e --- /dev/null +++ b/Mask2Former/configs/coco/instance-segmentation/maskformer2_R50_bs16_50ep.yaml @@ -0,0 +1,44 @@ +_BASE_: Base-COCO-InstanceSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 80 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: False + INSTANCE_ON: True + PANOPTIC_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml new file mode 100644 index 0000000..4732999 --- /dev/null +++ b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml @@ -0,0 +1,16 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_base_patch4_window12_384.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml new file mode 100644 index 0000000..5dde960 --- /dev/null +++ b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml @@ -0,0 +1,16 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_base_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml new file mode 100644 index 0000000..b685cdb --- /dev/null +++ b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml @@ -0,0 +1,21 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 200 +SOLVER: + STEPS: (655556, 710184) + MAX_ITER: 737500 diff --git a/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml new file mode 100644 index 0000000..f9b1c56 --- /dev/null +++ b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_small_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml new file mode 100644 index 0000000..7f27bc5 --- /dev/null +++ b/Mask2Former/configs/coco/instance-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 6, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_tiny_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/coco/panoptic-segmentation/Base-COCO-PanopticSegmentation.yaml b/Mask2Former/configs/coco/panoptic-segmentation/Base-COCO-PanopticSegmentation.yaml new file mode 100644 index 0000000..7560a73 --- /dev/null +++ b/Mask2Former/configs/coco/panoptic-segmentation/Base-COCO-PanopticSegmentation.yaml @@ -0,0 +1,47 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("coco_2017_train_panoptic",) + TEST: ("coco_2017_val_panoptic_with_sem_seg",) # to evaluate instance and semantic performance as well +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + STEPS: (327778, 355092) + MAX_ITER: 368750 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + IMAGE_SIZE: 1024 + MIN_SCALE: 0.1 + MAX_SCALE: 2.0 + FORMAT: "RGB" + DATASET_MAPPER_NAME: "coco_panoptic_lsj" +TEST: + EVAL_PERIOD: 5000 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/coco/panoptic-segmentation/maskformer2_R101_bs16_50ep.yaml b/Mask2Former/configs/coco/panoptic-segmentation/maskformer2_R101_bs16_50ep.yaml new file mode 100644 index 0000000..77defd0 --- /dev/null +++ b/Mask2Former/configs/coco/panoptic-segmentation/maskformer2_R101_bs16_50ep.yaml @@ -0,0 +1,11 @@ +_BASE_: maskformer2_R50_bs16_50ep.yaml +MODEL: + WEIGHTS: "R-101.pkl" + RESNETS: + DEPTH: 101 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used diff --git a/Mask2Former/configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml b/Mask2Former/configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml new file mode 100644 index 0000000..9ebf4f1 --- /dev/null +++ b/Mask2Former/configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml @@ -0,0 +1,45 @@ +_BASE_: Base-COCO-PanopticSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + IGNORE_VALUE: 255 + NUM_CLASSES: 133 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: True + PANOPTIC_ON: True + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml new file mode 100644 index 0000000..4732999 --- /dev/null +++ b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml @@ -0,0 +1,16 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_base_patch4_window12_384.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml new file mode 100644 index 0000000..5dde960 --- /dev/null +++ b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml @@ -0,0 +1,16 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_base_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml new file mode 100644 index 0000000..b685cdb --- /dev/null +++ b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml @@ -0,0 +1,21 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 200 +SOLVER: + STEPS: (655556, 710184) + MAX_ITER: 737500 diff --git a/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml new file mode 100644 index 0000000..f9b1c56 --- /dev/null +++ b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_small_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml new file mode 100644 index 0000000..7f27bc5 --- /dev/null +++ b/Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml @@ -0,0 +1,15 @@ +_BASE_: ../maskformer2_R50_bs16_50ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 6, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "swin_tiny_patch4_window7_224.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] diff --git a/Mask2Former/configs/mapillary-vistas/panoptic-segmentation/Base-MapillaryVistas-PanopticSegmentation.yaml b/Mask2Former/configs/mapillary-vistas/panoptic-segmentation/Base-MapillaryVistas-PanopticSegmentation.yaml new file mode 100644 index 0000000..86629a3 --- /dev/null +++ b/Mask2Former/configs/mapillary-vistas/panoptic-segmentation/Base-MapillaryVistas-PanopticSegmentation.yaml @@ -0,0 +1,56 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("mapillary_vistas_panoptic_train",) + TEST: ("mapillary_vistas_panoptic_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 300000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 2048) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 2048 + MAX_SIZE_TRAIN: 8192 + MAX_SIZE_TEST: 2048 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (1024, 1024) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 1024 # used in dataset mapper + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_panoptic" +TEST: + EVAL_PERIOD: 0 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 10 +VERSION: 2 diff --git a/Mask2Former/configs/mapillary-vistas/panoptic-segmentation/maskformer_R50_bs16_300k.yaml b/Mask2Former/configs/mapillary-vistas/panoptic-segmentation/maskformer_R50_bs16_300k.yaml new file mode 100644 index 0000000..b19c9d4 --- /dev/null +++ b/Mask2Former/configs/mapillary-vistas/panoptic-segmentation/maskformer_R50_bs16_300k.yaml @@ -0,0 +1,44 @@ +_BASE_: Base-MapillaryVistas-PanopticSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 65 + NUM_CLASSES: 65 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: False + PANOPTIC_ON: True + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.0 diff --git a/Mask2Former/configs/mapillary-vistas/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml b/Mask2Former/configs/mapillary-vistas/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml new file mode 100644 index 0000000..e7a8c4c --- /dev/null +++ b/Mask2Former/configs/mapillary-vistas/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml @@ -0,0 +1,18 @@ +_BASE_: ../maskformer_R50_bs16_300k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 200 diff --git a/Mask2Former/configs/mapillary-vistas/semantic-segmentation/Base-MapillaryVistas-SemanticSegmentation.yaml b/Mask2Former/configs/mapillary-vistas/semantic-segmentation/Base-MapillaryVistas-SemanticSegmentation.yaml new file mode 100644 index 0000000..f05fb28 --- /dev/null +++ b/Mask2Former/configs/mapillary-vistas/semantic-segmentation/Base-MapillaryVistas-SemanticSegmentation.yaml @@ -0,0 +1,56 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("mapillary_vistas_sem_seg_train",) + TEST: ("mapillary_vistas_sem_seg_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + MAX_ITER: 300000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 0 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + LR_SCHEDULER_NAME: "WarmupPolyLR" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 2048) for x in range(5, 21)]"] + MIN_SIZE_TRAIN_SAMPLING: "choice" + MIN_SIZE_TEST: 2048 + MAX_SIZE_TRAIN: 8192 + MAX_SIZE_TEST: 2048 + CROP: + ENABLED: True + TYPE: "absolute" + SIZE: (1024, 1024) + SINGLE_CATEGORY_MAX_AREA: 1.0 + COLOR_AUG_SSD: True + SIZE_DIVISIBILITY: 1024 # used in dataset mapper + FORMAT: "RGB" + DATASET_MAPPER_NAME: "mask_former_semantic" +TEST: + EVAL_PERIOD: 0 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: True + NUM_WORKERS: 10 +VERSION: 2 diff --git a/Mask2Former/configs/mapillary-vistas/semantic-segmentation/maskformer2_R50_bs16_300k.yaml b/Mask2Former/configs/mapillary-vistas/semantic-segmentation/maskformer2_R50_bs16_300k.yaml new file mode 100644 index 0000000..e9977a1 --- /dev/null +++ b/Mask2Former/configs/mapillary-vistas/semantic-segmentation/maskformer2_R50_bs16_300k.yaml @@ -0,0 +1,44 @@ +_BASE_: Base-MapillaryVistas-SemanticSegmentation.yaml +MODEL: + META_ARCHITECTURE: "MaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 65 + NUM_CLASSES: 65 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: True + INSTANCE_ON: False + PANOPTIC_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.0 diff --git a/Mask2Former/configs/mapillary-vistas/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml b/Mask2Former/configs/mapillary-vistas/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml new file mode 100644 index 0000000..e336a1b --- /dev/null +++ b/Mask2Former/configs/mapillary-vistas/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml @@ -0,0 +1,18 @@ +_BASE_: ../maskformer2_R50_bs16_300k.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "swin_large_patch4_window12_384_22k.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 100 diff --git a/Mask2Former/configs/youtubevis_2019/Base-YouTubeVIS-VideoInstanceSegmentation.yaml b/Mask2Former/configs/youtubevis_2019/Base-YouTubeVIS-VideoInstanceSegmentation.yaml new file mode 100644 index 0000000..80ed289 --- /dev/null +++ b/Mask2Former/configs/youtubevis_2019/Base-YouTubeVIS-VideoInstanceSegmentation.yaml @@ -0,0 +1,75 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_ON: True + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used + IFC: + APPLY_CLS_THRES: 0.01 + CLIP_STRIDE: 1 + DEC_LAYERS: 3 + DEEP_SUPERVISION: true + DICE_WEIGHT: 3.0 + DIM_FEEDFORWARD: 2048 + DROPOUT: 0.1 + ENC_LAYERS: 3 + HIDDEN_DIM: 256 + MASK_STRIDE: 4 + MASK_WEIGHT: 3.0 + MATCH_STRIDE: 4 + MERGE_ON_CPU: false + MULTI_CLS_ON: true + NHEADS: 8 + NO_OBJECT_WEIGHT: 0.1 + NUM_CLASSES: 40 + NUM_MEMORY_BUS: 8 + NUM_OBJECT_QUERIES: 100 + PRE_NORM: false + size_window: 3 #最佳为6 +DATASETS: + TRAIN: ("ytvis_2019_train",) + TEST: ("ytvis_2019_val",) +SOLVER: + IMS_PER_BATCH: 4 + BASE_LR: 0.0001 + STEPS: (4000,) #VITA:75,000 + MAX_ITER: 6000 #VITA:130,000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN_SAMPLING: "choice_by_clip" + RANDOM_FLIP: "flip_by_clip" + AUGMENTATIONS: [] + MIN_SIZE_TRAIN: (360, 480) + MIN_SIZE_TEST: 360 + CROP: + ENABLED: False + TYPE: "absolute_range" + SIZE: (600, 720) + FORMAT: "RGB" +TEST: + EVAL_PERIOD: 0 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: False + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml new file mode 100644 index 0000000..8068edf --- /dev/null +++ b/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml @@ -0,0 +1,18 @@ +_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "model_final_83d103.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] +INPUT: + MIN_SIZE_TEST: 480 diff --git a/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml new file mode 100644 index 0000000..3978882 --- /dev/null +++ b/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml @@ -0,0 +1,20 @@ +_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "model_final_e5f453.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 200 +INPUT: + MIN_SIZE_TEST: 480 diff --git a/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_small_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_small_bs16_8ep.yaml new file mode 100644 index 0000000..767d30a --- /dev/null +++ b/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_small_bs16_8ep.yaml @@ -0,0 +1,17 @@ +_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "model_final_1e7f22.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] +INPUT: + MIN_SIZE_TEST: 480 diff --git a/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml new file mode 100644 index 0000000..2d446e2 --- /dev/null +++ b/Mask2Former/configs/youtubevis_2019/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml @@ -0,0 +1,17 @@ +_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 6, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "model_final_86143f.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] +INPUT: + MIN_SIZE_TEST: 480 diff --git a/Mask2Former/configs/youtubevis_2019/video_maskformer2_R101_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2019/video_maskformer2_R101_bs16_8ep.yaml new file mode 100644 index 0000000..fcc9c49 --- /dev/null +++ b/Mask2Former/configs/youtubevis_2019/video_maskformer2_R101_bs16_8ep.yaml @@ -0,0 +1,11 @@ +_BASE_: video_maskformer2_R50_bs16_8ep.yaml +MODEL: + WEIGHTS: "model_final_eba159.pkl" + RESNETS: + DEPTH: 101 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used diff --git a/Mask2Former/configs/youtubevis_2019/video_maskformer2_R50_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2019/video_maskformer2_R50_bs16_8ep.yaml new file mode 100644 index 0000000..6c0cc58 --- /dev/null +++ b/Mask2Former/configs/youtubevis_2019/video_maskformer2_R50_bs16_8ep.yaml @@ -0,0 +1,45 @@ +_BASE_: Base-YouTubeVIS-VideoInstanceSegmentation.yaml +MODEL: + WEIGHTS: "/root/Mask2Former/weights/video_maskformer2_R50_bs16_8ep.pkl" + META_ARCHITECTURE: "VideoMaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 40 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "VideoMultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: False + INSTANCE_ON: True + PANOPTIC_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/configs/youtubevis_2021/Base-YouTubeVIS-VideoInstanceSegmentation.yaml b/Mask2Former/configs/youtubevis_2021/Base-YouTubeVIS-VideoInstanceSegmentation.yaml new file mode 100644 index 0000000..6545cd1 --- /dev/null +++ b/Mask2Former/configs/youtubevis_2021/Base-YouTubeVIS-VideoInstanceSegmentation.yaml @@ -0,0 +1,53 @@ +MODEL: + BACKBONE: + FREEZE_AT: 0 + NAME: "build_resnet_backbone" + WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_ON: True + RESNETS: + DEPTH: 50 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used +DATASETS: + TRAIN: ("ytvis_2021_train",) + TEST: ("ytvis_2021_val",) +SOLVER: + IMS_PER_BATCH: 16 + BASE_LR: 0.0001 + STEPS: (5500,) + MAX_ITER: 8000 + WARMUP_FACTOR: 1.0 + WARMUP_ITERS: 10 + WEIGHT_DECAY: 0.05 + OPTIMIZER: "ADAMW" + BACKBONE_MULTIPLIER: 0.1 + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 0.01 + NORM_TYPE: 2.0 + AMP: + ENABLED: True +INPUT: + MIN_SIZE_TRAIN_SAMPLING: "choice_by_clip" + RANDOM_FLIP: "flip_by_clip" + AUGMENTATIONS: [] + MIN_SIZE_TRAIN: (360, 480) + MIN_SIZE_TEST: 360 + CROP: + ENABLED: False + TYPE: "absolute_range" + SIZE: (600, 720) + FORMAT: "RGB" +TEST: + EVAL_PERIOD: 0 +DATALOADER: + FILTER_EMPTY_ANNOTATIONS: False + NUM_WORKERS: 4 +VERSION: 2 diff --git a/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml new file mode 100644 index 0000000..8068edf --- /dev/null +++ b/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml @@ -0,0 +1,18 @@ +_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 128 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [4, 8, 16, 32] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "model_final_83d103.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] +INPUT: + MIN_SIZE_TEST: 480 diff --git a/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml new file mode 100644 index 0000000..2d20903 --- /dev/null +++ b/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml @@ -0,0 +1,21 @@ +_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 192 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [6, 12, 24, 48] + WINDOW_SIZE: 12 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + PRETRAIN_IMG_SIZE: 384 + WEIGHTS: "model_final_e5f453.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + MASK_FORMER: + NUM_OBJECT_QUERIES: 200 +# OOM when using a larger test size +# INPUT: +# MIN_SIZE_TEST: 480 diff --git a/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_small_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_small_bs16_8ep.yaml new file mode 100644 index 0000000..767d30a --- /dev/null +++ b/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_small_bs16_8ep.yaml @@ -0,0 +1,17 @@ +_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 18, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "model_final_1e7f22.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] +INPUT: + MIN_SIZE_TEST: 480 diff --git a/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml new file mode 100644 index 0000000..2d446e2 --- /dev/null +++ b/Mask2Former/configs/youtubevis_2021/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml @@ -0,0 +1,17 @@ +_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml +MODEL: + BACKBONE: + NAME: "D2SwinTransformer" + SWIN: + EMBED_DIM: 96 + DEPTHS: [2, 2, 6, 2] + NUM_HEADS: [3, 6, 12, 24] + WINDOW_SIZE: 7 + APE: False + DROP_PATH_RATE: 0.3 + PATCH_NORM: True + WEIGHTS: "model_final_86143f.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] +INPUT: + MIN_SIZE_TEST: 480 diff --git a/Mask2Former/configs/youtubevis_2021/video_maskformer2_R101_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2021/video_maskformer2_R101_bs16_8ep.yaml new file mode 100644 index 0000000..fcc9c49 --- /dev/null +++ b/Mask2Former/configs/youtubevis_2021/video_maskformer2_R101_bs16_8ep.yaml @@ -0,0 +1,11 @@ +_BASE_: video_maskformer2_R50_bs16_8ep.yaml +MODEL: + WEIGHTS: "model_final_eba159.pkl" + RESNETS: + DEPTH: 101 + STEM_TYPE: "basic" # not used + STEM_OUT_CHANNELS: 64 + STRIDE_IN_1X1: False + OUT_FEATURES: ["res2", "res3", "res4", "res5"] + # NORM: "SyncBN" + RES5_MULTI_GRID: [1, 1, 1] # not used diff --git a/Mask2Former/configs/youtubevis_2021/video_maskformer2_R50_bs16_8ep.yaml b/Mask2Former/configs/youtubevis_2021/video_maskformer2_R50_bs16_8ep.yaml new file mode 100644 index 0000000..8af434d --- /dev/null +++ b/Mask2Former/configs/youtubevis_2021/video_maskformer2_R50_bs16_8ep.yaml @@ -0,0 +1,45 @@ +_BASE_: Base-YouTubeVIS-VideoInstanceSegmentation.yaml +MODEL: + WEIGHTS: "model_final_3c8ec9.pkl" + META_ARCHITECTURE: "VideoMaskFormer" + SEM_SEG_HEAD: + NAME: "MaskFormerHead" + IGNORE_VALUE: 255 + NUM_CLASSES: 40 + LOSS_WEIGHT: 1.0 + CONVS_DIM: 256 + MASK_DIM: 256 + NORM: "GN" + # pixel decoder + PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder" + IN_FEATURES: ["res2", "res3", "res4", "res5"] + DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"] + COMMON_STRIDE: 4 + TRANSFORMER_ENC_LAYERS: 6 + MASK_FORMER: + TRANSFORMER_DECODER_NAME: "VideoMultiScaleMaskedTransformerDecoder" + TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder" + DEEP_SUPERVISION: True + NO_OBJECT_WEIGHT: 0.1 + CLASS_WEIGHT: 2.0 + MASK_WEIGHT: 5.0 + DICE_WEIGHT: 5.0 + HIDDEN_DIM: 256 + NUM_OBJECT_QUERIES: 100 + NHEADS: 8 + DROPOUT: 0.0 + DIM_FEEDFORWARD: 2048 + ENC_LAYERS: 0 + PRE_NORM: False + ENFORCE_INPUT_PROJ: False + SIZE_DIVISIBILITY: 32 + DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query + TRAIN_NUM_POINTS: 12544 + OVERSAMPLE_RATIO: 3.0 + IMPORTANCE_SAMPLE_RATIO: 0.75 + TEST: + SEMANTIC_ON: False + INSTANCE_ON: True + PANOPTIC_ON: False + OVERLAP_THRESHOLD: 0.8 + OBJECT_MASK_THRESHOLD: 0.8 diff --git a/Mask2Former/datasets/README.md b/Mask2Former/datasets/README.md new file mode 100644 index 0000000..1d9bc8a --- /dev/null +++ b/Mask2Former/datasets/README.md @@ -0,0 +1,162 @@ +# Prepare Datasets for Mask2Former + +A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog) +for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc). +This document explains how to setup the builtin datasets so they can be used by the above APIs. +[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`, +and how to add new datasets to them. + +MaskFormer has builtin support for a few datasets. +The datasets are assumed to exist in a directory specified by the environment variable +`DETECTRON2_DATASETS`. +Under this directory, detectron2 will look for datasets in the structure described below, if needed. +``` +$DETECTRON2_DATASETS/ + ADEChallengeData2016/ + coco/ + cityscapes/ + mapillary_vistas/ +``` + +You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`. +If left unset, the default is `./datasets` relative to your current working directory. + +The [model zoo](https://github.com/facebookresearch/MaskFormer/blob/master/MODEL_ZOO.md) +contains configs and models that use these builtin datasets. + + +## Expected dataset structure for [COCO](https://cocodataset.org/#download): + +``` +coco/ + annotations/ + instances_{train,val}2017.json + panoptic_{train,val}2017.json + {train,val}2017/ + # image files that are mentioned in the corresponding json + panoptic_{train,val}2017/ # png annotations + panoptic_semseg_{train,val}2017/ # generated by the script mentioned below +``` + +Install panopticapi by: +``` +pip install git+https://github.com/cocodataset/panopticapi.git +``` +Then, run `python datasets/prepare_coco_semantic_annos_from_panoptic_annos.py`, to extract semantic annotations from panoptic annotations (only used for evaluation). + + +## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/): +``` +cityscapes/ + gtFine/ + train/ + aachen/ + color.png, instanceIds.png, labelIds.png, polygons.json, + labelTrainIds.png + ... + val/ + test/ + # below are generated Cityscapes panoptic annotation + cityscapes_panoptic_train.json + cityscapes_panoptic_train/ + cityscapes_panoptic_val.json + cityscapes_panoptic_val/ + cityscapes_panoptic_test.json + cityscapes_panoptic_test/ + leftImg8bit/ + train/ + val/ + test/ +``` +Install cityscapes scripts by: +``` +pip install git+https://github.com/mcordts/cityscapesScripts.git +``` + +Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with: +``` +CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py +``` +These files are not needed for instance segmentation. + +Note: to generate Cityscapes panoptic dataset, run cityscapesescript with: +``` +CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py +``` +These files are not needed for semantic and instance segmentation. + + +## Expected dataset structure for [ADE20k](http://sceneparsing.csail.mit.edu/): +``` +ADEChallengeData2016/ + images/ + annotations/ + objectInfo150.txt + # download instance annotation + annotations_instance/ + # generated by prepare_ade20k_sem_seg.py + annotations_detectron2/ + # below are generated by prepare_ade20k_pan_seg.py + ade20k_panoptic_{train,val}.json + ade20k_panoptic_{train,val}/ + # below are generated by prepare_ade20k_ins_seg.py + ade20k_instance_{train,val}.json +``` + +The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`. + +Install panopticapi by: +```bash +pip install git+https://github.com/cocodataset/panopticapi.git +``` + +Download the instance annotation from http://sceneparsing.csail.mit.edu/: +```bash +wget http://sceneparsing.csail.mit.edu/data/ChallengeData2017/annotations_instance.tar +``` + +Then, run `python datasets/prepare_ade20k_pan_seg.py`, to combine semantic and instance annotations for panoptic annotations. + +And run `python datasets/prepare_ade20k_ins_seg.py`, to extract instance annotations in COCO format. + + +## Expected dataset structure for [Mapillary Vistas](https://www.mapillary.com/dataset/vistas): +``` +mapillary_vistas/ + training/ + images/ + instances/ + labels/ + panoptic/ + validation/ + images/ + instances/ + labels/ + panoptic/ + mapillary_vistas_instance_{train,val}.json # generated by the script mentioned below +``` + +No preprocessing is needed for Mapillary Vistas on semantic and panoptic segmentation. + +If you want to evaluate instance segmentation on Mapillary Vistas, run `python datasets/prepare_mapillary_vistas_ins_seg.py` to generate COCO-style instance annotations. + + +## Expected dataset structure for [YouTubeVIS 2019](https://competitions.codalab.org/competitions/20128): + +``` +ytvis_2019/ + {train,valid,test}.json + {train,valid,test}/ + Annotations/ + JPEGImages/ +``` + +## Expected dataset structure for [YouTubeVIS 2021](https://competitions.codalab.org/competitions/28988): + +``` +ytvis_2021/ + {train,valid,test}.json + {train,valid,test}/ + Annotations/ + JPEGImages/ +``` diff --git a/Mask2Former/datasets/ade20k_instance_catid_mapping.txt b/Mask2Former/datasets/ade20k_instance_catid_mapping.txt new file mode 100644 index 0000000..ba388d0 --- /dev/null +++ b/Mask2Former/datasets/ade20k_instance_catid_mapping.txt @@ -0,0 +1,104 @@ +Instacne100 SceneParse150 FullADE20K +1 8 165 +2 9 3055 +3 11 350 +4 13 1831 +5 15 774 +5 15 783 +6 16 2684 +7 19 687 +8 20 471 +9 21 401 +10 23 1735 +11 24 2473 +12 25 2329 +13 28 1564 +14 31 57 +15 32 2272 +16 33 907 +17 34 724 +18 36 2985 +18 36 533 +19 37 1395 +20 38 155 +21 39 2053 +22 40 689 +23 42 266 +24 43 581 +25 44 2380 +26 45 491 +27 46 627 +28 48 2388 +29 50 943 +30 51 2096 +31 54 2530 +32 56 420 +33 57 1948 +34 58 1869 +35 59 2251 +36 63 239 +37 65 571 +38 66 2793 +39 67 978 +40 68 236 +41 70 181 +42 71 629 +43 72 2598 +44 73 1744 +45 74 1374 +46 75 591 +47 76 2679 +48 77 223 +49 79 47 +50 81 327 +51 82 2821 +52 83 1451 +53 84 2880 +54 86 480 +55 87 77 +56 88 2616 +57 89 246 +57 89 247 +58 90 2733 +59 91 14 +60 93 38 +61 94 1936 +62 96 120 +63 98 1702 +64 99 249 +65 103 2928 +66 104 2337 +67 105 1023 +68 108 2989 +69 109 1930 +70 111 2586 +71 112 131 +72 113 146 +73 116 95 +74 117 1563 +75 119 1708 +76 120 103 +77 121 1002 +78 122 2569 +79 124 2833 +80 125 1551 +81 126 1981 +82 127 29 +83 128 187 +84 130 747 +85 131 2254 +86 133 2262 +87 134 1260 +88 135 2243 +89 136 2932 +90 137 2836 +91 138 2850 +92 139 64 +93 140 894 +94 143 1919 +95 144 1583 +96 145 318 +97 147 2046 +98 148 1098 +99 149 530 +100 150 954 diff --git a/Mask2Former/datasets/ade20k_instance_imgCatIds.json b/Mask2Former/datasets/ade20k_instance_imgCatIds.json new file mode 100644 index 0000000..d142da4 --- /dev/null +++ b/Mask2Former/datasets/ade20k_instance_imgCatIds.json @@ -0,0 +1 @@ +{"images": [{"file_name": "ADE_train_00000001.jpg", "width": 683, "id": 0, "height": 512}, {"file_name": "ADE_train_00000002.jpg", "width": 711, "id": 1, "height": 512}, {"file_name": "ADE_train_00000003.jpg", "width": 683, "id": 2, "height": 512}, {"file_name": "ADE_train_00000004.jpg", "width": 384, "id": 3, "height": 251}, {"file_name": "ADE_train_00000005.jpg", "width": 683, "id": 4, "height": 512}, {"file_name": "ADE_train_00000006.jpg", "width": 512, "id": 5, "height": 973}, {"file_name": "ADE_train_00000007.jpg", "width": 512, "id": 6, "height": 735}, {"file_name": "ADE_train_00000008.jpg", "width": 538, "id": 7, "height": 512}, {"file_name": "ADE_train_00000009.jpg", "width": 683, "id": 8, "height": 512}, {"file_name": "ADE_train_00000010.jpg", "width": 512, "id": 9, "height": 764}, {"file_name": "ADE_train_00000011.jpg", "width": 356, "id": 10, "height": 247}, {"file_name": "ADE_train_00000012.jpg", "width": 512, "id": 11, "height": 683}, {"file_name": "ADE_train_00000013.jpg", "width": 683, "id": 12, "height": 512}, {"file_name": "ADE_train_00000014.jpg", "width": 683, "id": 13, "height": 512}, {"file_name": "ADE_train_00000015.jpg", "width": 637, "id": 14, "height": 512}, {"file_name": "ADE_train_00000016.jpg", "width": 683, "id": 15, "height": 512}, {"file_name": "ADE_train_00000017.jpg", "width": 512, "id": 16, "height": 683}, {"file_name": "ADE_train_00000018.jpg", "width": 770, "id": 17, "height": 512}, {"file_name": "ADE_train_00000019.jpg", "width": 640, "id": 18, "height": 431}, {"file_name": "ADE_train_00000020.jpg", "width": 512, "id": 19, "height": 517}, {"file_name": "ADE_train_00000021.jpg", "width": 233, "id": 20, "height": 311}, {"file_name": "ADE_train_00000022.jpg", "width": 200, "id": 21, "height": 303}, {"file_name": "ADE_train_00000023.jpg", "width": 512, "id": 22, "height": 708}, {"file_name": 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"monitor"}, {"id": 96, "name": "bulletin board"}, {"id": 97, "name": "radiator"}, {"id": 98, "name": "glass"}, {"id": 99, "name": "clock"}, {"id": 100, "name": "flag"}]} \ No newline at end of file diff --git a/Mask2Former/datasets/prepare_ade20k_ins_seg.py b/Mask2Former/datasets/prepare_ade20k_ins_seg.py new file mode 100644 index 0000000..e4e951a --- /dev/null +++ b/Mask2Former/datasets/prepare_ade20k_ins_seg.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +import glob +import json +import os +from collections import Counter + +import numpy as np +import tqdm +from panopticapi.utils import IdGenerator, save_json +from PIL import Image +import pycocotools.mask as mask_util + + +if __name__ == "__main__": + dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets") + + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/images/{dirname}/") + instance_dir = os.path.join( + dataset_dir, f"ADEChallengeData2016/annotations_instance/{dirname}/" + ) + + # img_id = 0 + ann_id = 1 + + # json + out_file = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_instance_{name}.json") + + # json config + instance_config_file = "datasets/ade20k_instance_imgCatIds.json" + with open(instance_config_file) as f: + category_dict = json.load(f)["categories"] + + # load catid mapping + # it is important to share category id for both instance and panoptic annotations + mapping_file = "datasets/ade20k_instance_catid_mapping.txt" + with open(mapping_file) as f: + map_id = {} + for i, line in enumerate(f.readlines()): + if i == 0: + continue + ins_id, sem_id, _ = line.strip().split() + # shift id by 1 because we want it to start from 0! + # ignore_label becomes 255 + map_id[int(ins_id)] = int(sem_id) - 1 + + for cat in category_dict: + cat["id"] = map_id[cat["id"]] + + filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg"))) + + ann_dict = {} + images = [] + annotations = [] + + for idx, filename in enumerate(tqdm.tqdm(filenames)): + image = {} + image_id = os.path.basename(filename).split(".")[0] + + image["id"] = image_id + image["file_name"] = os.path.basename(filename) + + original_format = np.array(Image.open(filename)) + image["width"] = original_format.shape[1] + image["height"] = original_format.shape[0] + + images.append(image) + + filename_instance = os.path.join(instance_dir, image_id + ".png") + ins_seg = np.asarray(Image.open(filename_instance)) + assert ins_seg.dtype == np.uint8 + + instance_cat_ids = ins_seg[..., 0] + # instance id starts from 1! + # because 0 is reserved as VOID label + instance_ins_ids = ins_seg[..., 1] + + # process things + for thing_id in np.unique(instance_ins_ids): + if thing_id == 0: + continue + mask = instance_ins_ids == thing_id + instance_cat_id = np.unique(instance_cat_ids[mask]) + assert len(instance_cat_id) == 1 + + anno = {} + anno['id'] = ann_id + ann_id += 1 + anno['image_id'] = image['id'] + anno["iscrowd"] = int(0) + anno["category_id"] = int(map_id[instance_cat_id[0]]) + + inds = np.nonzero(mask) + ymin, ymax = inds[0].min(), inds[0].max() + xmin, xmax = inds[1].min(), inds[1].max() + anno["bbox"] = [int(xmin), int(ymin), int(xmax - xmin + 1), int(ymax - ymin + 1)] + # if xmax <= xmin or ymax <= ymin: + # continue + rle = mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] + rle["counts"] = rle["counts"].decode("utf-8") + anno["segmentation"] = rle + anno["area"] = int(mask_util.area(rle)) + annotations.append(anno) + + # save this + ann_dict['images'] = images + ann_dict['categories'] = category_dict + ann_dict['annotations'] = annotations + + save_json(ann_dict, out_file) diff --git a/Mask2Former/datasets/prepare_ade20k_pan_seg.py b/Mask2Former/datasets/prepare_ade20k_pan_seg.py new file mode 100644 index 0000000..8b1b032 --- /dev/null +++ b/Mask2Former/datasets/prepare_ade20k_pan_seg.py @@ -0,0 +1,500 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +import glob +import json +import os +from collections import Counter + +import numpy as np +import tqdm +from panopticapi.utils import IdGenerator, save_json +from PIL import Image + +ADE20K_SEM_SEG_CATEGORIES = [ + "wall", + "building", + "sky", + "floor", + "tree", + "ceiling", + "road, route", + "bed", + "window ", + "grass", + "cabinet", + "sidewalk, pavement", + "person", + "earth, ground", + "door", + "table", + "mountain, mount", + "plant", + "curtain", + "chair", + "car", + "water", + "painting, picture", + "sofa", + "shelf", + "house", + "sea", + "mirror", + "rug", + "field", + "armchair", + "seat", + "fence", + "desk", + "rock, stone", + "wardrobe, closet, press", + "lamp", + "tub", + "rail", + "cushion", + "base, pedestal, stand", + "box", + "column, pillar", + "signboard, sign", + "chest of drawers, chest, bureau, dresser", + "counter", + "sand", + "sink", + "skyscraper", + "fireplace", + "refrigerator, icebox", + "grandstand, covered stand", + "path", + "stairs", + "runway", + "case, display case, showcase, vitrine", + "pool table, billiard table, snooker table", + "pillow", + "screen door, screen", + "stairway, staircase", + "river", + "bridge, span", + "bookcase", + "blind, screen", + "coffee table", + "toilet, can, commode, crapper, pot, potty, stool, throne", + "flower", + "book", + "hill", + "bench", + "countertop", + "stove", + "palm, palm tree", + "kitchen island", + "computer", + "swivel chair", + "boat", + "bar", + "arcade machine", + "hovel, hut, hutch, shack, shanty", + "bus", + "towel", + "light", + "truck", + "tower", + "chandelier", + "awning, sunshade, sunblind", + "street lamp", + "booth", + "tv", + "plane", + "dirt track", + "clothes", + "pole", + "land, ground, soil", + "bannister, banister, balustrade, balusters, handrail", + "escalator, moving staircase, moving stairway", + "ottoman, pouf, pouffe, puff, hassock", + "bottle", + "buffet, counter, sideboard", + "poster, posting, placard, notice, bill, card", + "stage", + "van", + "ship", + "fountain", + "conveyer belt, conveyor belt, conveyer, conveyor, transporter", + "canopy", + "washer, automatic washer, washing machine", + "plaything, toy", + "pool", + "stool", + "barrel, cask", + "basket, handbasket", + "falls", + "tent", + "bag", + "minibike, motorbike", + "cradle", + "oven", + "ball", + "food, solid food", + "step, stair", + "tank, storage tank", + "trade name", + "microwave", + "pot", + "animal", + "bicycle", + "lake", + "dishwasher", + "screen", + "blanket, cover", + "sculpture", + "hood, exhaust hood", + "sconce", + "vase", + "traffic light", + "tray", + "trash can", + "fan", + "pier", + "crt screen", + "plate", + "monitor", + "bulletin board", + "shower", + "radiator", + "glass, drinking glass", + "clock", + "flag", # noqa +] + +PALETTE = [ + [120, 120, 120], + [180, 120, 120], + [6, 230, 230], + [80, 50, 50], + [4, 200, 3], + [120, 120, 80], + [140, 140, 140], + [204, 5, 255], + [230, 230, 230], + [4, 250, 7], + [224, 5, 255], + [235, 255, 7], + [150, 5, 61], + [120, 120, 70], + [8, 255, 51], + [255, 6, 82], + [143, 255, 140], + [204, 255, 4], + [255, 51, 7], + [204, 70, 3], + [0, 102, 200], + [61, 230, 250], + [255, 6, 51], + [11, 102, 255], + [255, 7, 71], + [255, 9, 224], + [9, 7, 230], + [220, 220, 220], + [255, 9, 92], + [112, 9, 255], + [8, 255, 214], + [7, 255, 224], + [255, 184, 6], + [10, 255, 71], + [255, 41, 10], + [7, 255, 255], + [224, 255, 8], + [102, 8, 255], + [255, 61, 6], + [255, 194, 7], + [255, 122, 8], + [0, 255, 20], + [255, 8, 41], + [255, 5, 153], + [6, 51, 255], + [235, 12, 255], + [160, 150, 20], + [0, 163, 255], + [140, 140, 200], + [250, 10, 15], + [20, 255, 0], + [31, 255, 0], + [255, 31, 0], + [255, 224, 0], + [153, 255, 0], + [0, 0, 255], + [255, 71, 0], + [0, 235, 255], + [0, 173, 255], + [31, 0, 255], + [11, 200, 200], + [255, 82, 0], + [0, 255, 245], + [0, 61, 255], + [0, 255, 112], + [0, 255, 133], + [255, 0, 0], + [255, 163, 0], + [255, 102, 0], + [194, 255, 0], + [0, 143, 255], + [51, 255, 0], + [0, 82, 255], + [0, 255, 41], + [0, 255, 173], + [10, 0, 255], + [173, 255, 0], + [0, 255, 153], + [255, 92, 0], + [255, 0, 255], + [255, 0, 245], + [255, 0, 102], + [255, 173, 0], + [255, 0, 20], + [255, 184, 184], + [0, 31, 255], + [0, 255, 61], + [0, 71, 255], + [255, 0, 204], + [0, 255, 194], + [0, 255, 82], + [0, 10, 255], + [0, 112, 255], + [51, 0, 255], + [0, 194, 255], + [0, 122, 255], + [0, 255, 163], + [255, 153, 0], + [0, 255, 10], + [255, 112, 0], + [143, 255, 0], + [82, 0, 255], + [163, 255, 0], + [255, 235, 0], + [8, 184, 170], + [133, 0, 255], + [0, 255, 92], + [184, 0, 255], + [255, 0, 31], + [0, 184, 255], + [0, 214, 255], + [255, 0, 112], + [92, 255, 0], + [0, 224, 255], + [112, 224, 255], + [70, 184, 160], + [163, 0, 255], + [153, 0, 255], + [71, 255, 0], + [255, 0, 163], + [255, 204, 0], + [255, 0, 143], + [0, 255, 235], + [133, 255, 0], + [255, 0, 235], + [245, 0, 255], + [255, 0, 122], + [255, 245, 0], + [10, 190, 212], + [214, 255, 0], + [0, 204, 255], + [20, 0, 255], + [255, 255, 0], + [0, 153, 255], + [0, 41, 255], + [0, 255, 204], + [41, 0, 255], + [41, 255, 0], + [173, 0, 255], + [0, 245, 255], + [71, 0, 255], + [122, 0, 255], + [0, 255, 184], + [0, 92, 255], + [184, 255, 0], + [0, 133, 255], + [255, 214, 0], + [25, 194, 194], + [102, 255, 0], + [92, 0, 255], +] + + +if __name__ == "__main__": + dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets") + + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/images/{dirname}/") + semantic_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/annotations/{dirname}/") + instance_dir = os.path.join( + dataset_dir, f"ADEChallengeData2016/annotations_instance/{dirname}/" + ) + + # folder to store panoptic PNGs + out_folder = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_panoptic_{name}/") + # json with segmentations information + out_file = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_panoptic_{name}.json") + + if not os.path.isdir(out_folder): + print("Creating folder {} for panoptic segmentation PNGs".format(out_folder)) + os.mkdir(out_folder) + + # json config + config_file = "datasets/ade20k_instance_imgCatIds.json" + with open(config_file) as f: + config = json.load(f) + + # load catid mapping + mapping_file = "datasets/ade20k_instance_catid_mapping.txt" + with open(mapping_file) as f: + map_id = {} + for i, line in enumerate(f.readlines()): + if i == 0: + continue + ins_id, sem_id, _ = line.strip().split() + # shift id by 1 because we want it to start from 0! + # ignore_label becomes 255 + map_id[int(ins_id) - 1] = int(sem_id) - 1 + + ADE20K_150_CATEGORIES = [] + for cat_id, cat_name in enumerate(ADE20K_SEM_SEG_CATEGORIES): + ADE20K_150_CATEGORIES.append( + { + "name": cat_name, + "id": cat_id, + "isthing": int(cat_id in map_id.values()), + "color": PALETTE[cat_id], + } + ) + categories_dict = {cat["id"]: cat for cat in ADE20K_150_CATEGORIES} + + panoptic_json_categories = ADE20K_150_CATEGORIES[:] + panoptic_json_images = [] + panoptic_json_annotations = [] + + filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg"))) + for idx, filename in enumerate(tqdm.tqdm(filenames)): + panoptic_json_image = {} + panoptic_json_annotation = {} + + image_id = os.path.basename(filename).split(".")[0] + + panoptic_json_image["id"] = image_id + panoptic_json_image["file_name"] = os.path.basename(filename) + + original_format = np.array(Image.open(filename)) + panoptic_json_image["width"] = original_format.shape[1] + panoptic_json_image["height"] = original_format.shape[0] + + pan_seg = np.zeros( + (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8 + ) + id_generator = IdGenerator(categories_dict) + + filename_semantic = os.path.join(semantic_dir, image_id + ".png") + filename_instance = os.path.join(instance_dir, image_id + ".png") + + sem_seg = np.asarray(Image.open(filename_semantic)) + ins_seg = np.asarray(Image.open(filename_instance)) + + assert sem_seg.dtype == np.uint8 + assert ins_seg.dtype == np.uint8 + + semantic_cat_ids = sem_seg - 1 + instance_cat_ids = ins_seg[..., 0] - 1 + # instance id starts from 1! + # because 0 is reserved as VOID label + instance_ins_ids = ins_seg[..., 1] + + segm_info = [] + + # NOTE: there is some overlap between semantic and instance annotation + # thus we paste stuffs first + + # process stuffs + for semantic_cat_id in np.unique(semantic_cat_ids): + if semantic_cat_id == 255: + continue + if categories_dict[semantic_cat_id]["isthing"]: + continue + mask = semantic_cat_ids == semantic_cat_id + # should not have any overlap + assert pan_seg[mask].sum() == 0 + + segment_id, color = id_generator.get_id_and_color(semantic_cat_id) + pan_seg[mask] = color + + area = np.sum(mask) # segment area computation + # bbox computation for a segment + hor = np.sum(mask, axis=0) + hor_idx = np.nonzero(hor)[0] + x = hor_idx[0] + width = hor_idx[-1] - x + 1 + vert = np.sum(mask, axis=1) + vert_idx = np.nonzero(vert)[0] + y = vert_idx[0] + height = vert_idx[-1] - y + 1 + bbox = [int(x), int(y), int(width), int(height)] + + segm_info.append( + { + "id": int(segment_id), + "category_id": int(semantic_cat_id), + "area": int(area), + "bbox": bbox, + "iscrowd": 0, + } + ) + + # process things + for thing_id in np.unique(instance_ins_ids): + if thing_id == 0: + continue + mask = instance_ins_ids == thing_id + instance_cat_id = np.unique(instance_cat_ids[mask]) + assert len(instance_cat_id) == 1 + + semantic_cat_id = map_id[instance_cat_id[0]] + + segment_id, color = id_generator.get_id_and_color(semantic_cat_id) + pan_seg[mask] = color + + area = np.sum(mask) # segment area computation + # bbox computation for a segment + hor = np.sum(mask, axis=0) + hor_idx = np.nonzero(hor)[0] + x = hor_idx[0] + width = hor_idx[-1] - x + 1 + vert = np.sum(mask, axis=1) + vert_idx = np.nonzero(vert)[0] + y = vert_idx[0] + height = vert_idx[-1] - y + 1 + bbox = [int(x), int(y), int(width), int(height)] + + segm_info.append( + { + "id": int(segment_id), + "category_id": int(semantic_cat_id), + "area": int(area), + "bbox": bbox, + "iscrowd": 0, + } + ) + + panoptic_json_annotation = { + "image_id": image_id, + "file_name": image_id + ".png", + "segments_info": segm_info, + } + + Image.fromarray(pan_seg).save(os.path.join(out_folder, image_id + ".png")) + + panoptic_json_images.append(panoptic_json_image) + panoptic_json_annotations.append(panoptic_json_annotation) + + # save this + d = { + "images": panoptic_json_images, + "annotations": panoptic_json_annotations, + "categories": panoptic_json_categories, + } + + save_json(d, out_file) diff --git a/Mask2Former/datasets/prepare_ade20k_sem_seg.py b/Mask2Former/datasets/prepare_ade20k_sem_seg.py new file mode 100644 index 0000000..b0edfeb --- /dev/null +++ b/Mask2Former/datasets/prepare_ade20k_sem_seg.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +import os +from pathlib import Path + +import numpy as np +import tqdm +from PIL import Image + + +def convert(input, output): + img = np.asarray(Image.open(input)) + assert img.dtype == np.uint8 + img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1 + Image.fromarray(img).save(output) + + +if __name__ == "__main__": + dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "ADEChallengeData2016" + for name in ["training", "validation"]: + annotation_dir = dataset_dir / "annotations" / name + output_dir = dataset_dir / "annotations_detectron2" / name + output_dir.mkdir(parents=True, exist_ok=True) + for file in tqdm.tqdm(list(annotation_dir.iterdir())): + output_file = output_dir / file.name + convert(file, output_file) diff --git a/Mask2Former/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py b/Mask2Former/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py new file mode 100644 index 0000000..3090c9b --- /dev/null +++ b/Mask2Former/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. + +import functools +import json +import multiprocessing as mp +import numpy as np +import os +import time +from fvcore.common.download import download +from panopticapi.utils import rgb2id +from PIL import Image + +from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES + + +def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map): + panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32) + panoptic = rgb2id(panoptic) + output = np.zeros_like(panoptic, dtype=np.uint8) + 255 + for seg in segments: + cat_id = seg["category_id"] + new_cat_id = id_map[cat_id] + output[panoptic == seg["id"]] = new_cat_id + Image.fromarray(output).save(output_semantic) + + +def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories): + """ + Create semantic segmentation annotations from panoptic segmentation + annotations, to be used by PanopticFPN. + It maps all thing categories to class 0, and maps all unlabeled pixels to class 255. + It maps all stuff categories to contiguous ids starting from 1. + Args: + panoptic_json (str): path to the panoptic json file, in COCO's format. + panoptic_root (str): a directory with panoptic annotation files, in COCO's format. + sem_seg_root (str): a directory to output semantic annotation files + categories (list[dict]): category metadata. Each dict needs to have: + "id": corresponds to the "category_id" in the json annotations + "isthing": 0 or 1 + """ + os.makedirs(sem_seg_root, exist_ok=True) + + id_map = {} # map from category id to id in the output semantic annotation + assert len(categories) <= 254 + for i, k in enumerate(categories): + id_map[k["id"]] = i + # what is id = 0? + # id_map[0] = 255 + print(id_map) + + with open(panoptic_json) as f: + obj = json.load(f) + + pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4)) + + def iter_annotations(): + for anno in obj["annotations"]: + file_name = anno["file_name"] + segments = anno["segments_info"] + input = os.path.join(panoptic_root, file_name) + output = os.path.join(sem_seg_root, file_name) + yield input, output, segments + + print("Start writing to {} ...".format(sem_seg_root)) + start = time.time() + pool.starmap( + functools.partial(_process_panoptic_to_semantic, id_map=id_map), + iter_annotations(), + chunksize=100, + ) + print("Finished. time: {:.2f}s".format(time.time() - start)) + + +if __name__ == "__main__": + dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco") + for s in ["val2017", "train2017"]: + separate_coco_semantic_from_panoptic( + os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)), + os.path.join(dataset_dir, "panoptic_{}".format(s)), + os.path.join(dataset_dir, "panoptic_semseg_{}".format(s)), + COCO_CATEGORIES, + ) diff --git a/Mask2Former/mask2former/__init__.py b/Mask2Former/mask2former/__init__.py new file mode 100644 index 0000000..9b405c8 --- /dev/null +++ b/Mask2Former/mask2former/__init__.py @@ -0,0 +1,26 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import data # register all new datasets +from . import modeling + +# config +from .config import add_maskformer2_config + +# dataset loading +from .data.dataset_mappers.coco_instance_new_baseline_dataset_mapper import COCOInstanceNewBaselineDatasetMapper +from .data.dataset_mappers.coco_panoptic_new_baseline_dataset_mapper import COCOPanopticNewBaselineDatasetMapper +from .data.dataset_mappers.mask_former_instance_dataset_mapper import ( + MaskFormerInstanceDatasetMapper, +) +from .data.dataset_mappers.mask_former_panoptic_dataset_mapper import ( + MaskFormerPanopticDatasetMapper, +) +from .data.dataset_mappers.mask_former_semantic_dataset_mapper import ( + MaskFormerSemanticDatasetMapper, +) + +# models +from .maskformer_model import MaskFormer +from .test_time_augmentation import SemanticSegmentorWithTTA + +# evaluation +from .evaluation.instance_evaluation import InstanceSegEvaluator diff --git a/Mask2Former/mask2former/config.py b/Mask2Former/mask2former/config.py new file mode 100644 index 0000000..adc9309 --- /dev/null +++ b/Mask2Former/mask2former/config.py @@ -0,0 +1,114 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.config import CfgNode as CN + + +def add_maskformer2_config(cfg): + """ + Add config for MASK_FORMER. + """ + # NOTE: configs from original maskformer + # data config + # select the dataset mapper + cfg.INPUT.DATASET_MAPPER_NAME = "mask_former_semantic" + # Color augmentation + cfg.INPUT.COLOR_AUG_SSD = False + # We retry random cropping until no single category in semantic segmentation GT occupies more + # than `SINGLE_CATEGORY_MAX_AREA` part of the crop. + cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0 + # Pad image and segmentation GT in dataset mapper. + cfg.INPUT.SIZE_DIVISIBILITY = -1 + + # solver config + # weight decay on embedding + cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0 + # optimizer + cfg.SOLVER.OPTIMIZER = "ADAMW" + cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1 + + # mask_former model config + cfg.MODEL.MASK_FORMER = CN() + + # loss + cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True + cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1 + cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = 1.0 + cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0 + cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0 + + # transformer config + cfg.MODEL.MASK_FORMER.NHEADS = 8 + cfg.MODEL.MASK_FORMER.DROPOUT = 0.1 + cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048 + cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0 + cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6 + cfg.MODEL.MASK_FORMER.PRE_NORM = False + + cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256 + cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100 + + cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = "res5" + cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False + + # mask_former inference config + cfg.MODEL.MASK_FORMER.TEST = CN() + cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True + cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = False + cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False + cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0 + cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0 + cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False + + # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet) + # you can use this config to override + cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32 + + # pixel decoder config + cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256 + # adding transformer in pixel decoder + cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0 + # pixel decoder + cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder" + + # swin transformer backbone + cfg.MODEL.SWIN = CN() + cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224 + cfg.MODEL.SWIN.PATCH_SIZE = 4 + cfg.MODEL.SWIN.EMBED_DIM = 96 + cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2] + cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24] + cfg.MODEL.SWIN.WINDOW_SIZE = 7 + cfg.MODEL.SWIN.MLP_RATIO = 4.0 + cfg.MODEL.SWIN.QKV_BIAS = True + cfg.MODEL.SWIN.QK_SCALE = None + cfg.MODEL.SWIN.DROP_RATE = 0.0 + cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0 + cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3 + cfg.MODEL.SWIN.APE = False + cfg.MODEL.SWIN.PATCH_NORM = True + cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"] + cfg.MODEL.SWIN.USE_CHECKPOINT = False + + # NOTE: maskformer2 extra configs + # transformer module + cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME = "MultiScaleMaskedTransformerDecoder" + + # LSJ aug + cfg.INPUT.IMAGE_SIZE = 1024 + cfg.INPUT.MIN_SCALE = 0.1 + cfg.INPUT.MAX_SCALE = 2.0 + + # MSDeformAttn encoder configs + cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"] + cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4 + cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8 + + # point loss configs + # Number of points sampled during training for a mask point head. + cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = 112 * 112 + # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the + # original paper. + cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0 + # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in + # the original paper. + cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75 diff --git a/Mask2Former/mask2former/data/__init__.py b/Mask2Former/mask2former/data/__init__.py new file mode 100644 index 0000000..63ba265 --- /dev/null +++ b/Mask2Former/mask2former/data/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import datasets diff --git a/Mask2Former/mask2former/data/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/data/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..71a3668 Binary files /dev/null and b/Mask2Former/mask2former/data/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/data/dataset_mappers/__init__.py b/Mask2Former/mask2former/data/dataset_mappers/__init__.py new file mode 100644 index 0000000..9020c2d --- /dev/null +++ b/Mask2Former/mask2former/data/dataset_mappers/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. 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+# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py +import copy +import logging + +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.data.transforms import TransformGen +from detectron2.structures import BitMasks, Instances + +from pycocotools import mask as coco_mask + +__all__ = ["COCOInstanceNewBaselineDatasetMapper"] + + +def convert_coco_poly_to_mask(segmentations, height, width): + masks = [] + for polygons in segmentations: + rles = coco_mask.frPyObjects(polygons, height, width) + mask = coco_mask.decode(rles) + if len(mask.shape) < 3: + mask = mask[..., None] + mask = torch.as_tensor(mask, dtype=torch.uint8) + mask = mask.any(dim=2) + masks.append(mask) + if masks: + masks = torch.stack(masks, dim=0) + else: + masks = torch.zeros((0, height, width), dtype=torch.uint8) + return masks + + +def build_transform_gen(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + Returns: + list[Augmentation] + """ + assert is_train, "Only support training augmentation" + image_size = cfg.INPUT.IMAGE_SIZE + min_scale = cfg.INPUT.MIN_SCALE + max_scale = cfg.INPUT.MAX_SCALE + + augmentation = [] + + if cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + + augmentation.extend([ + T.ResizeScale( + min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size + ), + T.FixedSizeCrop(crop_size=(image_size, image_size)), + ]) + + return augmentation + + +# This is specifically designed for the COCO dataset. +class COCOInstanceNewBaselineDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer. + + This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + tfm_gens, + image_format, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + tfm_gens: data augmentation + image_format: an image format supported by :func:`detection_utils.read_image`. + """ + self.tfm_gens = tfm_gens + logging.getLogger(__name__).info( + "[COCOInstanceNewBaselineDatasetMapper] Full TransformGens used in training: {}".format(str(self.tfm_gens)) + ) + + self.img_format = image_format + self.is_train = is_train + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + tfm_gens = build_transform_gen(cfg, is_train) + + ret = { + "is_train": is_train, + "tfm_gens": tfm_gens, + "image_format": cfg.INPUT.FORMAT, + } + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + # TODO: get padding mask + # by feeding a "segmentation mask" to the same transforms + padding_mask = np.ones(image.shape[:2]) + + image, transforms = T.apply_transform_gens(self.tfm_gens, image) + # the crop transformation has default padding value 0 for segmentation + padding_mask = transforms.apply_segmentation(padding_mask) + padding_mask = ~ padding_mask.astype(bool) + + image_shape = image.shape[:2] # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + dataset_dict["padding_mask"] = torch.as_tensor(np.ascontiguousarray(padding_mask)) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + return dataset_dict + + if "annotations" in dataset_dict: + # USER: Modify this if you want to keep them for some reason. + for anno in dataset_dict["annotations"]: + # Let's always keep mask + # if not self.mask_on: + # anno.pop("segmentation", None) + anno.pop("keypoints", None) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations(obj, transforms, image_shape) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + # NOTE: does not support BitMask due to augmentation + # Current BitMask cannot handle empty objects + instances = utils.annotations_to_instances(annos, image_shape) + # After transforms such as cropping are applied, the bounding box may no longer + # tightly bound the object. As an example, imagine a triangle object + # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight + # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to + # the intersection of original bounding box and the cropping box. + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + # Need to filter empty instances first (due to augmentation) + instances = utils.filter_empty_instances(instances) + # Generate masks from polygon + h, w = instances.image_size + # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float) + if hasattr(instances, 'gt_masks'): + gt_masks = instances.gt_masks + gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w) + instances.gt_masks = gt_masks + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/Mask2Former/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py b/Mask2Former/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py new file mode 100644 index 0000000..901149f --- /dev/null +++ b/Mask2Former/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py +import copy +import logging + +import numpy as np +import torch + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.data.transforms import TransformGen +from detectron2.structures import BitMasks, Boxes, Instances + +__all__ = ["COCOPanopticNewBaselineDatasetMapper"] + + +def build_transform_gen(cfg, is_train): + """ + Create a list of default :class:`Augmentation` from config. + Now it includes resizing and flipping. + Returns: + list[Augmentation] + """ + assert is_train, "Only support training augmentation" + image_size = cfg.INPUT.IMAGE_SIZE + min_scale = cfg.INPUT.MIN_SCALE + max_scale = cfg.INPUT.MAX_SCALE + + augmentation = [] + + if cfg.INPUT.RANDOM_FLIP != "none": + augmentation.append( + T.RandomFlip( + horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal", + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + ) + ) + + augmentation.extend([ + T.ResizeScale( + min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size + ), + T.FixedSizeCrop(crop_size=(image_size, image_size)), + ]) + + return augmentation + + +# This is specifically designed for the COCO dataset. +class COCOPanopticNewBaselineDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer. + + This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + tfm_gens, + image_format, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + crop_gen: crop augmentation + tfm_gens: data augmentation + image_format: an image format supported by :func:`detection_utils.read_image`. + """ + self.tfm_gens = tfm_gens + logging.getLogger(__name__).info( + "[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: {}".format( + str(self.tfm_gens) + ) + ) + + self.img_format = image_format + self.is_train = is_train + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + tfm_gens = build_transform_gen(cfg, is_train) + + ret = { + "is_train": is_train, + "tfm_gens": tfm_gens, + "image_format": cfg.INPUT.FORMAT, + } + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + image, transforms = T.apply_transform_gens(self.tfm_gens, image) + image_shape = image.shape[:2] # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + return dataset_dict + + if "pan_seg_file_name" in dataset_dict: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + + pan_seg_gt = rgb2id(pan_seg_gt) + + instances = Instances(image_shape) + classes = [] + masks = [] + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + classes.append(class_id) + masks.append(pan_seg_gt == segment_info["id"]) + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + instances.gt_boxes = Boxes(torch.zeros((0, 4))) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + instances.gt_boxes = masks.get_bounding_boxes() + + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/Mask2Former/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py b/Mask2Former/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py new file mode 100644 index 0000000..63e312e --- /dev/null +++ b/Mask2Former/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py @@ -0,0 +1,180 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging + +import numpy as np +import pycocotools.mask as mask_util +import torch +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.projects.point_rend import ColorAugSSDTransform +from detectron2.structures import BitMasks, Instances, polygons_to_bitmask + +__all__ = ["MaskFormerInstanceDatasetMapper"] + + +class MaskFormerInstanceDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer for instance segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + augmentations, + image_format, + size_divisibility, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + size_divisibility: pad image size to be divisible by this value + """ + self.is_train = is_train + self.tfm_gens = augmentations + self.img_format = image_format + self.size_divisibility = size_divisibility + + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + augs = [ + T.ResizeShortestEdge( + cfg.INPUT.MIN_SIZE_TRAIN, + cfg.INPUT.MAX_SIZE_TRAIN, + cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING, + ) + ] + if cfg.INPUT.CROP.ENABLED: + augs.append( + T.RandomCrop( + cfg.INPUT.CROP.TYPE, + cfg.INPUT.CROP.SIZE, + ) + ) + if cfg.INPUT.COLOR_AUG_SSD: + augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT)) + augs.append(T.RandomFlip()) + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY, + } + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + assert self.is_train, "MaskFormerPanopticDatasetMapper should only be used for training!" + + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + aug_input = T.AugInput(image) + aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input) + image = aug_input.image + + # transform instnace masks + assert "annotations" in dataset_dict + for anno in dataset_dict["annotations"]: + anno.pop("keypoints", None) + + annos = [ + utils.transform_instance_annotations(obj, transforms, image.shape[:2]) + for obj in dataset_dict.pop("annotations") + if obj.get("iscrowd", 0) == 0 + ] + + if len(annos): + assert "segmentation" in annos[0] + segms = [obj["segmentation"] for obj in annos] + masks = [] + for segm in segms: + if isinstance(segm, list): + # polygon + masks.append(polygons_to_bitmask(segm, *image.shape[:2])) + elif isinstance(segm, dict): + # COCO RLE + masks.append(mask_util.decode(segm)) + elif isinstance(segm, np.ndarray): + assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format( + segm.ndim + ) + # mask array + masks.append(segm) + else: + raise ValueError( + "Cannot convert segmentation of type '{}' to BitMasks!" + "Supported types are: polygons as list[list[float] or ndarray]," + " COCO-style RLE as a dict, or a binary segmentation mask " + " in a 2D numpy array of shape HxW.".format(type(segm)) + ) + + # Pad image and segmentation label here! + image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks] + + classes = [int(obj["category_id"]) for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + + if self.size_divisibility > 0: + image_size = (image.shape[-2], image.shape[-1]) + padding_size = [ + 0, + self.size_divisibility - image_size[1], + 0, + self.size_divisibility - image_size[0], + ] + # pad image + image = F.pad(image, padding_size, value=128).contiguous() + # pad mask + masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks] + + image_shape = (image.shape[-2], image.shape[-1]) # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = image + + # Prepare per-category binary masks + instances = Instances(image_shape) + instances.gt_classes = classes + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1])) + else: + masks = BitMasks(torch.stack(masks)) + instances.gt_masks = masks.tensor + + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/Mask2Former/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py b/Mask2Former/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py new file mode 100644 index 0000000..ddbc2bd --- /dev/null +++ b/Mask2Former/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py @@ -0,0 +1,165 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging + +import numpy as np +import torch +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.structures import BitMasks, Instances + +from .mask_former_semantic_dataset_mapper import MaskFormerSemanticDatasetMapper + +__all__ = ["MaskFormerPanopticDatasetMapper"] + + +class MaskFormerPanopticDatasetMapper(MaskFormerSemanticDatasetMapper): + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer for panoptic segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + augmentations, + image_format, + ignore_label, + size_divisibility, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + ignore_label: the label that is ignored to evaluation + size_divisibility: pad image size to be divisible by this value + """ + super().__init__( + is_train, + augmentations=augmentations, + image_format=image_format, + ignore_label=ignore_label, + size_divisibility=size_divisibility, + ) + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + assert self.is_train, "MaskFormerPanopticDatasetMapper should only be used for training!" + + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + # semantic segmentation + if "sem_seg_file_name" in dataset_dict: + # PyTorch transformation not implemented for uint16, so converting it to double first + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double") + else: + sem_seg_gt = None + + # panoptic segmentation + if "pan_seg_file_name" in dataset_dict: + pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB") + segments_info = dataset_dict["segments_info"] + else: + pan_seg_gt = None + segments_info = None + + if pan_seg_gt is None: + raise ValueError( + "Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.".format( + dataset_dict["file_name"] + ) + ) + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input) + image = aug_input.image + if sem_seg_gt is not None: + sem_seg_gt = aug_input.sem_seg + + # apply the same transformation to panoptic segmentation + pan_seg_gt = transforms.apply_segmentation(pan_seg_gt) + + from panopticapi.utils import rgb2id + + pan_seg_gt = rgb2id(pan_seg_gt) + + # Pad image and segmentation label here! + image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long")) + pan_seg_gt = torch.as_tensor(pan_seg_gt.astype("long")) + + if self.size_divisibility > 0: + image_size = (image.shape[-2], image.shape[-1]) + padding_size = [ + 0, + self.size_divisibility - image_size[1], + 0, + self.size_divisibility - image_size[0], + ] + image = F.pad(image, padding_size, value=128).contiguous() + if sem_seg_gt is not None: + sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous() + pan_seg_gt = F.pad( + pan_seg_gt, padding_size, value=0 + ).contiguous() # 0 is the VOID panoptic label + + image_shape = (image.shape[-2], image.shape[-1]) # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = image + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = sem_seg_gt.long() + + if "annotations" in dataset_dict: + raise ValueError("Pemantic segmentation dataset should not have 'annotations'.") + + # Prepare per-category binary masks + pan_seg_gt = pan_seg_gt.numpy() + instances = Instances(image_shape) + classes = [] + masks = [] + for segment_info in segments_info: + class_id = segment_info["category_id"] + if not segment_info["iscrowd"]: + classes.append(class_id) + masks.append(pan_seg_gt == segment_info["id"]) + + classes = np.array(classes) + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1])) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/Mask2Former/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py b/Mask2Former/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py new file mode 100644 index 0000000..36ff315 --- /dev/null +++ b/Mask2Former/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py @@ -0,0 +1,184 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging + +import numpy as np +import torch +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T +from detectron2.projects.point_rend import ColorAugSSDTransform +from detectron2.structures import BitMasks, Instances + +__all__ = ["MaskFormerSemanticDatasetMapper"] + + +class MaskFormerSemanticDatasetMapper: + """ + A callable which takes a dataset dict in Detectron2 Dataset format, + and map it into a format used by MaskFormer for semantic segmentation. + + The callable currently does the following: + + 1. Read the image from "file_name" + 2. Applies geometric transforms to the image and annotation + 3. Find and applies suitable cropping to the image and annotation + 4. Prepare image and annotation to Tensors + """ + + @configurable + def __init__( + self, + is_train=True, + *, + augmentations, + image_format, + ignore_label, + size_divisibility, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: for training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + ignore_label: the label that is ignored to evaluation + size_divisibility: pad image size to be divisible by this value + """ + self.is_train = is_train + self.tfm_gens = augmentations + self.img_format = image_format + self.ignore_label = ignore_label + self.size_divisibility = size_divisibility + + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train=True): + # Build augmentation + augs = [ + T.ResizeShortestEdge( + cfg.INPUT.MIN_SIZE_TRAIN, + cfg.INPUT.MAX_SIZE_TRAIN, + cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING, + ) + ] + if cfg.INPUT.CROP.ENABLED: + augs.append( + T.RandomCrop_CategoryAreaConstraint( + cfg.INPUT.CROP.TYPE, + cfg.INPUT.CROP.SIZE, + cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA, + cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + ) + ) + if cfg.INPUT.COLOR_AUG_SSD: + augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT)) + augs.append(T.RandomFlip()) + + # Assume always applies to the training set. + dataset_names = cfg.DATASETS.TRAIN + meta = MetadataCatalog.get(dataset_names[0]) + ignore_label = meta.ignore_label + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "ignore_label": ignore_label, + "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY, + } + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + assert self.is_train, "MaskFormerSemanticDatasetMapper should only be used for training!" + + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + image = utils.read_image(dataset_dict["file_name"], format=self.img_format) + utils.check_image_size(dataset_dict, image) + + if "sem_seg_file_name" in dataset_dict: + # PyTorch transformation not implemented for uint16, so converting it to double first + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double") + else: + sem_seg_gt = None + + if sem_seg_gt is None: + raise ValueError( + "Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.".format( + dataset_dict["file_name"] + ) + ) + + aug_input = T.AugInput(image, sem_seg=sem_seg_gt) + aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input) + image = aug_input.image + sem_seg_gt = aug_input.sem_seg + + # Pad image and segmentation label here! + image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) + if sem_seg_gt is not None: + sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long")) + + if self.size_divisibility > 0: + image_size = (image.shape[-2], image.shape[-1]) + padding_size = [ + 0, + self.size_divisibility - image_size[1], + 0, + self.size_divisibility - image_size[0], + ] + image = F.pad(image, padding_size, value=128).contiguous() + if sem_seg_gt is not None: + sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous() + + image_shape = (image.shape[-2], image.shape[-1]) # h, w + + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"] = image + + if sem_seg_gt is not None: + dataset_dict["sem_seg"] = sem_seg_gt.long() + + if "annotations" in dataset_dict: + raise ValueError("Semantic segmentation dataset should not have 'annotations'.") + + # Prepare per-category binary masks + if sem_seg_gt is not None: + sem_seg_gt = sem_seg_gt.numpy() + instances = Instances(image_shape) + classes = np.unique(sem_seg_gt) + # remove ignored region + classes = classes[classes != self.ignore_label] + instances.gt_classes = torch.tensor(classes, dtype=torch.int64) + + masks = [] + for class_id in classes: + masks.append(sem_seg_gt == class_id) + + if len(masks) == 0: + # Some image does not have annotation (all ignored) + instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1])) + else: + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks]) + ) + instances.gt_masks = masks.tensor + + dataset_dict["instances"] = instances + + return dataset_dict diff --git a/Mask2Former/mask2former/data/datasets/__init__.py b/Mask2Former/mask2former/data/datasets/__init__.py new file mode 100644 index 0000000..403a678 --- /dev/null +++ b/Mask2Former/mask2former/data/datasets/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import ( + register_ade20k_full, + register_ade20k_panoptic, + register_coco_stuff_10k, + register_mapillary_vistas, + register_coco_panoptic_annos_semseg, + register_ade20k_instance, + register_mapillary_vistas_panoptic, +) diff --git a/Mask2Former/mask2former/data/datasets/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/data/datasets/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..bda55a0 Binary files /dev/null and b/Mask2Former/mask2former/data/datasets/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/data/datasets/__pycache__/register_ade20k_full.cpython-38.pyc b/Mask2Former/mask2former/data/datasets/__pycache__/register_ade20k_full.cpython-38.pyc new file mode 100644 index 0000000..794712f Binary files /dev/null and b/Mask2Former/mask2former/data/datasets/__pycache__/register_ade20k_full.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/data/datasets/__pycache__/register_ade20k_instance.cpython-38.pyc b/Mask2Former/mask2former/data/datasets/__pycache__/register_ade20k_instance.cpython-38.pyc new file mode 100644 index 0000000..d35dffd Binary files /dev/null and b/Mask2Former/mask2former/data/datasets/__pycache__/register_ade20k_instance.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/data/datasets/__pycache__/register_ade20k_panoptic.cpython-38.pyc b/Mask2Former/mask2former/data/datasets/__pycache__/register_ade20k_panoptic.cpython-38.pyc new file mode 100644 index 0000000..8f81c79 Binary files /dev/null and b/Mask2Former/mask2former/data/datasets/__pycache__/register_ade20k_panoptic.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/data/datasets/__pycache__/register_coco_panoptic_annos_semseg.cpython-38.pyc b/Mask2Former/mask2former/data/datasets/__pycache__/register_coco_panoptic_annos_semseg.cpython-38.pyc new file mode 100644 index 0000000..ae9d81e Binary files /dev/null and b/Mask2Former/mask2former/data/datasets/__pycache__/register_coco_panoptic_annos_semseg.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/data/datasets/__pycache__/register_coco_stuff_10k.cpython-38.pyc b/Mask2Former/mask2former/data/datasets/__pycache__/register_coco_stuff_10k.cpython-38.pyc new file mode 100644 index 0000000..0f597b7 Binary files /dev/null and b/Mask2Former/mask2former/data/datasets/__pycache__/register_coco_stuff_10k.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/data/datasets/__pycache__/register_mapillary_vistas.cpython-38.pyc b/Mask2Former/mask2former/data/datasets/__pycache__/register_mapillary_vistas.cpython-38.pyc new file mode 100644 index 0000000..9e625ef Binary files /dev/null and b/Mask2Former/mask2former/data/datasets/__pycache__/register_mapillary_vistas.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/data/datasets/__pycache__/register_mapillary_vistas_panoptic.cpython-38.pyc b/Mask2Former/mask2former/data/datasets/__pycache__/register_mapillary_vistas_panoptic.cpython-38.pyc new file mode 100644 index 0000000..4ea0e7a Binary files /dev/null and b/Mask2Former/mask2former/data/datasets/__pycache__/register_mapillary_vistas_panoptic.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/data/datasets/register_ade20k_full.py b/Mask2Former/mask2former/data/datasets/register_ade20k_full.py new file mode 100644 index 0000000..7121a22 --- /dev/null +++ b/Mask2Former/mask2former/data/datasets/register_ade20k_full.py @@ -0,0 +1,964 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg + +ADE20K_SEM_SEG_FULL_CATEGORIES = [ + {"name": "wall", "id": 2978, "trainId": 0}, + {"name": "building, edifice", "id": 312, "trainId": 1}, + {"name": "sky", "id": 2420, "trainId": 2}, + {"name": "tree", "id": 2855, "trainId": 3}, + {"name": "road, route", "id": 2131, "trainId": 4}, + {"name": "floor, flooring", "id": 976, "trainId": 5}, + {"name": "ceiling", "id": 447, "trainId": 6}, + {"name": "bed", "id": 165, "trainId": 7}, + {"name": "sidewalk, pavement", "id": 2377, "trainId": 8}, + {"name": "earth, ground", "id": 838, "trainId": 9}, + {"name": "cabinet", "id": 350, "trainId": 10}, + {"name": "person, individual, someone, somebody, mortal, soul", "id": 1831, "trainId": 11}, + {"name": "grass", "id": 1125, "trainId": 12}, + {"name": "windowpane, window", "id": 3055, "trainId": 13}, + {"name": "car, auto, automobile, machine, motorcar", "id": 401, "trainId": 14}, + {"name": "mountain, mount", "id": 1610, "trainId": 15}, + {"name": "plant, flora, plant life", "id": 1910, "trainId": 16}, + {"name": "table", "id": 2684, "trainId": 17}, + {"name": "chair", "id": 471, "trainId": 18}, + {"name": "curtain, drape, drapery, mantle, pall", "id": 687, "trainId": 19}, + {"name": "door", "id": 774, "trainId": 20}, + {"name": "sofa, couch, lounge", "id": 2473, "trainId": 21}, + {"name": "sea", "id": 2264, "trainId": 22}, + {"name": "painting, picture", "id": 1735, "trainId": 23}, + {"name": "water", "id": 2994, "trainId": 24}, + {"name": "mirror", "id": 1564, "trainId": 25}, + {"name": "house", "id": 1276, "trainId": 26}, + {"name": "rug, carpet, carpeting", "id": 2178, "trainId": 27}, + {"name": "shelf", "id": 2329, "trainId": 28}, + {"name": "armchair", "id": 57, "trainId": 29}, + {"name": "fence, fencing", "id": 907, "trainId": 30}, + {"name": "field", "id": 913, "trainId": 31}, + {"name": "lamp", "id": 1395, "trainId": 32}, + {"name": "rock, stone", "id": 2138, "trainId": 33}, + {"name": "seat", "id": 2272, "trainId": 34}, + {"name": "river", "id": 2128, "trainId": 35}, + {"name": "desk", "id": 724, 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2663, "trainId": 728}, + { + "name": "cellular telephone, cellular phone, cellphone, cell, mobile phone", + "id": 457, + "trainId": 729, + }, + {"name": "tallboy", "id": 2701, "trainId": 730}, + {"name": "stapler", "id": 2540, "trainId": 731}, + {"name": "sauna", "id": 2231, "trainId": 732}, + {"name": "test tube", "id": 2746, "trainId": 733}, + {"name": "palette", "id": 1738, "trainId": 734}, + {"name": "shopping carts", "id": 2350, "trainId": 735}, + {"name": "tools", "id": 2808, "trainId": 736}, + {"name": "push button, push, button", "id": 2025, "trainId": 737}, + {"name": "star", "id": 2541, "trainId": 738}, + {"name": "roof rack", "id": 2156, "trainId": 739}, + {"name": "barbed wire", "id": 126, "trainId": 740}, + {"name": "spray", "id": 2512, "trainId": 741}, + {"name": "ear", "id": 831, "trainId": 742}, + {"name": "sponge", "id": 2503, "trainId": 743}, + {"name": "racket", "id": 2039, "trainId": 744}, + {"name": "tins", "id": 2774, "trainId": 745}, + {"name": "eyeglasses", "id": 886, "trainId": 746}, + {"name": "file", "id": 919, "trainId": 747}, + {"name": "scarfs", "id": 2240, "trainId": 748}, + {"name": "sugar bowl", "id": 2636, "trainId": 749}, + {"name": "flip flop", "id": 963, "trainId": 750}, + {"name": "headstones", "id": 1218, "trainId": 751}, + {"name": "laptop bag", "id": 1406, "trainId": 752}, + {"name": "leash", "id": 1420, "trainId": 753}, + {"name": "climbing frame", "id": 526, "trainId": 754}, + {"name": "suit hanger", "id": 2639, "trainId": 755}, + {"name": "floor spotlight", "id": 975, "trainId": 756}, + {"name": "plate rack", "id": 1921, "trainId": 757}, + {"name": "sewer", "id": 2305, "trainId": 758}, + {"name": "hard drive", "id": 1193, "trainId": 759}, + {"name": "sprinkler", "id": 2517, "trainId": 760}, + {"name": "tools box", "id": 2809, "trainId": 761}, + {"name": "necklace", "id": 1647, "trainId": 762}, + {"name": "bulbs", "id": 314, "trainId": 763}, + {"name": "steel industry", "id": 2560, "trainId": 764}, + {"name": "club", "id": 545, "trainId": 765}, + {"name": "jack", "id": 1345, "trainId": 766}, + {"name": "door bars", "id": 775, "trainId": 767}, + { + "name": "control panel, instrument panel, control board, board, panel", + "id": 603, + "trainId": 768, + }, + {"name": "hairbrush", "id": 1163, "trainId": 769}, + {"name": "napkin holder", "id": 1641, "trainId": 770}, + {"name": "office", "id": 1678, "trainId": 771}, + {"name": "smoke detector", "id": 2450, "trainId": 772}, + {"name": "utensils", "id": 2915, "trainId": 773}, + {"name": "apron", "id": 42, "trainId": 774}, + {"name": "scissors", "id": 2242, "trainId": 775}, + {"name": "terminal", "id": 2741, "trainId": 776}, + {"name": "grinder", "id": 1143, "trainId": 777}, + {"name": "entry phone", "id": 862, "trainId": 778}, + {"name": "newspaper stand", "id": 1654, "trainId": 779}, + {"name": "pepper shaker", "id": 1826, "trainId": 780}, + {"name": "onions", "id": 1689, "trainId": 781}, + { + "name": "central processing unit, cpu, c p u , central processor, processor, mainframe", + "id": 3124, + "trainId": 782, + }, + {"name": "tape", "id": 2710, "trainId": 783}, + {"name": "bat", "id": 152, "trainId": 784}, + {"name": "coaster", "id": 549, "trainId": 785}, + {"name": "calculator", "id": 360, "trainId": 786}, + {"name": "potatoes", "id": 1982, "trainId": 787}, + {"name": "luggage rack", "id": 1478, "trainId": 788}, + {"name": "salt", "id": 2203, "trainId": 789}, + {"name": "street number", "id": 2612, "trainId": 790}, + {"name": "viewpoint", "id": 2956, "trainId": 791}, + {"name": "sword", "id": 2681, "trainId": 792}, + {"name": "cd", "id": 437, "trainId": 793}, + {"name": "rowing machine", "id": 2171, "trainId": 794}, + {"name": "plug", "id": 1933, "trainId": 795}, + {"name": "andiron, firedog, dog, dog-iron", "id": 3110, "trainId": 796}, + {"name": "pepper", "id": 1824, "trainId": 797}, + {"name": "tongs", "id": 2803, "trainId": 798}, + {"name": "bonfire", "id": 234, "trainId": 799}, + {"name": "dog dish", "id": 764, "trainId": 800}, + {"name": "belt", "id": 177, "trainId": 801}, + {"name": "dumbbells", "id": 817, "trainId": 802}, + {"name": "videocassette recorder, vcr", "id": 3145, "trainId": 803}, + {"name": "hook", "id": 1262, "trainId": 804}, + {"name": "envelopes", "id": 864, "trainId": 805}, + {"name": "shower faucet", "id": 2359, "trainId": 806}, + {"name": "watch", "id": 2992, "trainId": 807}, + {"name": "padlock", "id": 1725, "trainId": 808}, + {"name": "swimming pool ladder", "id": 2667, "trainId": 809}, + {"name": "spanners", "id": 2484, "trainId": 810}, + {"name": "gravy boat", "id": 1133, "trainId": 811}, + {"name": "notice board", "id": 1667, "trainId": 812}, + {"name": "trash bags", "id": 2847, "trainId": 813}, + {"name": "fire alarm", "id": 932, "trainId": 814}, + {"name": "ladle", "id": 1392, "trainId": 815}, + {"name": "stethoscope", "id": 2573, "trainId": 816}, + {"name": "rocket", "id": 2140, "trainId": 817}, + {"name": "funnel", "id": 1046, "trainId": 818}, + {"name": "bowling pins", "id": 264, "trainId": 819}, + {"name": "valve", "id": 2927, "trainId": 820}, + {"name": "thermometer", "id": 2752, "trainId": 821}, + {"name": "cups", "id": 679, "trainId": 822}, + {"name": "spice jar", "id": 2493, "trainId": 823}, + {"name": "night light", "id": 1658, "trainId": 824}, + {"name": "soaps", "id": 2466, "trainId": 825}, + {"name": "games table", "id": 1057, "trainId": 826}, + {"name": "slotted spoon", "id": 2444, "trainId": 827}, + {"name": "reel", "id": 2093, "trainId": 828}, + {"name": "scourer", "id": 2248, "trainId": 829}, + {"name": "sleeping robe", "id": 2432, "trainId": 830}, + {"name": "desk mat", "id": 726, "trainId": 831}, + {"name": "dumbbell", "id": 816, "trainId": 832}, + {"name": "hammer", "id": 1171, "trainId": 833}, + {"name": "tie", "id": 2766, "trainId": 834}, + {"name": "typewriter", "id": 2900, "trainId": 835}, + {"name": "shaker", "id": 2313, "trainId": 836}, + {"name": "cheese dish", "id": 488, "trainId": 837}, + {"name": "sea star", "id": 2265, "trainId": 838}, + {"name": "racquet", "id": 2043, "trainId": 839}, + {"name": "butane gas cylinder", "id": 332, "trainId": 840}, + {"name": "paper weight", "id": 1771, "trainId": 841}, + {"name": "shaving brush", "id": 2320, "trainId": 842}, + {"name": "sunglasses", "id": 2646, "trainId": 843}, + {"name": "gear shift", "id": 1089, "trainId": 844}, + {"name": "towel rail", "id": 2826, "trainId": 845}, + {"name": "adding machine, totalizer, totaliser", "id": 3148, "trainId": 846}, +] + + +def _get_ade20k_full_meta(): + # Id 0 is reserved for ignore_label, we change ignore_label for 0 + # to 255 in our pre-processing, so all ids are shifted by 1. + stuff_ids = [k["id"] for k in ADE20K_SEM_SEG_FULL_CATEGORIES] + assert len(stuff_ids) == 847, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 91], used in models) to ids in the dataset (used for processing results) + stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} + stuff_classes = [k["name"] for k in ADE20K_SEM_SEG_FULL_CATEGORIES] + + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + } + return ret + + +def register_all_ade20k_full(root): + root = os.path.join(root, "ADE20K_2021_17_01") + meta = _get_ade20k_full_meta() + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(root, "images_detectron2", dirname) + gt_dir = os.path.join(root, "annotations_detectron2", dirname) + name = f"ade20k_full_sem_seg_{name}" + DatasetCatalog.register( + name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="tif", image_ext="jpg") + ) + MetadataCatalog.get(name).set( + stuff_classes=meta["stuff_classes"][:], + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=65535, # NOTE: gt is saved in 16-bit TIFF images + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_ade20k_full(_root) diff --git a/Mask2Former/mask2former/data/datasets/register_ade20k_instance.py b/Mask2Former/mask2former/data/datasets/register_ade20k_instance.py new file mode 100644 index 0000000..1ded709 --- /dev/null +++ b/Mask2Former/mask2former/data/datasets/register_ade20k_instance.py @@ -0,0 +1,53 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import logging +import numpy as np +import os +from PIL import Image + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets.coco import load_coco_json, register_coco_instances +from detectron2.utils.file_io import PathManager + +ADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}] + + +_PREDEFINED_SPLITS = { + # point annotations without masks + "ade20k_instance_train": ( + "ADEChallengeData2016/images/training", + "ADEChallengeData2016/ade20k_instance_train.json", + ), + "ade20k_instance_val": ( + "ADEChallengeData2016/images/validation", + "ADEChallengeData2016/ade20k_instance_val.json", + ), +} + + +def _get_ade_instances_meta(): + thing_ids = [k["id"] for k in ADE_CATEGORIES] + assert len(thing_ids) == 100, len(thing_ids) + # Mapping from the incontiguous ADE category id to an id in [0, 99] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in ADE_CATEGORIES] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + } + return ret + + +def register_all_ade20k_instance(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS.items(): + # Assume pre-defined datasets live in `./datasets`. + register_coco_instances( + key, + _get_ade_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_ade20k_instance(_root) diff --git a/Mask2Former/mask2former/data/datasets/register_ade20k_panoptic.py b/Mask2Former/mask2former/data/datasets/register_ade20k_panoptic.py new file mode 100644 index 0000000..a76c999 --- /dev/null +++ b/Mask2Former/mask2former/data/datasets/register_ade20k_panoptic.py @@ -0,0 +1,390 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.file_io import PathManager + +ADE20K_150_CATEGORIES = [ + {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "wall"}, + {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "building"}, + {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "sky"}, + {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "floor"}, + {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "tree"}, + {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ceiling"}, + {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "road, route"}, + {"color": [204, 5, 255], "id": 7, "isthing": 1, "name": "bed"}, + {"color": [230, 230, 230], "id": 8, "isthing": 1, "name": "window "}, + {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "grass"}, + {"color": [224, 5, 255], "id": 10, "isthing": 1, "name": "cabinet"}, + {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "sidewalk, pavement"}, + {"color": [150, 5, 61], "id": 12, "isthing": 1, "name": "person"}, + {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "earth, ground"}, + {"color": [8, 255, 51], "id": 14, "isthing": 1, "name": "door"}, + {"color": [255, 6, 82], "id": 15, "isthing": 1, "name": "table"}, + {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "mountain, mount"}, + {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "plant"}, + {"color": [255, 51, 7], "id": 18, "isthing": 1, "name": "curtain"}, + {"color": [204, 70, 3], "id": 19, "isthing": 1, "name": "chair"}, + {"color": [0, 102, 200], "id": 20, "isthing": 1, "name": "car"}, + {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "water"}, + {"color": [255, 6, 51], "id": 22, "isthing": 1, "name": "painting, picture"}, + {"color": [11, 102, 255], "id": 23, "isthing": 1, "name": "sofa"}, + {"color": [255, 7, 71], "id": 24, "isthing": 1, "name": "shelf"}, + {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "house"}, + {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "sea"}, + {"color": [220, 220, 220], "id": 27, "isthing": 1, "name": "mirror"}, + {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "rug"}, + {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "field"}, + {"color": [8, 255, 214], "id": 30, "isthing": 1, "name": "armchair"}, + {"color": [7, 255, 224], "id": 31, "isthing": 1, "name": "seat"}, + {"color": [255, 184, 6], "id": 32, "isthing": 1, "name": "fence"}, + {"color": [10, 255, 71], "id": 33, "isthing": 1, "name": "desk"}, + {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "rock, stone"}, + {"color": [7, 255, 255], "id": 35, "isthing": 1, "name": "wardrobe, closet, press"}, + {"color": [224, 255, 8], "id": 36, "isthing": 1, "name": "lamp"}, + {"color": [102, 8, 255], "id": 37, "isthing": 1, "name": "tub"}, + {"color": [255, 61, 6], "id": 38, "isthing": 1, "name": "rail"}, + {"color": [255, 194, 7], "id": 39, "isthing": 1, "name": "cushion"}, + {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "base, pedestal, stand"}, + {"color": [0, 255, 20], "id": 41, "isthing": 1, "name": "box"}, + {"color": [255, 8, 41], "id": 42, "isthing": 1, "name": "column, pillar"}, + {"color": [255, 5, 153], "id": 43, "isthing": 1, "name": "signboard, sign"}, + { + "color": [6, 51, 255], + "id": 44, + "isthing": 1, + "name": "chest of drawers, chest, bureau, dresser", + }, + {"color": [235, 12, 255], "id": 45, "isthing": 1, "name": "counter"}, + {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "sand"}, + {"color": [0, 163, 255], "id": 47, "isthing": 1, "name": "sink"}, + {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "skyscraper"}, + {"color": [250, 10, 15], "id": 49, "isthing": 1, "name": "fireplace"}, + {"color": [20, 255, 0], "id": 50, "isthing": 1, "name": "refrigerator, icebox"}, + {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "grandstand, covered stand"}, + {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "path"}, + {"color": [255, 224, 0], "id": 53, "isthing": 1, "name": "stairs"}, + {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "runway"}, + {"color": [0, 0, 255], "id": 55, "isthing": 1, "name": "case, display case, showcase, vitrine"}, + { + "color": [255, 71, 0], + "id": 56, + "isthing": 1, + "name": "pool table, billiard table, snooker table", + }, + {"color": [0, 235, 255], "id": 57, "isthing": 1, "name": "pillow"}, + {"color": [0, 173, 255], "id": 58, "isthing": 1, "name": "screen door, screen"}, + {"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "stairway, staircase"}, + {"color": [11, 200, 200], "id": 60, "isthing": 0, "name": "river"}, + {"color": [255, 82, 0], "id": 61, "isthing": 0, "name": "bridge, span"}, + {"color": [0, 255, 245], "id": 62, "isthing": 1, "name": "bookcase"}, + {"color": [0, 61, 255], "id": 63, "isthing": 0, "name": "blind, screen"}, + {"color": [0, 255, 112], "id": 64, "isthing": 1, "name": "coffee table"}, + { + "color": [0, 255, 133], + "id": 65, + "isthing": 1, + "name": "toilet, can, commode, crapper, pot, potty, stool, throne", + }, + {"color": [255, 0, 0], "id": 66, "isthing": 1, "name": "flower"}, + {"color": [255, 163, 0], "id": 67, "isthing": 1, "name": "book"}, + {"color": [255, 102, 0], "id": 68, "isthing": 0, "name": "hill"}, + {"color": [194, 255, 0], "id": 69, "isthing": 1, "name": "bench"}, + {"color": [0, 143, 255], "id": 70, "isthing": 1, "name": "countertop"}, + {"color": [51, 255, 0], "id": 71, "isthing": 1, "name": "stove"}, + {"color": [0, 82, 255], "id": 72, "isthing": 1, "name": "palm, palm tree"}, + {"color": [0, 255, 41], "id": 73, "isthing": 1, "name": "kitchen island"}, + {"color": [0, 255, 173], "id": 74, "isthing": 1, "name": "computer"}, + {"color": [10, 0, 255], "id": 75, "isthing": 1, "name": "swivel chair"}, + {"color": [173, 255, 0], "id": 76, "isthing": 1, "name": "boat"}, + {"color": [0, 255, 153], "id": 77, "isthing": 0, "name": "bar"}, + {"color": [255, 92, 0], "id": 78, "isthing": 1, "name": "arcade machine"}, + {"color": [255, 0, 255], "id": 79, "isthing": 0, "name": "hovel, hut, hutch, shack, shanty"}, + {"color": [255, 0, 245], "id": 80, "isthing": 1, "name": "bus"}, + {"color": [255, 0, 102], "id": 81, "isthing": 1, "name": "towel"}, + {"color": [255, 173, 0], "id": 82, "isthing": 1, "name": "light"}, + {"color": [255, 0, 20], "id": 83, "isthing": 1, "name": "truck"}, + {"color": [255, 184, 184], "id": 84, "isthing": 0, "name": "tower"}, + {"color": [0, 31, 255], "id": 85, "isthing": 1, "name": "chandelier"}, + {"color": [0, 255, 61], "id": 86, "isthing": 1, "name": "awning, sunshade, sunblind"}, + {"color": [0, 71, 255], "id": 87, "isthing": 1, "name": "street lamp"}, + {"color": [255, 0, 204], "id": 88, "isthing": 1, "name": "booth"}, + {"color": [0, 255, 194], "id": 89, "isthing": 1, "name": "tv"}, + {"color": [0, 255, 82], "id": 90, "isthing": 1, "name": "plane"}, + {"color": [0, 10, 255], "id": 91, "isthing": 0, "name": "dirt track"}, + {"color": [0, 112, 255], "id": 92, "isthing": 1, "name": "clothes"}, + {"color": [51, 0, 255], "id": 93, "isthing": 1, "name": "pole"}, + {"color": [0, 194, 255], "id": 94, "isthing": 0, "name": "land, ground, soil"}, + { + "color": [0, 122, 255], + "id": 95, + "isthing": 1, + "name": "bannister, banister, balustrade, balusters, handrail", + }, + { + "color": [0, 255, 163], + "id": 96, + "isthing": 0, + "name": "escalator, moving staircase, moving stairway", + }, + { + "color": [255, 153, 0], + "id": 97, + "isthing": 1, + "name": "ottoman, pouf, pouffe, puff, hassock", + }, + {"color": [0, 255, 10], "id": 98, "isthing": 1, "name": "bottle"}, + {"color": [255, 112, 0], "id": 99, "isthing": 0, "name": "buffet, counter, sideboard"}, + { + "color": [143, 255, 0], + "id": 100, + "isthing": 0, + "name": "poster, posting, placard, notice, bill, card", + }, + {"color": [82, 0, 255], "id": 101, "isthing": 0, "name": "stage"}, + {"color": [163, 255, 0], "id": 102, "isthing": 1, "name": "van"}, + {"color": [255, 235, 0], "id": 103, "isthing": 1, "name": "ship"}, + {"color": [8, 184, 170], "id": 104, "isthing": 1, "name": "fountain"}, + { + "color": [133, 0, 255], + "id": 105, + "isthing": 0, + "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter", + }, + {"color": [0, 255, 92], "id": 106, "isthing": 0, "name": "canopy"}, + { + "color": [184, 0, 255], + "id": 107, + "isthing": 1, + "name": "washer, automatic washer, washing machine", + }, + {"color": [255, 0, 31], "id": 108, "isthing": 1, "name": "plaything, toy"}, + {"color": [0, 184, 255], "id": 109, "isthing": 0, "name": "pool"}, + {"color": [0, 214, 255], "id": 110, "isthing": 1, "name": "stool"}, + {"color": [255, 0, 112], "id": 111, "isthing": 1, "name": "barrel, cask"}, + {"color": [92, 255, 0], "id": 112, "isthing": 1, "name": "basket, handbasket"}, + {"color": [0, 224, 255], "id": 113, "isthing": 0, "name": "falls"}, + {"color": [112, 224, 255], "id": 114, "isthing": 0, "name": "tent"}, + {"color": [70, 184, 160], "id": 115, "isthing": 1, "name": "bag"}, + {"color": [163, 0, 255], "id": 116, "isthing": 1, "name": "minibike, motorbike"}, + {"color": [153, 0, 255], "id": 117, "isthing": 0, "name": "cradle"}, + {"color": [71, 255, 0], "id": 118, "isthing": 1, "name": "oven"}, + {"color": [255, 0, 163], "id": 119, "isthing": 1, "name": "ball"}, + {"color": [255, 204, 0], "id": 120, "isthing": 1, "name": "food, solid food"}, + {"color": [255, 0, 143], "id": 121, "isthing": 1, "name": "step, stair"}, + {"color": [0, 255, 235], "id": 122, "isthing": 0, "name": "tank, storage tank"}, + {"color": [133, 255, 0], "id": 123, "isthing": 1, "name": "trade name"}, + {"color": [255, 0, 235], "id": 124, "isthing": 1, "name": "microwave"}, + {"color": [245, 0, 255], "id": 125, "isthing": 1, "name": "pot"}, + {"color": [255, 0, 122], "id": 126, "isthing": 1, "name": "animal"}, + {"color": [255, 245, 0], "id": 127, "isthing": 1, "name": "bicycle"}, + {"color": [10, 190, 212], "id": 128, "isthing": 0, "name": "lake"}, + {"color": [214, 255, 0], "id": 129, "isthing": 1, "name": "dishwasher"}, + {"color": [0, 204, 255], "id": 130, "isthing": 1, "name": "screen"}, + {"color": [20, 0, 255], "id": 131, "isthing": 0, "name": "blanket, cover"}, + {"color": [255, 255, 0], "id": 132, "isthing": 1, "name": "sculpture"}, + {"color": [0, 153, 255], "id": 133, "isthing": 1, "name": "hood, exhaust hood"}, + {"color": [0, 41, 255], "id": 134, "isthing": 1, "name": "sconce"}, + {"color": [0, 255, 204], "id": 135, "isthing": 1, "name": "vase"}, + {"color": [41, 0, 255], "id": 136, "isthing": 1, "name": "traffic light"}, + {"color": [41, 255, 0], "id": 137, "isthing": 1, "name": "tray"}, + {"color": [173, 0, 255], "id": 138, "isthing": 1, "name": "trash can"}, + {"color": [0, 245, 255], "id": 139, "isthing": 1, "name": "fan"}, + {"color": [71, 0, 255], "id": 140, "isthing": 0, "name": "pier"}, + {"color": [122, 0, 255], "id": 141, "isthing": 0, "name": "crt screen"}, + {"color": [0, 255, 184], "id": 142, "isthing": 1, "name": "plate"}, + {"color": [0, 92, 255], "id": 143, "isthing": 1, "name": "monitor"}, + {"color": [184, 255, 0], "id": 144, "isthing": 1, "name": "bulletin board"}, + {"color": [0, 133, 255], "id": 145, "isthing": 0, "name": "shower"}, + {"color": [255, 214, 0], "id": 146, "isthing": 1, "name": "radiator"}, + {"color": [25, 194, 194], "id": 147, "isthing": 1, "name": "glass, drinking glass"}, + {"color": [102, 255, 0], "id": 148, "isthing": 1, "name": "clock"}, + {"color": [92, 0, 255], "id": 149, "isthing": 1, "name": "flag"}, +] + +ADE20k_COLORS = [k["color"] for k in ADE20K_150_CATEGORIES] + +MetadataCatalog.get("ade20k_sem_seg_train").set( + stuff_colors=ADE20k_COLORS[:], +) + +MetadataCatalog.get("ade20k_sem_seg_val").set( + stuff_colors=ADE20k_COLORS[:], +) + + +def load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = ann["image_id"] + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + sem_label_file = os.path.join(semseg_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "sem_seg_file_name": sem_label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] + return ret + + +def register_ade20k_panoptic( + name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None +): + """ + Register a "standard" version of ADE20k panoptic segmentation dataset named `name`. + The dictionaries in this registered dataset follows detectron2's standard format. + Hence it's called "standard". + Args: + name (str): the name that identifies a dataset, + e.g. "ade20k_panoptic_train" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images in COCO format + panoptic_json (str): path to the json panoptic annotation file in COCO format + sem_seg_root (none): not used, to be consistent with + `register_coco_panoptic_separated`. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + DatasetCatalog.register( + panoptic_name, + lambda: load_ade20k_panoptic_json( + panoptic_json, image_root, panoptic_root, semantic_root, metadata + ), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="ade20k_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **metadata, + ) + + +_PREDEFINED_SPLITS_ADE20K_PANOPTIC = { + "ade20k_panoptic_train": ( + "ADEChallengeData2016/images/training", + "ADEChallengeData2016/ade20k_panoptic_train", + "ADEChallengeData2016/ade20k_panoptic_train.json", + "ADEChallengeData2016/annotations_detectron2/training", + "ADEChallengeData2016/ade20k_instance_train.json", + ), + "ade20k_panoptic_val": ( + "ADEChallengeData2016/images/validation", + "ADEChallengeData2016/ade20k_panoptic_val", + "ADEChallengeData2016/ade20k_panoptic_val.json", + "ADEChallengeData2016/annotations_detectron2/validation", + "ADEChallengeData2016/ade20k_instance_val.json", + ), +} + + +def get_metadata(): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1] + stuff_classes = [k["name"] for k in ADE20K_150_CATEGORIES] + stuff_colors = [k["color"] for k in ADE20K_150_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(ADE20K_150_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + # else: + # stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + # in order to use sem_seg evaluator + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + + +def register_all_ade20k_panoptic(root): + metadata = get_metadata() + for ( + prefix, + (image_root, panoptic_root, panoptic_json, semantic_root, instance_json), + ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items(): + # The "standard" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic-DeepLab + register_ade20k_panoptic( + prefix, + metadata, + os.path.join(root, image_root), + os.path.join(root, panoptic_root), + os.path.join(root, semantic_root), + os.path.join(root, panoptic_json), + os.path.join(root, instance_json), + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_ade20k_panoptic(_root) diff --git a/Mask2Former/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py b/Mask2Former/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py new file mode 100644 index 0000000..eecd413 --- /dev/null +++ b/Mask2Former/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py @@ -0,0 +1,181 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg +from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES +from detectron2.utils.file_io import PathManager + + +_PREDEFINED_SPLITS_COCO_PANOPTIC = { + "coco_2017_train_panoptic": ( + # This is the original panoptic annotation directory + "coco/panoptic_train2017", + "coco/annotations/panoptic_train2017.json", + # This directory contains semantic annotations that are + # converted from panoptic annotations. + # It is used by PanopticFPN. + # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py + # to create these directories. + "coco/panoptic_semseg_train2017", + ), + "coco_2017_val_panoptic": ( + "coco/panoptic_val2017", + "coco/annotations/panoptic_val2017.json", + "coco/panoptic_semseg_val2017", + ), +} + + +def get_metadata(): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1] + thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1] + stuff_classes = [k["name"] for k in COCO_CATEGORIES] + stuff_colors = [k["color"] for k in COCO_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(COCO_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + # else: + # stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + # in order to use sem_seg evaluator + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + + +def load_coco_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = int(ann["image_id"]) + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + sem_label_file = os.path.join(semseg_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "sem_seg_file_name": sem_label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] + return ret + + +def register_coco_panoptic_annos_sem_seg( + name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json +): + panoptic_name = name + delattr(MetadataCatalog.get(panoptic_name), "thing_classes") + delattr(MetadataCatalog.get(panoptic_name), "thing_colors") + MetadataCatalog.get(panoptic_name).set( + thing_classes=metadata["thing_classes"], + thing_colors=metadata["thing_colors"], + # thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"], + ) + + # the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg" + semantic_name = name + "_with_sem_seg" + DatasetCatalog.register( + semantic_name, + lambda: load_coco_panoptic_json(panoptic_json, image_root, panoptic_root, sem_seg_root, metadata), + ) + MetadataCatalog.get(semantic_name).set( + sem_seg_root=sem_seg_root, + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="coco_panoptic_seg", + ignore_label=255, + label_divisor=1000, + **metadata, + ) + + +def register_all_coco_panoptic_annos_sem_seg(root): + for ( + prefix, + (panoptic_root, panoptic_json, semantic_root), + ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items(): + prefix_instances = prefix[: -len("_panoptic")] + instances_meta = MetadataCatalog.get(prefix_instances) + image_root, instances_json = instances_meta.image_root, instances_meta.json_file + + register_coco_panoptic_annos_sem_seg( + prefix, + get_metadata(), + image_root, + os.path.join(root, panoptic_root), + os.path.join(root, panoptic_json), + os.path.join(root, semantic_root), + instances_json, + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_coco_panoptic_annos_sem_seg(_root) diff --git a/Mask2Former/mask2former/data/datasets/register_coco_stuff_10k.py b/Mask2Former/mask2former/data/datasets/register_coco_stuff_10k.py new file mode 100644 index 0000000..a1ec037 --- /dev/null +++ b/Mask2Former/mask2former/data/datasets/register_coco_stuff_10k.py @@ -0,0 +1,223 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg + +COCO_CATEGORIES = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, + {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, + {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, + {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, + {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, + {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, + {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, + {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, + {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, + {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, + {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, + {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, + {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, + {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, + {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, + {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, + {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, + {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, + {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, + {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, + {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, + {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, + {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, + {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, + {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, + {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, + {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, + {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, + {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, + {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, + {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, + {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, + {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, + {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, + {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, + {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, + {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, + {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, + {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, + {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, + {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, + {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, + {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, + {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, + {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, + {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, + {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, + {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, + {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, + {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, + {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, + {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, + {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, + {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, + {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, + {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, + {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, + {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, + {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, + {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, + {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, + {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, + {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, + {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, + {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, + {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, + {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, + {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, + {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, + {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, + {"id": 92, "name": "banner", "supercategory": "textile"}, + {"id": 93, "name": "blanket", "supercategory": "textile"}, + {"id": 94, "name": "branch", "supercategory": "plant"}, + {"id": 95, "name": "bridge", "supercategory": "building"}, + {"id": 96, "name": "building-other", "supercategory": "building"}, + {"id": 97, "name": "bush", "supercategory": "plant"}, + {"id": 98, "name": "cabinet", "supercategory": "furniture-stuff"}, + {"id": 99, "name": "cage", "supercategory": "structural"}, + {"id": 100, "name": "cardboard", "supercategory": "raw-material"}, + {"id": 101, "name": "carpet", "supercategory": "floor"}, + {"id": 102, "name": "ceiling-other", "supercategory": "ceiling"}, + {"id": 103, "name": "ceiling-tile", "supercategory": "ceiling"}, + {"id": 104, "name": "cloth", "supercategory": "textile"}, + {"id": 105, "name": "clothes", "supercategory": "textile"}, + {"id": 106, "name": "clouds", "supercategory": "sky"}, + {"id": 107, "name": "counter", "supercategory": "furniture-stuff"}, + {"id": 108, "name": "cupboard", "supercategory": "furniture-stuff"}, + {"id": 109, "name": "curtain", "supercategory": "textile"}, + {"id": 110, "name": "desk-stuff", "supercategory": "furniture-stuff"}, + {"id": 111, "name": "dirt", "supercategory": "ground"}, + {"id": 112, "name": "door-stuff", "supercategory": "furniture-stuff"}, + {"id": 113, "name": "fence", "supercategory": "structural"}, + {"id": 114, "name": "floor-marble", "supercategory": "floor"}, + {"id": 115, "name": "floor-other", "supercategory": "floor"}, + {"id": 116, "name": "floor-stone", "supercategory": "floor"}, + {"id": 117, "name": "floor-tile", "supercategory": "floor"}, + {"id": 118, "name": "floor-wood", "supercategory": "floor"}, + {"id": 119, "name": "flower", "supercategory": "plant"}, + {"id": 120, "name": "fog", "supercategory": "water"}, + {"id": 121, "name": "food-other", "supercategory": "food-stuff"}, + {"id": 122, "name": "fruit", "supercategory": "food-stuff"}, + {"id": 123, "name": "furniture-other", "supercategory": "furniture-stuff"}, + {"id": 124, "name": "grass", "supercategory": "plant"}, + {"id": 125, "name": "gravel", "supercategory": "ground"}, + {"id": 126, "name": "ground-other", "supercategory": "ground"}, + {"id": 127, "name": "hill", "supercategory": "solid"}, + {"id": 128, "name": "house", "supercategory": "building"}, + {"id": 129, "name": "leaves", "supercategory": "plant"}, + {"id": 130, "name": "light", "supercategory": "furniture-stuff"}, + {"id": 131, "name": "mat", "supercategory": "textile"}, + {"id": 132, "name": "metal", "supercategory": "raw-material"}, + {"id": 133, "name": "mirror-stuff", "supercategory": "furniture-stuff"}, + {"id": 134, "name": "moss", "supercategory": "plant"}, + {"id": 135, "name": "mountain", "supercategory": "solid"}, + {"id": 136, "name": "mud", "supercategory": "ground"}, + {"id": 137, "name": "napkin", "supercategory": "textile"}, + {"id": 138, "name": "net", "supercategory": "structural"}, + {"id": 139, "name": "paper", "supercategory": "raw-material"}, + {"id": 140, "name": "pavement", "supercategory": "ground"}, + {"id": 141, "name": "pillow", "supercategory": "textile"}, + {"id": 142, "name": "plant-other", "supercategory": "plant"}, + {"id": 143, "name": "plastic", "supercategory": "raw-material"}, + {"id": 144, "name": "platform", "supercategory": "ground"}, + {"id": 145, "name": "playingfield", "supercategory": "ground"}, + {"id": 146, "name": "railing", "supercategory": "structural"}, + {"id": 147, "name": "railroad", "supercategory": "ground"}, + {"id": 148, "name": "river", "supercategory": "water"}, + {"id": 149, "name": "road", "supercategory": "ground"}, + {"id": 150, "name": "rock", "supercategory": "solid"}, + {"id": 151, "name": "roof", "supercategory": "building"}, + {"id": 152, "name": "rug", "supercategory": "textile"}, + {"id": 153, "name": "salad", "supercategory": "food-stuff"}, + {"id": 154, "name": "sand", "supercategory": "ground"}, + {"id": 155, "name": "sea", "supercategory": "water"}, + {"id": 156, "name": "shelf", "supercategory": "furniture-stuff"}, + {"id": 157, "name": "sky-other", "supercategory": "sky"}, + {"id": 158, "name": "skyscraper", "supercategory": "building"}, + {"id": 159, "name": "snow", "supercategory": "ground"}, + {"id": 160, "name": "solid-other", "supercategory": "solid"}, + {"id": 161, "name": "stairs", "supercategory": "furniture-stuff"}, + {"id": 162, "name": "stone", "supercategory": "solid"}, + {"id": 163, "name": "straw", "supercategory": "plant"}, + {"id": 164, "name": "structural-other", "supercategory": "structural"}, + {"id": 165, "name": "table", "supercategory": "furniture-stuff"}, + {"id": 166, "name": "tent", "supercategory": "building"}, + {"id": 167, "name": "textile-other", "supercategory": "textile"}, + {"id": 168, "name": "towel", "supercategory": "textile"}, + {"id": 169, "name": "tree", "supercategory": "plant"}, + {"id": 170, "name": "vegetable", "supercategory": "food-stuff"}, + {"id": 171, "name": "wall-brick", "supercategory": "wall"}, + {"id": 172, "name": "wall-concrete", "supercategory": "wall"}, + {"id": 173, "name": "wall-other", "supercategory": "wall"}, + {"id": 174, "name": "wall-panel", "supercategory": "wall"}, + {"id": 175, "name": "wall-stone", "supercategory": "wall"}, + {"id": 176, "name": "wall-tile", "supercategory": "wall"}, + {"id": 177, "name": "wall-wood", "supercategory": "wall"}, + {"id": 178, "name": "water-other", "supercategory": "water"}, + {"id": 179, "name": "waterdrops", "supercategory": "water"}, + {"id": 180, "name": "window-blind", "supercategory": "window"}, + {"id": 181, "name": "window-other", "supercategory": "window"}, + {"id": 182, "name": "wood", "supercategory": "solid"}, +] + + +def _get_coco_stuff_meta(): + # Id 0 is reserved for ignore_label, we change ignore_label for 0 + # to 255 in our pre-processing. + stuff_ids = [k["id"] for k in COCO_CATEGORIES] + assert len(stuff_ids) == 171, len(stuff_ids) + + # For semantic segmentation, this mapping maps from contiguous stuff id + # (in [0, 91], used in models) to ids in the dataset (used for processing results) + stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} + stuff_classes = [k["name"] for k in COCO_CATEGORIES] + + ret = { + "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, + "stuff_classes": stuff_classes, + } + return ret + + +def register_all_coco_stuff_10k(root): + root = os.path.join(root, "coco", "coco_stuff_10k") + meta = _get_coco_stuff_meta() + for name, image_dirname, sem_seg_dirname in [ + ("train", "images_detectron2/train", "annotations_detectron2/train"), + ("test", "images_detectron2/test", "annotations_detectron2/test"), + ]: + image_dir = os.path.join(root, image_dirname) + gt_dir = os.path.join(root, sem_seg_dirname) + name = f"coco_2017_{name}_stuff_10k_sem_seg" + DatasetCatalog.register( + name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg") + ) + MetadataCatalog.get(name).set( + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=255, + **meta, + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_coco_stuff_10k(_root) diff --git a/Mask2Former/mask2former/data/datasets/register_mapillary_vistas.py b/Mask2Former/mask2former/data/datasets/register_mapillary_vistas.py new file mode 100644 index 0000000..ce3874b --- /dev/null +++ b/Mask2Former/mask2former/data/datasets/register_mapillary_vistas.py @@ -0,0 +1,507 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.data.datasets import load_sem_seg + +MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [ + { + "color": [165, 42, 42], + "instances": True, + "readable": "Bird", + "name": "animal--bird", + "evaluate": True, + }, + { + "color": [0, 192, 0], + "instances": True, + "readable": "Ground Animal", + "name": "animal--ground-animal", + "evaluate": True, + }, + { + "color": [196, 196, 196], + "instances": False, + "readable": "Curb", + "name": "construction--barrier--curb", + "evaluate": True, + }, + { + "color": [190, 153, 153], + "instances": False, + "readable": "Fence", + "name": "construction--barrier--fence", + "evaluate": True, + }, + { + "color": [180, 165, 180], + "instances": False, + "readable": "Guard Rail", + "name": "construction--barrier--guard-rail", + "evaluate": True, + }, + { + "color": [90, 120, 150], + "instances": False, + "readable": "Barrier", + "name": "construction--barrier--other-barrier", + "evaluate": True, + }, + { + "color": [102, 102, 156], + "instances": False, + "readable": "Wall", + "name": "construction--barrier--wall", + "evaluate": True, + }, + { + "color": [128, 64, 255], + "instances": False, + "readable": "Bike Lane", + "name": "construction--flat--bike-lane", + "evaluate": True, + }, + { + "color": [140, 140, 200], + "instances": True, + "readable": "Crosswalk - Plain", + "name": "construction--flat--crosswalk-plain", + "evaluate": True, + }, + { + "color": [170, 170, 170], + "instances": False, + "readable": "Curb Cut", + "name": "construction--flat--curb-cut", + "evaluate": True, + }, + { + "color": [250, 170, 160], + "instances": False, + "readable": "Parking", + "name": "construction--flat--parking", + "evaluate": True, + }, + { + "color": [96, 96, 96], + "instances": False, + "readable": "Pedestrian Area", + "name": "construction--flat--pedestrian-area", + "evaluate": True, + }, + { + "color": [230, 150, 140], + "instances": False, + "readable": "Rail Track", + "name": "construction--flat--rail-track", + "evaluate": True, + }, + { + "color": [128, 64, 128], + "instances": False, + "readable": "Road", + "name": "construction--flat--road", + "evaluate": True, + }, + { + "color": [110, 110, 110], + "instances": False, + "readable": "Service Lane", + "name": "construction--flat--service-lane", + "evaluate": True, + }, + { + "color": [244, 35, 232], + "instances": False, + "readable": "Sidewalk", + "name": "construction--flat--sidewalk", + "evaluate": True, + }, + { + "color": [150, 100, 100], + "instances": False, + "readable": "Bridge", + "name": "construction--structure--bridge", + "evaluate": True, + }, + { + "color": [70, 70, 70], + "instances": False, + "readable": "Building", + "name": "construction--structure--building", + "evaluate": True, + }, + { + "color": [150, 120, 90], + "instances": False, + "readable": "Tunnel", + "name": "construction--structure--tunnel", + "evaluate": True, + }, + { + "color": [220, 20, 60], + "instances": True, + "readable": "Person", + "name": "human--person", + "evaluate": True, + }, + { + "color": [255, 0, 0], + "instances": True, + "readable": "Bicyclist", + "name": "human--rider--bicyclist", + "evaluate": True, + }, + { + "color": [255, 0, 100], + "instances": True, + "readable": "Motorcyclist", + "name": "human--rider--motorcyclist", + "evaluate": True, + }, + { + "color": [255, 0, 200], + "instances": True, + "readable": "Other Rider", + "name": "human--rider--other-rider", + "evaluate": True, + }, + { + "color": [200, 128, 128], + "instances": True, + "readable": "Lane Marking - Crosswalk", + "name": "marking--crosswalk-zebra", + "evaluate": True, + }, + { + "color": [255, 255, 255], + "instances": False, + "readable": "Lane Marking - General", + "name": "marking--general", + "evaluate": True, + }, + { + "color": [64, 170, 64], + "instances": False, + "readable": "Mountain", + "name": "nature--mountain", + "evaluate": True, + }, + { + "color": [230, 160, 50], + "instances": False, + "readable": "Sand", + "name": "nature--sand", + "evaluate": True, + }, + { + "color": [70, 130, 180], + "instances": False, + "readable": "Sky", + "name": "nature--sky", + "evaluate": True, + }, + { + "color": [190, 255, 255], + "instances": False, + "readable": "Snow", + "name": "nature--snow", + "evaluate": True, + }, + { + "color": [152, 251, 152], + "instances": False, + "readable": "Terrain", + "name": "nature--terrain", + "evaluate": True, + }, + { + "color": [107, 142, 35], + "instances": False, + "readable": "Vegetation", + "name": "nature--vegetation", + "evaluate": True, + }, + { + "color": [0, 170, 30], + "instances": False, + "readable": "Water", + "name": "nature--water", + "evaluate": True, + }, + { + "color": [255, 255, 128], + "instances": True, + "readable": "Banner", + "name": "object--banner", + "evaluate": True, + }, + { + "color": [250, 0, 30], + "instances": True, + "readable": "Bench", + "name": "object--bench", + "evaluate": True, + }, + { + "color": [100, 140, 180], + "instances": True, + "readable": "Bike Rack", + "name": "object--bike-rack", + "evaluate": True, + }, + { + "color": [220, 220, 220], + "instances": True, + "readable": "Billboard", + "name": "object--billboard", + "evaluate": True, + }, + { + "color": [220, 128, 128], + "instances": True, + "readable": "Catch Basin", + "name": "object--catch-basin", + "evaluate": True, + }, + { + "color": [222, 40, 40], + "instances": True, + "readable": "CCTV Camera", + "name": "object--cctv-camera", + "evaluate": True, + }, + { + "color": [100, 170, 30], + "instances": True, + "readable": "Fire Hydrant", + "name": "object--fire-hydrant", + "evaluate": True, + }, + { + "color": [40, 40, 40], + "instances": True, + "readable": "Junction Box", + "name": "object--junction-box", + "evaluate": True, + }, + { + "color": [33, 33, 33], + "instances": True, + "readable": "Mailbox", + "name": "object--mailbox", + "evaluate": True, + }, + { + "color": [100, 128, 160], + "instances": True, + "readable": "Manhole", + "name": "object--manhole", + "evaluate": True, + }, + { + "color": [142, 0, 0], + "instances": True, + "readable": "Phone Booth", + "name": "object--phone-booth", + "evaluate": True, + }, + { + "color": [70, 100, 150], + "instances": False, + "readable": "Pothole", + "name": "object--pothole", + "evaluate": True, + }, + { + "color": [210, 170, 100], + "instances": True, + "readable": "Street Light", + "name": "object--street-light", + "evaluate": True, + }, + { + "color": [153, 153, 153], + "instances": True, + "readable": "Pole", + "name": "object--support--pole", + "evaluate": True, + }, + { + "color": [128, 128, 128], + "instances": True, + "readable": "Traffic Sign Frame", + "name": "object--support--traffic-sign-frame", + "evaluate": True, + }, + { + "color": [0, 0, 80], + "instances": True, + "readable": "Utility Pole", + "name": "object--support--utility-pole", + "evaluate": True, + }, + { + "color": [250, 170, 30], + "instances": True, + "readable": "Traffic Light", + "name": "object--traffic-light", + "evaluate": True, + }, + { + "color": [192, 192, 192], + "instances": True, + "readable": "Traffic Sign (Back)", + "name": "object--traffic-sign--back", + "evaluate": True, + }, + { + "color": [220, 220, 0], + "instances": True, + "readable": "Traffic Sign (Front)", + "name": "object--traffic-sign--front", + "evaluate": True, + }, + { + "color": [140, 140, 20], + "instances": True, + "readable": "Trash Can", + "name": "object--trash-can", + "evaluate": True, + }, + { + "color": [119, 11, 32], + "instances": True, + "readable": "Bicycle", + "name": "object--vehicle--bicycle", + "evaluate": True, + }, + { + "color": [150, 0, 255], + "instances": True, + "readable": "Boat", + "name": "object--vehicle--boat", + "evaluate": True, + }, + { + "color": [0, 60, 100], + "instances": True, + "readable": "Bus", + "name": "object--vehicle--bus", + "evaluate": True, + }, + { + "color": [0, 0, 142], + "instances": True, + "readable": "Car", + "name": "object--vehicle--car", + "evaluate": True, + }, + { + "color": [0, 0, 90], + "instances": True, + "readable": "Caravan", + "name": "object--vehicle--caravan", + "evaluate": True, + }, + { + "color": [0, 0, 230], + "instances": True, + "readable": "Motorcycle", + "name": "object--vehicle--motorcycle", + "evaluate": True, + }, + { + "color": [0, 80, 100], + "instances": False, + "readable": "On Rails", + "name": "object--vehicle--on-rails", + "evaluate": True, + }, + { + "color": [128, 64, 64], + "instances": True, + "readable": "Other Vehicle", + "name": "object--vehicle--other-vehicle", + "evaluate": True, + }, + { + "color": [0, 0, 110], + "instances": True, + "readable": "Trailer", + "name": "object--vehicle--trailer", + "evaluate": True, + }, + { + "color": [0, 0, 70], + "instances": True, + "readable": "Truck", + "name": "object--vehicle--truck", + "evaluate": True, + }, + { + "color": [0, 0, 192], + "instances": True, + "readable": "Wheeled Slow", + "name": "object--vehicle--wheeled-slow", + "evaluate": True, + }, + { + "color": [32, 32, 32], + "instances": False, + "readable": "Car Mount", + "name": "void--car-mount", + "evaluate": True, + }, + { + "color": [120, 10, 10], + "instances": False, + "readable": "Ego Vehicle", + "name": "void--ego-vehicle", + "evaluate": True, + }, + { + "color": [0, 0, 0], + "instances": False, + "readable": "Unlabeled", + "name": "void--unlabeled", + "evaluate": False, + }, +] + + +def _get_mapillary_vistas_meta(): + stuff_classes = [k["readable"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]] + assert len(stuff_classes) == 65 + + stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]] + assert len(stuff_colors) == 65 + + ret = { + "stuff_classes": stuff_classes, + "stuff_colors": stuff_colors, + } + return ret + + +def register_all_mapillary_vistas(root): + root = os.path.join(root, "mapillary_vistas") + meta = _get_mapillary_vistas_meta() + for name, dirname in [("train", "training"), ("val", "validation")]: + image_dir = os.path.join(root, dirname, "images") + gt_dir = os.path.join(root, dirname, "labels") + name = f"mapillary_vistas_sem_seg_{name}" + DatasetCatalog.register( + name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg") + ) + MetadataCatalog.get(name).set( + image_root=image_dir, + sem_seg_root=gt_dir, + evaluator_type="sem_seg", + ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65 + **meta, + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_mapillary_vistas(_root) diff --git a/Mask2Former/mask2former/data/datasets/register_mapillary_vistas_panoptic.py b/Mask2Former/mask2former/data/datasets/register_mapillary_vistas_panoptic.py new file mode 100644 index 0000000..0123185 --- /dev/null +++ b/Mask2Former/mask2former/data/datasets/register_mapillary_vistas_panoptic.py @@ -0,0 +1,508 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import json +import os + +from detectron2.data import DatasetCatalog, MetadataCatalog +from detectron2.utils.file_io import PathManager + + +MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [ + {'color': [165, 42, 42], + 'id': 1, + 'isthing': 1, + 'name': 'Bird', + 'supercategory': 'animal--bird'}, + {'color': [0, 192, 0], + 'id': 2, + 'isthing': 1, + 'name': 'Ground Animal', + 'supercategory': 'animal--ground-animal'}, + {'color': [196, 196, 196], + 'id': 3, + 'isthing': 0, + 'name': 'Curb', + 'supercategory': 'construction--barrier--curb'}, + {'color': [190, 153, 153], + 'id': 4, + 'isthing': 0, + 'name': 'Fence', + 'supercategory': 'construction--barrier--fence'}, + {'color': [180, 165, 180], + 'id': 5, + 'isthing': 0, + 'name': 'Guard Rail', + 'supercategory': 'construction--barrier--guard-rail'}, + {'color': [90, 120, 150], + 'id': 6, + 'isthing': 0, + 'name': 'Barrier', + 'supercategory': 'construction--barrier--other-barrier'}, + {'color': [102, 102, 156], + 'id': 7, + 'isthing': 0, + 'name': 'Wall', + 'supercategory': 'construction--barrier--wall'}, + {'color': [128, 64, 255], + 'id': 8, + 'isthing': 0, + 'name': 'Bike Lane', + 'supercategory': 'construction--flat--bike-lane'}, + {'color': [140, 140, 200], + 'id': 9, + 'isthing': 1, + 'name': 'Crosswalk - Plain', + 'supercategory': 'construction--flat--crosswalk-plain'}, + {'color': [170, 170, 170], + 'id': 10, + 'isthing': 0, + 'name': 'Curb Cut', + 'supercategory': 'construction--flat--curb-cut'}, + {'color': [250, 170, 160], + 'id': 11, + 'isthing': 0, + 'name': 'Parking', + 'supercategory': 'construction--flat--parking'}, + {'color': [96, 96, 96], + 'id': 12, + 'isthing': 0, + 'name': 'Pedestrian Area', + 'supercategory': 'construction--flat--pedestrian-area'}, + {'color': [230, 150, 140], + 'id': 13, + 'isthing': 0, + 'name': 'Rail Track', + 'supercategory': 'construction--flat--rail-track'}, + {'color': [128, 64, 128], + 'id': 14, + 'isthing': 0, + 'name': 'Road', + 'supercategory': 'construction--flat--road'}, + {'color': [110, 110, 110], + 'id': 15, + 'isthing': 0, + 'name': 'Service Lane', + 'supercategory': 'construction--flat--service-lane'}, + {'color': [244, 35, 232], + 'id': 16, + 'isthing': 0, + 'name': 'Sidewalk', + 'supercategory': 'construction--flat--sidewalk'}, + {'color': [150, 100, 100], + 'id': 17, + 'isthing': 0, + 'name': 'Bridge', + 'supercategory': 'construction--structure--bridge'}, + {'color': [70, 70, 70], + 'id': 18, + 'isthing': 0, + 'name': 'Building', + 'supercategory': 'construction--structure--building'}, + {'color': [150, 120, 90], + 'id': 19, + 'isthing': 0, + 'name': 'Tunnel', + 'supercategory': 'construction--structure--tunnel'}, + {'color': [220, 20, 60], + 'id': 20, + 'isthing': 1, + 'name': 'Person', + 'supercategory': 'human--person'}, + {'color': [255, 0, 0], + 'id': 21, + 'isthing': 1, + 'name': 'Bicyclist', + 'supercategory': 'human--rider--bicyclist'}, + {'color': [255, 0, 100], + 'id': 22, + 'isthing': 1, + 'name': 'Motorcyclist', + 'supercategory': 'human--rider--motorcyclist'}, + {'color': [255, 0, 200], + 'id': 23, + 'isthing': 1, + 'name': 'Other Rider', + 'supercategory': 'human--rider--other-rider'}, + {'color': [200, 128, 128], + 'id': 24, + 'isthing': 1, + 'name': 'Lane Marking - Crosswalk', + 'supercategory': 'marking--crosswalk-zebra'}, + {'color': [255, 255, 255], + 'id': 25, + 'isthing': 0, + 'name': 'Lane Marking - General', + 'supercategory': 'marking--general'}, + {'color': [64, 170, 64], + 'id': 26, + 'isthing': 0, + 'name': 'Mountain', + 'supercategory': 'nature--mountain'}, + {'color': [230, 160, 50], + 'id': 27, + 'isthing': 0, + 'name': 'Sand', + 'supercategory': 'nature--sand'}, + {'color': [70, 130, 180], + 'id': 28, + 'isthing': 0, + 'name': 'Sky', + 'supercategory': 'nature--sky'}, + {'color': [190, 255, 255], + 'id': 29, + 'isthing': 0, + 'name': 'Snow', + 'supercategory': 'nature--snow'}, + {'color': [152, 251, 152], + 'id': 30, + 'isthing': 0, + 'name': 'Terrain', + 'supercategory': 'nature--terrain'}, + {'color': [107, 142, 35], + 'id': 31, + 'isthing': 0, + 'name': 'Vegetation', + 'supercategory': 'nature--vegetation'}, + {'color': [0, 170, 30], + 'id': 32, + 'isthing': 0, + 'name': 'Water', + 'supercategory': 'nature--water'}, + {'color': [255, 255, 128], + 'id': 33, + 'isthing': 1, + 'name': 'Banner', + 'supercategory': 'object--banner'}, + {'color': [250, 0, 30], + 'id': 34, + 'isthing': 1, + 'name': 'Bench', + 'supercategory': 'object--bench'}, + {'color': [100, 140, 180], + 'id': 35, + 'isthing': 1, + 'name': 'Bike Rack', + 'supercategory': 'object--bike-rack'}, + {'color': [220, 220, 220], + 'id': 36, + 'isthing': 1, + 'name': 'Billboard', + 'supercategory': 'object--billboard'}, + {'color': [220, 128, 128], + 'id': 37, + 'isthing': 1, + 'name': 'Catch Basin', + 'supercategory': 'object--catch-basin'}, + {'color': [222, 40, 40], + 'id': 38, + 'isthing': 1, + 'name': 'CCTV Camera', + 'supercategory': 'object--cctv-camera'}, + {'color': [100, 170, 30], + 'id': 39, + 'isthing': 1, + 'name': 'Fire Hydrant', + 'supercategory': 'object--fire-hydrant'}, + {'color': [40, 40, 40], + 'id': 40, + 'isthing': 1, + 'name': 'Junction Box', + 'supercategory': 'object--junction-box'}, + {'color': [33, 33, 33], + 'id': 41, + 'isthing': 1, + 'name': 'Mailbox', + 'supercategory': 'object--mailbox'}, + {'color': [100, 128, 160], + 'id': 42, + 'isthing': 1, + 'name': 'Manhole', + 'supercategory': 'object--manhole'}, + {'color': [142, 0, 0], + 'id': 43, + 'isthing': 1, + 'name': 'Phone Booth', + 'supercategory': 'object--phone-booth'}, + {'color': [70, 100, 150], + 'id': 44, + 'isthing': 0, + 'name': 'Pothole', + 'supercategory': 'object--pothole'}, + {'color': [210, 170, 100], + 'id': 45, + 'isthing': 1, + 'name': 'Street Light', + 'supercategory': 'object--street-light'}, + {'color': [153, 153, 153], + 'id': 46, + 'isthing': 1, + 'name': 'Pole', + 'supercategory': 'object--support--pole'}, + {'color': [128, 128, 128], + 'id': 47, + 'isthing': 1, + 'name': 'Traffic Sign Frame', + 'supercategory': 'object--support--traffic-sign-frame'}, + {'color': [0, 0, 80], + 'id': 48, + 'isthing': 1, + 'name': 'Utility Pole', + 'supercategory': 'object--support--utility-pole'}, + {'color': [250, 170, 30], + 'id': 49, + 'isthing': 1, + 'name': 'Traffic Light', + 'supercategory': 'object--traffic-light'}, + {'color': [192, 192, 192], + 'id': 50, + 'isthing': 1, + 'name': 'Traffic Sign (Back)', + 'supercategory': 'object--traffic-sign--back'}, + {'color': [220, 220, 0], + 'id': 51, + 'isthing': 1, + 'name': 'Traffic Sign (Front)', + 'supercategory': 'object--traffic-sign--front'}, + {'color': [140, 140, 20], + 'id': 52, + 'isthing': 1, + 'name': 'Trash Can', + 'supercategory': 'object--trash-can'}, + {'color': [119, 11, 32], + 'id': 53, + 'isthing': 1, + 'name': 'Bicycle', + 'supercategory': 'object--vehicle--bicycle'}, + {'color': [150, 0, 255], + 'id': 54, + 'isthing': 1, + 'name': 'Boat', + 'supercategory': 'object--vehicle--boat'}, + {'color': [0, 60, 100], + 'id': 55, + 'isthing': 1, + 'name': 'Bus', + 'supercategory': 'object--vehicle--bus'}, + {'color': [0, 0, 142], + 'id': 56, + 'isthing': 1, + 'name': 'Car', + 'supercategory': 'object--vehicle--car'}, + {'color': [0, 0, 90], + 'id': 57, + 'isthing': 1, + 'name': 'Caravan', + 'supercategory': 'object--vehicle--caravan'}, + {'color': [0, 0, 230], + 'id': 58, + 'isthing': 1, + 'name': 'Motorcycle', + 'supercategory': 'object--vehicle--motorcycle'}, + {'color': [0, 80, 100], + 'id': 59, + 'isthing': 0, + 'name': 'On Rails', + 'supercategory': 'object--vehicle--on-rails'}, + {'color': [128, 64, 64], + 'id': 60, + 'isthing': 1, + 'name': 'Other Vehicle', + 'supercategory': 'object--vehicle--other-vehicle'}, + {'color': [0, 0, 110], + 'id': 61, + 'isthing': 1, + 'name': 'Trailer', + 'supercategory': 'object--vehicle--trailer'}, + {'color': [0, 0, 70], + 'id': 62, + 'isthing': 1, + 'name': 'Truck', + 'supercategory': 'object--vehicle--truck'}, + {'color': [0, 0, 192], + 'id': 63, + 'isthing': 1, + 'name': 'Wheeled Slow', + 'supercategory': 'object--vehicle--wheeled-slow'}, + {'color': [32, 32, 32], + 'id': 64, + 'isthing': 0, + 'name': 'Car Mount', + 'supercategory': 'void--car-mount'}, + {'color': [120, 10, 10], + 'id': 65, + 'isthing': 0, + 'name': 'Ego Vehicle', + 'supercategory': 'void--ego-vehicle'} +] + + +def load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta): + """ + Args: + image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". + gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". + json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". + Returns: + list[dict]: a list of dicts in Detectron2 standard format. (See + `Using Custom Datasets `_ ) + """ + + def _convert_category_id(segment_info, meta): + if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: + segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = True + else: + segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ + segment_info["category_id"] + ] + segment_info["isthing"] = False + return segment_info + + with PathManager.open(json_file) as f: + json_info = json.load(f) + + ret = [] + for ann in json_info["annotations"]: + image_id = ann["image_id"] + # TODO: currently we assume image and label has the same filename but + # different extension, and images have extension ".jpg" for COCO. Need + # to make image extension a user-provided argument if we extend this + # function to support other COCO-like datasets. + image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") + label_file = os.path.join(gt_dir, ann["file_name"]) + sem_label_file = os.path.join(semseg_dir, ann["file_name"]) + segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] + ret.append( + { + "file_name": image_file, + "image_id": image_id, + "pan_seg_file_name": label_file, + "sem_seg_file_name": sem_label_file, + "segments_info": segments_info, + } + ) + assert len(ret), f"No images found in {image_dir}!" + assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] + assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] + assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] + return ret + + +def register_mapillary_vistas_panoptic( + name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None +): + """ + Register a "standard" version of ADE20k panoptic segmentation dataset named `name`. + The dictionaries in this registered dataset follows detectron2's standard format. + Hence it's called "standard". + Args: + name (str): the name that identifies a dataset, + e.g. "ade20k_panoptic_train" + metadata (dict): extra metadata associated with this dataset. + image_root (str): directory which contains all the images + panoptic_root (str): directory which contains panoptic annotation images in COCO format + panoptic_json (str): path to the json panoptic annotation file in COCO format + sem_seg_root (none): not used, to be consistent with + `register_coco_panoptic_separated`. + instances_json (str): path to the json instance annotation file + """ + panoptic_name = name + DatasetCatalog.register( + panoptic_name, + lambda: load_mapillary_vistas_panoptic_json( + panoptic_json, image_root, panoptic_root, semantic_root, metadata + ), + ) + MetadataCatalog.get(panoptic_name).set( + panoptic_root=panoptic_root, + image_root=image_root, + panoptic_json=panoptic_json, + json_file=instances_json, + evaluator_type="mapillary_vistas_panoptic_seg", + ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65 + label_divisor=1000, + **metadata, + ) + + +_PREDEFINED_SPLITS_ADE20K_PANOPTIC = { + "mapillary_vistas_panoptic_train": ( + "mapillary_vistas/training/images", + "mapillary_vistas/training/panoptic", + "mapillary_vistas/training/panoptic/panoptic_2018.json", + "mapillary_vistas/training/labels", + ), + "mapillary_vistas_panoptic_val": ( + "mapillary_vistas/validation/images", + "mapillary_vistas/validation/panoptic", + "mapillary_vistas/validation/panoptic/panoptic_2018.json", + "mapillary_vistas/validation/labels", + ), +} + + +def get_metadata(): + meta = {} + # The following metadata maps contiguous id from [0, #thing categories + + # #stuff categories) to their names and colors. We have to replica of the + # same name and color under "thing_*" and "stuff_*" because the current + # visualization function in D2 handles thing and class classes differently + # due to some heuristic used in Panoptic FPN. We keep the same naming to + # enable reusing existing visualization functions. + thing_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] + thing_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] + stuff_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] + stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES] + + meta["thing_classes"] = thing_classes + meta["thing_colors"] = thing_colors + meta["stuff_classes"] = stuff_classes + meta["stuff_colors"] = stuff_colors + + # Convert category id for training: + # category id: like semantic segmentation, it is the class id for each + # pixel. Since there are some classes not used in evaluation, the category + # id is not always contiguous and thus we have two set of category ids: + # - original category id: category id in the original dataset, mainly + # used for evaluation. + # - contiguous category id: [0, #classes), in order to train the linear + # softmax classifier. + thing_dataset_id_to_contiguous_id = {} + stuff_dataset_id_to_contiguous_id = {} + + for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES): + if cat["isthing"]: + thing_dataset_id_to_contiguous_id[cat["id"]] = i + # else: + # stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + # in order to use sem_seg evaluator + stuff_dataset_id_to_contiguous_id[cat["id"]] = i + + meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id + meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id + + return meta + + +def register_all_mapillary_vistas_panoptic(root): + metadata = get_metadata() + for ( + prefix, + (image_root, panoptic_root, panoptic_json, semantic_root), + ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items(): + # The "standard" version of COCO panoptic segmentation dataset, + # e.g. used by Panoptic-DeepLab + register_mapillary_vistas_panoptic( + prefix, + metadata, + os.path.join(root, image_root), + os.path.join(root, panoptic_root), + os.path.join(root, semantic_root), + os.path.join(root, panoptic_json), + ) + + +_root = os.getenv("DETECTRON2_DATASETS", "datasets") +register_all_mapillary_vistas_panoptic(_root) diff --git a/Mask2Former/mask2former/evaluation/__init__.py b/Mask2Former/mask2former/evaluation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Mask2Former/mask2former/evaluation/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/evaluation/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..0a97074 Binary files /dev/null and b/Mask2Former/mask2former/evaluation/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/evaluation/__pycache__/instance_evaluation.cpython-38.pyc b/Mask2Former/mask2former/evaluation/__pycache__/instance_evaluation.cpython-38.pyc new file mode 100644 index 0000000..8ebe077 Binary files /dev/null and b/Mask2Former/mask2former/evaluation/__pycache__/instance_evaluation.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/evaluation/instance_evaluation.py b/Mask2Former/mask2former/evaluation/instance_evaluation.py new file mode 100644 index 0000000..bc2face --- /dev/null +++ b/Mask2Former/mask2former/evaluation/instance_evaluation.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +import pickle +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.data.datasets.coco import convert_to_coco_json +from detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco +from detectron2.evaluation.fast_eval_api import COCOeval_opt +from detectron2.structures import Boxes, BoxMode, pairwise_iou +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + + +# modified from COCOEvaluator for instance segmetnat +class InstanceSegEvaluator(COCOEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means + the metric cannot be computed (e.g. due to no predictions made). + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def _eval_predictions(self, predictions, img_ids=None): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for COCO format ...") + coco_results = list(itertools.chain(*[x["instances"] for x in predictions])) + tasks = self._tasks or self._tasks_from_predictions(coco_results) + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + # all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + # num_classes = len(all_contiguous_ids) + # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in coco_results: + category_id = result["category_id"] + # assert category_id < num_classes, ( + # f"A prediction has class={category_id}, " + # f"but the dataset only has {num_classes} classes and " + # f"predicted class id should be in [0, {num_classes - 1}]." + # ) + assert category_id in reverse_id_mapping, ( + f"A prediction has class={category_id}, " + f"but the dataset only has class ids in {dataset_id_to_contiguous_id}." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "coco_instances_results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(coco_results)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + self._logger.info( + "Evaluating predictions with {} COCO API...".format( + "unofficial" if self._use_fast_impl else "official" + ) + ) + for task in sorted(tasks): + assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!" + coco_eval = ( + _evaluate_predictions_on_coco( + self._coco_api, + coco_results, + task, + kpt_oks_sigmas=self._kpt_oks_sigmas, + use_fast_impl=self._use_fast_impl, + img_ids=img_ids, + max_dets_per_image=self._max_dets_per_image, + ) + if len(coco_results) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, task, class_names=self._metadata.get("thing_classes") + ) + self._results[task] = res diff --git a/Mask2Former/mask2former/maskformer_model.py b/Mask2Former/mask2former/maskformer_model.py new file mode 100644 index 0000000..88ce76d --- /dev/null +++ b/Mask2Former/mask2former/maskformer_model.py @@ -0,0 +1,380 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import Tuple + +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head +from detectron2.modeling.backbone import Backbone +from detectron2.modeling.postprocessing import sem_seg_postprocess +from detectron2.structures import Boxes, ImageList, Instances, BitMasks +from detectron2.utils.memory import retry_if_cuda_oom + +from .modeling.criterion import SetCriterion +from .modeling.matcher import HungarianMatcher + + +@META_ARCH_REGISTRY.register() +class MaskFormer(nn.Module): + """ + Main class for mask classification semantic segmentation architectures. + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + sem_seg_head: nn.Module, + criterion: nn.Module, + num_queries: int, + object_mask_threshold: float, + overlap_threshold: float, + metadata, + size_divisibility: int, + sem_seg_postprocess_before_inference: bool, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + # inference + semantic_on: bool, + panoptic_on: bool, + instance_on: bool, + test_topk_per_image: int, + ): + """ + Args: + backbone: a backbone module, must follow detectron2's backbone interface + sem_seg_head: a module that predicts semantic segmentation from backbone features + criterion: a module that defines the loss + num_queries: int, number of queries + object_mask_threshold: float, threshold to filter query based on classification score + for panoptic segmentation inference + overlap_threshold: overlap threshold used in general inference for panoptic segmentation + metadata: dataset meta, get `thing` and `stuff` category names for panoptic + segmentation inference + size_divisibility: Some backbones require the input height and width to be divisible by a + specific integer. We can use this to override such requirement. + sem_seg_postprocess_before_inference: whether to resize the prediction back + to original input size before semantic segmentation inference or after. + For high-resolution dataset like Mapillary, resizing predictions before + inference will cause OOM error. + pixel_mean, pixel_std: list or tuple with #channels element, representing + the per-channel mean and std to be used to normalize the input image + semantic_on: bool, whether to output semantic segmentation prediction + instance_on: bool, whether to output instance segmentation prediction + panoptic_on: bool, whether to output panoptic segmentation prediction + test_topk_per_image: int, instance segmentation parameter, keep topk instances per image + """ + super().__init__() + self.backbone = backbone + self.sem_seg_head = sem_seg_head + self.criterion = criterion + self.num_queries = num_queries + self.overlap_threshold = overlap_threshold + self.object_mask_threshold = object_mask_threshold + self.metadata = metadata + if size_divisibility < 0: + # use backbone size_divisibility if not set + size_divisibility = self.backbone.size_divisibility + self.size_divisibility = size_divisibility + self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference + self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) + + # additional args + self.semantic_on = semantic_on + self.instance_on = instance_on + self.panoptic_on = panoptic_on + self.test_topk_per_image = test_topk_per_image + + if not self.semantic_on: + assert self.sem_seg_postprocess_before_inference + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape()) + + # Loss parameters: + deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION + no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT + + # loss weights + class_weight = cfg.MODEL.MASK_FORMER.CLASS_WEIGHT + dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT + mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT + + # building criterion + matcher = HungarianMatcher( + cost_class=class_weight, + cost_mask=mask_weight, + cost_dice=dice_weight, + num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, + ) + + weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, "loss_dice": dice_weight} + + if deep_supervision: + dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS + aux_weight_dict = {} + for i in range(dec_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + + losses = ["labels", "masks"] + + criterion = SetCriterion( + sem_seg_head.num_classes, + matcher=matcher, + weight_dict=weight_dict, + eos_coef=no_object_weight, + losses=losses, + num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, + oversample_ratio=cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO, + importance_sample_ratio=cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO, + ) + + return { + "backbone": backbone, + "sem_seg_head": sem_seg_head, + "criterion": criterion, + "num_queries": cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES, + "object_mask_threshold": cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD, + "overlap_threshold": cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD, + "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), + "size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY, + "sem_seg_postprocess_before_inference": ( + cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE + or cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON + or cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON + ), + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + # inference + "semantic_on": cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON, + "instance_on": cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON, + "panoptic_on": cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON, + "test_topk_per_image": cfg.TEST.DETECTIONS_PER_IMAGE, + } + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper`. + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + * "image": Tensor, image in (C, H, W) format. + * "instances": per-region ground truth + * Other information that's included in the original dicts, such as: + "height", "width" (int): the output resolution of the model (may be different + from input resolution), used in inference. + Returns: + list[dict]: + each dict has the results for one image. The dict contains the following keys: + + * "sem_seg": + A Tensor that represents the + per-pixel segmentation prediced by the head. + The prediction has shape KxHxW that represents the logits of + each class for each pixel. + * "panoptic_seg": + A tuple that represent panoptic output + panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment. + segments_info (list[dict]): Describe each segment in `panoptic_seg`. + Each dict contains keys "id", "category_id", "isthing". + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.size_divisibility) + + features = self.backbone(images.tensor) + outputs = self.sem_seg_head(features) + + if self.training: + # mask classification target + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + targets = self.prepare_targets(gt_instances, images) + else: + targets = None + + # bipartite matching-based loss + losses = self.criterion(outputs, targets) + + for k in list(losses.keys()): + if k in self.criterion.weight_dict: + losses[k] *= self.criterion.weight_dict[k] + else: + # remove this loss if not specified in `weight_dict` + losses.pop(k) + return losses + else: + mask_cls_results = outputs["pred_logits"] + mask_pred_results = outputs["pred_masks"] + # upsample masks + mask_pred_results = F.interpolate( + mask_pred_results, + size=(images.tensor.shape[-2], images.tensor.shape[-1]), + mode="bilinear", + align_corners=False, + ) + + del outputs + + processed_results = [] + for mask_cls_result, mask_pred_result, input_per_image, image_size in zip( + mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + processed_results.append({}) + + if self.sem_seg_postprocess_before_inference: + mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)( + mask_pred_result, image_size, height, width + ) + mask_cls_result = mask_cls_result.to(mask_pred_result) + + # semantic segmentation inference + if self.semantic_on: + r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result) + if not self.sem_seg_postprocess_before_inference: + r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width) + processed_results[-1]["sem_seg"] = r + + # panoptic segmentation inference + if self.panoptic_on: + panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result) + processed_results[-1]["panoptic_seg"] = panoptic_r + + # instance segmentation inference + if self.instance_on: + instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result) + processed_results[-1]["instances"] = instance_r + + return processed_results + + def prepare_targets(self, targets, images): + h_pad, w_pad = images.tensor.shape[-2:] + new_targets = [] + for targets_per_image in targets: + # pad gt + gt_masks = targets_per_image.gt_masks + padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device) + padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks + new_targets.append( + { + "labels": targets_per_image.gt_classes, + "masks": padded_masks, + } + ) + return new_targets + + def semantic_inference(self, mask_cls, mask_pred): + mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1] + mask_pred = mask_pred.sigmoid() + semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred) + return semseg + + def panoptic_inference(self, mask_cls, mask_pred): + scores, labels = F.softmax(mask_cls, dim=-1).max(-1) + mask_pred = mask_pred.sigmoid() + + keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold) + cur_scores = scores[keep] + cur_classes = labels[keep] + cur_masks = mask_pred[keep] + cur_mask_cls = mask_cls[keep] + cur_mask_cls = cur_mask_cls[:, :-1] + + cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks + + h, w = cur_masks.shape[-2:] + panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device) + segments_info = [] + + current_segment_id = 0 + + if cur_masks.shape[0] == 0: + # We didn't detect any mask :( + return panoptic_seg, segments_info + else: + # take argmax + cur_mask_ids = cur_prob_masks.argmax(0) + stuff_memory_list = {} + for k in range(cur_classes.shape[0]): + pred_class = cur_classes[k].item() + isthing = pred_class in self.metadata.thing_dataset_id_to_contiguous_id.values() + mask_area = (cur_mask_ids == k).sum().item() + original_area = (cur_masks[k] >= 0.5).sum().item() + mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5) + + if mask_area > 0 and original_area > 0 and mask.sum().item() > 0: + if mask_area / original_area < self.overlap_threshold: + continue + + # merge stuff regions + if not isthing: + if int(pred_class) in stuff_memory_list.keys(): + panoptic_seg[mask] = stuff_memory_list[int(pred_class)] + continue + else: + stuff_memory_list[int(pred_class)] = current_segment_id + 1 + + current_segment_id += 1 + panoptic_seg[mask] = current_segment_id + + segments_info.append( + { + "id": current_segment_id, + "isthing": bool(isthing), + "category_id": int(pred_class), + } + ) + + return panoptic_seg, segments_info + + def instance_inference(self, mask_cls, mask_pred): + # mask_pred is already processed to have the same shape as original input + image_size = mask_pred.shape[-2:] + + # [Q, K] + scores = F.softmax(mask_cls, dim=-1)[:, :-1] + labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1) + # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False) + scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False) + labels_per_image = labels[topk_indices] + + topk_indices = topk_indices // self.sem_seg_head.num_classes + # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1) + mask_pred = mask_pred[topk_indices] + + # if this is panoptic segmentation, we only keep the "thing" classes + if self.panoptic_on: + keep = torch.zeros_like(scores_per_image).bool() + for i, lab in enumerate(labels_per_image): + keep[i] = lab in self.metadata.thing_dataset_id_to_contiguous_id.values() + + scores_per_image = scores_per_image[keep] + labels_per_image = labels_per_image[keep] + mask_pred = mask_pred[keep] + + result = Instances(image_size) + # mask (before sigmoid) + result.pred_masks = (mask_pred > 0).float() + result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4)) + # Uncomment the following to get boxes from masks (this is slow) + # result.pred_boxes = BitMasks(mask_pred > 0).get_bounding_boxes() + + # calculate average mask prob + mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * result.pred_masks.flatten(1)).sum(1) / (result.pred_masks.flatten(1).sum(1) + 1e-6) + result.scores = scores_per_image * mask_scores_per_image + result.pred_classes = labels_per_image + return result diff --git a/Mask2Former/mask2former/modeling/__init__.py b/Mask2Former/mask2former/modeling/__init__.py new file mode 100644 index 0000000..7aed7be --- /dev/null +++ b/Mask2Former/mask2former/modeling/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .backbone.swin import D2SwinTransformer +from .pixel_decoder.fpn import BasePixelDecoder +from .pixel_decoder.msdeformattn import MSDeformAttnPixelDecoder +from .meta_arch.mask_former_head import MaskFormerHead +from .meta_arch.per_pixel_baseline import PerPixelBaselineHead, PerPixelBaselinePlusHead diff --git a/Mask2Former/mask2former/modeling/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/modeling/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..fa9880a Binary files 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affiliates. diff --git a/Mask2Former/mask2former/modeling/backbone/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/modeling/backbone/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..e266c5f Binary files /dev/null and b/Mask2Former/mask2former/modeling/backbone/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/backbone/__pycache__/swin.cpython-38.pyc b/Mask2Former/mask2former/modeling/backbone/__pycache__/swin.cpython-38.pyc new file mode 100644 index 0000000..b69052f Binary files /dev/null and b/Mask2Former/mask2former/modeling/backbone/__pycache__/swin.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/backbone/swin.py b/Mask2Former/mask2former/modeling/backbone/swin.py new file mode 100644 index 0000000..3b099d8 --- /dev/null +++ b/Mask2Former/mask2former/modeling/backbone/swin.py @@ -0,0 +1,770 @@ +# -------------------------------------------------------- +# Swin Transformer +# Copyright (c) 2021 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ze Liu, Yutong Lin, Yixuan Wei +# -------------------------------------------------------- + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from timm.models.layers import DropPath, to_2tuple, trunc_normal_ + +from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec + + +class Mlp(nn.Module): + """Multilayer perceptron.""" + + def __init__( + self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0 + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + """Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B_, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1 + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwinTransformerBlock(nn.Module): + """Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop + ) + + self.H = None + self.W = None + + def forward(self, x, mask_matrix): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + mask_matrix: Attention mask for cyclic shift. + """ + B, L, C = x.shape + H, W = self.H, self.W + assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # pad feature maps to multiples of window size + pad_l = pad_t = 0 + pad_r = (self.window_size - W % self.window_size) % self.window_size + pad_b = (self.window_size - H % self.window_size) % self.window_size + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + _, Hp, Wp, _ = x.shape + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + + if pad_r > 0 or pad_b > 0: + x = x[:, :H, :W, :].contiguous() + + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + """Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + + x = x.view(B, H, W, C) + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (int): Local window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + depth, + num_heads, + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.window_size = window_size + self.shift_size = window_size // 2 + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, H, W): + """Forward function. + Args: + x: Input feature, tensor size (B, H*W, C). + H, W: Spatial resolution of the input feature. + """ + + # calculate attention mask for SW-MSA + Hp = int(np.ceil(H / self.window_size)) * self.window_size + Wp = int(np.ceil(W / self.window_size)) * self.window_size + img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + for blk in self.blocks: + blk.H, blk.W = H, W + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, attn_mask) + else: + x = blk(x, attn_mask) + if self.downsample is not None: + x_down = self.downsample(x, H, W) + Wh, Ww = (H + 1) // 2, (W + 1) // 2 + return x, H, W, x_down, Wh, Ww + else: + return x, H, W, x, H, W + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding + Args: + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, H, W = x.size() + if W % self.patch_size[1] != 0: + x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1])) + if H % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])) + + x = self.proj(x) # B C Wh Ww + if self.norm is not None: + Wh, Ww = x.size(2), x.size(3) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww) + + return x + + +class SwinTransformer(nn.Module): + """Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + pretrain_img_size (int): Input image size for training the pretrained model, + used in absolute postion embedding. Default 224. + patch_size (int | tuple(int)): Patch size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False. + patch_norm (bool): If True, add normalization after patch embedding. Default: True. + out_indices (Sequence[int]): Output from which stages. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + pretrain_img_size=224, + patch_size=4, + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + use_checkpoint=False, + ): + super().__init__() + + self.pretrain_img_size = pretrain_img_size + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.out_indices = out_indices + self.frozen_stages = frozen_stages + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + pretrain_img_size = to_2tuple(pretrain_img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + pretrain_img_size[0] // patch_size[0], + pretrain_img_size[1] // patch_size[1], + ] + + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = BasicLayer( + dim=int(embed_dim * 2 ** i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], + norm_layer=norm_layer, + downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint, + ) + self.layers.append(layer) + + num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)] + self.num_features = num_features + + # add a norm layer for each output + for i_layer in out_indices: + layer = norm_layer(num_features[i_layer]) + layer_name = f"norm{i_layer}" + self.add_module(layer_name, layer) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1 and self.ape: + self.absolute_pos_embed.requires_grad = False + + if self.frozen_stages >= 2: + self.pos_drop.eval() + for i in range(0, self.frozen_stages - 1): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x): + """Forward function.""" + x = self.patch_embed(x) + + Wh, Ww = x.size(2), x.size(3) + if self.ape: + # interpolate the position embedding to the corresponding size + absolute_pos_embed = F.interpolate( + self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic" + ) + x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C + else: + x = x.flatten(2).transpose(1, 2) + x = self.pos_drop(x) + + outs = {} + for i in range(self.num_layers): + layer = self.layers[i] + x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww) + + if i in self.out_indices: + norm_layer = getattr(self, f"norm{i}") + x_out = norm_layer(x_out) + + out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous() + outs["res{}".format(i + 2)] = out + + return outs + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer, self).train(mode) + self._freeze_stages() + + +@BACKBONE_REGISTRY.register() +class D2SwinTransformer(SwinTransformer, Backbone): + def __init__(self, cfg, input_shape): + + pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE + patch_size = cfg.MODEL.SWIN.PATCH_SIZE + in_chans = 3 + embed_dim = cfg.MODEL.SWIN.EMBED_DIM + depths = cfg.MODEL.SWIN.DEPTHS + num_heads = cfg.MODEL.SWIN.NUM_HEADS + window_size = cfg.MODEL.SWIN.WINDOW_SIZE + mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO + qkv_bias = cfg.MODEL.SWIN.QKV_BIAS + qk_scale = cfg.MODEL.SWIN.QK_SCALE + drop_rate = cfg.MODEL.SWIN.DROP_RATE + attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE + drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE + norm_layer = nn.LayerNorm + ape = cfg.MODEL.SWIN.APE + patch_norm = cfg.MODEL.SWIN.PATCH_NORM + use_checkpoint = cfg.MODEL.SWIN.USE_CHECKPOINT + + super().__init__( + pretrain_img_size, + patch_size, + in_chans, + embed_dim, + depths, + num_heads, + window_size, + mlp_ratio, + qkv_bias, + qk_scale, + drop_rate, + attn_drop_rate, + drop_path_rate, + norm_layer, + ape, + patch_norm, + use_checkpoint=use_checkpoint, + ) + + self._out_features = cfg.MODEL.SWIN.OUT_FEATURES + + self._out_feature_strides = { + "res2": 4, + "res3": 8, + "res4": 16, + "res5": 32, + } + self._out_feature_channels = { + "res2": self.num_features[0], + "res3": self.num_features[1], + "res4": self.num_features[2], + "res5": self.num_features[3], + } + + def forward(self, x): + """ + Args: + x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``. + Returns: + dict[str->Tensor]: names and the corresponding features + """ + assert ( + x.dim() == 4 + ), f"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!" + outputs = {} + y = super().forward(x) + for k in y.keys(): + if k in self._out_features: + outputs[k] = y[k] + return outputs + + def output_shape(self): + return { + name: ShapeSpec( + channels=self._out_feature_channels[name], stride=self._out_feature_strides[name] + ) + for name in self._out_features + } + + @property + def size_divisibility(self): + return 32 diff --git a/Mask2Former/mask2former/modeling/criterion.py b/Mask2Former/mask2former/modeling/criterion.py new file mode 100644 index 0000000..878ae75 --- /dev/null +++ b/Mask2Former/mask2former/modeling/criterion.py @@ -0,0 +1,263 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py +""" +MaskFormer criterion. +""" +import logging + +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.utils.comm import get_world_size +from detectron2.projects.point_rend.point_features import ( + get_uncertain_point_coords_with_randomness, + point_sample, +) + +from ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list + + +def dice_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(-1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_masks + + +dice_loss_jit = torch.jit.script( + dice_loss +) # type: torch.jit.ScriptModule + + +def sigmoid_ce_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + + return loss.mean(1).sum() / num_masks + + +sigmoid_ce_loss_jit = torch.jit.script( + sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +def calculate_uncertainty(logits): + """ + We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the + foreground class in `classes`. + Args: + logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or + class-agnostic, where R is the total number of predicted masks in all images and C is + the number of foreground classes. The values are logits. + Returns: + scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with + the most uncertain locations having the highest uncertainty score. + """ + assert logits.shape[1] == 1 + gt_class_logits = logits.clone() + return -(torch.abs(gt_class_logits)) + + +class SetCriterion(nn.Module): + """This class computes the loss for DETR. + The process happens in two steps: + 1) we compute hungarian assignment between ground truth boxes and the outputs of the model + 2) we supervise each pair of matched ground-truth / prediction (supervise class and box) + """ + + def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses, + num_points, oversample_ratio, importance_sample_ratio): + """Create the criterion. + Parameters: + num_classes: number of object categories, omitting the special no-object category + matcher: module able to compute a matching between targets and proposals + weight_dict: dict containing as key the names of the losses and as values their relative weight. + eos_coef: relative classification weight applied to the no-object category + losses: list of all the losses to be applied. See get_loss for list of available losses. + """ + super().__init__() + self.num_classes = num_classes + self.matcher = matcher + self.weight_dict = weight_dict + self.eos_coef = eos_coef + self.losses = losses + empty_weight = torch.ones(self.num_classes + 1) + empty_weight[-1] = self.eos_coef + self.register_buffer("empty_weight", empty_weight) + + # pointwise mask loss parameters + self.num_points = num_points + self.oversample_ratio = oversample_ratio + self.importance_sample_ratio = importance_sample_ratio + + def loss_labels(self, outputs, targets, indices, num_masks): + """Classification loss (NLL) + targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] + """ + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"].float() + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device + ) + target_classes[idx] = target_classes_o + + loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) + losses = {"loss_ce": loss_ce} + return losses + + def loss_masks(self, outputs, targets, indices, num_masks): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] + """ + assert "pred_masks" in outputs + + src_idx = self._get_src_permutation_idx(indices) + tgt_idx = self._get_tgt_permutation_idx(indices) + src_masks = outputs["pred_masks"] + src_masks = src_masks[src_idx] + masks = [t["masks"] for t in targets] + # TODO use valid to mask invalid areas due to padding in loss + target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() + target_masks = target_masks.to(src_masks) + target_masks = target_masks[tgt_idx] + + # No need to upsample predictions as we are using normalized coordinates :) + # N x 1 x H x W + src_masks = src_masks[:, None] + target_masks = target_masks[:, None] + + with torch.no_grad(): + # sample point_coords + point_coords = get_uncertain_point_coords_with_randomness( + src_masks, + lambda logits: calculate_uncertainty(logits), + self.num_points, + self.oversample_ratio, + self.importance_sample_ratio, + ) + # get gt labels + point_labels = point_sample( + target_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + point_logits = point_sample( + src_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + losses = { + "loss_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks), + "loss_dice": dice_loss_jit(point_logits, point_labels, num_masks), + } + + del src_masks + del target_masks + return losses + + def _get_src_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + src_idx = torch.cat([src for (src, _) in indices]) + return batch_idx, src_idx + + def _get_tgt_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + tgt_idx = torch.cat([tgt for (_, tgt) in indices]) + return batch_idx, tgt_idx + + def get_loss(self, loss, outputs, targets, indices, num_masks): + loss_map = { + 'labels': self.loss_labels, + 'masks': self.loss_masks, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_masks) + + def forward(self, outputs, targets): + """This performs the loss computation. + Parameters: + outputs: dict of tensors, see the output specification of the model for the format + targets: list of dicts, such that len(targets) == batch_size. + The expected keys in each dict depends on the losses applied, see each loss' doc + """ + outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets) + + # Compute the average number of target boxes accross all nodes, for normalization purposes + num_masks = sum(len(t["labels"]) for t in targets) + num_masks = torch.as_tensor( + [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device + ) + if is_dist_avail_and_initialized(): + torch.distributed.all_reduce(num_masks) + num_masks = torch.clamp(num_masks / get_world_size(), min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + losses.update(self.get_loss(loss, outputs, targets, indices, num_masks)) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if "aux_outputs" in outputs: + for i, aux_outputs in enumerate(outputs["aux_outputs"]): + indices = self.matcher(aux_outputs, targets) + for loss in self.losses: + l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses + + def __repr__(self): + head = "Criterion " + self.__class__.__name__ + body = [ + "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)), + "losses: {}".format(self.losses), + "weight_dict: {}".format(self.weight_dict), + "num_classes: {}".format(self.num_classes), + "eos_coef: {}".format(self.eos_coef), + "num_points: {}".format(self.num_points), + "oversample_ratio: {}".format(self.oversample_ratio), + "importance_sample_ratio: {}".format(self.importance_sample_ratio), + ] + _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/Mask2Former/mask2former/modeling/matcher.py b/Mask2Former/mask2former/modeling/matcher.py new file mode 100644 index 0000000..7c6af7f --- /dev/null +++ b/Mask2Former/mask2former/modeling/matcher.py @@ -0,0 +1,189 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/matcher.py +""" +Modules to compute the matching cost and solve the corresponding LSAP. +""" +import torch +import torch.nn.functional as F +from scipy.optimize import linear_sum_assignment +from torch import nn +from torch.cuda.amp import autocast + +from detectron2.projects.point_rend.point_features import point_sample + + +def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets) + denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :] + loss = 1 - (numerator + 1) / (denominator + 1) + return loss + + +batch_dice_loss_jit = torch.jit.script( + batch_dice_loss +) # type: torch.jit.ScriptModule + + +def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + hw = inputs.shape[1] + + pos = F.binary_cross_entropy_with_logits( + inputs, torch.ones_like(inputs), reduction="none" + ) + neg = F.binary_cross_entropy_with_logits( + inputs, torch.zeros_like(inputs), reduction="none" + ) + + loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum( + "nc,mc->nm", neg, (1 - targets) + ) + + return loss / hw + + +batch_sigmoid_ce_loss_jit = torch.jit.script( + batch_sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +class HungarianMatcher(nn.Module): + """This class computes an assignment between the targets and the predictions of the network + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, + there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, + while the others are un-matched (and thus treated as non-objects). + """ + + def __init__(self, cost_class: float = 1, cost_mask: float = 1, cost_dice: float = 1, num_points: int = 0): + """Creates the matcher + + Params: + cost_class: This is the relative weight of the classification error in the matching cost + cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost + cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost + """ + super().__init__() + self.cost_class = cost_class + self.cost_mask = cost_mask + self.cost_dice = cost_dice + + assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0" + + self.num_points = num_points + + @torch.no_grad() + def memory_efficient_forward(self, outputs, targets): + """More memory-friendly matching""" + bs, num_queries = outputs["pred_logits"].shape[:2] + + indices = [] + + # Iterate through batch size + for b in range(bs): + + out_prob = outputs["pred_logits"][b].softmax(-1) # [num_queries, num_classes] + tgt_ids = targets[b]["labels"] + + # Compute the classification cost. Contrary to the loss, we don't use the NLL, + # but approximate it in 1 - proba[target class]. + # The 1 is a constant that doesn't change the matching, it can be ommitted. + cost_class = -out_prob[:, tgt_ids] + + out_mask = outputs["pred_masks"][b] # [num_queries, H_pred, W_pred] + # gt masks are already padded when preparing target + tgt_mask = targets[b]["masks"].to(out_mask) + + out_mask = out_mask[:, None] + tgt_mask = tgt_mask[:, None] + # all masks share the same set of points for efficient matching! + point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device) + # get gt labels + tgt_mask = point_sample( + tgt_mask, + point_coords.repeat(tgt_mask.shape[0], 1, 1), + align_corners=False, + ).squeeze(1) + + out_mask = point_sample( + out_mask, + point_coords.repeat(out_mask.shape[0], 1, 1), + align_corners=False, + ).squeeze(1) + + with autocast(enabled=False): + out_mask = out_mask.float() + tgt_mask = tgt_mask.float() + # Compute the focal loss between masks + cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask) + + # Compute the dice loss betwen masks + cost_dice = batch_dice_loss_jit(out_mask, tgt_mask) + + # Final cost matrix + C = ( + self.cost_mask * cost_mask + + self.cost_class * cost_class + + self.cost_dice * cost_dice + ) + C = C.reshape(num_queries, -1).cpu() + + indices.append(linear_sum_assignment(C)) + + return [ + (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) + for i, j in indices + ] + + @torch.no_grad() + def forward(self, outputs, targets): + """Performs the matching + + Params: + outputs: This is a dict that contains at least these entries: + "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + "pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks + + targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: + "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth + objects in the target) containing the class labels + "masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks + + Returns: + A list of size batch_size, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: + len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + return self.memory_efficient_forward(outputs, targets) + + def __repr__(self, _repr_indent=4): + head = "Matcher " + self.__class__.__name__ + body = [ + "cost_class: {}".format(self.cost_class), + "cost_mask: {}".format(self.cost_mask), + "cost_dice: {}".format(self.cost_dice), + ] + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/Mask2Former/mask2former/modeling/meta_arch/__init__.py b/Mask2Former/mask2former/modeling/meta_arch/__init__.py new file mode 100644 index 0000000..9020c2d --- /dev/null +++ b/Mask2Former/mask2former/modeling/meta_arch/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/Mask2Former/mask2former/modeling/meta_arch/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/modeling/meta_arch/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..8a11c74 Binary files /dev/null and b/Mask2Former/mask2former/modeling/meta_arch/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/meta_arch/__pycache__/mask_former_head.cpython-38.pyc b/Mask2Former/mask2former/modeling/meta_arch/__pycache__/mask_former_head.cpython-38.pyc new file mode 100644 index 0000000..465bb29 Binary files /dev/null and b/Mask2Former/mask2former/modeling/meta_arch/__pycache__/mask_former_head.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/meta_arch/__pycache__/per_pixel_baseline.cpython-38.pyc b/Mask2Former/mask2former/modeling/meta_arch/__pycache__/per_pixel_baseline.cpython-38.pyc new file mode 100644 index 0000000..bbdd25e Binary files /dev/null and b/Mask2Former/mask2former/modeling/meta_arch/__pycache__/per_pixel_baseline.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/meta_arch/mask_former_head.py b/Mask2Former/mask2former/modeling/meta_arch/mask_former_head.py new file mode 100644 index 0000000..7051173 --- /dev/null +++ b/Mask2Former/mask2former/modeling/meta_arch/mask_former_head.py @@ -0,0 +1,132 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +from copy import deepcopy +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.maskformer_transformer_decoder import build_transformer_decoder +from ..pixel_decoder.fpn import build_pixel_decoder + + +@SEM_SEG_HEADS_REGISTRY.register() +class MaskFormerHead(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "sem_seg_head" in k and not k.startswith(prefix + "predictor"): + newk = k.replace(prefix, prefix + "pixel_decoder.") + # logger.debug(f"{k} ==> {newk}") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + num_classes: int, + pixel_decoder: nn.Module, + loss_weight: float = 1.0, + ignore_value: int = -1, + # extra parameters + transformer_predictor: nn.Module, + transformer_in_feature: str, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + num_classes: number of classes to predict + pixel_decoder: the pixel decoder module + loss_weight: loss weight + ignore_value: category id to be ignored during training. + transformer_predictor: the transformer decoder that makes prediction + transformer_in_feature: input feature name to the transformer_predictor + """ + super().__init__() + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + self.ignore_value = ignore_value + self.common_stride = 4 + self.loss_weight = loss_weight + + self.pixel_decoder = pixel_decoder + self.predictor = transformer_predictor #mask-attention 共9层 + self.transformer_in_feature = transformer_in_feature #multi_scale_pixel_decoder + + self.num_classes = num_classes + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + # figure out in_channels to transformer predictor + if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder": + transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + elif cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "pixel_embedding": + transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + elif cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "multi_scale_pixel_decoder": # for maskformer2 + transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + else: + transformer_predictor_in_channels = input_shape[cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE].channels + + return { + "input_shape": { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + }, + "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + "pixel_decoder": build_pixel_decoder(cfg, input_shape), + "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT, + "transformer_in_feature": cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE, + "transformer_predictor": build_transformer_decoder( + cfg, + transformer_predictor_in_channels, + mask_classification=True, + ), + } + + def forward(self, features, mask=None): + return self.layers(features, mask) + + def layers(self, features, mask=None): + mask_features, transformer_encoder_features, multi_scale_features = self.pixel_decoder.forward_features(features) + if self.transformer_in_feature == "multi_scale_pixel_decoder": + predictions = self.predictor(multi_scale_features, mask_features, mask) + else: + if self.transformer_in_feature == "transformer_encoder": + assert ( + transformer_encoder_features is not None + ), "Please use the TransformerEncoderPixelDecoder." + predictions = self.predictor(transformer_encoder_features, mask_features, mask) + elif self.transformer_in_feature == "pixel_embedding": + predictions = self.predictor(mask_features, mask_features, mask) + else: + predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask) + return predictions diff --git a/Mask2Former/mask2former/modeling/meta_arch/per_pixel_baseline.py b/Mask2Former/mask2former/modeling/meta_arch/per_pixel_baseline.py new file mode 100644 index 0000000..4ce7573 --- /dev/null +++ b/Mask2Former/mask2former/modeling/meta_arch/per_pixel_baseline.py @@ -0,0 +1,243 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.maskformer_transformer_decoder import StandardTransformerDecoder +from ..pixel_decoder.fpn import build_pixel_decoder + + +@SEM_SEG_HEADS_REGISTRY.register() +class PerPixelBaselineHead(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + logger = logging.getLogger(__name__) + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "sem_seg_head" in k and not k.startswith(prefix + "predictor"): + newk = k.replace(prefix, prefix + "pixel_decoder.") + # logger.warning(f"{k} ==> {newk}") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + num_classes: int, + pixel_decoder: nn.Module, + loss_weight: float = 1.0, + ignore_value: int = -1, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + num_classes: number of classes to predict + pixel_decoder: the pixel decoder module + loss_weight: loss weight + ignore_value: category id to be ignored during training. + """ + super().__init__() + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + self.ignore_value = ignore_value + self.common_stride = 4 + self.loss_weight = loss_weight + + self.pixel_decoder = pixel_decoder + self.predictor = Conv2d( + self.pixel_decoder.mask_dim, num_classes, kernel_size=1, stride=1, padding=0 + ) + weight_init.c2_msra_fill(self.predictor) + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + return { + "input_shape": { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + }, + "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE, + "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + "pixel_decoder": build_pixel_decoder(cfg, input_shape), + "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT, + } + + def forward(self, features, targets=None): + """ + Returns: + In training, returns (None, dict of losses) + In inference, returns (CxHxW logits, {}) + """ + x = self.layers(features) + if self.training: + return None, self.losses(x, targets) + else: + x = F.interpolate( + x, scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + return x, {} + + def layers(self, features): + x, _, _ = self.pixel_decoder.forward_features(features) + x = self.predictor(x) + return x + + def losses(self, predictions, targets): + predictions = predictions.float() # https://github.com/pytorch/pytorch/issues/48163 + predictions = F.interpolate( + predictions, scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + loss = F.cross_entropy( + predictions, targets, reduction="mean", ignore_index=self.ignore_value + ) + losses = {"loss_sem_seg": loss * self.loss_weight} + return losses + + +@SEM_SEG_HEADS_REGISTRY.register() +class PerPixelBaselinePlusHead(PerPixelBaselineHead): + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "sem_seg_head" in k and not k.startswith(prefix + "predictor"): + newk = k.replace(prefix, prefix + "pixel_decoder.") + logger.debug(f"{k} ==> {newk}") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + # extra parameters + transformer_predictor: nn.Module, + transformer_in_feature: str, + deep_supervision: bool, + # inherit parameters + num_classes: int, + pixel_decoder: nn.Module, + loss_weight: float = 1.0, + ignore_value: int = -1, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + transformer_predictor: the transformer decoder that makes prediction + transformer_in_feature: input feature name to the transformer_predictor + deep_supervision: whether or not to add supervision to the output of + every transformer decoder layer + num_classes: number of classes to predict + pixel_decoder: the pixel decoder module + loss_weight: loss weight + ignore_value: category id to be ignored during training. + """ + super().__init__( + input_shape, + num_classes=num_classes, + pixel_decoder=pixel_decoder, + loss_weight=loss_weight, + ignore_value=ignore_value, + ) + + del self.predictor + + self.predictor = transformer_predictor + self.transformer_in_feature = transformer_in_feature + self.deep_supervision = deep_supervision + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = super().from_config(cfg, input_shape) + ret["transformer_in_feature"] = cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE + if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder": + in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + else: + in_channels = input_shape[ret["transformer_in_feature"]].channels + ret["transformer_predictor"] = StandardTransformerDecoder( + cfg, in_channels, mask_classification=False + ) + ret["deep_supervision"] = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION + return ret + + def forward(self, features, targets=None): + """ + Returns: + In training, returns (None, dict of losses) + In inference, returns (CxHxW logits, {}) + """ + x, aux_outputs = self.layers(features) + if self.training: + if self.deep_supervision: + losses = self.losses(x, targets) + for i, aux_output in enumerate(aux_outputs): + losses["loss_sem_seg" + f"_{i}"] = self.losses( + aux_output["pred_masks"], targets + )["loss_sem_seg"] + return None, losses + else: + return None, self.losses(x, targets) + else: + x = F.interpolate( + x, scale_factor=self.common_stride, mode="bilinear", align_corners=False + ) + return x, {} + + def layers(self, features): + mask_features, transformer_encoder_features, _ = self.pixel_decoder.forward_features(features) + if self.transformer_in_feature == "transformer_encoder": + assert ( + transformer_encoder_features is not None + ), "Please use the TransformerEncoderPixelDecoder." + predictions = self.predictor(transformer_encoder_features, mask_features) + else: + predictions = self.predictor(features[self.transformer_in_feature], mask_features) + if self.deep_supervision: + return predictions["pred_masks"], predictions["aux_outputs"] + else: + return predictions["pred_masks"], None diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/__init__.py b/Mask2Former/mask2former/modeling/pixel_decoder/__init__.py new file mode 100644 index 0000000..9020c2d --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/modeling/pixel_decoder/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..f07f23f Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/__pycache__/fpn.cpython-38.pyc b/Mask2Former/mask2former/modeling/pixel_decoder/__pycache__/fpn.cpython-38.pyc new file mode 100644 index 0000000..5753e3e Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/__pycache__/fpn.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/__pycache__/msdeformattn.cpython-38.pyc b/Mask2Former/mask2former/modeling/pixel_decoder/__pycache__/msdeformattn.cpython-38.pyc new file mode 100644 index 0000000..ab8e85e Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/__pycache__/msdeformattn.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/fpn.py b/Mask2Former/mask2former/modeling/pixel_decoder/fpn.py new file mode 100644 index 0000000..7df65a1 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/fpn.py @@ -0,0 +1,312 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_ +from torch.cuda.amp import autocast + +from detectron2.config import configurable +from detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.position_encoding import PositionEmbeddingSine +from ..transformer_decoder.transformer import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn + + +def build_pixel_decoder(cfg, input_shape): + """ + Build a pixel decoder from `cfg.MODEL.MASK_FORMER.PIXEL_DECODER_NAME`. + """ + name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME + model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape) + forward_features = getattr(model, "forward_features", None) + if not callable(forward_features): + raise ValueError( + "Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. " + f"Please implement forward_features for {name} to only return mask features." + ) + return model + + +# This is a modified FPN decoder. +@SEM_SEG_HEADS_REGISTRY.register() +class BasePixelDecoder(nn.Module): + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + conv_dim: int, + mask_dim: int, + norm: Optional[Union[str, Callable]] = None, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + conv_dims: number of output channels for the intermediate conv layers. + mask_dim: number of output channels for the final conv layer. + norm (str or callable): normalization for all conv layers + """ + super().__init__() + + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" + feature_channels = [v.channels for k, v in input_shape] + + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(feature_channels): + if idx == len(self.in_features) - 1: + output_norm = get_norm(norm, conv_dim) + output_conv = Conv2d( + in_channels, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(output_conv) + self.add_module("layer_{}".format(idx + 1), output_conv) + + lateral_convs.append(None) + output_convs.append(output_conv) + else: + lateral_norm = get_norm(norm, conv_dim) + output_norm = get_norm(norm, conv_dim) + + lateral_conv = Conv2d( + in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + conv_dim, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + self.add_module("adapter_{}".format(idx + 1), lateral_conv) + self.add_module("layer_{}".format(idx + 1), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + + self.mask_dim = mask_dim + self.mask_features = Conv2d( + conv_dim, + mask_dim, + kernel_size=3, + stride=1, + padding=1, + ) + weight_init.c2_xavier_fill(self.mask_features) + + self.maskformer_num_feature_levels = 3 # always use 3 scales + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = {} + ret["input_shape"] = { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + } + ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM + return ret + + def forward_features(self, features): + multi_scale_features = [] + num_cur_levels = 0 + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.in_features[::-1]): + x = features[f] + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + if lateral_conv is None: + y = output_conv(x) + else: + cur_fpn = lateral_conv(x) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest") + y = output_conv(y) + if num_cur_levels < self.maskformer_num_feature_levels: + multi_scale_features.append(y) + num_cur_levels += 1 + return self.mask_features(y), None, multi_scale_features + + def forward(self, features, targets=None): + logger = logging.getLogger(__name__) + logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.") + return self.forward_features(features) + + +class TransformerEncoderOnly(nn.Module): + def __init__( + self, + d_model=512, + nhead=8, + num_encoder_layers=6, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + + encoder_layer = TransformerEncoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before + ) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + self._reset_parameters() + + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, src, mask, pos_embed): + # flatten NxCxHxW to HWxNxC + bs, c, h, w = src.shape + src = src.flatten(2).permute(2, 0, 1) + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + if mask is not None: + mask = mask.flatten(1) + + memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) + return memory.permute(1, 2, 0).view(bs, c, h, w) + + +# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map. +@SEM_SEG_HEADS_REGISTRY.register() +class TransformerEncoderPixelDecoder(BasePixelDecoder): + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + transformer_dropout: float, + transformer_nheads: int, + transformer_dim_feedforward: int, + transformer_enc_layers: int, + transformer_pre_norm: bool, + conv_dim: int, + mask_dim: int, + norm: Optional[Union[str, Callable]] = None, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + transformer_dropout: dropout probability in transformer + transformer_nheads: number of heads in transformer + transformer_dim_feedforward: dimension of feedforward network + transformer_enc_layers: number of transformer encoder layers + transformer_pre_norm: whether to use pre-layernorm or not + conv_dims: number of output channels for the intermediate conv layers. + mask_dim: number of output channels for the final conv layer. + norm (str or callable): normalization for all conv layers + """ + super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm) + + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" + feature_strides = [v.stride for k, v in input_shape] + feature_channels = [v.channels for k, v in input_shape] + + in_channels = feature_channels[len(self.in_features) - 1] + self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1) + weight_init.c2_xavier_fill(self.input_proj) + self.transformer = TransformerEncoderOnly( + d_model=conv_dim, + dropout=transformer_dropout, + nhead=transformer_nheads, + dim_feedforward=transformer_dim_feedforward, + num_encoder_layers=transformer_enc_layers, + normalize_before=transformer_pre_norm, + ) + N_steps = conv_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + # update layer + use_bias = norm == "" + output_norm = get_norm(norm, conv_dim) + output_conv = Conv2d( + conv_dim, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(output_conv) + delattr(self, "layer_{}".format(len(self.in_features))) + self.add_module("layer_{}".format(len(self.in_features)), output_conv) + self.output_convs[0] = output_conv + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = super().from_config(cfg, input_shape) + ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT + ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + ret[ + "transformer_enc_layers" + ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config + ret["transformer_pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM + return ret + + def forward_features(self, features): + multi_scale_features = [] + num_cur_levels = 0 + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.in_features[::-1]): + x = features[f] + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + if lateral_conv is None: + transformer = self.input_proj(x) + pos = self.pe_layer(x) + transformer = self.transformer(transformer, None, pos) + y = output_conv(transformer) + # save intermediate feature as input to Transformer decoder + transformer_encoder_features = transformer + else: + cur_fpn = lateral_conv(x) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest") + y = output_conv(y) + if num_cur_levels < self.maskformer_num_feature_levels: + multi_scale_features.append(y) + num_cur_levels += 1 + return self.mask_features(y), transformer_encoder_features, multi_scale_features + + def forward(self, features, targets=None): + logger = logging.getLogger(__name__) + logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.") + return self.forward_features(features) diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/msdeformattn.py b/Mask2Former/mask2former/modeling/pixel_decoder/msdeformattn.py new file mode 100644 index 0000000..0ff1a81 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/msdeformattn.py @@ -0,0 +1,358 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import numpy as np +from typing import Callable, Dict, List, Optional, Tuple, Union + +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_ +from torch.cuda.amp import autocast + +from detectron2.config import configurable +from detectron2.layers import Conv2d, ShapeSpec, get_norm +from detectron2.modeling import SEM_SEG_HEADS_REGISTRY + +from ..transformer_decoder.position_encoding import PositionEmbeddingSine +from ..transformer_decoder.transformer import _get_clones, _get_activation_fn +from .ops.modules import MSDeformAttn + + +# MSDeformAttn Transformer encoder in deformable detr +class MSDeformAttnTransformerEncoderOnly(nn.Module): + def __init__(self, d_model=256, nhead=8, + num_encoder_layers=6, dim_feedforward=1024, dropout=0.1, + activation="relu", + num_feature_levels=4, enc_n_points=4, + ): + super().__init__() + + self.d_model = d_model + self.nhead = nhead + + encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward, + dropout, activation, + num_feature_levels, nhead, enc_n_points) + self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers) + + self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model)) + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MSDeformAttn): + m._reset_parameters() + normal_(self.level_embed) + + def get_valid_ratio(self, mask): + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def forward(self, srcs, pos_embeds): + masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs] + # prepare input for encoder + src_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)): + bs, c, h, w = src.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + src = src.flatten(2).transpose(1, 2) + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) + lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + src_flatten.append(src) + mask_flatten.append(mask) + src_flatten = torch.cat(src_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device) + level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1])) + valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1) + + # encoder + memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten) + + return memory, spatial_shapes, level_start_index + + +class MSDeformAttnTransformerEncoderLayer(nn.Module): + def __init__(self, + d_model=256, d_ffn=1024, + dropout=0.1, activation="relu", + n_levels=4, n_heads=8, n_points=4): + super().__init__() + + # self attention + self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points) + self.dropout1 = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # ffn + self.linear1 = nn.Linear(d_model, d_ffn) + self.activation = _get_activation_fn(activation) + self.dropout2 = nn.Dropout(dropout) + self.linear2 = nn.Linear(d_ffn, d_model) + self.dropout3 = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward_ffn(self, src): + src2 = self.linear2(self.dropout2(self.activation(self.linear1(src)))) + src = src + self.dropout3(src2) + src = self.norm2(src) + return src + + def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None): + # self attention + src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask) + src = src + self.dropout1(src2) + src = self.norm1(src) + + # ffn + src = self.forward_ffn(src) + + return src + + +class MSDeformAttnTransformerEncoder(nn.Module): + def __init__(self, encoder_layer, num_layers): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + reference_points_list = [] + for lvl, (H_, W_) in enumerate(spatial_shapes): + + ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device), + torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device)) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None): + output = src + reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device) + for _, layer in enumerate(self.layers): + output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask) + + return output + + +@SEM_SEG_HEADS_REGISTRY.register() +class MSDeformAttnPixelDecoder(nn.Module): + @configurable + def __init__( + self, + input_shape: Dict[str, ShapeSpec], + *, + transformer_dropout: float, + transformer_nheads: int, + transformer_dim_feedforward: int, + transformer_enc_layers: int, + conv_dim: int, + mask_dim: int, + norm: Optional[Union[str, Callable]] = None, + # deformable transformer encoder args + transformer_in_features: List[str], + common_stride: int, + ): + """ + NOTE: this interface is experimental. + Args: + input_shape: shapes (channels and stride) of the input features + transformer_dropout: dropout probability in transformer + transformer_nheads: number of heads in transformer + transformer_dim_feedforward: dimension of feedforward network + transformer_enc_layers: number of transformer encoder layers + conv_dims: number of output channels for the intermediate conv layers. + mask_dim: number of output channels for the final conv layer. + norm (str or callable): normalization for all conv layers + """ + super().__init__() + transformer_input_shape = { + k: v for k, v in input_shape.items() if k in transformer_in_features + } + + # this is the input shape of pixel decoder + input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride) + self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5" + self.feature_strides = [v.stride for k, v in input_shape] + self.feature_channels = [v.channels for k, v in input_shape] + + # this is the input shape of transformer encoder (could use less features than pixel decoder + transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride) + self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from "res2" to "res5" + transformer_in_channels = [v.channels for k, v in transformer_input_shape] + self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # to decide extra FPN layers + + self.transformer_num_feature_levels = len(self.transformer_in_features) + if self.transformer_num_feature_levels > 1: + input_proj_list = [] + # from low resolution to high resolution (res5 -> res2) + for in_channels in transformer_in_channels[::-1]: + input_proj_list.append(nn.Sequential( + nn.Conv2d(in_channels, conv_dim, kernel_size=1), + nn.GroupNorm(32, conv_dim), + )) + self.input_proj = nn.ModuleList(input_proj_list) + else: + self.input_proj = nn.ModuleList([ + nn.Sequential( + nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1), + nn.GroupNorm(32, conv_dim), + )]) + + for proj in self.input_proj: + nn.init.xavier_uniform_(proj[0].weight, gain=1) + nn.init.constant_(proj[0].bias, 0) + + self.transformer = MSDeformAttnTransformerEncoderOnly( + d_model=conv_dim, + dropout=transformer_dropout, + nhead=transformer_nheads, + dim_feedforward=transformer_dim_feedforward, + num_encoder_layers=transformer_enc_layers, + num_feature_levels=self.transformer_num_feature_levels, + ) + N_steps = conv_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + self.mask_dim = mask_dim + # use 1x1 conv instead + self.mask_features = Conv2d( + conv_dim, + mask_dim, + kernel_size=1, + stride=1, + padding=0, + ) + weight_init.c2_xavier_fill(self.mask_features) + + self.maskformer_num_feature_levels = 3 # always use 3 scales + self.common_stride = common_stride + + # extra fpn levels + stride = min(self.transformer_feature_strides) + self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride)) + + lateral_convs = [] + output_convs = [] + + use_bias = norm == "" + for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]): + lateral_norm = get_norm(norm, conv_dim) + output_norm = get_norm(norm, conv_dim) + + lateral_conv = Conv2d( + in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm + ) + output_conv = Conv2d( + conv_dim, + conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + norm=output_norm, + activation=F.relu, + ) + weight_init.c2_xavier_fill(lateral_conv) + weight_init.c2_xavier_fill(output_conv) + self.add_module("adapter_{}".format(idx + 1), lateral_conv) + self.add_module("layer_{}".format(idx + 1), output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + + @classmethod + def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]): + ret = {} + ret["input_shape"] = { + k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES + } + ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM + ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT + ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + # ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + ret["transformer_dim_feedforward"] = 1024 # use 1024 for deformable transformer encoder + ret[ + "transformer_enc_layers" + ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config + ret["transformer_in_features"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES + ret["common_stride"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE + return ret + + @autocast(enabled=False) + def forward_features(self, features): + srcs = [] + pos = [] + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.transformer_in_features[::-1]): + x = features[f].float() # deformable detr does not support half precision + srcs.append(self.input_proj[idx](x)) + pos.append(self.pe_layer(x)) + + y, spatial_shapes, level_start_index = self.transformer(srcs, pos) + bs = y.shape[0] + + split_size_or_sections = [None] * self.transformer_num_feature_levels + for i in range(self.transformer_num_feature_levels): + if i < self.transformer_num_feature_levels - 1: + split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i] + else: + split_size_or_sections[i] = y.shape[1] - level_start_index[i] + y = torch.split(y, split_size_or_sections, dim=1) + + out = [] + multi_scale_features = [] + num_cur_levels = 0 + for i, z in enumerate(y): + out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1])) + + # append `out` with extra FPN levels + # Reverse feature maps into top-down order (from low to high resolution) + for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]): + x = features[f].float() + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + cur_fpn = lateral_conv(x) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False) + y = output_conv(y) + out.append(y) + + for o in out: + if num_cur_levels < self.maskformer_num_feature_levels: + multi_scale_features.append(o) + num_cur_levels += 1 + + return self.mask_features(out[-1]), out[0], multi_scale_features diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO b/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO new file mode 100644 index 0000000..18ccefb --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO @@ -0,0 +1,11 @@ +Metadata-Version: 2.1 +Name: MultiScaleDeformableAttention +Version: 1.0 +Summary: PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention +Home-page: https://github.com/fundamentalvision/Deformable-DETR +Author: Weijie Su +License: UNKNOWN +Platform: UNKNOWN + +UNKNOWN + diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt b/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt new file mode 100644 index 0000000..aa2cd28 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt @@ -0,0 +1,12 @@ +setup.py +/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/vision.cpp +/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp +/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu +MultiScaleDeformableAttention.egg-info/PKG-INFO +MultiScaleDeformableAttention.egg-info/SOURCES.txt +MultiScaleDeformableAttention.egg-info/dependency_links.txt +MultiScaleDeformableAttention.egg-info/top_level.txt +functions/__init__.py +functions/ms_deform_attn_func.py +modules/__init__.py +modules/ms_deform_attn.py \ No newline at end of file diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt b/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/top_level.txt b/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/top_level.txt new file mode 100644 index 0000000..25d8f77 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/MultiScaleDeformableAttention.egg-info/top_level.txt @@ -0,0 +1,3 @@ +MultiScaleDeformableAttention +functions +modules diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/MultiScaleDeformableAttention.cpython-38-x86_64-linux-gnu.so b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/MultiScaleDeformableAttention.cpython-38-x86_64-linux-gnu.so new file mode 100644 index 0000000..86f4cc1 Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/MultiScaleDeformableAttention.cpython-38-x86_64-linux-gnu.so differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/functions/__init__.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/functions/__init__.py new file mode 100644 index 0000000..2b06b5a --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/functions/__init__.py @@ -0,0 +1,13 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from .ms_deform_attn_func import MSDeformAttnFunction + diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/functions/ms_deform_attn_func.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/functions/ms_deform_attn_func.py new file mode 100644 index 0000000..94a36ab --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/functions/ms_deform_attn_func.py @@ -0,0 +1,72 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import torch.nn.functional as F +from torch.autograd import Function +from torch.autograd.function import once_differentiable + +try: + import MultiScaleDeformableAttention as MSDA +except ModuleNotFoundError as e: + info_string = ( + "\n\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\n" + "\t`cd mask2former/modeling/pixel_decoder/ops`\n" + "\t`sh make.sh`\n" + ) + raise ModuleNotFoundError(info_string) + + +class MSDeformAttnFunction(Function): + @staticmethod + def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step): + ctx.im2col_step = im2col_step + output = MSDA.ms_deform_attn_forward( + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step) + ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors + grad_value, grad_sampling_loc, grad_attn_weight = \ + MSDA.ms_deform_attn_backward( + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step) + + return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None + + +def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): + # for debug and test only, + # need to use cuda version instead + N_, S_, M_, D_ = value.shape + _, Lq_, M_, L_, P_, _ = sampling_locations.shape + value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for lid_, (H_, W_) in enumerate(value_spatial_shapes): + # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ + value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_) + # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 + sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) + # N_*M_, D_, Lq_, P_ + sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_, + mode='bilinear', padding_mode='zeros', align_corners=False) + sampling_value_list.append(sampling_value_l_) + # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) + attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_) + output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_) + return output.transpose(1, 2).contiguous() diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/__init__.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/__init__.py new file mode 100644 index 0000000..6fdbf03 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/__init__.py @@ -0,0 +1,12 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from .ms_deform_attn import MSDeformAttn diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/ms_deform_attn.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/ms_deform_attn.py new file mode 100644 index 0000000..e7b4c42 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/lib.linux-x86_64-3.8/modules/ms_deform_attn.py @@ -0,0 +1,125 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import warnings +import math + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.init import xavier_uniform_, constant_ + +from ..functions import MSDeformAttnFunction +from ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch + + +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n-1) == 0) and n != 0 + + +class MSDeformAttn(nn.Module): + def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4): + """ + Multi-Scale Deformable Attention Module + :param d_model hidden dimension + :param n_levels number of feature levels + :param n_heads number of attention heads + :param n_points number of sampling points per attention head per feature level + """ + super().__init__() + if d_model % n_heads != 0: + raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads)) + _d_per_head = d_model // n_heads + # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation + if not _is_power_of_2(_d_per_head): + warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 " + "which is more efficient in our CUDA implementation.") + + self.im2col_step = 128 + + self.d_model = d_model + self.n_levels = n_levels + self.n_heads = n_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) + self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) + self.value_proj = nn.Linear(d_model, d_model) + self.output_proj = nn.Linear(d_model, d_model) + + self._reset_parameters() + + def _reset_parameters(self): + constant_(self.sampling_offsets.weight.data, 0.) + thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1) + for i in range(self.n_points): + grid_init[:, :, i, :] *= i + 1 + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.) + constant_(self.attention_weights.bias.data, 0.) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.) + + def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None): + """ + :param query (N, Length_{query}, C) + :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area + or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes + :param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C) + :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] + :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] + :param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements + + :return output (N, Length_{query}, C) + """ + N, Len_q, _ = query.shape + N, Len_in, _ = input_flatten.shape + assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in + + value = self.value_proj(input_flatten) + if input_padding_mask is not None: + value = value.masked_fill(input_padding_mask[..., None], float(0)) + value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) + attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) + attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) + # N, Len_q, n_heads, n_levels, n_points, 2 + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :] \ + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + elif reference_points.shape[-1] == 4: + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + else: + raise ValueError( + 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1])) + try: + output = MSDeformAttnFunction.apply( + value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step) + except: + # CPU + output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + # # For FLOPs calculation only + # output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + output = self.output_proj(output) + return output diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.o b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.o new file mode 100644 index 0000000..c07db28 Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.o differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.o b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.o new file mode 100644 index 0000000..3950c91 Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.o differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/vision.o b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/vision.o new file mode 100644 index 0000000..7ada15f Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/ops/build/temp.linux-x86_64-3.8/root/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/vision.o differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/dist/MultiScaleDeformableAttention-1.0-py3.8-linux-x86_64.egg b/Mask2Former/mask2former/modeling/pixel_decoder/ops/dist/MultiScaleDeformableAttention-1.0-py3.8-linux-x86_64.egg new file mode 100644 index 0000000..775a1c1 Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/ops/dist/MultiScaleDeformableAttention-1.0-py3.8-linux-x86_64.egg differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__init__.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__init__.py new file mode 100644 index 0000000..2b06b5a --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__init__.py @@ -0,0 +1,13 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from .ms_deform_attn_func import MSDeformAttnFunction + diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..b0cdc9c Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__pycache__/ms_deform_attn_func.cpython-38.pyc b/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__pycache__/ms_deform_attn_func.cpython-38.pyc new file mode 100644 index 0000000..17fa319 Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/__pycache__/ms_deform_attn_func.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py new file mode 100644 index 0000000..94a36ab --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py @@ -0,0 +1,72 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import torch.nn.functional as F +from torch.autograd import Function +from torch.autograd.function import once_differentiable + +try: + import MultiScaleDeformableAttention as MSDA +except ModuleNotFoundError as e: + info_string = ( + "\n\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\n" + "\t`cd mask2former/modeling/pixel_decoder/ops`\n" + "\t`sh make.sh`\n" + ) + raise ModuleNotFoundError(info_string) + + +class MSDeformAttnFunction(Function): + @staticmethod + def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step): + ctx.im2col_step = im2col_step + output = MSDA.ms_deform_attn_forward( + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step) + ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors + grad_value, grad_sampling_loc, grad_attn_weight = \ + MSDA.ms_deform_attn_backward( + value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step) + + return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None + + +def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): + # for debug and test only, + # need to use cuda version instead + N_, S_, M_, D_ = value.shape + _, Lq_, M_, L_, P_, _ = sampling_locations.shape + value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for lid_, (H_, W_) in enumerate(value_spatial_shapes): + # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ + value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_) + # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 + sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) + # N_*M_, D_, Lq_, P_ + sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_, + mode='bilinear', padding_mode='zeros', align_corners=False) + sampling_value_list.append(sampling_value_l_) + # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) + attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_) + output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_) + return output.transpose(1, 2).contiguous() diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/make.sh b/Mask2Former/mask2former/modeling/pixel_decoder/ops/make.sh new file mode 100644 index 0000000..7b38cdb --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/make.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +python setup.py build install diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/__init__.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/__init__.py new file mode 100644 index 0000000..6fdbf03 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/__init__.py @@ -0,0 +1,12 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from .ms_deform_attn import MSDeformAttn diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..cc04788 Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/__pycache__/ms_deform_attn.cpython-38.pyc b/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/__pycache__/ms_deform_attn.cpython-38.pyc new file mode 100644 index 0000000..1632e32 Binary files /dev/null and b/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/__pycache__/ms_deform_attn.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py new file mode 100644 index 0000000..e7b4c42 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py @@ -0,0 +1,125 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import warnings +import math + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn.init import xavier_uniform_, constant_ + +from ..functions import MSDeformAttnFunction +from ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch + + +def _is_power_of_2(n): + if (not isinstance(n, int)) or (n < 0): + raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n))) + return (n & (n-1) == 0) and n != 0 + + +class MSDeformAttn(nn.Module): + def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4): + """ + Multi-Scale Deformable Attention Module + :param d_model hidden dimension + :param n_levels number of feature levels + :param n_heads number of attention heads + :param n_points number of sampling points per attention head per feature level + """ + super().__init__() + if d_model % n_heads != 0: + raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads)) + _d_per_head = d_model // n_heads + # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation + if not _is_power_of_2(_d_per_head): + warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 " + "which is more efficient in our CUDA implementation.") + + self.im2col_step = 128 + + self.d_model = d_model + self.n_levels = n_levels + self.n_heads = n_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2) + self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points) + self.value_proj = nn.Linear(d_model, d_model) + self.output_proj = nn.Linear(d_model, d_model) + + self._reset_parameters() + + def _reset_parameters(self): + constant_(self.sampling_offsets.weight.data, 0.) + thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1) + for i in range(self.n_points): + grid_init[:, :, i, :] *= i + 1 + with torch.no_grad(): + self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1)) + constant_(self.attention_weights.weight.data, 0.) + constant_(self.attention_weights.bias.data, 0.) + xavier_uniform_(self.value_proj.weight.data) + constant_(self.value_proj.bias.data, 0.) + xavier_uniform_(self.output_proj.weight.data) + constant_(self.output_proj.bias.data, 0.) + + def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None): + """ + :param query (N, Length_{query}, C) + :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area + or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes + :param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C) + :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})] + :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}] + :param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements + + :return output (N, Length_{query}, C) + """ + N, Len_q, _ = query.shape + N, Len_in, _ = input_flatten.shape + assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in + + value = self.value_proj(input_flatten) + if input_padding_mask is not None: + value = value.masked_fill(input_padding_mask[..., None], float(0)) + value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2) + attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points) + attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points) + # N, Len_q, n_heads, n_levels, n_points, 2 + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1) + sampling_locations = reference_points[:, :, None, :, None, :] \ + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + elif reference_points.shape[-1] == 4: + sampling_locations = reference_points[:, :, None, :, None, :2] \ + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + else: + raise ValueError( + 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1])) + try: + output = MSDeformAttnFunction.apply( + value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step) + except: + # CPU + output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + # # For FLOPs calculation only + # output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights) + output = self.output_proj(output) + return output diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/setup.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/setup.py new file mode 100644 index 0000000..3b57ad3 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/setup.py @@ -0,0 +1,78 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +import os +import glob + +import torch + +from torch.utils.cpp_extension import CUDA_HOME +from torch.utils.cpp_extension import CppExtension +from torch.utils.cpp_extension import CUDAExtension + +from setuptools import find_packages +from setuptools import setup + +requirements = ["torch", "torchvision"] + +def get_extensions(): + this_dir = os.path.dirname(os.path.abspath(__file__)) + extensions_dir = os.path.join(this_dir, "src") + + main_file = glob.glob(os.path.join(extensions_dir, "*.cpp")) + source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp")) + source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu")) + + sources = main_file + source_cpu + extension = CppExtension + extra_compile_args = {"cxx": []} + define_macros = [] + + # Force cuda since torch ask for a device, not if cuda is in fact available. + if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None: + extension = CUDAExtension + sources += source_cuda + define_macros += [("WITH_CUDA", None)] + extra_compile_args["nvcc"] = [ + "-DCUDA_HAS_FP16=1", + "-D__CUDA_NO_HALF_OPERATORS__", + "-D__CUDA_NO_HALF_CONVERSIONS__", + "-D__CUDA_NO_HALF2_OPERATORS__", + ] + else: + if CUDA_HOME is None: + raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.') + else: + raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().') + + sources = [os.path.join(extensions_dir, s) for s in sources] + include_dirs = [extensions_dir] + ext_modules = [ + extension( + "MultiScaleDeformableAttention", + sources, + include_dirs=include_dirs, + define_macros=define_macros, + extra_compile_args=extra_compile_args, + ) + ] + return ext_modules + +setup( + name="MultiScaleDeformableAttention", + version="1.0", + author="Weijie Su", + url="https://github.com/fundamentalvision/Deformable-DETR", + description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention", + packages=find_packages(exclude=("configs", "tests",)), + ext_modules=get_extensions(), + cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, +) diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp new file mode 100644 index 0000000..48757e2 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp @@ -0,0 +1,46 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include + +#include +#include + + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + AT_ERROR("Not implement on cpu"); +} + diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h new file mode 100644 index 0000000..51bb27e --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h @@ -0,0 +1,38 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#pragma once +#include + +at::Tensor +ms_deform_attn_cpu_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector +ms_deform_attn_cpu_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + + diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu new file mode 100644 index 0000000..0c465da --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu @@ -0,0 +1,158 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include +#include "cuda/ms_deform_im2col_cuda.cuh" + +#include +#include +#include +#include + + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto output = at::zeros({batch, num_query, num_heads, channels}, value.options()); + + const int batch_n = im2col_step_; + auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto columns = output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] { + ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + columns.data()); + + })); + } + + output = output.view({batch, num_query, num_heads*channels}); + + return output; +} + + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + + AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous"); + AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous"); + AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous"); + AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous"); + AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous"); + AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous"); + + AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor"); + AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor"); + AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor"); + AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor"); + AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor"); + AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor"); + + const int batch = value.size(0); + const int spatial_size = value.size(1); + const int num_heads = value.size(2); + const int channels = value.size(3); + + const int num_levels = spatial_shapes.size(0); + + const int num_query = sampling_loc.size(1); + const int num_point = sampling_loc.size(4); + + const int im2col_step_ = std::min(batch, im2col_step); + + AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_); + + auto grad_value = at::zeros_like(value); + auto grad_sampling_loc = at::zeros_like(sampling_loc); + auto grad_attn_weight = at::zeros_like(attn_weight); + + const int batch_n = im2col_step_; + auto per_value_size = spatial_size * num_heads * channels; + auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2; + auto per_attn_weight_size = num_query * num_heads * num_levels * num_point; + auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels}); + + for (int n = 0; n < batch/im2col_step_; ++n) + { + auto grad_output_g = grad_output_n.select(0, n); + AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] { + ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(), + grad_output_g.data(), + value.data() + n * im2col_step_ * per_value_size, + spatial_shapes.data(), + level_start_index.data(), + sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + attn_weight.data() + n * im2col_step_ * per_attn_weight_size, + batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point, + grad_value.data() + n * im2col_step_ * per_value_size, + grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size, + grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size); + + })); + } + + return { + grad_value, grad_sampling_loc, grad_attn_weight + }; +} \ No newline at end of file diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h new file mode 100644 index 0000000..4f0658e --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h @@ -0,0 +1,35 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#pragma once +#include + +at::Tensor ms_deform_attn_cuda_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step); + +std::vector ms_deform_attn_cuda_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step); + diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh new file mode 100644 index 0000000..c04e0d4 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh @@ -0,0 +1,1332 @@ +/*! +************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************** +* Modified from DCN (https://github.com/msracver/Deformable-ConvNets) +* Copyright (c) 2018 Microsoft +************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include +#include +#include + +#include +#include + +#include + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; \ + i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +inline int GET_BLOCKS(const int N, const int num_threads) +{ + return (N + num_threads - 1) / num_threads; +} + + +template +__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + } + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + *grad_attn_weight = top_grad * val; + *grad_sampling_loc = width * grad_w_weight * top_grad_value; + *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value; +} + + +template +__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, + const int &height, const int &width, const int &nheads, const int &channels, + const scalar_t &h, const scalar_t &w, const int &m, const int &c, + const scalar_t &top_grad, + const scalar_t &attn_weight, + scalar_t* &grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int h_low = floor(h); + const int w_low = floor(w); + const int h_high = h_low + 1; + const int w_high = w_low + 1; + + const scalar_t lh = h - h_low; + const scalar_t lw = w - w_low; + const scalar_t hh = 1 - lh, hw = 1 - lw; + + const int w_stride = nheads * channels; + const int h_stride = width * w_stride; + const int h_low_ptr_offset = h_low * h_stride; + const int h_high_ptr_offset = h_low_ptr_offset + h_stride; + const int w_low_ptr_offset = w_low * w_stride; + const int w_high_ptr_offset = w_low_ptr_offset + w_stride; + const int base_ptr = m * channels + c; + + const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + const scalar_t top_grad_value = top_grad * attn_weight; + scalar_t grad_h_weight = 0, grad_w_weight = 0; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + { + const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr; + v1 = bottom_data[ptr1]; + grad_h_weight -= hw * v1; + grad_w_weight -= hh * v1; + atomicAdd(grad_value+ptr1, w1*top_grad_value); + } + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + { + const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr; + v2 = bottom_data[ptr2]; + grad_h_weight -= lw * v2; + grad_w_weight += hh * v2; + atomicAdd(grad_value+ptr2, w2*top_grad_value); + } + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + { + const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr; + v3 = bottom_data[ptr3]; + grad_h_weight += hw * v3; + grad_w_weight -= lh * v3; + atomicAdd(grad_value+ptr3, w3*top_grad_value); + } + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + { + const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr; + v4 = bottom_data[ptr4]; + grad_h_weight += lw * v4; + grad_w_weight += lh * v4; + atomicAdd(grad_value+ptr4, w4*top_grad_value); + } + + const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + atomicAdd(grad_attn_weight, top_grad * val); + atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value); + atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value); +} + + +template +__global__ void ms_deformable_im2col_gpu_kernel(const int n, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + scalar_t *data_col_ptr = data_col + index; + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + scalar_t col = 0; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride); + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight; + } + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + } + } + *data_col_ptr = col; + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockSize; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2]; + __shared__ scalar_t cache_grad_attn_weight[blockSize]; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockSize/2; s>0; s>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + if (tid == 0) + { + scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0]; + int sid=2; + for (unsigned int tid = 1; tid < blockDim.x; ++tid) + { + _grad_w += cache_grad_sampling_loc[sid]; + _grad_h += cache_grad_sampling_loc[sid + 1]; + _grad_a += cache_grad_attn_weight[tid]; + sid += 2; + } + + + *grad_sampling_loc = _grad_w; + *(grad_sampling_loc + 1) = _grad_h; + *grad_attn_weight = _grad_a; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + *grad_sampling_loc = cache_grad_sampling_loc[0]; + *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1]; + *grad_attn_weight = cache_grad_attn_weight[0]; + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + +template +__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + extern __shared__ int _s[]; + scalar_t* cache_grad_sampling_loc = (scalar_t*)_s; + scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x; + unsigned int tid = threadIdx.x; + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0; + *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0; + *(cache_grad_attn_weight+threadIdx.x)=0; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x); + } + + __syncthreads(); + + for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1) + { + if (tid < s) { + const unsigned int xid1 = tid << 1; + const unsigned int xid2 = (tid + s) << 1; + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1]; + if (tid + (s << 1) < spre) + { + cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)]; + cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)]; + cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)]; + } + } + __syncthreads(); + } + + if (tid == 0) + { + atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]); + atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]); + atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]); + } + __syncthreads(); + + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n, + const scalar_t *grad_col, + const scalar_t *data_value, + const int64_t *data_spatial_shapes, + const int64_t *data_level_start_index, + const scalar_t *data_sampling_loc, + const scalar_t *data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t *grad_value, + scalar_t *grad_sampling_loc, + scalar_t *grad_attn_weight) +{ + CUDA_KERNEL_LOOP(index, n) + { + int _temp = index; + const int c_col = _temp % channels; + _temp /= channels; + const int sampling_index = _temp; + const int m_col = _temp % num_heads; + _temp /= num_heads; + const int q_col = _temp % num_query; + _temp /= num_query; + const int b_col = _temp; + + const scalar_t top_grad = grad_col[index]; + + int data_weight_ptr = sampling_index * num_levels * num_point; + int data_loc_w_ptr = data_weight_ptr << 1; + const int grad_sampling_ptr = data_weight_ptr; + grad_sampling_loc += grad_sampling_ptr << 1; + grad_attn_weight += grad_sampling_ptr; + const int grad_weight_stride = 1; + const int grad_loc_stride = 2; + const int qid_stride = num_heads * channels; + const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride; + + for (int l_col=0; l_col < num_levels; ++l_col) + { + const int level_start_id = data_level_start_index[l_col]; + const int spatial_h_ptr = l_col << 1; + const int spatial_h = data_spatial_shapes[spatial_h_ptr]; + const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1]; + const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride; + const scalar_t *data_value_ptr = data_value + value_ptr_offset; + scalar_t *grad_value_ptr = grad_value + value_ptr_offset; + + for (int p_col=0; p_col < num_point; ++p_col) + { + const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr]; + const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1]; + const scalar_t weight = data_attn_weight[data_weight_ptr]; + + const scalar_t h_im = loc_h * spatial_h - 0.5; + const scalar_t w_im = loc_w * spatial_w - 0.5; + if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w) + { + ms_deform_attn_col2im_bilinear_gm( + data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col, + top_grad, weight, grad_value_ptr, + grad_sampling_loc, grad_attn_weight); + } + data_weight_ptr += 1; + data_loc_w_ptr += 2; + grad_attn_weight += grad_weight_stride; + grad_sampling_loc += grad_loc_stride; + } + } + } +} + + +template +void ms_deformable_im2col_cuda(cudaStream_t stream, + const scalar_t* data_value, + const int64_t* data_spatial_shapes, + const int64_t* data_level_start_index, + const scalar_t* data_sampling_loc, + const scalar_t* data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* data_col) +{ + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + const int num_threads = CUDA_NUM_THREADS; + ms_deformable_im2col_gpu_kernel + <<>>( + num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, + batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } + +} + +template +void ms_deformable_col2im_cuda(cudaStream_t stream, + const scalar_t* grad_col, + const scalar_t* data_value, + const int64_t * data_spatial_shapes, + const int64_t * data_level_start_index, + const scalar_t * data_sampling_loc, + const scalar_t * data_attn_weight, + const int batch_size, + const int spatial_size, + const int num_heads, + const int channels, + const int num_levels, + const int num_query, + const int num_point, + scalar_t* grad_value, + scalar_t* grad_sampling_loc, + scalar_t* grad_attn_weight) +{ + const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels; + const int num_kernels = batch_size * num_query * num_heads * channels; + const int num_actual_kernels = batch_size * num_query * num_heads * channels; + if (channels > 1024) + { + if ((channels & 1023) == 0) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_gm + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + else{ + switch(channels) + { + case 1: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 2: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 4: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 8: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 16: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 32: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 64: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 128: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 256: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 512: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + case 1024: + ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + break; + default: + if (channels < 64) + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v1 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + else + { + ms_deformable_col2im_gpu_kernel_shm_reduce_v2 + <<>>( + num_kernels, + grad_col, + data_value, + data_spatial_shapes, + data_level_start_index, + data_sampling_loc, + data_attn_weight, + batch_size, + spatial_size, + num_heads, + channels, + num_levels, + num_query, + num_point, + grad_value, + grad_sampling_loc, + grad_attn_weight); + } + } + } + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } + +} \ No newline at end of file diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/ms_deform_attn.h b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/ms_deform_attn.h new file mode 100644 index 0000000..2f80a1b --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/ms_deform_attn.h @@ -0,0 +1,67 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#pragma once + +#include "cpu/ms_deform_attn_cpu.h" + +#ifdef WITH_CUDA +#include "cuda/ms_deform_attn_cuda.h" +#endif + + +at::Tensor +ms_deform_attn_forward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_forward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + +std::vector +ms_deform_attn_backward( + const at::Tensor &value, + const at::Tensor &spatial_shapes, + const at::Tensor &level_start_index, + const at::Tensor &sampling_loc, + const at::Tensor &attn_weight, + const at::Tensor &grad_output, + const int im2col_step) +{ + if (value.type().is_cuda()) + { +#ifdef WITH_CUDA + return ms_deform_attn_cuda_backward( + value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + AT_ERROR("Not implemented on the CPU"); +} + diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/vision.cpp b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/vision.cpp new file mode 100644 index 0000000..4a08821 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/src/vision.cpp @@ -0,0 +1,21 @@ +/*! +************************************************************************************************** +* Deformable DETR +* Copyright (c) 2020 SenseTime. All Rights Reserved. +* Licensed under the Apache License, Version 2.0 [see LICENSE for details] +************************************************************************************************** +* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +************************************************************************************************** +*/ + +/*! +* Copyright (c) Facebook, Inc. and its affiliates. +* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR +*/ + +#include "ms_deform_attn.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward"); + m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward"); +} diff --git a/Mask2Former/mask2former/modeling/pixel_decoder/ops/test.py b/Mask2Former/mask2former/modeling/pixel_decoder/ops/test.py new file mode 100644 index 0000000..6e1b545 --- /dev/null +++ b/Mask2Former/mask2former/modeling/pixel_decoder/ops/test.py @@ -0,0 +1,92 @@ +# ------------------------------------------------------------------------------------------------ +# Deformable DETR +# Copyright (c) 2020 SenseTime. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------------------------------ +# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 +# ------------------------------------------------------------------------------------------------ + +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import time +import torch +import torch.nn as nn +from torch.autograd import gradcheck + +from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch + + +N, M, D = 1, 2, 2 +Lq, L, P = 2, 2, 2 +shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda() +level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1])) +S = sum([(H*W).item() for H, W in shapes]) + + +torch.manual_seed(3) + + +@torch.no_grad() +def check_forward_equal_with_pytorch_double(): + value = torch.rand(N, S, M, D).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu() + output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu() + fwdok = torch.allclose(output_cuda, output_pytorch) + max_abs_err = (output_cuda - output_pytorch).abs().max() + max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() + + print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}') + + +@torch.no_grad() +def check_forward_equal_with_pytorch_float(): + value = torch.rand(N, S, M, D).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu() + output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu() + fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3) + max_abs_err = (output_cuda - output_pytorch).abs().max() + max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max() + + print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}') + + +def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True): + + value = torch.rand(N, S, M, channels).cuda() * 0.01 + sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda() + attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5 + attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True) + im2col_step = 2 + func = MSDeformAttnFunction.apply + + value.requires_grad = grad_value + sampling_locations.requires_grad = grad_sampling_loc + attention_weights.requires_grad = grad_attn_weight + + gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step)) + + print(f'* {gradok} check_gradient_numerical(D={channels})') + + +if __name__ == '__main__': + check_forward_equal_with_pytorch_double() + check_forward_equal_with_pytorch_float() + + for channels in [30, 32, 64, 71, 1025, 2048, 3096]: + check_gradient_numerical(channels, True, True, True) + + + diff --git a/Mask2Former/mask2former/modeling/transformer_decoder/__init__.py b/Mask2Former/mask2former/modeling/transformer_decoder/__init__.py new file mode 100644 index 0000000..ddcf38e --- /dev/null +++ b/Mask2Former/mask2former/modeling/transformer_decoder/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .maskformer_transformer_decoder import StandardTransformerDecoder +from .mask2former_transformer_decoder import MultiScaleMaskedTransformerDecoder diff --git a/Mask2Former/mask2former/modeling/transformer_decoder/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/modeling/transformer_decoder/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..fc5ea68 Binary files /dev/null and b/Mask2Former/mask2former/modeling/transformer_decoder/__pycache__/__init__.cpython-38.pyc differ diff --git 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+1,461 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py +import logging +import fvcore.nn.weight_init as weight_init +from typing import Optional +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d + +from .position_encoding import PositionEmbeddingSine +from .maskformer_transformer_decoder import TRANSFORMER_DECODER_REGISTRY + + +class SelfAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + return self.forward_post(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + + +class CrossAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + return self.forward_post(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + + +class FFNLayer(nn.Module): + + def __init__(self, d_model, dim_feedforward=2048, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm = nn.LayerNorm(d_model) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt): + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + return tgt + + def forward_pre(self, tgt): + tgt2 = self.norm(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout(tgt2) + return tgt + + def forward(self, tgt): + if self.normalize_before: + return self.forward_pre(tgt) + return self.forward_post(tgt) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") + + +class MLP(nn.Module): + """ Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +@TRANSFORMER_DECODER_REGISTRY.register() +class MultiScaleMaskedTransformerDecoder(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "static_query" in k: + newk = k.replace("static_query", "query_feat") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + in_channels, + mask_classification=True, + *, + num_classes: int, + hidden_dim: int, + num_queries: int, + nheads: int, + dim_feedforward: int, + dec_layers: int, + pre_norm: bool, + mask_dim: int, + enforce_input_project: bool, + ): + """ + NOTE: this interface is experimental. + Args: + in_channels: channels of the input features + mask_classification: whether to add mask classifier or not + num_classes: number of classes + hidden_dim: Transformer feature dimension + num_queries: number of queries + nheads: number of heads + dim_feedforward: feature dimension in feedforward network + enc_layers: number of Transformer encoder layers + dec_layers: number of Transformer decoder layers + pre_norm: whether to use pre-LayerNorm or not + mask_dim: mask feature dimension + enforce_input_project: add input project 1x1 conv even if input + channels and hidden dim is identical + """ + super().__init__() + + assert mask_classification, "Only support mask classification model" + self.mask_classification = mask_classification + + # positional encoding + N_steps = hidden_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + # define Transformer decoder here + self.num_heads = nheads + self.num_layers = dec_layers + self.transformer_self_attention_layers = nn.ModuleList() + self.transformer_cross_attention_layers = nn.ModuleList() + self.transformer_ffn_layers = nn.ModuleList() + + for _ in range(self.num_layers): + self.transformer_self_attention_layers.append( + SelfAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_cross_attention_layers.append( + CrossAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_ffn_layers.append( + FFNLayer( + d_model=hidden_dim, + dim_feedforward=dim_feedforward, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.decoder_norm = nn.LayerNorm(hidden_dim) + + self.num_queries = num_queries + # learnable query features + self.query_feat = nn.Embedding(num_queries, hidden_dim) + # learnable query p.e. + self.query_embed = nn.Embedding(num_queries, hidden_dim) + + # level embedding (we always use 3 scales) + self.num_feature_levels = 3 + self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim) + self.input_proj = nn.ModuleList() + for _ in range(self.num_feature_levels): + if in_channels != hidden_dim or enforce_input_project: + self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1)) + weight_init.c2_xavier_fill(self.input_proj[-1]) + else: + self.input_proj.append(nn.Sequential()) + + # output FFNs + if self.mask_classification: + self.class_embed = nn.Linear(hidden_dim, num_classes + 1) + self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3) + + @classmethod + def from_config(cls, cfg, in_channels, mask_classification): + ret = {} + ret["in_channels"] = in_channels + ret["mask_classification"] = mask_classification + + ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES + ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM + ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES + # Transformer parameters: + ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + + # NOTE: because we add learnable query features which requires supervision, + # we add minus 1 to decoder layers to be consistent with our loss + # implementation: that is, number of auxiliary losses is always + # equal to number of decoder layers. With learnable query features, the number of + # auxiliary losses equals number of decoders plus 1. + assert cfg.MODEL.MASK_FORMER.DEC_LAYERS >= 1 + ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS - 1 + ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM + ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ + + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + + return ret + + def forward(self, x, mask_features, mask = None): + # x is a list of multi-scale feature + assert len(x) == self.num_feature_levels + src = [] + pos = [] + size_list = [] + + # disable mask, it does not affect performance + del mask + + for i in range(self.num_feature_levels): + size_list.append(x[i].shape[-2:]) + pos.append(self.pe_layer(x[i], None).flatten(2)) + src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None]) + + # flatten NxCxHxW to HWxNxC + pos[-1] = pos[-1].permute(2, 0, 1) + src[-1] = src[-1].permute(2, 0, 1) + + _, bs, _ = src[0].shape + + # QxNxC + query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1) + output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1) + + predictions_class = [] + predictions_mask = [] + + # prediction heads on learnable query features + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0]) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + for i in range(self.num_layers): + level_index = i % self.num_feature_levels + attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False + # attention: cross-attention first + output = self.transformer_cross_attention_layers[i]( + output, src[level_index], + memory_mask=attn_mask, + memory_key_padding_mask=None, # here we do not apply masking on padded region + pos=pos[level_index], query_pos=query_embed + ) + + output = self.transformer_self_attention_layers[i]( + output, tgt_mask=None, + tgt_key_padding_mask=None, + query_pos=query_embed + ) + + # FFN + output = self.transformer_ffn_layers[i]( + output + ) + + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels]) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + assert len(predictions_class) == self.num_layers + 1 + + out = { + 'pred_logits': predictions_class[-1], + 'pred_masks': predictions_mask[-1], + 'aux_outputs': self._set_aux_loss( + predictions_class if self.mask_classification else None, predictions_mask + ) + } + return out + + def forward_prediction_heads(self, output, mask_features, attn_mask_target_size): + decoder_output = self.decoder_norm(output) + decoder_output = decoder_output.transpose(0, 1) + outputs_class = self.class_embed(decoder_output) + mask_embed = self.mask_embed(decoder_output) + outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features) + + # NOTE: prediction is of higher-resolution + # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW] + attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False) + # must use bool type + # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged. + attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool() + attn_mask = attn_mask.detach() + + return outputs_class, outputs_mask, attn_mask + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_seg_masks): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + if self.mask_classification: + return [ + {"pred_logits": a, "pred_masks": b} + for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1]) + ] + else: + return [{"pred_masks": b} for b in outputs_seg_masks[:-1]] diff --git a/Mask2Former/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py b/Mask2Former/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py new file mode 100644 index 0000000..79f09fa --- /dev/null +++ b/Mask2Former/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py +import fvcore.nn.weight_init as weight_init +import torch +from torch import nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d +from detectron2.utils.registry import Registry + +from .position_encoding import PositionEmbeddingSine +from .transformer import Transformer + + +TRANSFORMER_DECODER_REGISTRY = Registry("TRANSFORMER_MODULE") +TRANSFORMER_DECODER_REGISTRY.__doc__ = """ +Registry for transformer module in MaskFormer. +""" + + +def build_transformer_decoder(cfg, in_channels, mask_classification=True): + """ + Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`. + """ + name = cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME + return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification) + + +@TRANSFORMER_DECODER_REGISTRY.register() +class StandardTransformerDecoder(nn.Module): + @configurable + def __init__( + self, + in_channels, + mask_classification=True, + *, + num_classes: int, + hidden_dim: int, + num_queries: int, + nheads: int, + dropout: float, + dim_feedforward: int, + enc_layers: int, + dec_layers: int, + pre_norm: bool, + deep_supervision: bool, + mask_dim: int, + enforce_input_project: bool, + ): + """ + NOTE: this interface is experimental. + Args: + in_channels: channels of the input features + mask_classification: whether to add mask classifier or not + num_classes: number of classes + hidden_dim: Transformer feature dimension + num_queries: number of queries + nheads: number of heads + dropout: dropout in Transformer + dim_feedforward: feature dimension in feedforward network + enc_layers: number of Transformer encoder layers + dec_layers: number of Transformer decoder layers + pre_norm: whether to use pre-LayerNorm or not + deep_supervision: whether to add supervision to every decoder layers + mask_dim: mask feature dimension + enforce_input_project: add input project 1x1 conv even if input + channels and hidden dim is identical + """ + super().__init__() + + self.mask_classification = mask_classification + + # positional encoding + N_steps = hidden_dim // 2 + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) + + transformer = Transformer( + d_model=hidden_dim, + dropout=dropout, + nhead=nheads, + dim_feedforward=dim_feedforward, + num_encoder_layers=enc_layers, + num_decoder_layers=dec_layers, + normalize_before=pre_norm, + return_intermediate_dec=deep_supervision, + ) + + self.num_queries = num_queries + self.transformer = transformer + hidden_dim = transformer.d_model + + self.query_embed = nn.Embedding(num_queries, hidden_dim) + + if in_channels != hidden_dim or enforce_input_project: + self.input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1) + weight_init.c2_xavier_fill(self.input_proj) + else: + self.input_proj = nn.Sequential() + self.aux_loss = deep_supervision + + # output FFNs + if self.mask_classification: + self.class_embed = nn.Linear(hidden_dim, num_classes + 1) + self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3) + + @classmethod + def from_config(cls, cfg, in_channels, mask_classification): + ret = {} + ret["in_channels"] = in_channels + ret["mask_classification"] = mask_classification + + ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES + ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM + ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES + # Transformer parameters: + ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + ret["dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT + ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + ret["enc_layers"] = cfg.MODEL.MASK_FORMER.ENC_LAYERS + ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS + ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM + ret["deep_supervision"] = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION + ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ + + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + + return ret + + def forward(self, x, mask_features, mask=None): + if mask is not None: + mask = F.interpolate(mask[None].float(), size=x.shape[-2:]).to(torch.bool)[0] + pos = self.pe_layer(x, mask) + + src = x + hs, memory = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos) + + if self.mask_classification: + outputs_class = self.class_embed(hs) + out = {"pred_logits": outputs_class[-1]} + else: + out = {} + + if self.aux_loss: + # [l, bs, queries, embed] + mask_embed = self.mask_embed(hs) + outputs_seg_masks = torch.einsum("lbqc,bchw->lbqhw", mask_embed, mask_features) + out["pred_masks"] = outputs_seg_masks[-1] + out["aux_outputs"] = self._set_aux_loss( + outputs_class if self.mask_classification else None, outputs_seg_masks + ) + else: + # FIXME h_boxes takes the last one computed, keep this in mind + # [bs, queries, embed] + mask_embed = self.mask_embed(hs[-1]) + outputs_seg_masks = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features) + out["pred_masks"] = outputs_seg_masks + return out + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_seg_masks): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + if self.mask_classification: + return [ + {"pred_logits": a, "pred_masks": b} + for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1]) + ] + else: + return [{"pred_masks": b} for b in outputs_seg_masks[:-1]] + + +class MLP(nn.Module): + """Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList( + nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) + ) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x diff --git a/Mask2Former/mask2former/modeling/transformer_decoder/position_encoding.py b/Mask2Former/mask2former/modeling/transformer_decoder/position_encoding.py new file mode 100644 index 0000000..f32532e --- /dev/null +++ b/Mask2Former/mask2former/modeling/transformer_decoder/position_encoding.py @@ -0,0 +1,64 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py +""" +Various positional encodings for the transformer. +""" +import math + +import torch +from torch import nn + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + if mask is None: + mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + def __repr__(self, _repr_indent=4): + head = "Positional encoding " + self.__class__.__name__ + body = [ + "num_pos_feats: {}".format(self.num_pos_feats), + "temperature: {}".format(self.temperature), + "normalize: {}".format(self.normalize), + "scale: {}".format(self.scale), + ] + # _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/Mask2Former/mask2former/modeling/transformer_decoder/transformer.py b/Mask2Former/mask2former/modeling/transformer_decoder/transformer.py new file mode 100644 index 0000000..ea8caa0 --- /dev/null +++ b/Mask2Former/mask2former/modeling/transformer_decoder/transformer.py @@ -0,0 +1,369 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/transformer.py +""" +Transformer class. + +Copy-paste from torch.nn.Transformer with modifications: + * positional encodings are passed in MHattention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers +""" +import copy +from typing import List, Optional + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + + +class Transformer(nn.Module): + def __init__( + self, + d_model=512, + nhead=8, + num_encoder_layers=6, + num_decoder_layers=6, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + return_intermediate_dec=False, + ): + super().__init__() + + encoder_layer = TransformerEncoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before + ) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + decoder_layer = TransformerDecoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before + ) + decoder_norm = nn.LayerNorm(d_model) + self.decoder = TransformerDecoder( + decoder_layer, + num_decoder_layers, + decoder_norm, + return_intermediate=return_intermediate_dec, + ) + + self._reset_parameters() + + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, src, mask, query_embed, pos_embed): + # flatten NxCxHxW to HWxNxC + bs, c, h, w = src.shape + src = src.flatten(2).permute(2, 0, 1) + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) + if mask is not None: + mask = mask.flatten(1) + + tgt = torch.zeros_like(query_embed) + memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) + hs = self.decoder( + tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed + ) + return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w) + + +class TransformerEncoder(nn.Module): + def __init__(self, encoder_layer, num_layers, norm=None): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + + def forward( + self, + src, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + output = src + + for layer in self.layers: + output = layer( + output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos + ) + + if self.norm is not None: + output = self.norm(output) + + return output + + +class TransformerDecoder(nn.Module): + def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): + super().__init__() + self.layers = _get_clones(decoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + self.return_intermediate = return_intermediate + + def forward( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + output = tgt + + intermediate = [] + + for layer in self.layers: + output = layer( + output, + memory, + tgt_mask=tgt_mask, + memory_mask=memory_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + pos=pos, + query_pos=query_pos, + ) + if self.return_intermediate: + intermediate.append(self.norm(output)) + + if self.norm is not None: + output = self.norm(output) + if self.return_intermediate: + intermediate.pop() + intermediate.append(output) + + if self.return_intermediate: + return torch.stack(intermediate) + + return output.unsqueeze(0) + + +class TransformerEncoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + q = k = self.with_pos_embed(src, pos) + src2 = self.self_attn( + q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask + )[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src + + def forward_pre( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + src2 = self.norm1(src) + q = k = self.with_pos_embed(src2, pos) + src2 = self.self_attn( + q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask + )[0] + src = src + self.dropout1(src2) + src2 = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) + src = src + self.dropout2(src2) + return src + + def forward( + self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + ): + if self.normalize_before: + return self.forward_pre(src, src_mask, src_key_padding_mask, pos) + return self.forward_post(src, src_mask, src_key_padding_mask, pos) + + +class TransformerDecoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn( + q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask + )[0] + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + tgt2 = self.multihead_attn( + query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + )[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout3(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward_pre( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn( + q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask + )[0] + tgt = tgt + self.dropout1(tgt2) + tgt2 = self.norm2(tgt) + tgt2 = self.multihead_attn( + query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + )[0] + tgt = tgt + self.dropout2(tgt2) + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + def forward( + self, + tgt, + memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + if self.normalize_before: + return self.forward_pre( + tgt, + memory, + tgt_mask, + memory_mask, + tgt_key_padding_mask, + memory_key_padding_mask, + pos, + query_pos, + ) + return self.forward_post( + tgt, + memory, + tgt_mask, + memory_mask, + tgt_key_padding_mask, + memory_key_padding_mask, + pos, + query_pos, + ) + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") diff --git a/Mask2Former/mask2former/test_time_augmentation.py b/Mask2Former/mask2former/test_time_augmentation.py new file mode 100644 index 0000000..b02568d --- /dev/null +++ b/Mask2Former/mask2former/test_time_augmentation.py @@ -0,0 +1,103 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import copy +import logging +from itertools import count + +import numpy as np +import torch +from fvcore.transforms import HFlipTransform +from torch import nn +from torch.nn.parallel import DistributedDataParallel + +from detectron2.data.detection_utils import read_image +from detectron2.modeling import DatasetMapperTTA + + +__all__ = [ + "SemanticSegmentorWithTTA", +] + + +class SemanticSegmentorWithTTA(nn.Module): + """ + A SemanticSegmentor with test-time augmentation enabled. + Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`. + """ + + def __init__(self, cfg, model, tta_mapper=None, batch_size=1): + """ + Args: + cfg (CfgNode): + model (SemanticSegmentor): a SemanticSegmentor to apply TTA on. + tta_mapper (callable): takes a dataset dict and returns a list of + augmented versions of the dataset dict. Defaults to + `DatasetMapperTTA(cfg)`. + batch_size (int): batch the augmented images into this batch size for inference. + """ + super().__init__() + if isinstance(model, DistributedDataParallel): + model = model.module + self.cfg = cfg.clone() + + self.model = model + + if tta_mapper is None: + tta_mapper = DatasetMapperTTA(cfg) + self.tta_mapper = tta_mapper + self.batch_size = batch_size + + def __call__(self, batched_inputs): + """ + Same input/output format as :meth:`SemanticSegmentor.forward` + """ + + def _maybe_read_image(dataset_dict): + ret = copy.copy(dataset_dict) + if "image" not in ret: + image = read_image(ret.pop("file_name"), self.model.input_format) + image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW + ret["image"] = image + if "height" not in ret and "width" not in ret: + ret["height"] = image.shape[1] + ret["width"] = image.shape[2] + return ret + + processed_results = [] + for x in batched_inputs: + result = self._inference_one_image(_maybe_read_image(x)) + processed_results.append(result) + return processed_results + + def _inference_one_image(self, input): + """ + Args: + input (dict): one dataset dict with "image" field being a CHW tensor + Returns: + dict: one output dict + """ + orig_shape = (input["height"], input["width"]) + augmented_inputs, tfms = self._get_augmented_inputs(input) + + final_predictions = None + count_predictions = 0 + for input, tfm in zip(augmented_inputs, tfms): + count_predictions += 1 + with torch.no_grad(): + if final_predictions is None: + if any(isinstance(t, HFlipTransform) for t in tfm.transforms): + final_predictions = self.model([input])[0].pop("sem_seg").flip(dims=[2]) + else: + final_predictions = self.model([input])[0].pop("sem_seg") + else: + if any(isinstance(t, HFlipTransform) for t in tfm.transforms): + final_predictions += self.model([input])[0].pop("sem_seg").flip(dims=[2]) + else: + final_predictions += self.model([input])[0].pop("sem_seg") + + final_predictions = final_predictions / count_predictions + return {"sem_seg": final_predictions} + + def _get_augmented_inputs(self, input): + augmented_inputs = self.tta_mapper(input) + tfms = [x.pop("transforms") for x in augmented_inputs] + return augmented_inputs, tfms diff --git a/Mask2Former/mask2former/utils/__init__.py b/Mask2Former/mask2former/utils/__init__.py new file mode 100644 index 0000000..9020c2d --- /dev/null +++ b/Mask2Former/mask2former/utils/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/Mask2Former/mask2former/utils/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former/utils/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..ffcb61e Binary files /dev/null and b/Mask2Former/mask2former/utils/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/utils/__pycache__/misc.cpython-38.pyc b/Mask2Former/mask2former/utils/__pycache__/misc.cpython-38.pyc new file mode 100644 index 0000000..bf70256 Binary files /dev/null and b/Mask2Former/mask2former/utils/__pycache__/misc.cpython-38.pyc differ diff --git a/Mask2Former/mask2former/utils/misc.py b/Mask2Former/mask2former/utils/misc.py new file mode 100644 index 0000000..874d980 --- /dev/null +++ b/Mask2Former/mask2former/utils/misc.py @@ -0,0 +1,111 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +from typing import List, Optional + +import torch +import torch.distributed as dist +import torchvision +from torch import Tensor + + +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device): + # type: (Device) -> NestedTensor # noqa + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + # TODO make this more general + if tensor_list[0].ndim == 3: + if torchvision._is_tracing(): + # nested_tensor_from_tensor_list() does not export well to ONNX + # call _onnx_nested_tensor_from_tensor_list() instead + return _onnx_nested_tensor_from_tensor_list(tensor_list) + + # TODO make it support different-sized images + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], : img.shape[2]] = False + else: + raise ValueError("not supported") + return NestedTensor(tensor, mask) + + +# _onnx_nested_tensor_from_tensor_list() is an implementation of +# nested_tensor_from_tensor_list() that is supported by ONNX tracing. +@torch.jit.unused +def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: + max_size = [] + for i in range(tensor_list[0].dim()): + max_size_i = torch.max( + torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32) + ).to(torch.int64) + max_size.append(max_size_i) + max_size = tuple(max_size) + + # work around for + # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + # m[: img.shape[1], :img.shape[2]] = False + # which is not yet supported in onnx + padded_imgs = [] + padded_masks = [] + for img in tensor_list: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) + padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) + padded_masks.append(padded_mask.to(torch.bool)) + + tensor = torch.stack(padded_imgs) + mask = torch.stack(padded_masks) + + return NestedTensor(tensor, mask=mask) + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True diff --git a/Mask2Former/mask2former_video/__init__.py b/Mask2Former/mask2former_video/__init__.py new file mode 100644 index 0000000..b6b2f54 --- /dev/null +++ b/Mask2Former/mask2former_video/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from . import modeling + +# config +from .config import add_maskformer2_video_config + +# models +from .video_maskformer_model import VideoMaskFormer + +# video +from .data_video import ( + YTVISDatasetMapper, + YTVISEvaluator, + build_detection_train_loader, + build_detection_test_loader, + get_detection_dataset_dicts, +) diff --git a/Mask2Former/mask2former_video/config.py b/Mask2Former/mask2former_video/config.py new file mode 100644 index 0000000..960d563 --- /dev/null +++ b/Mask2Former/mask2former_video/config.py @@ -0,0 +1,49 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +from detectron2.config import CfgNode as CN + + +def add_maskformer2_video_config(cfg): + # video data + # DataLoader + cfg.INPUT.SAMPLING_FRAME_NUM = 6 #IFC为5 + cfg.INPUT.SAMPLING_FRAME_RANGE = 20 + cfg.INPUT.SAMPLING_FRAME_SHUFFLE = False + cfg.INPUT.AUGMENTATIONS = [] # "brightness", "contrast", "saturation", "rotation" + + + ############## Add config for IFC. ############## + cfg.MODEL.IFC = CN() + cfg.MODEL.IFC.NUM_CLASSES = 40 + + # LOSS + cfg.MODEL.IFC.MASK_WEIGHT = 3.0 + cfg.MODEL.IFC.DICE_WEIGHT = 3.0 + cfg.MODEL.IFC.DEEP_SUPERVISION = True + cfg.MODEL.IFC.NO_OBJECT_WEIGHT = 0.1 + cfg.MODEL.IFC.MASK_STRIDE = 4 + cfg.MODEL.IFC.MATCH_STRIDE = 4 + + # TRANSFORMER + cfg.MODEL.IFC.NHEADS = 8 + cfg.MODEL.IFC.DROPOUT = 0.1 + cfg.MODEL.IFC.DIM_FEEDFORWARD = 2048 + cfg.MODEL.IFC.ENC_LAYERS = 3 #显存不够 + cfg.MODEL.IFC.DEC_LAYERS = 3 #显存不够 + cfg.MODEL.IFC.PRE_NORM = False + cfg.MODEL.IFC.NUM_MEMORY_BUS = 8 + + cfg.MODEL.IFC.HIDDEN_DIM = 256 + cfg.MODEL.IFC.NUM_OBJECT_QUERIES = 100 + + # Evaluation + cfg.MODEL.IFC.CLIP_STRIDE = 1 + cfg.MODEL.IFC.MERGE_ON_CPU = False + cfg.MODEL.IFC.MULTI_CLS_ON = True + cfg.MODEL.IFC.APPLY_CLS_THRES = 0.01 + + # Window of obeject encoder + cfg.MODEL.IFC.size_window = 3 + +size_window = 3 +DIM_FEEDFORWARD = 2048 \ No newline at end of file diff --git a/Mask2Former/mask2former_video/data_video/__init__.py b/Mask2Former/mask2former_video/data_video/__init__.py new file mode 100644 index 0000000..3085ed7 --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +from .dataset_mapper import YTVISDatasetMapper, CocoClipDatasetMapper +from .build import * + +from .datasets import * +from .ytvis_eval import YTVISEvaluator diff --git a/Mask2Former/mask2former_video/data_video/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..c57a509 Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/__pycache__/augmentation.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/__pycache__/augmentation.cpython-38.pyc new file mode 100644 index 0000000..6dd419b Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/__pycache__/augmentation.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/__pycache__/build.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/__pycache__/build.cpython-38.pyc new file mode 100644 index 0000000..7606640 Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/__pycache__/build.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/__pycache__/dataset_mapper.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/__pycache__/dataset_mapper.cpython-38.pyc new file mode 100644 index 0000000..5ee7367 Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/__pycache__/dataset_mapper.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/__pycache__/ytvis_eval.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/__pycache__/ytvis_eval.cpython-38.pyc new file mode 100644 index 0000000..2b7ca0b Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/__pycache__/ytvis_eval.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/augmentation.py b/Mask2Former/mask2former_video/data_video/augmentation.py new file mode 100644 index 0000000..5739721 --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/augmentation.py @@ -0,0 +1,167 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import numpy as np +import logging +import sys +from fvcore.transforms.transform import ( + HFlipTransform, + NoOpTransform, + VFlipTransform, +) +from PIL import Image + +from detectron2.data import transforms as T + + +class ResizeShortestEdge(T.Augmentation): + """ + Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge. + If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. + """ + + def __init__( + self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR, clip_frame_cnt=1 + ): + """ + Args: + short_edge_length (list[int]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the shortest edge length. + If ``sample_style=="choice"``, a list of shortest edge lengths to sample from. + max_size (int): maximum allowed longest edge length. + sample_style (str): either "range" or "choice". + """ + super().__init__() + assert sample_style in ["range", "choice", "range_by_clip", "choice_by_clip"], sample_style + + self.is_range = ("range" in sample_style) + if isinstance(short_edge_length, int): + short_edge_length = (short_edge_length, short_edge_length) + if self.is_range: + assert len(short_edge_length) == 2, ( + "short_edge_length must be two values using 'range' sample style." + f" Got {short_edge_length}!" + ) + self._cnt = 0 + self._init(locals()) + + def get_transform(self, image): + if self._cnt % self.clip_frame_cnt == 0: + if self.is_range: + self.size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1) + else: + self.size = np.random.choice(self.short_edge_length) + if self.size == 0: + return NoOpTransform() + + self._cnt = 0 # avoiding overflow + self._cnt += 1 + + h, w = image.shape[:2] + + scale = self.size * 1.0 / min(h, w) + if h < w: + newh, neww = self.size, scale * w + else: + newh, neww = scale * h, self.size + if max(newh, neww) > self.max_size: + scale = self.max_size * 1.0 / max(newh, neww) + newh = newh * scale + neww = neww * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return T.ResizeTransform(h, w, newh, neww, self.interp) + + +class RandomFlip(T.Augmentation): + """ + Flip the image horizontally or vertically with the given probability. + """ + + def __init__(self, prob=0.5, *, horizontal=True, vertical=False, clip_frame_cnt=1): + """ + Args: + prob (float): probability of flip. + horizontal (boolean): whether to apply horizontal flipping + vertical (boolean): whether to apply vertical flipping + """ + super().__init__() + + if horizontal and vertical: + raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.") + if not horizontal and not vertical: + raise ValueError("At least one of horiz or vert has to be True!") + self._cnt = 0 + + self._init(locals()) + + def get_transform(self, image): + if self._cnt % self.clip_frame_cnt == 0: + self.do = self._rand_range() < self.prob + self._cnt = 0 # avoiding overflow + self._cnt += 1 + + h, w = image.shape[:2] + + if self.do: + if self.horizontal: + return HFlipTransform(w) + elif self.vertical: + return VFlipTransform(h) + else: + return NoOpTransform() + + +def build_augmentation(cfg, is_train): + logger = logging.getLogger(__name__) + aug_list = [] + if is_train: + # Crop + if cfg.INPUT.CROP.ENABLED: + aug_list.append(T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)) + + # Resize + min_size = cfg.INPUT.MIN_SIZE_TRAIN + max_size = cfg.INPUT.MAX_SIZE_TRAIN + sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING + ms_clip_frame_cnt = cfg.INPUT.SAMPLING_FRAME_NUM if "by_clip" in cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING else 1 + aug_list.append(ResizeShortestEdge(min_size, max_size, sample_style, clip_frame_cnt=ms_clip_frame_cnt)) + + # Flip + if cfg.INPUT.RANDOM_FLIP != "none": + if cfg.INPUT.RANDOM_FLIP == "flip_by_clip": + flip_clip_frame_cnt = cfg.INPUT.SAMPLING_FRAME_NUM + else: + flip_clip_frame_cnt = 1 + + aug_list.append( + # NOTE using RandomFlip modified for the support of flip maintenance + RandomFlip( + horizontal=(cfg.INPUT.RANDOM_FLIP == "horizontal") or (cfg.INPUT.RANDOM_FLIP == "flip_by_clip"), + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + clip_frame_cnt=flip_clip_frame_cnt, + ) + ) + + # Additional augmentations : brightness, contrast, saturation, rotation + augmentations = cfg.INPUT.AUGMENTATIONS + if "brightness" in augmentations: + aug_list.append(T.RandomBrightness(0.9, 1.1)) + if "contrast" in augmentations: + aug_list.append(T.RandomContrast(0.9, 1.1)) + if "saturation" in augmentations: + aug_list.append(T.RandomSaturation(0.9, 1.1)) + if "rotation" in augmentations: + aug_list.append( + T.RandomRotation( + [-15, 15], expand=False, center=[(0.4, 0.4), (0.6, 0.6)], sample_style="range" + ) + ) + else: + # Resize + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + sample_style = "choice" + aug_list.append(T.ResizeShortestEdge(min_size, max_size, sample_style)) + + return aug_list diff --git a/Mask2Former/mask2former_video/data_video/build.py b/Mask2Former/mask2former_video/data_video/build.py new file mode 100644 index 0000000..a4f37e4 --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/build.py @@ -0,0 +1,250 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import itertools +import logging +import torch.utils.data + +from detectron2.config import CfgNode, configurable +from detectron2.data.build import ( + build_batch_data_loader, + load_proposals_into_dataset, + trivial_batch_collator, +) +from detectron2.data.catalog import DatasetCatalog +from detectron2.data.common import DatasetFromList, MapDataset +from detectron2.data.dataset_mapper import DatasetMapper +from detectron2.data.samplers import InferenceSampler, TrainingSampler +from detectron2.utils.comm import get_world_size + + +def _compute_num_images_per_worker(cfg: CfgNode): + num_workers = get_world_size() + images_per_batch = cfg.SOLVER.IMS_PER_BATCH + assert ( + images_per_batch % num_workers == 0 + ), "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of workers ({}).".format( + images_per_batch, num_workers + ) + assert ( + images_per_batch >= num_workers + ), "SOLVER.IMS_PER_BATCH ({}) must be larger than the number of workers ({}).".format( + images_per_batch, num_workers + ) + images_per_worker = images_per_batch // num_workers + return images_per_worker + + +def filter_images_with_only_crowd_annotations(dataset_dicts, dataset_names): + """ + Filter out images with none annotations or only crowd annotations + (i.e., images without non-crowd annotations). + A common training-time preprocessing on COCO dataset. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format, but filtered. + """ + num_before = len(dataset_dicts) + + def valid(anns): + for ann in anns: + if isinstance(ann, list): + for instance in ann: + if instance.get("iscrowd", 0) == 0: + return True + else: + if ann.get("iscrowd", 0) == 0: + return True + return False + + dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with no usable annotations. {} images left.".format( + num_before - num_after, num_after + ) + ) + return dataset_dicts + + +def get_detection_dataset_dicts( + dataset_names, filter_empty=True, proposal_files=None +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + dataset_names (str or list[str]): a dataset name or a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `dataset_names`. + + Returns: + list[dict]: a list of dicts following the standard dataset dict format. + """ + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + assert len(dataset_names) + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] + for dataset_name, dicts in zip(dataset_names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + if proposal_files is not None: + assert len(dataset_names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts, dataset_names) + + assert len(dataset_dicts), "No valid data found in {}.".format(",".join(dataset_names)) + return dataset_dicts + + +def _train_loader_from_config(cfg, mapper, *, dataset=None, sampler=None): + if dataset is None: + dataset = get_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + + if mapper is None: + mapper = DatasetMapper(cfg, True) + + if sampler is None: + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + logger.info("Using training sampler {}".format(sampler_name)) + sampler = TrainingSampler(len(dataset)) + + return { + "dataset": dataset, + "sampler": sampler, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + } + + +# TODO can allow dataset as an iterable or IterableDataset to make this function more general +@configurable(from_config=_train_loader_from_config) +def build_detection_train_loader( + dataset, *, mapper, sampler=None, total_batch_size, aspect_ratio_grouping=True, num_workers=0 +): + """ + Build a dataloader for object detection with some default features. + This interface is experimental. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a map-style pytorch dataset. They can be obtained by using + :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that + produces indices to be applied on ``dataset``. + Default to :class:`TrainingSampler`, which coordinates a random shuffle + sequence across all workers. + total_batch_size (int): total batch size across all workers. Batching + simply puts data into a list. + aspect_ratio_grouping (bool): whether to group images with similar + aspect ratio for efficiency. When enabled, it requires each + element in dataset be a dict with keys "width" and "height". + num_workers (int): number of parallel data loading workers + + Returns: + torch.utils.data.DataLoader: a dataloader. Each output from it is a + ``list[mapped_element]`` of length ``total_batch_size / num_workers``, + where ``mapped_element`` is produced by the ``mapper``. + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torch.utils.data.sampler.Sampler) + return build_batch_data_loader( + dataset, + sampler, + total_batch_size, + aspect_ratio_grouping=aspect_ratio_grouping, + num_workers=num_workers, + ) + + +def _test_loader_from_config(cfg, dataset_name, mapper=None): + """ + Uses the given `dataset_name` argument (instead of the names in cfg), because the + standard practice is to evaluate each test set individually (not combining them). + """ + dataset = get_detection_dataset_dicts( + [dataset_name], + filter_empty=False, + proposal_files=[ + cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(dataset_name)] + ] + if cfg.MODEL.LOAD_PROPOSALS + else None, + ) + if mapper is None: + mapper = DatasetMapper(cfg, False) + return {"dataset": dataset, "mapper": mapper, "num_workers": cfg.DATALOADER.NUM_WORKERS} + + +@configurable(from_config=_test_loader_from_config) +def build_detection_test_loader(dataset, *, mapper, num_workers=0): + """ + Similar to `build_detection_train_loader`, but uses a batch size of 1. + This interface is experimental. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a map-style pytorch dataset. They can be obtained by using + :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset + and returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``. + num_workers (int): number of parallel data loading workers + + Returns: + DataLoader: a torch DataLoader, that loads the given detection + dataset, with test-time transformation and batching. + + Examples: + :: + data_loader = build_detection_test_loader( + DatasetRegistry.get("my_test"), + mapper=DatasetMapper(...)) + + # or, instantiate with a CfgNode: + data_loader = build_detection_test_loader(cfg, "my_test") + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + sampler = InferenceSampler(len(dataset)) + # Always use 1 image per worker during inference since this is the + # standard when reporting inference time in papers. + batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, 1, drop_last=False) + data_loader = torch.utils.data.DataLoader( + dataset, + num_workers=num_workers, + batch_sampler=batch_sampler, + collate_fn=trivial_batch_collator, + ) + return data_loader diff --git a/Mask2Former/mask2former_video/data_video/dataset_mapper.py b/Mask2Former/mask2former_video/data_video/dataset_mapper.py new file mode 100644 index 0000000..b89d943 --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/dataset_mapper.py @@ -0,0 +1,383 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import copy +import logging +import random +import numpy as np +from typing import List, Union +import torch + +from detectron2.config import configurable +from detectron2.structures import ( + BitMasks, + Boxes, + BoxMode, + Instances, +) + +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T + +from .augmentation import build_augmentation + +__all__ = ["YTVISDatasetMapper", "CocoClipDatasetMapper"] + + +def filter_empty_instances(instances, by_box=True, by_mask=True, box_threshold=1e-5): + """ + Filter out empty instances in an `Instances` object. + + Args: + instances (Instances): + by_box (bool): whether to filter out instances with empty boxes + by_mask (bool): whether to filter out instances with empty masks + box_threshold (float): minimum width and height to be considered non-empty + + Returns: + Instances: the filtered instances. + """ + assert by_box or by_mask + r = [] + if by_box: + r.append(instances.gt_boxes.nonempty(threshold=box_threshold)) + if instances.has("gt_masks") and by_mask: + r.append(instances.gt_masks.nonempty()) + + if not r: + return instances + m = r[0] + for x in r[1:]: + m = m & x + + instances.gt_ids[~m] = -1 + return instances + + +def _get_dummy_anno(num_classes): + return { + "iscrowd": 0, + "category_id": num_classes, + "id": -1, + "bbox": np.array([0, 0, 0, 0]), + "bbox_mode": BoxMode.XYXY_ABS, + "segmentation": [np.array([0.0] * 6)] + } + + +def ytvis_annotations_to_instances(annos, image_size): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + It will contain fields "gt_boxes", "gt_classes", "gt_ids", + "gt_masks", if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos] + target = Instances(image_size) + target.gt_boxes = Boxes(boxes) + + classes = [int(obj["category_id"]) for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + ids = [int(obj["id"]) for obj in annos] + ids = torch.tensor(ids, dtype=torch.int64) + target.gt_ids = ids + + if len(annos) and "segmentation" in annos[0]: + segms = [obj["segmentation"] for obj in annos] + masks = [] + for segm in segms: + assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format( + segm.ndim + ) + # mask array + masks.append(segm) + # torch.from_numpy does not support array with negative stride. + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x)) for x in masks]) + ) + target.gt_masks = masks + + return target + + +class YTVISDatasetMapper: + """ + A callable which takes a dataset dict in YouTube-VIS Dataset format, + and map it into a format used by the model. + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + sampling_frame_num: int = 2, + sampling_frame_range: int = 5, + sampling_frame_shuffle: bool = False, + num_classes: int = 40, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + """ + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.sampling_frame_num = sampling_frame_num + self.sampling_frame_range = sampling_frame_range + self.sampling_frame_shuffle = sampling_frame_shuffle + self.num_classes = num_classes + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = build_augmentation(cfg, is_train) + + sampling_frame_num = cfg.INPUT.SAMPLING_FRAME_NUM + sampling_frame_range = cfg.INPUT.SAMPLING_FRAME_RANGE + sampling_frame_shuffle = cfg.INPUT.SAMPLING_FRAME_SHUFFLE + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "sampling_frame_num": sampling_frame_num, + "sampling_frame_range": sampling_frame_range, + "sampling_frame_shuffle": sampling_frame_shuffle, + "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, + } + + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one video, in YTVIS Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + # TODO consider examining below deepcopy as it costs huge amount of computations. + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + + video_length = dataset_dict["length"] + if self.is_train: + ref_frame = random.randrange(video_length) + + start_idx = max(0, ref_frame-self.sampling_frame_range) + end_idx = min(video_length, ref_frame+self.sampling_frame_range + 1) + + selected_idx = np.random.choice( + np.array(list(range(start_idx, ref_frame)) + list(range(ref_frame+1, end_idx))), + self.sampling_frame_num - 1, + ) + selected_idx = selected_idx.tolist() + [ref_frame] + selected_idx = sorted(selected_idx) + if self.sampling_frame_shuffle: + random.shuffle(selected_idx) + else: + selected_idx = range(video_length) + + video_annos = dataset_dict.pop("annotations", None) + file_names = dataset_dict.pop("file_names", None) + + if self.is_train: + _ids = set() + for frame_idx in selected_idx: + _ids.update([anno["id"] for anno in video_annos[frame_idx]]) + ids = dict() + for i, _id in enumerate(_ids): + ids[_id] = i + + dataset_dict["image"] = [] + dataset_dict["instances"] = [] + dataset_dict["file_names"] = [] + for frame_idx in selected_idx: + dataset_dict["file_names"].append(file_names[frame_idx]) + + # Read image + image = utils.read_image(file_names[frame_idx], format=self.image_format) + utils.check_image_size(dataset_dict, image) + + aug_input = T.AugInput(image) + transforms = self.augmentations(aug_input) + image = aug_input.image + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"].append(torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))) + + if (video_annos is None) or (not self.is_train): + continue + + # NOTE copy() is to prevent annotations getting changed from applying augmentations + _frame_annos = [] + for anno in video_annos[frame_idx]: + _anno = {} + for k, v in anno.items(): + _anno[k] = copy.deepcopy(v) + _frame_annos.append(_anno) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations(obj, transforms, image_shape) + for obj in _frame_annos + if obj.get("iscrowd", 0) == 0 + ] + sorted_annos = [_get_dummy_anno(self.num_classes) for _ in range(len(ids))] + + for _anno in annos: + idx = ids[_anno["id"]] + sorted_annos[idx] = _anno + _gt_ids = [_anno["id"] for _anno in sorted_annos] + + instances = utils.annotations_to_instances(sorted_annos, image_shape, mask_format="bitmask") + instances.gt_ids = torch.tensor(_gt_ids) + if instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + instances = filter_empty_instances(instances) + else: + instances.gt_masks = BitMasks(torch.empty((0, *image_shape))) + dataset_dict["instances"].append(instances) + + return dataset_dict + + +class CocoClipDatasetMapper: + """ + A callable which takes a COCO image which converts into multiple frames, + and map it into a format used by the model. + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + sampling_frame_num: int = 2, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + """ + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.sampling_frame_num = sampling_frame_num + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = build_augmentation(cfg, is_train) + + sampling_frame_num = cfg.INPUT.SAMPLING_FRAME_NUM + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "sampling_frame_num": sampling_frame_num, + } + + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + + img_annos = dataset_dict.pop("annotations", None) + file_name = dataset_dict.pop("file_name", None) + original_image = utils.read_image(file_name, format=self.image_format) + + dataset_dict["image"] = [] + dataset_dict["instances"] = [] + dataset_dict["file_names"] = [file_name] * self.sampling_frame_num + for _ in range(self.sampling_frame_num): + utils.check_image_size(dataset_dict, original_image) + + aug_input = T.AugInput(original_image) + transforms = self.augmentations(aug_input) + image = aug_input.image + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"].append(torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))) + + if (img_annos is None) or (not self.is_train): + continue + + _img_annos = [] + for anno in img_annos: + _anno = {} + for k, v in anno.items(): + _anno[k] = copy.deepcopy(v) + _img_annos.append(_anno) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations(obj, transforms, image_shape) + for obj in _img_annos + if obj.get("iscrowd", 0) == 0 + ] + _gt_ids = list(range(len(annos))) + for idx in range(len(annos)): + if len(annos[idx]["segmentation"]) == 0: + annos[idx]["segmentation"] = [np.array([0.0] * 6)] + + instances = utils.annotations_to_instances(annos, image_shape, mask_format="bitmask") + instances.gt_ids = torch.tensor(_gt_ids) + if instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + instances = filter_empty_instances(instances) + else: + instances.gt_masks = BitMasks(torch.empty((0, *image_shape))) + dataset_dict["instances"].append(instances) + + return dataset_dict diff --git a/Mask2Former/mask2former_video/data_video/datasets/__init__.py b/Mask2Former/mask2former_video/data_video/datasets/__init__.py new file mode 100644 index 0000000..a95cb6c --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/datasets/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +from . import builtin # ensure the builtin datasets are registered + +__all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")] diff --git a/Mask2Former/mask2former_video/data_video/datasets/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/datasets/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..ca604f9 Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/datasets/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/datasets/__pycache__/builtin.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/datasets/__pycache__/builtin.cpython-38.pyc new file mode 100644 index 0000000..95d7dad Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/datasets/__pycache__/builtin.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/datasets/__pycache__/ytvis.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/datasets/__pycache__/ytvis.cpython-38.pyc new file mode 100644 index 0000000..38f8a43 Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/datasets/__pycache__/ytvis.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/datasets/builtin.py b/Mask2Former/mask2former_video/data_video/datasets/builtin.py new file mode 100644 index 0000000..dd51787 --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/datasets/builtin.py @@ -0,0 +1,60 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import os + +from .ytvis import ( + register_ytvis_instances, + _get_ytvis_2019_instances_meta, + _get_ytvis_2021_instances_meta, +) + +# ==== Predefined splits for YTVIS 2019 =========== +_PREDEFINED_SPLITS_YTVIS_2019 = { + "ytvis_2019_train": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/train.json"), + "ytvis_2019_val": ("ytvis_2019/valid/JPEGImages", + "ytvis_2019/valid.json"), + "ytvis_2019_test": ("ytvis_2019/test/JPEGImages", + "ytvis_2019/test.json"), +} + + +# ==== Predefined splits for YTVIS 2021 =========== +_PREDEFINED_SPLITS_YTVIS_2021 = { + "ytvis_2021_train": ("ytvis_2021/train/JPEGImages", + "ytvis_2021/train.json"), + "ytvis_2021_val": ("ytvis_2021/valid/JPEGImages", + "ytvis_2021/valid.json"), + "ytvis_2021_test": ("ytvis_2021/test/JPEGImages", + "ytvis_2021/test.json"), +} + + +def register_all_ytvis_2019(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_YTVIS_2019.items(): + # Assume pre-defined datasets live in `./datasets`. + register_ytvis_instances( + key, + _get_ytvis_2019_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +def register_all_ytvis_2021(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_YTVIS_2021.items(): + # Assume pre-defined datasets live in `./datasets`. + register_ytvis_instances( + key, + _get_ytvis_2021_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".builtin"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_ytvis_2019(_root) + register_all_ytvis_2021(_root) diff --git a/Mask2Former/mask2former_video/data_video/datasets/ytvis.py b/Mask2Former/mask2former_video/data_video/datasets/ytvis.py new file mode 100644 index 0000000..e8c7d95 --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/datasets/ytvis.py @@ -0,0 +1,340 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import contextlib +import io +import json +import logging +import numpy as np +import os +import pycocotools.mask as mask_util +from fvcore.common.file_io import PathManager +from fvcore.common.timer import Timer + +from detectron2.structures import Boxes, BoxMode, PolygonMasks +from detectron2.data import DatasetCatalog, MetadataCatalog + +""" +This file contains functions to parse YTVIS dataset of +COCO-format annotations into dicts in "Detectron2 format". +""" + +logger = logging.getLogger(__name__) + +__all__ = ["load_ytvis_json", "register_ytvis_instances"] + + +YTVIS_CATEGORIES_2019 = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [0, 82, 0], "isthing": 1, "id": 2, "name": "giant_panda"}, + {"color": [119, 11, 32], "isthing": 1, "id": 3, "name": "lizard"}, + {"color": [165, 42, 42], "isthing": 1, "id": 4, "name": "parrot"}, + {"color": [134, 134, 103], "isthing": 1, "id": 5, "name": "skateboard"}, + {"color": [0, 0, 142], "isthing": 1, "id": 6, "name": "sedan"}, + {"color": [255, 109, 65], "isthing": 1, "id": 7, "name": "ape"}, + {"color": [0, 226, 252], "isthing": 1, "id": 8, "name": "dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 9, "name": "snake"}, + {"color": [0, 60, 100], "isthing": 1, "id": 10, "name": "monkey"}, + {"color": [250, 170, 30], "isthing": 1, "id": 11, "name": "hand"}, + {"color": [100, 170, 30], "isthing": 1, "id": 12, "name": "rabbit"}, + {"color": [179, 0, 194], "isthing": 1, "id": 13, "name": "duck"}, + {"color": [255, 77, 255], "isthing": 1, "id": 14, "name": "cat"}, + {"color": [120, 166, 157], "isthing": 1, "id": 15, "name": "cow"}, + {"color": [73, 77, 174], "isthing": 1, "id": 16, "name": "fish"}, + {"color": [0, 80, 100], "isthing": 1, "id": 17, "name": "train"}, + {"color": [182, 182, 255], "isthing": 1, "id": 18, "name": "horse"}, + {"color": [0, 143, 149], "isthing": 1, "id": 19, "name": "turtle"}, + {"color": [174, 57, 255], "isthing": 1, "id": 20, "name": "bear"}, + {"color": [0, 0, 230], "isthing": 1, "id": 21, "name": "motorbike"}, + {"color": [72, 0, 118], "isthing": 1, "id": 22, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 23, "name": "leopard"}, + {"color": [0, 125, 92], "isthing": 1, "id": 24, "name": "fox"}, + {"color": [209, 0, 151], "isthing": 1, "id": 25, "name": "deer"}, + {"color": [188, 208, 182], "isthing": 1, "id": 26, "name": "owl"}, + {"color": [145, 148, 174], "isthing": 1, "id": 27, "name": "surfboard"}, + {"color": [106, 0, 228], "isthing": 1, "id": 28, "name": "airplane"}, + {"color": [0, 0, 70], "isthing": 1, "id": 29, "name": "truck"}, + {"color": [199, 100, 0], "isthing": 1, "id": 30, "name": "zebra"}, + {"color": [166, 196, 102], "isthing": 1, "id": 31, "name": "tiger"}, + {"color": [110, 76, 0], "isthing": 1, "id": 32, "name": "elephant"}, + {"color": [133, 129, 255], "isthing": 1, "id": 33, "name": "snowboard"}, + {"color": [0, 0, 192], "isthing": 1, "id": 34, "name": "boat"}, + {"color": [183, 130, 88], "isthing": 1, "id": 35, "name": "shark"}, + {"color": [130, 114, 135], "isthing": 1, "id": 36, "name": "mouse"}, + {"color": [107, 142, 35], "isthing": 1, "id": 37, "name": "frog"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "eagle"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "earless_seal"}, + {"color": [255, 208, 186], "isthing": 1, "id": 40, "name": "tennis_racket"}, +] + + +YTVIS_CATEGORIES_2021 = [ + {"color": [106, 0, 228], "isthing": 1, "id": 1, "name": "airplane"}, + {"color": [174, 57, 255], "isthing": 1, "id": 2, "name": "bear"}, + {"color": [255, 109, 65], "isthing": 1, "id": 3, "name": "bird"}, + {"color": [0, 0, 192], "isthing": 1, "id": 4, "name": "boat"}, + {"color": [0, 0, 142], "isthing": 1, "id": 5, "name": "car"}, + {"color": [255, 77, 255], "isthing": 1, "id": 6, "name": "cat"}, + {"color": [120, 166, 157], "isthing": 1, "id": 7, "name": "cow"}, + {"color": [209, 0, 151], "isthing": 1, "id": 8, "name": "deer"}, + {"color": [0, 226, 252], "isthing": 1, "id": 9, "name": "dog"}, + {"color": [179, 0, 194], "isthing": 1, "id": 10, "name": "duck"}, + {"color": [174, 255, 243], "isthing": 1, "id": 11, "name": "earless_seal"}, + {"color": [110, 76, 0], "isthing": 1, "id": 12, "name": "elephant"}, + {"color": [73, 77, 174], "isthing": 1, "id": 13, "name": "fish"}, + {"color": [250, 170, 30], "isthing": 1, "id": 14, "name": "flying_disc"}, + {"color": [0, 125, 92], "isthing": 1, "id": 15, "name": "fox"}, + {"color": [107, 142, 35], "isthing": 1, "id": 16, "name": "frog"}, + {"color": [0, 82, 0], "isthing": 1, "id": 17, "name": "giant_panda"}, + {"color": [72, 0, 118], "isthing": 1, "id": 18, "name": "giraffe"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [255, 179, 240], "isthing": 1, "id": 20, "name": "leopard"}, + {"color": [119, 11, 32], "isthing": 1, "id": 21, "name": "lizard"}, + {"color": [0, 60, 100], "isthing": 1, "id": 22, "name": "monkey"}, + {"color": [0, 0, 230], "isthing": 1, "id": 23, "name": "motorbike"}, + {"color": [130, 114, 135], "isthing": 1, "id": 24, "name": "mouse"}, + {"color": [165, 42, 42], "isthing": 1, "id": 25, "name": "parrot"}, + {"color": [220, 20, 60], "isthing": 1, "id": 26, "name": "person"}, + {"color": [100, 170, 30], "isthing": 1, "id": 27, "name": "rabbit"}, + {"color": [183, 130, 88], "isthing": 1, "id": 28, "name": "shark"}, + {"color": [134, 134, 103], "isthing": 1, "id": 29, "name": "skateboard"}, + {"color": [5, 121, 0], "isthing": 1, "id": 30, "name": "snake"}, + {"color": [133, 129, 255], "isthing": 1, "id": 31, "name": "snowboard"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "squirrel"}, + {"color": [145, 148, 174], "isthing": 1, "id": 33, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 34, "name": "tennis_racket"}, + {"color": [166, 196, 102], "isthing": 1, "id": 35, "name": "tiger"}, + {"color": [0, 80, 100], "isthing": 1, "id": 36, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 37, "name": "truck"}, + {"color": [0, 143, 149], "isthing": 1, "id": 38, "name": "turtle"}, + {"color": [0, 228, 0], "isthing": 1, "id": 39, "name": "whale"}, + {"color": [199, 100, 0], "isthing": 1, "id": 40, "name": "zebra"}, +] + + +def _get_ytvis_2019_instances_meta(): + thing_ids = [k["id"] for k in YTVIS_CATEGORIES_2019 if k["isthing"] == 1] + thing_colors = [k["color"] for k in YTVIS_CATEGORIES_2019 if k["isthing"] == 1] + assert len(thing_ids) == 40, len(thing_ids) + # Mapping from the incontiguous YTVIS category id to an id in [0, 39] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in YTVIS_CATEGORIES_2019 if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + + +def _get_ytvis_2021_instances_meta(): + thing_ids = [k["id"] for k in YTVIS_CATEGORIES_2021 if k["isthing"] == 1] + thing_colors = [k["color"] for k in YTVIS_CATEGORIES_2021 if k["isthing"] == 1] + assert len(thing_ids) == 40, len(thing_ids) + # Mapping from the incontiguous YTVIS category id to an id in [0, 39] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in YTVIS_CATEGORIES_2021 if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + + +def load_ytvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + from .ytvis_api.ytvos import YTVOS + + timer = Timer() + ####修改路径 + json_file = '/root/'+json_file + image_root = '/root/'+image_root + # dataset_name = '/root/'+dataset_name + #### + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + ytvis_api = YTVOS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(ytvis_api.getCatIds()) + cats = ytvis_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + # sort indices for reproducible results + vid_ids = sorted(ytvis_api.vids.keys()) + # vids is a list of dicts, each looks something like: + # {'license': 1, + # 'flickr_url': ' ', + # 'file_names': ['ff25f55852/00000.jpg', 'ff25f55852/00005.jpg', ..., 'ff25f55852/00175.jpg'], + # 'height': 720, + # 'width': 1280, + # 'length': 36, + # 'date_captured': '2019-04-11 00:55:41.903902', + # 'id': 2232} + vids = ytvis_api.loadVids(vid_ids) + + anns = [ytvis_api.vidToAnns[vid_id] for vid_id in vid_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(ytvis_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + vids_anns = list(zip(vids, anns)) + logger.info("Loaded {} videos in YTVIS format from {}".format(len(vids_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "category_id", "id"] + (extra_annotation_keys or []) + + num_instances_without_valid_segmentation = 0 + + for (vid_dict, anno_dict_list) in vids_anns: + record = {} + record["file_names"] = [os.path.join(image_root, vid_dict["file_names"][i]) for i in range(vid_dict["length"])] + record["height"] = vid_dict["height"] + record["width"] = vid_dict["width"] + record["length"] = vid_dict["length"] + video_id = record["video_id"] = vid_dict["id"] + + video_objs = [] + for frame_idx in range(record["length"]): + frame_objs = [] + for anno in anno_dict_list: + assert anno["video_id"] == video_id + + obj = {key: anno[key] for key in ann_keys if key in anno} + + _bboxes = anno.get("bboxes", None) + _segm = anno.get("segmentations", None) + + if not (_bboxes and _segm and _bboxes[frame_idx] and _segm[frame_idx]): + continue + + bbox = _bboxes[frame_idx] + segm = _segm[frame_idx] + + obj["bbox"] = bbox + obj["bbox_mode"] = BoxMode.XYWH_ABS + + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + elif segm: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + if id_map: + obj["category_id"] = id_map[obj["category_id"]] + frame_objs.append(obj) + video_objs.append(frame_objs) + record["annotations"] = video_objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. " + "A valid polygon should be a list[float] with even length >= 6." + ) + return dataset_dicts + + +def register_ytvis_instances(name, metadata, json_file, image_root): + """ + Register a dataset in YTVIS's json annotation format for + instance tracking. + + Args: + name (str): the name that identifies a dataset, e.g. "ytvis_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_ytvis_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="ytvis", **metadata + ) + + +if __name__ == "__main__": + """ + Test the YTVIS json dataset loader. + """ + from detectron2.utils.logger import setup_logger + from detectron2.utils.visualizer import Visualizer + import detectron2.data.datasets # noqa # add pre-defined metadata + import sys + from PIL import Image + + logger = setup_logger(name=__name__) + #assert sys.argv[3] in DatasetCatalog.list() + meta = MetadataCatalog.get("ytvis_2019_train") + + json_file = "./datasets/ytvis/instances_train_sub.json" + image_root = "./datasets/ytvis/train/JPEGImages" + dicts = load_ytvis_json(json_file, image_root, dataset_name="ytvis_2019_train") + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "ytvis-data-vis" + os.makedirs(dirname, exist_ok=True) + + def extract_frame_dic(dic, frame_idx): + import copy + frame_dic = copy.deepcopy(dic) + annos = frame_dic.get("annotations", None) + if annos: + frame_dic["annotations"] = annos[frame_idx] + + return frame_dic + + for d in dicts: + vid_name = d["file_names"][0].split('/')[-2] + os.makedirs(os.path.join(dirname, vid_name), exist_ok=True) + for idx, file_name in enumerate(d["file_names"]): + img = np.array(Image.open(file_name)) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(extract_frame_dic(d, idx)) + fpath = os.path.join(dirname, vid_name, file_name.split('/')[-1]) + vis.save(fpath) diff --git a/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__init__.py b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__init__.py new file mode 100644 index 0000000..d7c9624 --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/youtubevos/cocoapi diff --git a/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..cb1975c Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__pycache__/ytvos.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__pycache__/ytvos.cpython-38.pyc new file mode 100644 index 0000000..e397087 Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__pycache__/ytvos.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__pycache__/ytvoseval.cpython-38.pyc b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__pycache__/ytvoseval.cpython-38.pyc new file mode 100644 index 0000000..146a38e Binary files /dev/null and b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/__pycache__/ytvoseval.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/ytvos.py b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/ytvos.py new file mode 100644 index 0000000..f8f8792 --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/ytvos.py @@ -0,0 +1,290 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/youtubevos/cocoapi + +__author__ = 'ychfan' +# Interface for accessing the YouTubeVIS dataset. + +# The following API functions are defined: +# YTVOS - YTVOS api class that loads YouTubeVIS annotation file and prepare data structures. +# decodeMask - Decode binary mask M encoded via run-length encoding. +# encodeMask - Encode binary mask M using run-length encoding. +# getAnnIds - Get ann ids that satisfy given filter conditions. +# getCatIds - Get cat ids that satisfy given filter conditions. +# getImgIds - Get img ids that satisfy given filter conditions. +# loadAnns - Load anns with the specified ids. +# loadCats - Load cats with the specified ids. +# loadImgs - Load imgs with the specified ids. +# annToMask - Convert segmentation in an annotation to binary mask. +# loadRes - Load algorithm results and create API for accessing them. + +# Microsoft COCO Toolbox. version 2.0 +# Data, paper, and tutorials available at: http://mscoco.org/ +# Code written by Piotr Dollar and Tsung-Yi Lin, 2014. +# Licensed under the Simplified BSD License [see bsd.txt] + +import json +import time +import matplotlib.pyplot as plt +from matplotlib.collections import PatchCollection +from matplotlib.patches import Polygon +import numpy as np +import copy +import itertools +from pycocotools import mask as maskUtils +import os +from collections import defaultdict +import sys +PYTHON_VERSION = sys.version_info[0] +if PYTHON_VERSION == 2: + from urllib import urlretrieve +elif PYTHON_VERSION == 3: + from urllib.request import urlretrieve + + +def _isArrayLike(obj): + return hasattr(obj, '__iter__') and hasattr(obj, '__len__') + + +class YTVOS: + def __init__(self, annotation_file=None): + """ + Constructor of Microsoft COCO helper class for reading and visualizing annotations. + :param annotation_file (str): location of annotation file + :param image_folder (str): location to the folder that hosts images. + :return: + """ + # load dataset + self.dataset,self.anns,self.cats,self.vids = dict(),dict(),dict(),dict() + self.vidToAnns, self.catToVids = defaultdict(list), defaultdict(list) + if not annotation_file == None: + print('loading annotations into memory...') + tic = time.time() + dataset = json.load(open(annotation_file, 'r')) + assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset)) + print('Done (t={:0.2f}s)'.format(time.time()- tic)) + self.dataset = dataset + self.createIndex() + + def createIndex(self): + # create index + print('creating index...') + anns, cats, vids = {}, {}, {} + vidToAnns,catToVids = defaultdict(list),defaultdict(list) + if 'annotations' in self.dataset: + for ann in self.dataset['annotations']: + vidToAnns[ann['video_id']].append(ann) + anns[ann['id']] = ann + + if 'videos' in self.dataset: + for vid in self.dataset['videos']: + vids[vid['id']] = vid + + if 'categories' in self.dataset: + for cat in self.dataset['categories']: + cats[cat['id']] = cat + + if 'annotations' in self.dataset and 'categories' in self.dataset: + for ann in self.dataset['annotations']: + catToVids[ann['category_id']].append(ann['video_id']) + + print('index created!') + + # create class members + self.anns = anns + self.vidToAnns = vidToAnns + self.catToVids = catToVids + self.vids = vids + self.cats = cats + + def info(self): + """ + Print information about the annotation file. + :return: + """ + for key, value in self.dataset['info'].items(): + print('{}: {}'.format(key, value)) + + def getAnnIds(self, vidIds=[], catIds=[], areaRng=[], iscrowd=None): + """ + Get ann ids that satisfy given filter conditions. default skips that filter + :param vidIds (int array) : get anns for given vids + catIds (int array) : get anns for given cats + areaRng (float array) : get anns for given area range (e.g. [0 inf]) + iscrowd (boolean) : get anns for given crowd label (False or True) + :return: ids (int array) : integer array of ann ids + """ + vidIds = vidIds if _isArrayLike(vidIds) else [vidIds] + catIds = catIds if _isArrayLike(catIds) else [catIds] + + if len(vidIds) == len(catIds) == len(areaRng) == 0: + anns = self.dataset['annotations'] + else: + if not len(vidIds) == 0: + lists = [self.vidToAnns[vidId] for vidId in vidIds if vidId in self.vidToAnns] + anns = list(itertools.chain.from_iterable(lists)) + else: + anns = self.dataset['annotations'] + anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds] + anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['avg_area'] > areaRng[0] and ann['avg_area'] < areaRng[1]] + if not iscrowd == None: + ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] + else: + ids = [ann['id'] for ann in anns] + return ids + + def getCatIds(self, catNms=[], supNms=[], catIds=[]): + """ + filtering parameters. default skips that filter. + :param catNms (str array) : get cats for given cat names + :param supNms (str array) : get cats for given supercategory names + :param catIds (int array) : get cats for given cat ids + :return: ids (int array) : integer array of cat ids + """ + catNms = catNms if _isArrayLike(catNms) else [catNms] + supNms = supNms if _isArrayLike(supNms) else [supNms] + catIds = catIds if _isArrayLike(catIds) else [catIds] + + if len(catNms) == len(supNms) == len(catIds) == 0: + cats = self.dataset['categories'] + else: + cats = self.dataset['categories'] + cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms] + cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms] + cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds] + ids = [cat['id'] for cat in cats] + return ids + + def getVidIds(self, vidIds=[], catIds=[]): + ''' + Get vid ids that satisfy given filter conditions. + :param vidIds (int array) : get vids for given ids + :param catIds (int array) : get vids with all given cats + :return: ids (int array) : integer array of vid ids + ''' + vidIds = vidIds if _isArrayLike(vidIds) else [vidIds] + catIds = catIds if _isArrayLike(catIds) else [catIds] + + if len(vidIds) == len(catIds) == 0: + ids = self.vids.keys() + else: + ids = set(vidIds) + for i, catId in enumerate(catIds): + if i == 0 and len(ids) == 0: + ids = set(self.catToVids[catId]) + else: + ids &= set(self.catToVids[catId]) + return list(ids) + + def loadAnns(self, ids=[]): + """ + Load anns with the specified ids. + :param ids (int array) : integer ids specifying anns + :return: anns (object array) : loaded ann objects + """ + if _isArrayLike(ids): + return [self.anns[id] for id in ids] + elif type(ids) == int: + return [self.anns[ids]] + + def loadCats(self, ids=[]): + """ + Load cats with the specified ids. + :param ids (int array) : integer ids specifying cats + :return: cats (object array) : loaded cat objects + """ + if _isArrayLike(ids): + return [self.cats[id] for id in ids] + elif type(ids) == int: + return [self.cats[ids]] + + def loadVids(self, ids=[]): + """ + Load anns with the specified ids. + :param ids (int array) : integer ids specifying vid + :return: vids (object array) : loaded vid objects + """ + if _isArrayLike(ids): + return [self.vids[id] for id in ids] + elif type(ids) == int: + return [self.vids[ids]] + + + def loadRes(self, resFile): + """ + Load result file and return a result api object. + :param resFile (str) : file name of result file + :return: res (obj) : result api object + """ + res = YTVOS() + res.dataset['videos'] = [img for img in self.dataset['videos']] + + print('Loading and preparing results...') + tic = time.time() + if type(resFile) == str or (PYTHON_VERSION == 2 and type(resFile) == unicode): + anns = json.load(open(resFile)) + elif type(resFile) == np.ndarray: + anns = self.loadNumpyAnnotations(resFile) + else: + anns = resFile + assert type(anns) == list, 'results in not an array of objects' + annsVidIds = [ann['video_id'] for ann in anns] + assert set(annsVidIds) == (set(annsVidIds) & set(self.getVidIds())), \ + 'Results do not correspond to current coco set' + if 'segmentations' in anns[0]: + res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) + for id, ann in enumerate(anns): + ann['areas'] = [] + if not 'bboxes' in ann: + ann['bboxes'] = [] + for seg in ann['segmentations']: + # now only support compressed RLE format as segmentation results + if seg: + ann['areas'].append(maskUtils.area(seg)) + if len(ann['bboxes']) < len(ann['areas']): + ann['bboxes'].append(maskUtils.toBbox(seg)) + else: + ann['areas'].append(None) + if len(ann['bboxes']) < len(ann['areas']): + ann['bboxes'].append(None) + ann['id'] = id+1 + l = [a for a in ann['areas'] if a] + if len(l)==0: + ann['avg_area'] = 0 + else: + ann['avg_area'] = np.array(l).mean() + ann['iscrowd'] = 0 + print('DONE (t={:0.2f}s)'.format(time.time()- tic)) + + res.dataset['annotations'] = anns + res.createIndex() + return res + + def annToRLE(self, ann, frameId): + """ + Convert annotation which can be polygons, uncompressed RLE to RLE. + :return: binary mask (numpy 2D array) + """ + t = self.vids[ann['video_id']] + h, w = t['height'], t['width'] + segm = ann['segmentations'][frameId] + if type(segm) == list: + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = maskUtils.frPyObjects(segm, h, w) + rle = maskUtils.merge(rles) + elif type(segm['counts']) == list: + # uncompressed RLE + rle = maskUtils.frPyObjects(segm, h, w) + else: + # rle + rle = segm + return rle + + def annToMask(self, ann, frameId): + """ + Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. + :return: binary mask (numpy 2D array) + """ + rle = self.annToRLE(ann, frameId) + m = maskUtils.decode(rle) + return m diff --git a/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/ytvoseval.py b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/ytvoseval.py new file mode 100644 index 0000000..f2cb8be --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/datasets/ytvis_api/ytvoseval.py @@ -0,0 +1,567 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/youtubevos/cocoapi + +__author__ = 'ychfan' + +import numpy as np +import datetime +import time +from collections import defaultdict +from pycocotools import mask as maskUtils +import copy + +class YTVOSeval: + # Interface for evaluating video instance segmentation on the YouTubeVIS dataset. + # + # The usage for YTVOSeval is as follows: + # cocoGt=..., cocoDt=... # load dataset and results + # E = YTVOSeval(cocoGt,cocoDt); # initialize YTVOSeval object + # E.params.recThrs = ...; # set parameters as desired + # E.evaluate(); # run per image evaluation + # E.accumulate(); # accumulate per image results + # E.summarize(); # display summary metrics of results + # For example usage see evalDemo.m and http://mscoco.org/. + # + # The evaluation parameters are as follows (defaults in brackets): + # imgIds - [all] N img ids to use for evaluation + # catIds - [all] K cat ids to use for evaluation + # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation + # recThrs - [0:.01:1] R=101 recall thresholds for evaluation + # areaRng - [...] A=4 object area ranges for evaluation + # maxDets - [1 10 100] M=3 thresholds on max detections per image + # iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints' + # iouType replaced the now DEPRECATED useSegm parameter. + # useCats - [1] if true use category labels for evaluation + # Note: if useCats=0 category labels are ignored as in proposal scoring. + # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified. + # + # evaluate(): evaluates detections on every image and every category and + # concats the results into the "evalImgs" with fields: + # dtIds - [1xD] id for each of the D detections (dt) + # gtIds - [1xG] id for each of the G ground truths (gt) + # dtMatches - [TxD] matching gt id at each IoU or 0 + # gtMatches - [TxG] matching dt id at each IoU or 0 + # dtScores - [1xD] confidence of each dt + # gtIgnore - [1xG] ignore flag for each gt + # dtIgnore - [TxD] ignore flag for each dt at each IoU + # + # accumulate(): accumulates the per-image, per-category evaluation + # results in "evalImgs" into the dictionary "eval" with fields: + # params - parameters used for evaluation + # date - date evaluation was performed + # counts - [T,R,K,A,M] parameter dimensions (see above) + # precision - [TxRxKxAxM] precision for every evaluation setting + # recall - [TxKxAxM] max recall for every evaluation setting + # Note: precision and recall==-1 for settings with no gt objects. + # + # See also coco, mask, pycocoDemo, pycocoEvalDemo + # + # Microsoft COCO Toolbox. version 2.0 + # Data, paper, and tutorials available at: http://mscoco.org/ + # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. + # Licensed under the Simplified BSD License [see coco/license.txt] + def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'): + ''' + Initialize CocoEval using coco APIs for gt and dt + :param cocoGt: coco object with ground truth annotations + :param cocoDt: coco object with detection results + :return: None + ''' + if not iouType: + print('iouType not specified. use default iouType segm') + self.cocoGt = cocoGt # ground truth COCO API + self.cocoDt = cocoDt # detections COCO API + self.params = {} # evaluation parameters + self.evalVids = defaultdict(list) # per-image per-category evaluation results [KxAxI] elements + self.eval = {} # accumulated evaluation results + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self.params = Params(iouType=iouType) # parameters + self._paramsEval = {} # parameters for evaluation + self.stats = [] # result summarization + self.ious = {} # ious between all gts and dts + if not cocoGt is None: + self.params.vidIds = sorted(cocoGt.getVidIds()) + self.params.catIds = sorted(cocoGt.getCatIds()) + + + def _prepare(self): + ''' + Prepare ._gts and ._dts for evaluation based on params + :return: None + ''' + def _toMask(anns, coco): + # modify ann['segmentation'] by reference + for ann in anns: + for i, a in enumerate(ann['segmentations']): + if a: + rle = coco.annToRLE(ann, i) + ann['segmentations'][i] = rle + l = [a for a in ann['areas'] if a] + if len(l)==0: + ann['avg_area'] = 0 + else: + ann['avg_area'] = np.array(l).mean() + p = self.params + if p.useCats: + gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(vidIds=p.vidIds, catIds=p.catIds)) + dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(vidIds=p.vidIds, catIds=p.catIds)) + else: + gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(vidIds=p.vidIds)) + dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(vidIds=p.vidIds)) + + # convert ground truth to mask if iouType == 'segm' + if p.iouType == 'segm': + _toMask(gts, self.cocoGt) + _toMask(dts, self.cocoDt) + # set ignore flag + for gt in gts: + gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0 + gt['ignore'] = 'iscrowd' in gt and gt['iscrowd'] + if p.iouType == 'keypoints': + gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore'] + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + for gt in gts: + self._gts[gt['video_id'], gt['category_id']].append(gt) + for dt in dts: + self._dts[dt['video_id'], dt['category_id']].append(dt) + self.evalVids = defaultdict(list) # per-image per-category evaluation results + self.eval = {} # accumulated evaluation results + + def evaluate(self): + ''' + Run per image evaluation on given images and store results (a list of dict) in self.evalVids + :return: None + ''' + tic = time.time() + print('Running per image evaluation...') + p = self.params + # add backward compatibility if useSegm is specified in params + if not p.useSegm is None: + p.iouType = 'segm' if p.useSegm == 1 else 'bbox' + print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType)) + print('Evaluate annotation type *{}*'.format(p.iouType)) + p.vidIds = list(np.unique(p.vidIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + self.params=p + + self._prepare() + # loop through images, area range, max detection number + catIds = p.catIds if p.useCats else [-1] + + if p.iouType == 'segm' or p.iouType == 'bbox': + computeIoU = self.computeIoU + elif p.iouType == 'keypoints': + computeIoU = self.computeOks + self.ious = {(vidId, catId): computeIoU(vidId, catId) \ + for vidId in p.vidIds + for catId in catIds} + + evaluateVid = self.evaluateVid + maxDet = p.maxDets[-1] + + + self.evalImgs = [evaluateVid(vidId, catId, areaRng, maxDet) + for catId in catIds + for areaRng in p.areaRng + for vidId in p.vidIds + ] + self._paramsEval = copy.deepcopy(self.params) + toc = time.time() + print('DONE (t={:0.2f}s).'.format(toc-tic)) + + def computeIoU(self, vidId, catId): + p = self.params + if p.useCats: + gt = self._gts[vidId,catId] + dt = self._dts[vidId,catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[vidId,cId]] + dt = [_ for cId in p.catIds for _ in self._dts[vidId,cId]] + if len(gt) == 0 and len(dt) ==0: + return [] + inds = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in inds] + if len(dt) > p.maxDets[-1]: + dt=dt[0:p.maxDets[-1]] + + if p.iouType == 'segm': + g = [g['segmentations'] for g in gt] + d = [d['segmentations'] for d in dt] + elif p.iouType == 'bbox': + g = [g['bboxes'] for g in gt] + d = [d['bboxes'] for d in dt] + else: + raise Exception('unknown iouType for iou computation') + + # compute iou between each dt and gt region + iscrowd = [int(o['iscrowd']) for o in gt] + #ious = maskUtils.iou(d,g,iscrowd) + def iou_seq(d_seq, g_seq): + i = .0 + u = .0 + for d, g in zip(d_seq, g_seq): + if d and g: + i += maskUtils.area(maskUtils.merge([d, g], True)) + u += maskUtils.area(maskUtils.merge([d, g], False)) + elif not d and g: + u += maskUtils.area(g) + elif d and not g: + u += maskUtils.area(d) + if not u > .0: + print("Mask sizes in video {} and category {} may not match!".format(vidId, catId)) + iou = i / u if u > .0 else .0 + return iou + ious = np.zeros([len(d), len(g)]) + for i, j in np.ndindex(ious.shape): + ious[i, j] = iou_seq(d[i], g[j]) + #print(vidId, catId, ious.shape, ious) + return ious + + def computeOks(self, imgId, catId): + p = self.params + # dimention here should be Nxm + gts = self._gts[imgId, catId] + dts = self._dts[imgId, catId] + inds = np.argsort([-d['score'] for d in dts], kind='mergesort') + dts = [dts[i] for i in inds] + if len(dts) > p.maxDets[-1]: + dts = dts[0:p.maxDets[-1]] + # if len(gts) == 0 and len(dts) == 0: + if len(gts) == 0 or len(dts) == 0: + return [] + ious = np.zeros((len(dts), len(gts))) + sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0 + vars = (sigmas * 2)**2 + k = len(sigmas) + # compute oks between each detection and ground truth object + for j, gt in enumerate(gts): + # create bounds for ignore regions(double the gt bbox) + g = np.array(gt['keypoints']) + xg = g[0::3]; yg = g[1::3]; vg = g[2::3] + k1 = np.count_nonzero(vg > 0) + bb = gt['bbox'] + x0 = bb[0] - bb[2]; x1 = bb[0] + bb[2] * 2 + y0 = bb[1] - bb[3]; y1 = bb[1] + bb[3] * 2 + for i, dt in enumerate(dts): + d = np.array(dt['keypoints']) + xd = d[0::3]; yd = d[1::3] + if k1>0: + # measure the per-keypoint distance if keypoints visible + dx = xd - xg + dy = yd - yg + else: + # measure minimum distance to keypoints in (x0,y0) & (x1,y1) + z = np.zeros((k)) + dx = np.max((z, x0-xd),axis=0)+np.max((z, xd-x1),axis=0) + dy = np.max((z, y0-yd),axis=0)+np.max((z, yd-y1),axis=0) + e = (dx**2 + dy**2) / vars / (gt['avg_area']+np.spacing(1)) / 2 + if k1 > 0: + e=e[vg > 0] + ious[i, j] = np.sum(np.exp(-e)) / e.shape[0] + return ious + + def evaluateVid(self, vidId, catId, aRng, maxDet): + ''' + perform evaluation for single category and image + :return: dict (single image results) + ''' + p = self.params + if p.useCats: + gt = self._gts[vidId,catId] + dt = self._dts[vidId,catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[vidId,cId]] + dt = [_ for cId in p.catIds for _ in self._dts[vidId,cId]] + if len(gt) == 0 and len(dt) ==0: + return None + + for g in gt: + if g['ignore'] or (g['avg_area']aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = self.ious[vidId, catId][:, gtind] if len(self.ious[vidId, catId]) > 0 else self.ious[vidId, catId] + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + gtm = np.zeros((T,G)) + dtm = np.zeros((T,D)) + gtIg = np.array([g['_ignore'] for g in gt]) + dtIg = np.zeros((T,D)) + if not len(ious)==0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t,1-1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind,gind]>0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m>-1 and gtIg[m]==0 and gtIg[gind]==1: + break + # continue to next gt unless better match made + if ious[dind,gind] < iou: + continue + # if match successful and best so far, store appropriately + iou=ious[dind,gind] + m=gind + # if match made store id of match for both dt and gt + if m ==-1: + continue + dtIg[tind,dind] = gtIg[m] + dtm[tind,dind] = gt[m]['id'] + gtm[tind,m] = d['id'] + # set unmatched detections outside of area range to ignore + a = np.array([d['avg_area']aRng[1] for d in dt]).reshape((1, len(dt))) + dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0))) + # store results for given image and category + return { + 'video_id': vidId, + 'category_id': catId, + 'aRng': aRng, + 'maxDet': maxDet, + 'dtIds': [d['id'] for d in dt], + 'gtIds': [g['id'] for g in gt], + 'dtMatches': dtm, + 'gtMatches': gtm, + 'dtScores': [d['score'] for d in dt], + 'gtIgnore': gtIg, + 'dtIgnore': dtIg, + } + + def accumulate(self, p = None): + ''' + Accumulate per image evaluation results and store the result in self.eval + :param p: input params for evaluation + :return: None + ''' + print('Accumulating evaluation results...') + tic = time.time() + if not self.evalImgs: + print('Please run evaluate() first') + # allows input customized parameters + if p is None: + p = self.params + p.catIds = p.catIds if p.useCats == 1 else [-1] + T = len(p.iouThrs) + R = len(p.recThrs) + K = len(p.catIds) if p.useCats else 1 + A = len(p.areaRng) + M = len(p.maxDets) + precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories + recall = -np.ones((T,K,A,M)) + scores = -np.ones((T,R,K,A,M)) + + # create dictionary for future indexing + _pe = self._paramsEval + catIds = _pe.catIds if _pe.useCats else [-1] + setK = set(catIds) + setA = set(map(tuple, _pe.areaRng)) + setM = set(_pe.maxDets) + setI = set(_pe.vidIds) + # get inds to evaluate + k_list = [n for n, k in enumerate(p.catIds) if k in setK] + m_list = [m for n, m in enumerate(p.maxDets) if m in setM] + a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA] + i_list = [n for n, i in enumerate(p.vidIds) if i in setI] + I0 = len(_pe.vidIds) + A0 = len(_pe.areaRng) + # retrieve E at each category, area range, and max number of detections + for k, k0 in enumerate(k_list): + Nk = k0*A0*I0 + for a, a0 in enumerate(a_list): + Na = a0*I0 + for m, maxDet in enumerate(m_list): + E = [self.evalImgs[Nk + Na + i] for i in i_list] + E = [e for e in E if not e is None] + if len(E) == 0: + continue + dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E]) + + # different sorting method generates slightly different results. + # mergesort is used to be consistent as Matlab implementation. + inds = np.argsort(-dtScores, kind='mergesort') + dtScoresSorted = dtScores[inds] + + dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds] + dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds] + gtIg = np.concatenate([e['gtIgnore'] for e in E]) + npig = np.count_nonzero(gtIg==0 ) + if npig == 0: + continue + tps = np.logical_and( dtm, np.logical_not(dtIg) ) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) ) + + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + nd = len(tp) + rc = tp / npig + pr = tp / (fp+tp+np.spacing(1)) + q = np.zeros((R,)) + ss = np.zeros((R,)) + + if nd: + recall[t,k,a,m] = rc[-1] + else: + recall[t,k,a,m] = 0 + + # numpy is slow without cython optimization for accessing elements + # use python array gets significant speed improvement + pr = pr.tolist(); q = q.tolist() + + for i in range(nd-1, 0, -1): + if pr[i] > pr[i-1]: + pr[i-1] = pr[i] + + inds = np.searchsorted(rc, p.recThrs, side='left') + try: + for ri, pi in enumerate(inds): + q[ri] = pr[pi] + ss[ri] = dtScoresSorted[pi] + except: + pass + precision[t,:,k,a,m] = np.array(q) + scores[t,:,k,a,m] = np.array(ss) + self.eval = { + 'params': p, + 'counts': [T, R, K, A, M], + 'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + 'precision': precision, + 'recall': recall, + 'scores': scores, + } + toc = time.time() + print('DONE (t={:0.2f}s).'.format( toc-tic)) + + def summarize(self): + ''' + Compute and display summary metrics for evaluation results. + Note this functin can *only* be applied on the default parameter setting + ''' + def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ): + p = self.params + iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}' + titleStr = 'Average Precision' if ap == 1 else 'Average Recall' + typeStr = '(AP)' if ap==1 else '(AR)' + iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \ + if iouThr is None else '{:0.2f}'.format(iouThr) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval['precision'] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:,:,:,aind,mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval['recall'] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:,:,aind,mind] + if len(s[s>-1])==0: + mean_s = -1 + else: + mean_s = np.mean(s[s>-1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + def _summarizeDets(): + stats = np.zeros((12,)) + stats[0] = _summarize(1) + stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2]) + return stats + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=.5) + stats[2] = _summarize(1, maxDets=20, iouThr=.75) + stats[3] = _summarize(1, maxDets=20, areaRng='medium') + stats[4] = _summarize(1, maxDets=20, areaRng='large') + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=.5) + stats[7] = _summarize(0, maxDets=20, iouThr=.75) + stats[8] = _summarize(0, maxDets=20, areaRng='medium') + stats[9] = _summarize(0, maxDets=20, areaRng='large') + return stats + if not self.eval: + raise Exception('Please run accumulate() first') + iouType = self.params.iouType + if iouType == 'segm' or iouType == 'bbox': + summarize = _summarizeDets + elif iouType == 'keypoints': + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() + +class Params: + ''' + Params for coco evaluation api + ''' + def setDetParams(self): + self.vidIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + #self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True) + #self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True) + self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) + self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01)) + 1, endpoint=True) + self.maxDets = [1, 10, 100] + self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 128 ** 2], [ 128 ** 2, 256 ** 2], [256 ** 2, 1e5 ** 2]] + self.areaRngLbl = ['all', 'small', 'medium', 'large'] + self.useCats = 1 + + def setKpParams(self): + self.vidIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True) + self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True) + self.maxDets = [20] + self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] + self.areaRngLbl = ['all', 'medium', 'large'] + self.useCats = 1 + + def __init__(self, iouType='segm'): + if iouType == 'segm' or iouType == 'bbox': + self.setDetParams() + elif iouType == 'keypoints': + self.setKpParams() + else: + raise Exception('iouType not supported') + self.iouType = iouType + # useSegm is deprecated + self.useSegm = None diff --git a/Mask2Former/mask2former_video/data_video/ytvis_eval.py b/Mask2Former/mask2former_video/data_video/ytvis_eval.py new file mode 100644 index 0000000..8af1dba --- /dev/null +++ b/Mask2Former/mask2former_video/data_video/ytvis_eval.py @@ -0,0 +1,330 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/sukjunhwang/IFC + +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from .datasets.ytvis_api.ytvos import YTVOS +from .datasets.ytvis_api.ytvoseval import YTVOSeval +from tabulate import tabulate + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.evaluation import DatasetEvaluator +from detectron2.utils.file_io import PathManager +from detectron2.utils.logger import create_small_table + + +class YTVISEvaluator(DatasetEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + use_fast_impl=True, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file in torch serialization + format that contains all the raw original predictions. + 2. "coco_instances_results.json" a json file in COCO's result + format. + use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. + Although the results should be very close to the official implementation in COCO + API, it is still recommended to compute results with the official API for use in + papers. The faster implementation also uses more RAM. + """ + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + self._use_fast_impl = use_fast_impl + + if tasks is not None and isinstance(tasks, CfgNode): + self._logger.warning( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + + json_file = PathManager.get_local_path(self._metadata.json_file) + + json_file = '/root/' + json_file #修改 + + with contextlib.redirect_stdout(io.StringIO()): + self._ytvis_api = YTVOS(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._ytvis_api.dataset + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + prediction = instances_to_coco_json_video(inputs, outputs) + self._predictions.extend(prediction) + + def evaluate(self): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + self._eval_predictions(predictions) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _eval_predictions(self, predictions): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for YTVIS format ...") + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in predictions: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(predictions)) + f.flush() + + if not self._do_evaluation: + self._logger.info("Annotations are not available for evaluation.") + return + + coco_eval = ( + _evaluate_predictions_on_coco( + self._ytvis_api, + predictions, + ) + if len(predictions) > 0 + else None # cocoapi does not handle empty results very well + ) + + res = self._derive_coco_results( + coco_eval, class_names=self._metadata.get("thing_classes") + ) + self._results["segm"] = res + + def _derive_coco_results(self, coco_eval, class_names=None): + """ + Derive the desired score numbers from summarized COCOeval. + Args: + coco_eval (None or COCOEval): None represents no predictions from model. + iou_type (str): + class_names (None or list[str]): if provided, will use it to predict + per-category AP. + Returns: + a dict of {metric name: score} + """ + + metrics = ["AP", "AP50", "AP75", "APs", "APm", "APl", "AR1", "AR10"] + + if coco_eval is None: + self._logger.warn("No predictions from the model!") + return {metric: float("nan") for metric in metrics} + + # the standard metrics + results = { + metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan") + for idx, metric in enumerate(metrics) + } + self._logger.info( + "Evaluation results for {}: \n".format("segm") + create_small_table(results) + ) + if not np.isfinite(sum(results.values())): + self._logger.info("Some metrics cannot be computed and is shown as NaN.") + + if class_names is None or len(class_names) <= 1: + return results + # Compute per-category AP + # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa + precisions = coco_eval.eval["precision"] + # precision has dims (iou, recall, cls, area range, max dets) + assert len(class_names) == precisions.shape[2] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + # tabulate it + N_COLS = min(6, len(results_per_category) * 2) + results_flatten = list(itertools.chain(*results_per_category)) + results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)]) + table = tabulate( + results_2d, + tablefmt="pipe", + floatfmt=".3f", + headers=["category", "AP"] * (N_COLS // 2), + numalign="left", + ) + self._logger.info("Per-category {} AP: \n".format("segm") + table) + + results.update({"AP-" + name: ap for name, ap in results_per_category}) + return results + + +def instances_to_coco_json_video(inputs, outputs): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + video_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + assert len(inputs) == 1, "More than one inputs are loaded for inference!" + + video_id = inputs[0]["video_id"] + video_length = inputs[0]["length"] + + scores = outputs["pred_scores"] + labels = outputs["pred_labels"] + masks = outputs["pred_masks"] + + ytvis_results = [] + for instance_id, (s, l, m) in enumerate(zip(scores, labels, masks)): + segms = [ + mask_util.encode(np.array(_mask[:, :, None], order="F", dtype="uint8"))[0] + for _mask in m + ] + for rle in segms: + rle["counts"] = rle["counts"].decode("utf-8") + + res = { + "video_id": video_id, + "score": s, + "category_id": l, + "segmentations": segms, + } + ytvis_results.append(res) + + return ytvis_results + + +def _evaluate_predictions_on_coco( + coco_gt, + coco_results, + img_ids=None, +): + """ + Evaluate the coco results using COCOEval API. + """ + assert len(coco_results) > 0 + + coco_results = copy.deepcopy(coco_results) + # When evaluating mask AP, if the results contain bbox, cocoapi will + # use the box area as the area of the instance, instead of the mask area. + # This leads to a different definition of small/medium/large. + # We remove the bbox field to let mask AP use mask area. + for c in coco_results: + c.pop("bbox", None) + + coco_dt = coco_gt.loadRes(coco_results) + coco_eval = YTVOSeval(coco_gt, coco_dt) + # For COCO, the default max_dets_per_image is [1, 10, 100]. + max_dets_per_image = [1, 10, 100] # Default from COCOEval + coco_eval.params.maxDets = max_dets_per_image + + if img_ids is not None: + coco_eval.params.imgIds = img_ids + + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + return coco_eval diff --git a/Mask2Former/mask2former_video/ifc/__init__.py b/Mask2Former/mask2former_video/ifc/__init__.py new file mode 100644 index 0000000..3cb3eb0 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/__init__.py @@ -0,0 +1,3 @@ +from .config import add_ifc_config +from .ifc import Ifc +# from .data import YTVISDatasetMapper, build_detection_train_loader, build_detection_test_loader diff --git a/Mask2Former/mask2former_video/ifc/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..b5c6466 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/__pycache__/config.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/__pycache__/config.cpython-38.pyc new file mode 100644 index 0000000..e51aa5c Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/__pycache__/config.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/__pycache__/ifc.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/__pycache__/ifc.cpython-38.pyc new file mode 100644 index 0000000..4a3fcc1 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/__pycache__/ifc.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/config.py b/Mask2Former/mask2former_video/ifc/config.py new file mode 100644 index 0000000..3d0f080 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/config.py @@ -0,0 +1,45 @@ +# -*- coding: utf-8 -*- +from detectron2.config import CfgNode as CN + + +def add_ifc_config(cfg): + """ + Add config for IFC. + """ + cfg.MODEL.IFC = CN() + cfg.MODEL.IFC.NUM_CLASSES = 80 + + # DataLoader + cfg.INPUT.SAMPLING_FRAME_NUM = 5 + cfg.INPUT.SAMPLING_FRAME_RANGE = 20 + cfg.INPUT.SAMPLING_FRAME_SHUFFLE = False + cfg.INPUT.AUGMENTATIONS = [] # "brightness", "contrast", "saturation", "rotation" + + # LOSS + cfg.MODEL.IFC.MASK_WEIGHT = 3.0 + cfg.MODEL.IFC.DICE_WEIGHT = 3.0 + cfg.MODEL.IFC.DEEP_SUPERVISION = True + cfg.MODEL.IFC.NO_OBJECT_WEIGHT = 0.1 + cfg.MODEL.IFC.MASK_STRIDE = 4 + cfg.MODEL.IFC.MATCH_STRIDE = 4 + + # TRANSFORMER + cfg.MODEL.IFC.NHEADS = 8 + cfg.MODEL.IFC.DROPOUT = 0.1 + cfg.MODEL.IFC.DIM_FEEDFORWARD = 2048 + cfg.MODEL.IFC.ENC_LAYERS = 3 + cfg.MODEL.IFC.DEC_LAYERS = 3 + cfg.MODEL.IFC.PRE_NORM = False + cfg.MODEL.IFC.NUM_MEMORY_BUS = 8 + + cfg.MODEL.IFC.HIDDEN_DIM = 256 + cfg.MODEL.IFC.NUM_OBJECT_QUERIES = 100 + + # Evaluation + cfg.MODEL.IFC.CLIP_STRIDE = 1 + cfg.MODEL.IFC.MERGE_ON_CPU = False + cfg.MODEL.IFC.MULTI_CLS_ON = True + cfg.MODEL.IFC.APPLY_CLS_THRES = 0.01 + + cfg.SOLVER.OPTIMIZER = "ADAMW" + cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1 diff --git a/Mask2Former/mask2former_video/ifc/data/__init__.py b/Mask2Former/mask2former_video/ifc/data/__init__.py new file mode 100644 index 0000000..e632c0d --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/data/__init__.py @@ -0,0 +1,5 @@ +from .dataset_mapper import YTVISDatasetMapper, CocoClipDatasetMapper +from .build import * + +from .datasets import * +from .ytvis_eval import YTVISEvaluator diff --git 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import ( + HFlipTransform, + NoOpTransform, + VFlipTransform, +) +from PIL import Image + +from detectron2.data import transforms as T + + +class ResizeShortestEdge(T.Augmentation): + """ + Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge. + If `max_size` is reached, then downscale so that the longer edge does not exceed max_size. + """ + + def __init__( + self, short_edge_length, max_size=sys.maxsize, sample_style="range", interp=Image.BILINEAR, clip_frame_cnt=1 + ): + """ + Args: + short_edge_length (list[int]): If ``sample_style=="range"``, + a [min, max] interval from which to sample the shortest edge length. + If ``sample_style=="choice"``, a list of shortest edge lengths to sample from. + max_size (int): maximum allowed longest edge length. + sample_style (str): either "range" or "choice". + """ + super().__init__() + assert sample_style in ["range", "choice", "range_by_clip", "choice_by_clip"], sample_style + + self.is_range = ("range" in sample_style) + if isinstance(short_edge_length, int): + short_edge_length = (short_edge_length, short_edge_length) + if self.is_range: + assert len(short_edge_length) == 2, ( + "short_edge_length must be two values using 'range' sample style." + f" Got {short_edge_length}!" + ) + self._cnt = 0 + self._init(locals()) + + def get_transform(self, image): + if self._cnt % self.clip_frame_cnt == 0: + if self.is_range: + self.size = np.random.randint(self.short_edge_length[0], self.short_edge_length[1] + 1) + else: + self.size = np.random.choice(self.short_edge_length) + if self.size == 0: + return NoOpTransform() + + self._cnt = 0 # avoiding overflow + self._cnt += 1 + + h, w = image.shape[:2] + + scale = self.size * 1.0 / min(h, w) + if h < w: + newh, neww = self.size, scale * w + else: + newh, neww = scale * h, self.size + if max(newh, neww) > self.max_size: + scale = self.max_size * 1.0 / max(newh, neww) + newh = newh * scale + neww = neww * scale + neww = int(neww + 0.5) + newh = int(newh + 0.5) + return T.ResizeTransform(h, w, newh, neww, self.interp) + + +class RandomFlip(T.Augmentation): + """ + Flip the image horizontally or vertically with the given probability. + """ + + def __init__(self, prob=0.5, *, horizontal=True, vertical=False, clip_frame_cnt=1): + """ + Args: + prob (float): probability of flip. + horizontal (boolean): whether to apply horizontal flipping + vertical (boolean): whether to apply vertical flipping + """ + super().__init__() + + if horizontal and vertical: + raise ValueError("Cannot do both horiz and vert. Please use two Flip instead.") + if not horizontal and not vertical: + raise ValueError("At least one of horiz or vert has to be True!") + self._cnt = 0 + + self._init(locals()) + + def get_transform(self, image): + if self._cnt % self.clip_frame_cnt == 0: + self.do = self._rand_range() < self.prob + self._cnt = 0 # avoiding overflow + self._cnt += 1 + + h, w = image.shape[:2] + + if self.do: + if self.horizontal: + return HFlipTransform(w) + elif self.vertical: + return VFlipTransform(h) + else: + return NoOpTransform() + + +def build_augmentation(cfg, is_train): + logger = logging.getLogger(__name__) + aug_list = [] + if is_train: + # Crop + if cfg.INPUT.CROP.ENABLED: + aug_list.append(T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)) + + # Resize + min_size = cfg.INPUT.MIN_SIZE_TRAIN + max_size = cfg.INPUT.MAX_SIZE_TRAIN + sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING + ms_clip_frame_cnt = cfg.INPUT.SAMPLING_FRAME_NUM if "by_clip" in cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING else 1 + aug_list.append(ResizeShortestEdge(min_size, max_size, sample_style, clip_frame_cnt=ms_clip_frame_cnt)) + + # Flip + if cfg.INPUT.RANDOM_FLIP != "none": + if cfg.INPUT.RANDOM_FLIP == "flip_by_clip": + flip_clip_frame_cnt = cfg.INPUT.SAMPLING_FRAME_NUM + else: + flip_clip_frame_cnt = 1 + + aug_list.append( + # NOTE using RandomFlip modified for the support of flip maintenance + RandomFlip( + horizontal=(cfg.INPUT.RANDOM_FLIP == "horizontal") or (cfg.INPUT.RANDOM_FLIP == "flip_by_clip"), + vertical=cfg.INPUT.RANDOM_FLIP == "vertical", + clip_frame_cnt=flip_clip_frame_cnt, + ) + ) + + # Additional augmentations : brightness, contrast, saturation, rotation + augmentations = cfg.INPUT.AUGMENTATIONS + if "brightness" in augmentations: + aug_list.append(T.RandomBrightness(0.9, 1.1)) + if "contrast" in augmentations: + aug_list.append(T.RandomContrast(0.9, 1.1)) + if "saturation" in augmentations: + aug_list.append(T.RandomSaturation(0.9, 1.1)) + if "rotation" in augmentations: + aug_list.append( + T.RandomRotation( + [-15, 15], expand=False, center=[(0.4, 0.4), (0.6, 0.6)], sample_style="range" + ) + ) + else: + # Resize + min_size = cfg.INPUT.MIN_SIZE_TEST + max_size = cfg.INPUT.MAX_SIZE_TEST + sample_style = "choice" + aug_list.append(T.ResizeShortestEdge(min_size, max_size, sample_style)) + + return aug_list diff --git a/Mask2Former/mask2former_video/ifc/data/build.py b/Mask2Former/mask2former_video/ifc/data/build.py new file mode 100644 index 0000000..a015021 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/data/build.py @@ -0,0 +1,247 @@ +import itertools +import logging +import torch.utils.data + +from detectron2.config import CfgNode, configurable +from detectron2.data.build import ( + build_batch_data_loader, + load_proposals_into_dataset, + trivial_batch_collator, +) +from detectron2.data.catalog import DatasetCatalog +from detectron2.data.common import DatasetFromList, MapDataset +from detectron2.data.dataset_mapper import DatasetMapper +from detectron2.data.samplers import InferenceSampler, TrainingSampler +from detectron2.utils.comm import get_world_size + + +def _compute_num_images_per_worker(cfg: CfgNode): + num_workers = get_world_size() + images_per_batch = cfg.SOLVER.IMS_PER_BATCH + assert ( + images_per_batch % num_workers == 0 + ), "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of workers ({}).".format( + images_per_batch, num_workers + ) + assert ( + images_per_batch >= num_workers + ), "SOLVER.IMS_PER_BATCH ({}) must be larger than the number of workers ({}).".format( + images_per_batch, num_workers + ) + images_per_worker = images_per_batch // num_workers + return images_per_worker + + +def filter_images_with_only_crowd_annotations(dataset_dicts, dataset_names): + """ + Filter out images with none annotations or only crowd annotations + (i.e., images without non-crowd annotations). + A common training-time preprocessing on COCO dataset. + + Args: + dataset_dicts (list[dict]): annotations in Detectron2 Dataset format. + + Returns: + list[dict]: the same format, but filtered. + """ + num_before = len(dataset_dicts) + + def valid(anns): + for ann in anns: + if isinstance(ann, list): + for instance in ann: + if instance.get("iscrowd", 0) == 0: + return True + else: + if ann.get("iscrowd", 0) == 0: + return True + return False + + dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])] + num_after = len(dataset_dicts) + logger = logging.getLogger(__name__) + logger.info( + "Removed {} images with no usable annotations. {} images left.".format( + num_before - num_after, num_after + ) + ) + return dataset_dicts + + +def get_detection_dataset_dicts( + dataset_names, filter_empty=True, proposal_files=None +): + """ + Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation. + + Args: + dataset_names (str or list[str]): a dataset name or a list of dataset names + filter_empty (bool): whether to filter out images without instance annotations + proposal_files (list[str]): if given, a list of object proposal files + that match each dataset in `dataset_names`. + + Returns: + list[dict]: a list of dicts following the standard dataset dict format. + """ + if isinstance(dataset_names, str): + dataset_names = [dataset_names] + assert len(dataset_names) + dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] + for dataset_name, dicts in zip(dataset_names, dataset_dicts): + assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) + + if proposal_files is not None: + assert len(dataset_names) == len(proposal_files) + # load precomputed proposals from proposal files + dataset_dicts = [ + load_proposals_into_dataset(dataset_i_dicts, proposal_file) + for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files) + ] + + dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) + + has_instances = "annotations" in dataset_dicts[0] + if filter_empty and has_instances: + dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts, dataset_names) + + assert len(dataset_dicts), "No valid data found in {}.".format(",".join(dataset_names)) + return dataset_dicts + + +def _train_loader_from_config(cfg, mapper, *, dataset=None, sampler=None): + if dataset is None: + dataset = get_detection_dataset_dicts( + cfg.DATASETS.TRAIN, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + + if mapper is None: + mapper = DatasetMapper(cfg, True) + + if sampler is None: + sampler_name = cfg.DATALOADER.SAMPLER_TRAIN + logger = logging.getLogger(__name__) + logger.info("Using training sampler {}".format(sampler_name)) + sampler = TrainingSampler(len(dataset)) + + return { + "dataset": dataset, + "sampler": sampler, + "mapper": mapper, + "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, + "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING, + "num_workers": cfg.DATALOADER.NUM_WORKERS, + } + + +# TODO can allow dataset as an iterable or IterableDataset to make this function more general +@configurable(from_config=_train_loader_from_config) +def build_detection_train_loader( + dataset, *, mapper, sampler=None, total_batch_size, aspect_ratio_grouping=True, num_workers=0 +): + """ + Build a dataloader for object detection with some default features. + This interface is experimental. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a map-style pytorch dataset. They can be obtained by using + :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset and + returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``. + sampler (torch.utils.data.sampler.Sampler or None): a sampler that + produces indices to be applied on ``dataset``. + Default to :class:`TrainingSampler`, which coordinates a random shuffle + sequence across all workers. + total_batch_size (int): total batch size across all workers. Batching + simply puts data into a list. + aspect_ratio_grouping (bool): whether to group images with similar + aspect ratio for efficiency. When enabled, it requires each + element in dataset be a dict with keys "width" and "height". + num_workers (int): number of parallel data loading workers + + Returns: + torch.utils.data.DataLoader: a dataloader. Each output from it is a + ``list[mapped_element]`` of length ``total_batch_size / num_workers``, + where ``mapped_element`` is produced by the ``mapper``. + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + if sampler is None: + sampler = TrainingSampler(len(dataset)) + assert isinstance(sampler, torch.utils.data.sampler.Sampler) + return build_batch_data_loader( + dataset, + sampler, + total_batch_size, + aspect_ratio_grouping=aspect_ratio_grouping, + num_workers=num_workers, + ) + + +def _test_loader_from_config(cfg, dataset_name, mapper=None): + """ + Uses the given `dataset_name` argument (instead of the names in cfg), because the + standard practice is to evaluate each test set individually (not combining them). + """ + dataset = get_detection_dataset_dicts( + [dataset_name], + filter_empty=False, + proposal_files=[ + cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(dataset_name)] + ] + if cfg.MODEL.LOAD_PROPOSALS + else None, + ) + if mapper is None: + mapper = DatasetMapper(cfg, False) + return {"dataset": dataset, "mapper": mapper, "num_workers": cfg.DATALOADER.NUM_WORKERS} + + +@configurable(from_config=_test_loader_from_config) +def build_detection_test_loader(dataset, *, mapper, num_workers=0): + """ + Similar to `build_detection_train_loader`, but uses a batch size of 1. + This interface is experimental. + + Args: + dataset (list or torch.utils.data.Dataset): a list of dataset dicts, + or a map-style pytorch dataset. They can be obtained by using + :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`. + mapper (callable): a callable which takes a sample (dict) from dataset + and returns the format to be consumed by the model. + When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``. + num_workers (int): number of parallel data loading workers + + Returns: + DataLoader: a torch DataLoader, that loads the given detection + dataset, with test-time transformation and batching. + + Examples: + :: + data_loader = build_detection_test_loader( + DatasetRegistry.get("my_test"), + mapper=DatasetMapper(...)) + + # or, instantiate with a CfgNode: + data_loader = build_detection_test_loader(cfg, "my_test") + """ + if isinstance(dataset, list): + dataset = DatasetFromList(dataset, copy=False) + if mapper is not None: + dataset = MapDataset(dataset, mapper) + sampler = InferenceSampler(len(dataset)) + # Always use 1 image per worker during inference since this is the + # standard when reporting inference time in papers. + batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, 1, drop_last=False) + data_loader = torch.utils.data.DataLoader( + dataset, + num_workers=num_workers, + batch_sampler=batch_sampler, + collate_fn=trivial_batch_collator, + ) + return data_loader diff --git a/Mask2Former/mask2former_video/ifc/data/dataset_mapper.py b/Mask2Former/mask2former_video/ifc/data/dataset_mapper.py new file mode 100644 index 0000000..67e3b91 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/data/dataset_mapper.py @@ -0,0 +1,380 @@ +import copy +import logging +import random +import numpy as np +from typing import List, Union +import torch + +from detectron2.config import configurable +from detectron2.structures import ( + BitMasks, + Boxes, + BoxMode, + Instances, +) + +from detectron2.data import detection_utils as utils +from detectron2.data import transforms as T + +from .augmentation import build_augmentation + +__all__ = ["YTVISDatasetMapper", "CocoClipDatasetMapper"] + + +def filter_empty_instances(instances, by_box=True, by_mask=True, box_threshold=1e-5): + """ + Filter out empty instances in an `Instances` object. + + Args: + instances (Instances): + by_box (bool): whether to filter out instances with empty boxes + by_mask (bool): whether to filter out instances with empty masks + box_threshold (float): minimum width and height to be considered non-empty + + Returns: + Instances: the filtered instances. + """ + assert by_box or by_mask + r = [] + if by_box: + r.append(instances.gt_boxes.nonempty(threshold=box_threshold)) + if instances.has("gt_masks") and by_mask: + r.append(instances.gt_masks.nonempty()) + + if not r: + return instances + m = r[0] + for x in r[1:]: + m = m & x + + instances.gt_ids[~m] = -1 + return instances + + +def _get_dummy_anno(num_classes): + return { + "iscrowd": 0, + "category_id": num_classes, + "id": -1, + "bbox": np.array([0, 0, 0, 0]), + "bbox_mode": BoxMode.XYXY_ABS, + "segmentation": [np.array([0.0] * 6)] + } + + +def ytvis_annotations_to_instances(annos, image_size): + """ + Create an :class:`Instances` object used by the models, + from instance annotations in the dataset dict. + + Args: + annos (list[dict]): a list of instance annotations in one image, each + element for one instance. + image_size (tuple): height, width + + Returns: + Instances: + It will contain fields "gt_boxes", "gt_classes", "gt_ids", + "gt_masks", if they can be obtained from `annos`. + This is the format that builtin models expect. + """ + boxes = [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos] + target = Instances(image_size) + target.gt_boxes = Boxes(boxes) + + classes = [int(obj["category_id"]) for obj in annos] + classes = torch.tensor(classes, dtype=torch.int64) + target.gt_classes = classes + + ids = [int(obj["id"]) for obj in annos] + ids = torch.tensor(ids, dtype=torch.int64) + target.gt_ids = ids + + if len(annos) and "segmentation" in annos[0]: + segms = [obj["segmentation"] for obj in annos] + masks = [] + for segm in segms: + assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format( + segm.ndim + ) + # mask array + masks.append(segm) + # torch.from_numpy does not support array with negative stride. + masks = BitMasks( + torch.stack([torch.from_numpy(np.ascontiguousarray(x)) for x in masks]) + ) + target.gt_masks = masks + + return target + + +class YTVISDatasetMapper: + """ + A callable which takes a dataset dict in YouTube-VIS Dataset format, + and map it into a format used by the model. + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + sampling_frame_num: int = 2, + sampling_frame_range: int = 5, + sampling_frame_shuffle: bool = False, + num_classes: int = 40, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + """ + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.sampling_frame_num = sampling_frame_num + self.sampling_frame_range = sampling_frame_range + self.sampling_frame_shuffle = sampling_frame_shuffle + self.num_classes = num_classes + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = build_augmentation(cfg, is_train) + + sampling_frame_num = cfg.INPUT.SAMPLING_FRAME_NUM + sampling_frame_range = cfg.INPUT.SAMPLING_FRAME_RANGE + sampling_frame_shuffle = cfg.INPUT.SAMPLING_FRAME_SHUFFLE + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "sampling_frame_num": sampling_frame_num, + "sampling_frame_range": sampling_frame_range, + "sampling_frame_shuffle": sampling_frame_shuffle, + "num_classes": cfg.MODEL.IFC.NUM_CLASSES, + } + + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one video, in YTVIS Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + # TODO consider examining below deepcopy as it costs huge amount of computations. + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + + video_length = dataset_dict["length"] + if self.is_train: + ref_frame = random.randrange(video_length) + + start_idx = max(0, ref_frame-self.sampling_frame_range) + end_idx = min(video_length, ref_frame+self.sampling_frame_range + 1) + + selected_idx = np.random.choice( + np.array(list(range(start_idx, ref_frame)) + list(range(ref_frame+1, end_idx))), + self.sampling_frame_num - 1, + ) + selected_idx = selected_idx.tolist() + [ref_frame] + selected_idx = sorted(selected_idx) + if self.sampling_frame_shuffle: + random.shuffle(selected_idx) + else: + selected_idx = range(video_length) + + video_annos = dataset_dict.pop("annotations", None) + file_names = dataset_dict.pop("file_names", None) + + if self.is_train: + _ids = set() + for frame_idx in selected_idx: + _ids.update([anno["id"] for anno in video_annos[frame_idx]]) + ids = dict() + for i, _id in enumerate(_ids): + ids[_id] = i + + dataset_dict["image"] = [] + dataset_dict["instances"] = [] + dataset_dict["file_names"] = [] + for frame_idx in selected_idx: + dataset_dict["file_names"].append(file_names[frame_idx]) + + # Read image + image = utils.read_image(file_names[frame_idx], format=self.image_format) + utils.check_image_size(dataset_dict, image) + + aug_input = T.AugInput(image) + transforms = self.augmentations(aug_input) + image = aug_input.image + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"].append(torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))) + + if (video_annos is None) or (not self.is_train): + continue + + # NOTE copy() is to prevent annotations getting changed from applying augmentations + _frame_annos = [] + for anno in video_annos[frame_idx]: + _anno = {} + for k, v in anno.items(): + _anno[k] = copy.deepcopy(v) + _frame_annos.append(_anno) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations(obj, transforms, image_shape) + for obj in _frame_annos + if obj.get("iscrowd", 0) == 0 + ] + sorted_annos = [_get_dummy_anno(self.num_classes) for _ in range(len(ids))] + + for _anno in annos: + idx = ids[_anno["id"]] + sorted_annos[idx] = _anno + _gt_ids = [_anno["id"] for _anno in sorted_annos] + + instances = utils.annotations_to_instances(sorted_annos, image_shape, mask_format="bitmask") + instances.gt_ids = torch.tensor(_gt_ids) + if instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + instances = filter_empty_instances(instances) + else: + instances.gt_masks = BitMasks(torch.empty((0, *image_shape))) + dataset_dict["instances"].append(instances) + + return dataset_dict + + +class CocoClipDatasetMapper: + """ + A callable which takes a COCO image which converts into multiple frames, + and map it into a format used by the model. + """ + + @configurable + def __init__( + self, + is_train: bool, + *, + augmentations: List[Union[T.Augmentation, T.Transform]], + image_format: str, + use_instance_mask: bool = False, + sampling_frame_num: int = 2, + ): + """ + NOTE: this interface is experimental. + Args: + is_train: whether it's used in training or inference + augmentations: a list of augmentations or deterministic transforms to apply + image_format: an image format supported by :func:`detection_utils.read_image`. + use_instance_mask: whether to process instance segmentation annotations, if available + """ + # fmt: off + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) + self.image_format = image_format + self.use_instance_mask = use_instance_mask + self.sampling_frame_num = sampling_frame_num + # fmt: on + logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}") + + @classmethod + def from_config(cls, cfg, is_train: bool = True): + augs = build_augmentation(cfg, is_train) + + sampling_frame_num = cfg.INPUT.SAMPLING_FRAME_NUM + + ret = { + "is_train": is_train, + "augmentations": augs, + "image_format": cfg.INPUT.FORMAT, + "use_instance_mask": cfg.MODEL.MASK_ON, + "sampling_frame_num": sampling_frame_num, + } + + return ret + + def __call__(self, dataset_dict): + """ + Args: + dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. + + Returns: + dict: a format that builtin models in detectron2 accept + """ + dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below + + img_annos = dataset_dict.pop("annotations", None) + file_name = dataset_dict.pop("file_name", None) + original_image = utils.read_image(file_name, format=self.image_format) + + dataset_dict["image"] = [] + dataset_dict["instances"] = [] + dataset_dict["file_names"] = [file_name] * self.sampling_frame_num + for _ in range(self.sampling_frame_num): + utils.check_image_size(dataset_dict, original_image) + + aug_input = T.AugInput(original_image) + transforms = self.augmentations(aug_input) + image = aug_input.image + + image_shape = image.shape[:2] # h, w + # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, + # but not efficient on large generic data structures due to the use of pickle & mp.Queue. + # Therefore it's important to use torch.Tensor. + dataset_dict["image"].append(torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))) + + if (img_annos is None) or (not self.is_train): + continue + + _img_annos = [] + for anno in img_annos: + _anno = {} + for k, v in anno.items(): + _anno[k] = copy.deepcopy(v) + _img_annos.append(_anno) + + # USER: Implement additional transformations if you have other types of data + annos = [ + utils.transform_instance_annotations(obj, transforms, image_shape) + for obj in _img_annos + if obj.get("iscrowd", 0) == 0 + ] + _gt_ids = list(range(len(annos))) + for idx in range(len(annos)): + if len(annos[idx]["segmentation"]) == 0: + annos[idx]["segmentation"] = [np.array([0.0] * 6)] + + instances = utils.annotations_to_instances(annos, image_shape, mask_format="bitmask") + instances.gt_ids = torch.tensor(_gt_ids) + if instances.has("gt_masks"): + instances.gt_boxes = instances.gt_masks.get_bounding_boxes() + instances = filter_empty_instances(instances) + else: + instances.gt_masks = BitMasks(torch.empty((0, *image_shape))) + dataset_dict["instances"].append(instances) + + return dataset_dict diff --git a/Mask2Former/mask2former_video/ifc/data/datasets/__init__.py b/Mask2Former/mask2former_video/ifc/data/datasets/__init__.py new file mode 100644 index 0000000..29c8b8b --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/data/datasets/__init__.py @@ -0,0 +1,3 @@ +from . import builtin # ensure the builtin datasets are registered + +__all__ = [k for k in globals().keys() if "builtin" not in k and not k.startswith("_")] diff --git a/Mask2Former/mask2former_video/ifc/data/datasets/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/data/datasets/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..5b9e9b6 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/data/datasets/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/data/datasets/__pycache__/builtin.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/data/datasets/__pycache__/builtin.cpython-38.pyc new file mode 100644 index 0000000..b26e403 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/data/datasets/__pycache__/builtin.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/data/datasets/__pycache__/ytvis.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/data/datasets/__pycache__/ytvis.cpython-38.pyc new file mode 100644 index 0000000..23e0bef Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/data/datasets/__pycache__/ytvis.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/data/datasets/builtin.py b/Mask2Former/mask2former_video/ifc/data/datasets/builtin.py new file mode 100644 index 0000000..96d9bf5 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/data/datasets/builtin.py @@ -0,0 +1,63 @@ +# -*- coding: utf-8 -*- + +import os + +from .ytvis import ( + register_ytvis_instances, + _get_ytvis_2019_instances_meta, + _get_ytvis_2021_instances_meta, +) + +# ==== Predefined splits for YTVIS 2019 =========== +_PREDEFINED_SPLITS_YTVIS_2019 = { + "ytvis_2019_train": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/train.json"), + "ytvis_2019_val": ("ytvis_2019/valid/JPEGImages", + "ytvis_2019/valid.json"), + "ytvis_2019_test": ("ytvis_2019/test/JPEGImages", + "ytvis_2019/test.json"), + "ytvis_2019_dev": ("ytvis_2019/train/JPEGImages", + "ytvis_2019/instances_train_sub.json"), +} + + +# ==== Predefined splits for YTVIS 2021 =========== +_PREDEFINED_SPLITS_YTVIS_2021 = { + "ytvis_2021_train": ("ytvis_2021/train/JPEGImages", + "ytvis_2021/train.json"), + "ytvis_2021_val": ("ytvis_2021/valid/JPEGImages", + "ytvis_2021/valid.json"), + "ytvis_2021_test": ("ytvis_2021/test/JPEGImages", + "ytvis_2021/test.json"), + "ytvis_2021_dev": ("ytvis_2021/train/JPEGImages", + "ytvis_2021/instances_train_sub.json"), +} + + +def register_all_ytvis_2019(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_YTVIS_2019.items(): + # Assume pre-defined datasets live in `./datasets`. + register_ytvis_instances( + key, + _get_ytvis_2019_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +def register_all_ytvis_2021(root): + for key, (image_root, json_file) in _PREDEFINED_SPLITS_YTVIS_2021.items(): + # Assume pre-defined datasets live in `./datasets`. + register_ytvis_instances( + key, + _get_ytvis_2021_instances_meta(), + os.path.join(root, json_file) if "://" not in json_file else json_file, + os.path.join(root, image_root), + ) + + +if __name__.endswith(".builtin"): + # Assume pre-defined datasets live in `./datasets`. + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + register_all_ytvis_2019(_root) + register_all_ytvis_2021(_root) diff --git a/Mask2Former/mask2former_video/ifc/data/datasets/ytvis.py b/Mask2Former/mask2former_video/ifc/data/datasets/ytvis.py new file mode 100644 index 0000000..15ddd45 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/data/datasets/ytvis.py @@ -0,0 +1,333 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import contextlib +import io +import json +import logging +import numpy as np +import os +import pycocotools.mask as mask_util +from fvcore.common.file_io import PathManager +from fvcore.common.timer import Timer + +from detectron2.structures import Boxes, BoxMode, PolygonMasks +from detectron2.data import DatasetCatalog, MetadataCatalog + +""" +This file contains functions to parse YTVIS dataset of +COCO-format annotations into dicts in "Detectron2 format". +""" + +logger = logging.getLogger(__name__) + +__all__ = ["load_ytvis_json", "register_ytvis_instances"] + + +YTVIS_CATEGORIES_2019 = [ + {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, + {"color": [0, 82, 0], "isthing": 1, "id": 2, "name": "giant_panda"}, + {"color": [119, 11, 32], "isthing": 1, "id": 3, "name": "lizard"}, + {"color": [165, 42, 42], "isthing": 1, "id": 4, "name": "parrot"}, + {"color": [134, 134, 103], "isthing": 1, "id": 5, "name": "skateboard"}, + {"color": [0, 0, 142], "isthing": 1, "id": 6, "name": "sedan"}, + {"color": [255, 109, 65], "isthing": 1, "id": 7, "name": "ape"}, + {"color": [0, 226, 252], "isthing": 1, "id": 8, "name": "dog"}, + {"color": [5, 121, 0], "isthing": 1, "id": 9, "name": "snake"}, + {"color": [0, 60, 100], "isthing": 1, "id": 10, "name": "monkey"}, + {"color": [250, 170, 30], "isthing": 1, "id": 11, "name": "hand"}, + {"color": [100, 170, 30], "isthing": 1, "id": 12, "name": "rabbit"}, + {"color": [179, 0, 194], "isthing": 1, "id": 13, "name": "duck"}, + {"color": [255, 77, 255], "isthing": 1, "id": 14, "name": "cat"}, + {"color": [120, 166, 157], "isthing": 1, "id": 15, "name": "cow"}, + {"color": [73, 77, 174], "isthing": 1, "id": 16, "name": "fish"}, + {"color": [0, 80, 100], "isthing": 1, "id": 17, "name": "train"}, + {"color": [182, 182, 255], "isthing": 1, "id": 18, "name": "horse"}, + {"color": [0, 143, 149], "isthing": 1, "id": 19, "name": "turtle"}, + {"color": [174, 57, 255], "isthing": 1, "id": 20, "name": "bear"}, + {"color": [0, 0, 230], "isthing": 1, "id": 21, "name": "motorbike"}, + {"color": [72, 0, 118], "isthing": 1, "id": 22, "name": "giraffe"}, + {"color": [255, 179, 240], "isthing": 1, "id": 23, "name": "leopard"}, + {"color": [0, 125, 92], "isthing": 1, "id": 24, "name": "fox"}, + {"color": [209, 0, 151], "isthing": 1, "id": 25, "name": "deer"}, + {"color": [188, 208, 182], "isthing": 1, "id": 26, "name": "owl"}, + {"color": [145, 148, 174], "isthing": 1, "id": 27, "name": "surfboard"}, + {"color": [106, 0, 228], "isthing": 1, "id": 28, "name": "airplane"}, + {"color": [0, 0, 70], "isthing": 1, "id": 29, "name": "truck"}, + {"color": [199, 100, 0], "isthing": 1, "id": 30, "name": "zebra"}, + {"color": [166, 196, 102], "isthing": 1, "id": 31, "name": "tiger"}, + {"color": [110, 76, 0], "isthing": 1, "id": 32, "name": "elephant"}, + {"color": [133, 129, 255], "isthing": 1, "id": 33, "name": "snowboard"}, + {"color": [0, 0, 192], "isthing": 1, "id": 34, "name": "boat"}, + {"color": [183, 130, 88], "isthing": 1, "id": 35, "name": "shark"}, + {"color": [130, 114, 135], "isthing": 1, "id": 36, "name": "mouse"}, + {"color": [107, 142, 35], "isthing": 1, "id": 37, "name": "frog"}, + {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "eagle"}, + {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "earless_seal"}, + {"color": [255, 208, 186], "isthing": 1, "id": 40, "name": "tennis_racket"}, +] + + +YTVIS_CATEGORIES_2021 = [ + {"color": [106, 0, 228], "isthing": 1, "id": 1, "name": "airplane"}, + {"color": [174, 57, 255], "isthing": 1, "id": 2, "name": "bear"}, + {"color": [255, 109, 65], "isthing": 1, "id": 3, "name": "bird"}, + {"color": [0, 0, 192], "isthing": 1, "id": 4, "name": "boat"}, + {"color": [0, 0, 142], "isthing": 1, "id": 5, "name": "car"}, + {"color": [255, 77, 255], "isthing": 1, "id": 6, "name": "cat"}, + {"color": [120, 166, 157], "isthing": 1, "id": 7, "name": "cow"}, + {"color": [209, 0, 151], "isthing": 1, "id": 8, "name": "deer"}, + {"color": [0, 226, 252], "isthing": 1, "id": 9, "name": "dog"}, + {"color": [179, 0, 194], "isthing": 1, "id": 10, "name": "duck"}, + {"color": [174, 255, 243], "isthing": 1, "id": 11, "name": "earless_seal"}, + {"color": [110, 76, 0], "isthing": 1, "id": 12, "name": "elephant"}, + {"color": [73, 77, 174], "isthing": 1, "id": 13, "name": "fish"}, + {"color": [250, 170, 30], "isthing": 1, "id": 14, "name": "flying_disc"}, + {"color": [0, 125, 92], "isthing": 1, "id": 15, "name": "fox"}, + {"color": [107, 142, 35], "isthing": 1, "id": 16, "name": "frog"}, + {"color": [0, 82, 0], "isthing": 1, "id": 17, "name": "giant_panda"}, + {"color": [72, 0, 118], "isthing": 1, "id": 18, "name": "giraffe"}, + {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, + {"color": [255, 179, 240], "isthing": 1, "id": 20, "name": "leopard"}, + {"color": [119, 11, 32], "isthing": 1, "id": 21, "name": "lizard"}, + {"color": [0, 60, 100], "isthing": 1, "id": 22, "name": "monkey"}, + {"color": [0, 0, 230], "isthing": 1, "id": 23, "name": "motorbike"}, + {"color": [130, 114, 135], "isthing": 1, "id": 24, "name": "mouse"}, + {"color": [165, 42, 42], "isthing": 1, "id": 25, "name": "parrot"}, + {"color": [220, 20, 60], "isthing": 1, "id": 26, "name": "person"}, + {"color": [100, 170, 30], "isthing": 1, "id": 27, "name": "rabbit"}, + {"color": [183, 130, 88], "isthing": 1, "id": 28, "name": "shark"}, + {"color": [134, 134, 103], "isthing": 1, "id": 29, "name": "skateboard"}, + {"color": [5, 121, 0], "isthing": 1, "id": 30, "name": "snake"}, + {"color": [133, 129, 255], "isthing": 1, "id": 31, "name": "snowboard"}, + {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "squirrel"}, + {"color": [145, 148, 174], "isthing": 1, "id": 33, "name": "surfboard"}, + {"color": [255, 208, 186], "isthing": 1, "id": 34, "name": "tennis_racket"}, + {"color": [166, 196, 102], "isthing": 1, "id": 35, "name": "tiger"}, + {"color": [0, 80, 100], "isthing": 1, "id": 36, "name": "train"}, + {"color": [0, 0, 70], "isthing": 1, "id": 37, "name": "truck"}, + {"color": [0, 143, 149], "isthing": 1, "id": 38, "name": "turtle"}, + {"color": [0, 228, 0], "isthing": 1, "id": 39, "name": "whale"}, + {"color": [199, 100, 0], "isthing": 1, "id": 40, "name": "zebra"}, +] + + +def _get_ytvis_2019_instances_meta(): + thing_ids = [k["id"] for k in YTVIS_CATEGORIES_2019 if k["isthing"] == 1] + thing_colors = [k["color"] for k in YTVIS_CATEGORIES_2019 if k["isthing"] == 1] + assert len(thing_ids) == 40, len(thing_ids) + # Mapping from the incontiguous YTVIS category id to an id in [0, 39] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in YTVIS_CATEGORIES_2019 if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + + +def _get_ytvis_2021_instances_meta(): + thing_ids = [k["id"] for k in YTVIS_CATEGORIES_2021 if k["isthing"] == 1] + thing_colors = [k["color"] for k in YTVIS_CATEGORIES_2021 if k["isthing"] == 1] + assert len(thing_ids) == 40, len(thing_ids) + # Mapping from the incontiguous YTVIS category id to an id in [0, 39] + thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)} + thing_classes = [k["name"] for k in YTVIS_CATEGORIES_2021 if k["isthing"] == 1] + ret = { + "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id, + "thing_classes": thing_classes, + "thing_colors": thing_colors, + } + return ret + + +def load_ytvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): + from pycocotools.ytvos import YTVOS + + timer = Timer() + json_file = PathManager.get_local_path(json_file) + with contextlib.redirect_stdout(io.StringIO()): + ytvis_api = YTVOS(json_file) + if timer.seconds() > 1: + logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) + + id_map = None + if dataset_name is not None: + meta = MetadataCatalog.get(dataset_name) + cat_ids = sorted(ytvis_api.getCatIds()) + cats = ytvis_api.loadCats(cat_ids) + # The categories in a custom json file may not be sorted. + thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] + meta.thing_classes = thing_classes + + # In COCO, certain category ids are artificially removed, + # and by convention they are always ignored. + # We deal with COCO's id issue and translate + # the category ids to contiguous ids in [0, 80). + + # It works by looking at the "categories" field in the json, therefore + # if users' own json also have incontiguous ids, we'll + # apply this mapping as well but print a warning. + if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): + if "coco" not in dataset_name: + logger.warning( + """ +Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. +""" + ) + id_map = {v: i for i, v in enumerate(cat_ids)} + meta.thing_dataset_id_to_contiguous_id = id_map + + # sort indices for reproducible results + vid_ids = sorted(ytvis_api.vids.keys()) + # vids is a list of dicts, each looks something like: + # {'license': 1, + # 'flickr_url': ' ', + # 'file_names': ['ff25f55852/00000.jpg', 'ff25f55852/00005.jpg', ..., 'ff25f55852/00175.jpg'], + # 'height': 720, + # 'width': 1280, + # 'length': 36, + # 'date_captured': '2019-04-11 00:55:41.903902', + # 'id': 2232} + vids = ytvis_api.loadVids(vid_ids) + + anns = [ytvis_api.vidToAnns[vid_id] for vid_id in vid_ids] + total_num_valid_anns = sum([len(x) for x in anns]) + total_num_anns = len(ytvis_api.anns) + if total_num_valid_anns < total_num_anns: + logger.warning( + f"{json_file} contains {total_num_anns} annotations, but only " + f"{total_num_valid_anns} of them match to images in the file." + ) + + vids_anns = list(zip(vids, anns)) + logger.info("Loaded {} videos in YTVIS format from {}".format(len(vids_anns), json_file)) + + dataset_dicts = [] + + ann_keys = ["iscrowd", "category_id", "id"] + (extra_annotation_keys or []) + + num_instances_without_valid_segmentation = 0 + + for (vid_dict, anno_dict_list) in vids_anns: + record = {} + record["file_names"] = [os.path.join(image_root, vid_dict["file_names"][i]) for i in range(vid_dict["length"])] + record["height"] = vid_dict["height"] + record["width"] = vid_dict["width"] + record["length"] = vid_dict["length"] + video_id = record["video_id"] = vid_dict["id"] + + video_objs = [] + for frame_idx in range(record["length"]): + frame_objs = [] + for anno in anno_dict_list: + assert anno["video_id"] == video_id + + obj = {key: anno[key] for key in ann_keys if key in anno} + + _bboxes = anno.get("bboxes", None) + _segm = anno.get("segmentations", None) + + if not (_bboxes and _segm and _bboxes[frame_idx] and _segm[frame_idx]): + continue + + bbox = _bboxes[frame_idx] + segm = _segm[frame_idx] + + obj["bbox"] = bbox + obj["bbox_mode"] = BoxMode.XYWH_ABS + + if isinstance(segm, dict): + if isinstance(segm["counts"], list): + # convert to compressed RLE + segm = mask_util.frPyObjects(segm, *segm["size"]) + elif segm: + # filter out invalid polygons (< 3 points) + segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] + if len(segm) == 0: + num_instances_without_valid_segmentation += 1 + continue # ignore this instance + obj["segmentation"] = segm + + if id_map: + obj["category_id"] = id_map[obj["category_id"]] + frame_objs.append(obj) + video_objs.append(frame_objs) + record["annotations"] = video_objs + dataset_dicts.append(record) + + if num_instances_without_valid_segmentation > 0: + logger.warning( + "Filtered out {} instances without valid segmentation. ".format( + num_instances_without_valid_segmentation + ) + + "There might be issues in your dataset generation process. " + "A valid polygon should be a list[float] with even length >= 6." + ) + return dataset_dicts + + +def register_ytvis_instances(name, metadata, json_file, image_root): + """ + Register a dataset in YTVIS's json annotation format for + instance tracking. + + Args: + name (str): the name that identifies a dataset, e.g. "ytvis_train". + metadata (dict): extra metadata associated with this dataset. You can + leave it as an empty dict. + json_file (str): path to the json instance annotation file. + image_root (str or path-like): directory which contains all the images. + """ + assert isinstance(name, str), name + assert isinstance(json_file, (str, os.PathLike)), json_file + assert isinstance(image_root, (str, os.PathLike)), image_root + # 1. register a function which returns dicts + DatasetCatalog.register(name, lambda: load_ytvis_json(json_file, image_root, name)) + + # 2. Optionally, add metadata about this dataset, + # since they might be useful in evaluation, visualization or logging + MetadataCatalog.get(name).set( + json_file=json_file, image_root=image_root, evaluator_type="ytvis", **metadata + ) + + +if __name__ == "__main__": + """ + Test the YTVIS json dataset loader. + """ + from detectron2.utils.logger import setup_logger + from detectron2.utils.visualizer import Visualizer + import detectron2.data.datasets # noqa # add pre-defined metadata + import sys + from PIL import Image + + logger = setup_logger(name=__name__) + #assert sys.argv[3] in DatasetCatalog.list() + meta = MetadataCatalog.get("ytvis_2019_train") + + json_file = "./datasets/ytvis/instances_train_sub.json" + image_root = "./datasets/ytvis/train/JPEGImages" + dicts = load_ytvis_json(json_file, image_root, dataset_name="ytvis_2019_train") + logger.info("Done loading {} samples.".format(len(dicts))) + + dirname = "ytvis-data-vis" + os.makedirs(dirname, exist_ok=True) + + def extract_frame_dic(dic, frame_idx): + import copy + frame_dic = copy.deepcopy(dic) + annos = frame_dic.get("annotations", None) + if annos: + frame_dic["annotations"] = annos[frame_idx] + + return frame_dic + + for d in dicts: + vid_name = d["file_names"][0].split('/')[-2] + os.makedirs(os.path.join(dirname, vid_name), exist_ok=True) + for idx, file_name in enumerate(d["file_names"]): + img = np.array(Image.open(file_name)) + visualizer = Visualizer(img, metadata=meta) + vis = visualizer.draw_dataset_dict(extract_frame_dic(d, idx)) + fpath = os.path.join(dirname, vid_name, file_name.split('/')[-1]) + vis.save(fpath) diff --git a/Mask2Former/mask2former_video/ifc/data/ytvis_eval.py b/Mask2Former/mask2former_video/ifc/data/ytvis_eval.py new file mode 100644 index 0000000..5d9fdea --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/data/ytvis_eval.py @@ -0,0 +1,211 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import contextlib +import copy +import io +import itertools +import json +import logging +import numpy as np +import os +from collections import OrderedDict +import pycocotools.mask as mask_util +import torch +from pycocotools.ytvos import YTVOS + +import detectron2.utils.comm as comm +from detectron2.config import CfgNode +from detectron2.data import MetadataCatalog +from detectron2.evaluation import DatasetEvaluator +from detectron2.utils.file_io import PathManager + + +class YTVISEvaluator(DatasetEvaluator): + """ + Evaluate AR for object proposals, AP for instance detection/segmentation, AP + for keypoint detection outputs using COCO's metrics. + See http://cocodataset.org/#detection-eval and + http://cocodataset.org/#keypoints-eval to understand its metrics. + + In addition to COCO, this evaluator is able to support any bounding box detection, + instance segmentation, or keypoint detection dataset. + """ + + def __init__( + self, + dataset_name, + tasks=None, + distributed=True, + output_dir=None, + *, + use_fast_impl=True, + ): + """ + Args: + dataset_name (str): name of the dataset to be evaluated. + It must have either the following corresponding metadata: + + "json_file": the path to the COCO format annotation + + Or it must be in detectron2's standard dataset format + so it can be converted to COCO format automatically. + tasks (tuple[str]): tasks that can be evaluated under the given + configuration. A task is one of "bbox", "segm", "keypoints". + By default, will infer this automatically from predictions. + distributed (True): if True, will collect results from all ranks and run evaluation + in the main process. + Otherwise, will only evaluate the results in the current process. + output_dir (str): optional, an output directory to dump all + results predicted on the dataset. The dump contains two files: + + 1. "instances_predictions.pth" a file in torch serialization + format that contains all the raw original predictions. + 2. "coco_instances_results.json" a json file in COCO's result + format. + use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP. + Although the results should be very close to the official implementation in COCO + API, it is still recommended to compute results with the official API for use in + papers. The faster implementation also uses more RAM. + """ + self._logger = logging.getLogger(__name__) + self._distributed = distributed + self._output_dir = output_dir + self._use_fast_impl = use_fast_impl + + if tasks is not None and isinstance(tasks, CfgNode): + self._logger.warning( + "COCO Evaluator instantiated using config, this is deprecated behavior." + " Please pass in explicit arguments instead." + ) + self._tasks = None # Infering it from predictions should be better + else: + self._tasks = tasks + + self._cpu_device = torch.device("cpu") + + self._metadata = MetadataCatalog.get(dataset_name) + + json_file = PathManager.get_local_path(self._metadata.json_file) + with contextlib.redirect_stdout(io.StringIO()): + self._ytvis_api = YTVOS(json_file) + + # Test set json files do not contain annotations (evaluation must be + # performed using the COCO evaluation server). + self._do_evaluation = "annotations" in self._ytvis_api.dataset + + def reset(self): + self._predictions = [] + + def process(self, inputs, outputs): + """ + Args: + inputs: the inputs to a COCO model (e.g., GeneralizedRCNN). + It is a list of dict. Each dict corresponds to an image and + contains keys like "height", "width", "file_name", "image_id". + outputs: the outputs of a COCO model. It is a list of dicts with key + "instances" that contains :class:`Instances`. + """ + prediction = instances_to_coco_json_video(inputs, outputs) + self._predictions.extend(prediction) + + def evaluate(self): + """ + Args: + img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset + """ + if self._distributed: + comm.synchronize() + predictions = comm.gather(self._predictions, dst=0) + predictions = list(itertools.chain(*predictions)) + + if not comm.is_main_process(): + return {} + else: + predictions = self._predictions + + if len(predictions) == 0: + self._logger.warning("[COCOEvaluator] Did not receive valid predictions.") + return {} + + if self._output_dir: + PathManager.mkdirs(self._output_dir) + file_path = os.path.join(self._output_dir, "instances_predictions.pth") + with PathManager.open(file_path, "wb") as f: + torch.save(predictions, f) + + self._results = OrderedDict() + self._eval_predictions(predictions) + # Copy so the caller can do whatever with results + return copy.deepcopy(self._results) + + def _eval_predictions(self, predictions): + """ + Evaluate predictions. Fill self._results with the metrics of the tasks. + """ + self._logger.info("Preparing results for YTVIS format ...") + + # unmap the category ids for COCO + if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"): + dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id + all_contiguous_ids = list(dataset_id_to_contiguous_id.values()) + num_classes = len(all_contiguous_ids) + assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1 + + reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()} + for result in predictions: + category_id = result["category_id"] + assert category_id < num_classes, ( + f"A prediction has class={category_id}, " + f"but the dataset only has {num_classes} classes and " + f"predicted class id should be in [0, {num_classes - 1}]." + ) + result["category_id"] = reverse_id_mapping[category_id] + + if self._output_dir: + file_path = os.path.join(self._output_dir, "results.json") + self._logger.info("Saving results to {}".format(file_path)) + with PathManager.open(file_path, "w") as f: + f.write(json.dumps(predictions)) + f.flush() + + self._logger.info("Annotations are not available for evaluation.") + return + + +def instances_to_coco_json_video(inputs, outputs): + """ + Dump an "Instances" object to a COCO-format json that's used for evaluation. + + Args: + instances (Instances): + video_id (int): the image id + + Returns: + list[dict]: list of json annotations in COCO format. + """ + assert len(inputs) == 1, "More than one inputs are loaded for inference!" + + video_id = inputs[0]["video_id"] + video_length = inputs[0]["length"] + + scores = outputs["pred_scores"] + labels = outputs["pred_labels"] + masks = outputs["pred_masks"] + + ytvis_results = [] + for instance_id, (s, l, m) in enumerate(zip(scores, labels, masks)): + segms = [ + mask_util.encode(np.array(_mask[:, :, None], order="F", dtype="uint8"))[0] + for _mask in m + ] + for rle in segms: + rle["counts"] = rle["counts"].decode("utf-8") + + res = { + "video_id": video_id, + "score": s, + "category_id": l, + "segmentations": segms, + } + ytvis_results.append(res) + + return ytvis_results diff --git a/Mask2Former/mask2former_video/ifc/ifc.py b/Mask2Former/mask2former_video/ifc/ifc.py new file mode 100644 index 0000000..689493f --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/ifc.py @@ -0,0 +1,408 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import math +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.modeling import META_ARCH_REGISTRY, build_backbone +from detectron2.structures import Boxes, ImageList, Instances + +from .models.backbone import Joiner +from .models.ifc import IFC, SetCriterion +from .models.matcher import HungarianMatcher +from .models.position_encoding import PositionEmbeddingSine +from .models.segmentation import MaskHead, segmentation_postprocess +from .models.transformer import IFCTransformer +from .structures.clip_output import Videos, Clips +from .util.misc import NestedTensor, _max_by_axis, interpolate + +__all__ = ["Ifc"] + + +class MaskedBackbone(nn.Module): + """ This is a thin wrapper around D2's backbone to provide padding masking""" + + def __init__(self, cfg): + super().__init__() + self.backbone = build_backbone(cfg) + backbone_shape = self.backbone.output_shape() + self.feature_strides = [backbone_shape[f].stride for f in backbone_shape.keys()] + self.num_channels = backbone_shape[list(backbone_shape.keys())[-1]].channels + + def forward(self, images): + features = self.backbone(images.tensor) + masks = self.mask_out_padding( + [features_per_level.shape for features_per_level in features.values()], + images.image_sizes, + images.tensor.device, + ) + assert len(features) == len(masks) + for i, k in enumerate(features.keys()): + features[k] = NestedTensor(features[k], masks[i]) + return features + + def mask_out_padding(self, feature_shapes, image_sizes, device): + masks = [] + assert len(feature_shapes) == len(self.feature_strides) + for idx, shape in enumerate(feature_shapes): + N, _, H, W = shape + masks_per_feature_level = torch.ones((N, H, W), dtype=torch.bool, device=device) + for img_idx, (h, w) in enumerate(image_sizes): + masks_per_feature_level[ + img_idx, + : int(np.ceil(float(h) / self.feature_strides[idx])), + : int(np.ceil(float(w) / self.feature_strides[idx])), + ] = 0 + masks.append(masks_per_feature_level) + return masks + + +@META_ARCH_REGISTRY.register() +class Ifc(nn.Module): + + def __init__(self, cfg): + super().__init__() + + self.num_frames = cfg.INPUT.SAMPLING_FRAME_NUM + + self.device = torch.device(cfg.MODEL.DEVICE) + self.clip_stride = cfg.MODEL.IFC.CLIP_STRIDE + self.merge_on_cpu = cfg.MODEL.IFC.MERGE_ON_CPU + self.is_multi_cls = cfg.MODEL.IFC.MULTI_CLS_ON + self.apply_cls_thres = cfg.MODEL.IFC.APPLY_CLS_THRES + + self.is_coco = cfg.DATASETS.TEST[0].startswith("coco") + self.num_classes = cfg.MODEL.IFC.NUM_CLASSES + self.mask_stride = cfg.MODEL.IFC.MASK_STRIDE + self.match_stride = cfg.MODEL.IFC.MATCH_STRIDE + self.mask_on = cfg.MODEL.MASK_ON + hidden_dim = cfg.MODEL.IFC.HIDDEN_DIM + num_queries = cfg.MODEL.IFC.NUM_OBJECT_QUERIES + + # Transformer parameters: + nheads = cfg.MODEL.IFC.NHEADS + dropout = cfg.MODEL.IFC.DROPOUT + dim_feedforward = cfg.MODEL.IFC.DIM_FEEDFORWARD + enc_layers = cfg.MODEL.IFC.ENC_LAYERS + dec_layers = cfg.MODEL.IFC.DEC_LAYERS + pre_norm = cfg.MODEL.IFC.PRE_NORM + num_memory_bus = cfg.MODEL.IFC.NUM_MEMORY_BUS + + # Loss parameters: + mask_weight = cfg.MODEL.IFC.MASK_WEIGHT + dice_weight = cfg.MODEL.IFC.DICE_WEIGHT + deep_supervision = cfg.MODEL.IFC.DEEP_SUPERVISION + no_object_weight = cfg.MODEL.IFC.NO_OBJECT_WEIGHT + + N_steps = hidden_dim // 2 + d2_backbone = MaskedBackbone(cfg) + backbone = Joiner(d2_backbone, PositionEmbeddingSine(N_steps, normalize=True)) + backbone.num_channels = d2_backbone.num_channels + + transformer = IFCTransformer( + num_frames=self.num_frames, + d_model=hidden_dim, + dropout=dropout, + nhead=nheads, + num_memory_bus=num_memory_bus, + dim_feedforward=dim_feedforward, + num_encoder_layers=enc_layers, + num_decoder_layers=dec_layers, + normalize_before=pre_norm, + return_intermediate_dec=deep_supervision, + ) + mask_head = MaskHead(hidden_dim, [1024, 512], self.num_frames) + + self.detr = IFC( + backbone, transformer, mask_head, + num_classes=self.num_classes, num_queries=num_queries, + num_frames=self.num_frames, aux_loss=deep_supervision + ) + self.detr.to(self.device) + + # building criterion + matcher = HungarianMatcher( + cost_class=1, + cost_dice=dice_weight, + num_classes=self.num_classes, + ) + weight_dict = {"loss_ce": 1, "loss_mask": mask_weight, "loss_dice": dice_weight} + if deep_supervision: + aux_weight_dict = {} + for i in range(dec_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + losses = ["labels", "masks", "cardinality"] + self.criterion = SetCriterion( + self.num_classes, matcher=matcher, weight_dict=weight_dict, eos_coef=no_object_weight, losses=losses, + num_frames=self.num_frames + ) + self.criterion.to(self.device) + + pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1) + pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1) + self.normalizer = lambda x: (x - pixel_mean) / pixel_std + self.to(self.device) + + self.merge_device = "cpu" if self.merge_on_cpu else self.device + + def forward(self, batched_inputs): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + + * image: Tensor, image in (C, H, W) format. + * instances: Instances + + Other information that's included in the original dicts, such as: + + * "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + Returns: + dict[str: Tensor]: + mapping from a named loss to a tensor storing the loss. Used during training only. + """ + if self.training or self.is_coco: + images = self.preprocess_image(batched_inputs) + output = self.detr(images) + + if self.training: + gt_instances = [] + for video in batched_inputs: + for frame in video["instances"]: + gt_instances.append(frame.to(self.device)) + + targets = self.prepare_targets(batched_inputs) + loss_dict = self.criterion(output, targets) + weight_dict = self.criterion.weight_dict + for k in loss_dict.keys(): + if k in weight_dict: + loss_dict[k] *= weight_dict[k] + return loss_dict + else: + return self.inference_image(output, batched_inputs, images) + + # Youtube-VIS evaluation should be treated in a different manner. + else: + # TODO provide sequential runner for long videos. + # NOTE we assume that only a single video is taken as an input. + video = self.preprocess_image(batched_inputs) + + backbone_tensor, backbone_pos = self.detr.forward_pre_backbone(video) + + video_length = len(video) + image_size = video.tensor.shape[-2:] + interim_size = (math.ceil(image_size[0] / 8), math.ceil(image_size[1] / 8)) + + video_output = Videos( + self.num_frames, video_length, self.num_classes, interim_size, self.merge_device + ) + + is_last_clip = False + for start_idx in range(0, video_length, self.clip_stride): + end_idx = start_idx + self.num_frames + if end_idx >= video_length: + is_last_clip = True + start_idx, end_idx = max(0, video_length - self.num_frames), video_length + + frame_idx = list(range(start_idx, end_idx)) + + clip_backbone_tensor = [t[frame_idx] for t in backbone_tensor] + clip_backbone_pos = [p[frame_idx] for p in backbone_pos] + #####从这里修改 + output = self.detr.forward_post_backbone(clip_backbone_tensor, clip_backbone_pos, is_train=False) + + _clip_results = self.inference_clip(output, image_size) + clip_results = Clips(frame_idx, _clip_results.to(self.merge_device)) + + video_output.update(clip_results) + + if is_last_clip: + break + + height = batched_inputs[0].get("height", image_size[0]) + width = batched_inputs[0].get("width", image_size[1]) + + pred_cls, pred_masks = video_output.get_result((height, width)) # NxHxW / NxC + + return self.inference_video(pred_cls, pred_masks, (height, width)) + + def prepare_targets(self, targets): + gt_instances = [] + + _sizes = [] + for targets_per_video in targets: + _sizes += [list(t.image_size) for t in targets_per_video["instances"]] + max_size = _max_by_axis(_sizes) + + for targets_per_video in targets: + _num_instance = len(targets_per_video["instances"][0]) + mask_shape = [_num_instance, self.num_frames] + max_size + gt_masks_per_video = torch.zeros(mask_shape, dtype=torch.bool, device=self.device) + + gt_ids_per_video = [] + gt_classes_per_video = torch.full((_num_instance,), self.num_classes, device=self.device) + for f_i, targets_per_frame in enumerate(targets_per_video["instances"]): + targets_per_frame = targets_per_frame.to(self.device) + h, w = targets_per_frame.image_size + + valid_cls_idx = (targets_per_frame.gt_classes != self.num_classes) + gt_classes_per_video[valid_cls_idx] = targets_per_frame.gt_classes[valid_cls_idx] + gt_ids_per_video.append(targets_per_frame.gt_ids[:, None]) + gt_masks_per_video[:, f_i, :h, :w] = targets_per_frame.gt_masks.tensor + + gt_ids_per_video = torch.cat(gt_ids_per_video, dim=1) + valid_idx = (gt_ids_per_video != -1).any(dim=-1) + + gt_classes_per_video = gt_classes_per_video[valid_idx] # N, + gt_ids_per_video = gt_ids_per_video[valid_idx] # N, num_frames + + gt_instances.append({"labels": gt_classes_per_video, "ids": gt_ids_per_video}) + gt_masks_per_video = gt_masks_per_video[valid_idx].float() # N, num_frames, H, W + o_h, o_w = gt_masks_per_video.shape[-2:] + l_h, l_w = math.ceil(o_h/self.mask_stride), math.ceil(o_w/self.mask_stride) + m_h, m_w = math.ceil(o_h/self.match_stride), math.ceil(o_w/self.match_stride) + + gt_masks_for_loss = interpolate(gt_masks_per_video, size=(l_h, l_w), mode="bilinear", align_corners=False) + gt_masks_for_match = interpolate(gt_masks_per_video, size=(m_h, m_w), mode="bilinear", align_corners=False) + gt_instances[-1].update({"masks": gt_masks_for_loss, "match_masks": gt_masks_for_match}) + + return gt_instances + + def inference_clip(self, output, image_size): + mask_cls = output["pred_logits"][0] + mask_pred = output["pred_masks"][0] + + # For each mask we assign the best class or the second best if the best on is `no_object`. + _idx = self.num_classes + 1 + mask_cls = F.softmax(mask_cls, dim=-1)[:, :_idx] + scores, labels = mask_cls.max(-1) + + valid = (labels < self.num_classes) + scores = scores[valid] + labels = labels[valid] + mask_cls = mask_cls[valid] + mask_pred = mask_pred[valid] + + results = Instances(image_size) + results.scores = scores + results.pred_classes = labels + results.cls_probs = mask_cls + results.pred_masks = mask_pred + + return results + + def inference_video(self, pred_cls, pred_masks, image_size): + if len(pred_cls) > 0: + if self.is_multi_cls: + is_above_thres = torch.where(pred_cls[:, :-1] > self.apply_cls_thres) + scores = pred_cls[is_above_thres] + labels = is_above_thres[1] + pred_masks = pred_masks[is_above_thres[0]] + else: + scores, labels = pred_cls[:, :-1].max(-1) + + masks = pred_masks > 0.5 + + out_scores = scores.tolist() + out_labels = labels.tolist() + out_masks = [m for m in masks.cpu()] + else: + out_scores = [] + out_labels = [] + out_masks = [] + + video_output = { + "image_size": image_size, + "pred_scores": out_scores, + "pred_labels": out_labels, + "pred_masks": out_masks, + } + + return video_output + + def inference_image(self, output, batched_inputs, images): + mask_cls = output["pred_logits"] + mask_pred = output["pred_masks"] + + results = self._inference_image( + mask_cls, mask_pred, images.image_sizes + ) + processed_results = [] + for results_per_image, input_per_image, image_size in zip(results, batched_inputs, images.image_sizes): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = segmentation_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + def _inference_image(self, mask_cls, mask_pred, image_sizes): + """ + Arguments: + mask_cls (Tensor): tensor of shape (batch_size, num_queries, K). + The tensor predicts the classification probability for each query. + image_sizes (List[torch.Size]): the input image sizes + + Returns: + results (List[Instances]): a list of #images elements. + """ + assert len(mask_cls) == len(image_sizes) // self.num_frames + results = [] + + max_size = _max_by_axis([list(s) for s in image_sizes]) + b, n = mask_pred.shape[:2] + mask_pred = interpolate( + mask_pred.flatten(0,1), size=max_size, mode='bilinear', align_corners=False + ) > 0.0 + mask_pred = mask_pred.view(b, n, *mask_pred.shape[-3:]) + + # For each mask we assign the best class or the second best if the best on is `no_object`. + _idx = self.num_classes + 1 + scores, labels = F.softmax(mask_cls, dim=-1)[:, :, :_idx].max(-1) + + for i, (scores_per_clip, labels_per_clip, mask_pred_per_clip) in enumerate(zip( + scores, labels, mask_pred + )): + valid = (labels_per_clip < self.num_classes) + scores_per_clip = scores_per_clip[valid] + labels_per_clip = labels_per_clip[valid] + mask_pred_per_clip = mask_pred_per_clip[valid] + + for j in range(self.num_frames): + image_size = image_sizes[i*self.num_frames + j] + result = Instances(image_size) + + pred_masks = mask_pred_per_clip[:, j, :image_size[0], :image_size[1]] + + pred_boxes = torch.zeros(pred_masks.shape[0], 4, dtype=torch.float32) + x_any = torch.any(pred_masks, dim=1) + y_any = torch.any(pred_masks, dim=2) + for idx in range(pred_masks.shape[0]): + x = torch.where(x_any[idx, :])[0] + y = torch.where(y_any[idx, :])[0] + if len(x) > 0 and len(y) > 0: + pred_boxes[idx, :] = torch.as_tensor( + [x[0], y[0], x[-1] + 1, y[-1] + 1], dtype=torch.float32 + ) + pred_boxes = Boxes(pred_boxes) + + result.scores = scores_per_clip + result.pred_classes = labels_per_clip + result.pred_masks = pred_masks + result.pred_boxes = pred_boxes + results.append(result) + return results + + def preprocess_image(self, batched_inputs): + """ + Normalize, pad and batch the input images. + """ + images = [] + for video in batched_inputs: + for frame in video["image"]: + images.append(self.normalizer(frame.to(self.device))) + images = ImageList.from_tensors(images) + return images diff --git a/Mask2Former/mask2former_video/ifc/models/__init__.py b/Mask2Former/mask2former_video/ifc/models/__init__.py new file mode 100644 index 0000000..99c0104 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/models/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .ifc import * diff --git a/Mask2Former/mask2former_video/ifc/models/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/models/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..69e751e Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/models/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/models/__pycache__/backbone.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/models/__pycache__/backbone.cpython-38.pyc new file mode 100644 index 0000000..4881954 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/models/__pycache__/backbone.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/models/__pycache__/ifc.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/models/__pycache__/ifc.cpython-38.pyc new file mode 100644 index 0000000..bae664e Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/models/__pycache__/ifc.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/models/__pycache__/matcher.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/models/__pycache__/matcher.cpython-38.pyc new file mode 100644 index 0000000..dfa6f16 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/models/__pycache__/matcher.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/models/__pycache__/misc.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/models/__pycache__/misc.cpython-38.pyc new file mode 100644 index 0000000..1b5ff34 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/models/__pycache__/misc.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/models/__pycache__/position_encoding.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/models/__pycache__/position_encoding.cpython-38.pyc new file mode 100644 index 0000000..070f844 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/models/__pycache__/position_encoding.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/models/__pycache__/segmentation.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/models/__pycache__/segmentation.cpython-38.pyc new file mode 100644 index 0000000..18ef9e6 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/models/__pycache__/segmentation.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/models/__pycache__/transformer.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/models/__pycache__/transformer.cpython-38.pyc new file mode 100644 index 0000000..e564e72 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/models/__pycache__/transformer.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/models/backbone.py b/Mask2Former/mask2former_video/ifc/models/backbone.py new file mode 100644 index 0000000..ea67575 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/models/backbone.py @@ -0,0 +1,118 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Backbone modules. +""" + +import torch +import torch.nn.functional as F +import torchvision +from torch import nn +from torchvision.models._utils import IntermediateLayerGetter +from typing import Dict, List + +from ..util.misc import NestedTensor, is_main_process + +from .position_encoding import build_position_encoding + + +class FrozenBatchNorm2d(torch.nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, + without which any other models than torchvision.models.resnet[18,34,50,101] + produce nans. + """ + + def __init__(self, n): + super(FrozenBatchNorm2d, self).__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + num_batches_tracked_key = prefix + 'num_batches_tracked' + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super(FrozenBatchNorm2d, self)._load_from_state_dict( + state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + + def forward(self, x): + # move reshapes to the beginning + # to make it fuser-friendly + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + rv = self.running_var.reshape(1, -1, 1, 1) + rm = self.running_mean.reshape(1, -1, 1, 1) + eps = 1e-5 + scale = w * (rv + eps).rsqrt() + bias = b - rm * scale + return x * scale + bias + + +class BackboneBase(nn.Module): + + def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool): + super().__init__() + for name, parameter in backbone.named_parameters(): + if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name: + parameter.requires_grad_(False) + if return_interm_layers: + return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} + else: + return_layers = {'layer4': "0"} + self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) + self.num_channels = num_channels + + def forward(self, tensor_list: NestedTensor): + xs = self.body(tensor_list.tensors) + out: Dict[str, NestedTensor] = {} + for name, x in xs.items(): + m = tensor_list.mask + assert m is not None + mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] + out[name] = NestedTensor(x, mask) + return out + + +class Backbone(BackboneBase): + """ResNet backbone with frozen BatchNorm.""" + def __init__(self, name: str, + train_backbone: bool, + return_interm_layers: bool, + dilation: bool): + backbone = getattr(torchvision.models, name)( + replace_stride_with_dilation=[False, False, dilation], + pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d) + num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 + super().__init__(backbone, train_backbone, num_channels, return_interm_layers) + + +class Joiner(nn.Sequential): + def __init__(self, backbone, position_embedding): + super().__init__(backbone, position_embedding) + + def forward(self, tensor_list: NestedTensor, res_outs): + xs = self[0](tensor_list, res_outs) + out: List[NestedTensor] = [] + pos = [] + for name, x in xs.items(): + out.append(x) + # position encoding + pos.append(self[1](x).to(x.tensors.dtype)) + + return out, pos + + +def build_backbone(args): + position_embedding = build_position_encoding(args) + train_backbone = args.lr_backbone > 0 + return_interm_layers = args.masks + backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation) + model = Joiner(backbone, position_embedding) + model.num_channels = backbone.num_channels + return model diff --git a/Mask2Former/mask2former_video/ifc/models/ifc.py b/Mask2Former/mask2former_video/ifc/models/ifc.py new file mode 100644 index 0000000..d5efe11 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/models/ifc.py @@ -0,0 +1,247 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +IFC model and criterion classes. +Modified by the authors of Video Instance Segmentation using Inter-Frame Communication Transformer. +""" +import torch +import torch.nn.functional as F +from torch import nn + +from ..util.misc import (NestedTensor, nested_tensor_from_tensor_list, interpolate, + accuracy, get_world_size, is_dist_avail_and_initialized) +from .misc import MLP +from .segmentation import dice_loss, sigmoid_focal_loss +import sys, os +sys.path.append(os.getcwd()) + +from mask2former_video.config import * + +class IFC(nn.Module): + """ This is the IFC module that performs object detection """ + def __init__(self, backbone, transformer, mask_head, num_classes, num_queries, num_frames, aux_loss=False): + """ Initializes the model. + Parameters: + backbone: torch module of the backbone to be used. See backbone.py + transformer: torch module of the transformer architecture. See transformer.py + num_classes: number of object classes + num_queries: number of object queries, ie detection slot. This is the maximal number of objects + IFC can detect in a single image. For COCO, we recommend 100 queries. + aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. + """ + super().__init__() + self.num_queries = num_queries + self.transformer = transformer + hidden_dim = transformer.d_model + self.class_embed = MLP(hidden_dim, hidden_dim, num_classes + 1, 2) + self.query_embed = nn.Embedding(num_queries, hidden_dim) + self.input_proj = nn.Conv2d(DIM_FEEDFORWARD, hidden_dim, kernel_size=1) + self.backbone = backbone + self.num_frames = num_frames + self.aux_loss = aux_loss + + self.mask_head = mask_head + + # def forward(self, samples: NestedTensor, window_tokens, res_outs): + def forward(self, window_tokens, res_outs): + """ The forward expects a NestedTensor, which consists of: + - samples.tensor: batched images, of shape [batch_size x 3 x H x W] + - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels + + It returns a dict with the following elements: + - "pred_logits": the classification logits (including no-object) for all queries. + Shape= [batch_size x num_queries x (num_classes + 1)] + - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of + dictionnaries containing the two above keys for each decoder layer. + """ + # features, pos = self.forward_pre_backbone(samples, res_outs) + out = self.forward_post_backbone(window_tokens, res_outs) #此处与之前Mask2Former交接 + + return out + + def forward_pre_backbone(self, samples: NestedTensor, res_outs): + if isinstance(samples, (list, torch.Tensor)): + samples = nested_tensor_from_tensor_list(samples) + features, pos = self.backbone(samples, res_outs) + + return features, pos + + # def forward_post_backbone(self, features, pos, res_outs, is_train=True): + def forward_post_backbone(self, window_tokens, res_outs, is_train=True): + # src_proj, mask = features.decompose() #取经过backbone后的最有一层输出res5 + + # assert mask is not None + encoded_src = self.input_proj(res_outs['res5']) #[40, 256, 15, 27] + + # clip_hs, encoded_src = self.transformer( # LxQxBxTxC #此处进入 与之前Mask2Former交接 + # src_proj, mask, self.query_embed.weight, pos, is_train + # ) + clip_hs = self.transformer( # LxQxBxTxC #此处进入 与之前Mask2Former交接 + window_tokens, self.query_embed.weight, is_train + ) + clip_hs = clip_hs.transpose(1,2) # LxBxQxC #[3,8,100,256] + + outputs_class = self.class_embed(clip_hs) #[3,8,100,41] + outputs_masks = self.mask_head( + encoded_src, #[40, 256, 15, 27] + [res_outs['res4'], res_outs['res3']], + clip_hs + ) # L, B, N, T, H, W [3, 8, 100, 5, 60, 107] + out = {'pred_logits': outputs_class[-1]} #[8, 100, 41] + out.update({'pred_masks': outputs_masks[-1]}) #[8, 100, 5, 60, 107] + + if self.aux_loss: + out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_masks) + return out + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_masks): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + return [{'pred_logits': a, 'pred_masks': b} + for a, b in zip(outputs_class[:-1], outputs_masks[:-1])] + + +class SetCriterion(nn.Module): + """ This class computes the loss for IFC. + The process happens in two steps: + 1) we compute hungarian assignment between ground truth masks and the outputs of the model + 2) we supervise each pair of matched ground-truth / prediction (supervise class and mask) + """ + def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses, num_frames): + """ Create the criterion. + Parameters: + num_classes: number of object categories, omitting the special no-object category + matcher: module able to compute a matching between targets and proposals + weight_dict: dict containing as key the names of the losses and as values their relative weight. + eos_coef: relative classification weight applied to the no-object category + losses: list of all the losses to be applied. See get_loss for list of available losses. + """ + super().__init__() + self.num_classes = num_classes + self.matcher = matcher + self.weight_dict = weight_dict + self.eos_coef = eos_coef + self.losses = losses + self.num_frames = num_frames + empty_weight = torch.ones(num_classes + 1) + empty_weight[-1] = self.eos_coef + self.register_buffer('empty_weight', empty_weight) + + def loss_labels(self, outputs, targets, indices, num_masks, log=True): + """Classification loss (NLL) + targets dicts must contain the key "labels" containing a tensor of dim [nb_target_masks] + """ + src_logits = outputs['pred_logits'] + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full(src_logits.shape[:2], self.num_classes, + dtype=torch.int64, device=src_logits.device) + target_classes[idx] = target_classes_o + + loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) + losses = {'loss_ce': loss_ce} + + if log: + # TODO this should probably be a separate loss, not hacked in this one here + losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0] + return losses + + @torch.no_grad() + def loss_cardinality(self, outputs, targets, indices, num_masks): + """ Compute the cardinality error, ie the absolute error in the number of predicted non-empty boxes + This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients + """ + pred_logits = outputs['pred_logits'] + device = pred_logits.device + tgt_lengths = torch.as_tensor([len(v["labels"]) for v in targets], device=device) + # Count the number of predictions that are NOT "no-object" (which is the last class) + card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1) + card_err = F.l1_loss(card_pred.float(), tgt_lengths.float()) + losses = {'cardinality_error': card_err} + return losses + + def loss_masks(self, outputs, targets, indices, num_masks): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_masks, h, w] + """ + assert "pred_masks" in outputs + + idx = self._get_src_permutation_idx(indices) + src_masks = outputs["pred_masks"][idx] + target_masks = torch.cat([t['masks'][i] for t, (_, i) in zip(targets, indices)]).to(src_masks) + + n, t = src_masks.shape[:2] + t_h, t_w = target_masks.shape[-2:] + + src_masks = interpolate(src_masks, size=(t_h, t_w), mode="bilinear", align_corners=False) + + src_masks = src_masks.flatten(1) + target_masks = target_masks.flatten(1) + + losses = { + "loss_mask": sigmoid_focal_loss(src_masks, target_masks, num_masks), + "loss_dice": dice_loss(src_masks, target_masks, num_masks), + } + return losses + + def _get_src_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + src_idx = torch.cat([src for (src, _) in indices]) + return batch_idx, src_idx + + def _get_tgt_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + tgt_idx = torch.cat([tgt for (_, tgt) in indices]) + return batch_idx, tgt_idx + + def get_loss(self, loss, outputs, targets, indices, num_masks, **kwargs): + loss_map = { + 'labels': self.loss_labels, + 'cardinality': self.loss_cardinality, + 'masks': self.loss_masks + } + assert loss in loss_map, f'do you really want to compute {loss} loss?' + return loss_map[loss](outputs, targets, indices, num_masks, **kwargs) + + def forward(self, outputs, targets): + """ This performs the loss computation. + Parameters: + outputs: dict of tensors, see the output specification of the model for the format + targets: list of dicts, such that len(targets) == batch_size. + The expected keys in each dict depends on the losses applied, see each loss' doc + """ + outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets) + + # Compute the average number of target masks accross all nodes, for normalization purposes + num_masks = sum(len(t["labels"]) for t in targets) + num_masks = torch.as_tensor([num_masks], dtype=torch.float, device=next(iter(outputs.values())).device) + if is_dist_avail_and_initialized(): + torch.distributed.all_reduce(num_masks) + num_masks = torch.clamp(num_masks / get_world_size(), min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + losses.update(self.get_loss(loss, outputs_without_aux, targets, indices, num_masks)) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if 'aux_outputs' in outputs: + for i, aux_outputs in enumerate(outputs['aux_outputs']): + indices = self.matcher(aux_outputs, targets) + for loss in self.losses: + kwargs = {} + if loss == 'labels': + # Logging is enabled only for the last layer + kwargs = {'log': False} + l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks, **kwargs) + l_dict = {k + f'_{i}': v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses diff --git a/Mask2Former/mask2former_video/ifc/models/matcher.py b/Mask2Former/mask2former_video/ifc/models/matcher.py new file mode 100644 index 0000000..1278af3 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/models/matcher.py @@ -0,0 +1,82 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Modules to compute the matching cost and solve the corresponding LSAP. +Modified by the authors of Video Instance Segmentation using Inter-Frame Communication Transformer. +""" +import numpy as np +import torch +from scipy.optimize import linear_sum_assignment +from torch import nn +import torch.nn.functional as F +from typing import List + +from detectron2.utils.memory import retry_if_cuda_oom + +from .segmentation import dice_coef +from ..util.misc import interpolate + + +class HungarianMatcher(nn.Module): + """This class computes an assignment between the targets and the predictions of the network + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, + there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, + while the others are un-matched (and thus treated as non-objects). + """ + + def __init__( + self, + cost_class: float = 1, + cost_dice: float = 1, + num_classes: int = 80, + ): + """Creates the matcher + + Params: + cost_class: This is the relative weight of the classification error in the matching cost + cost_mask: This is the relative weight of the sigmoid_focal error of the masks in the matching cost + cost_dice: This is the relative weight of the dice loss of the masks in the matching cost + """ + super().__init__() + self.cost_class = cost_class + self.cost_dice = cost_dice + assert cost_class != 0 or cost_dice != 0, "all costs cant be 0" + + self.num_classes = num_classes + self.num_cum_classes = [0] + np.cumsum(np.array(num_classes) + 1).tolist() + + @torch.no_grad() + def forward(self, outputs, targets): + # We flatten to compute the cost matrices in a batch + out_prob = outputs["pred_logits"].softmax(-1) + out_mask = outputs["pred_masks"] + B, Q, T, s_h, s_w = out_mask.shape + t_h, t_w = targets[0]["match_masks"].shape[-2:] + + if (s_h, s_w) != (t_h, t_w): + out_mask = out_mask.reshape(B, Q*T, s_h, s_w) + out_mask = interpolate(out_mask, size=(t_h, t_w), mode="bilinear", align_corners=False) + out_mask = out_mask.view(B, Q, T, t_h, t_w) + + indices = [] + for b_i in range(B): + b_tgt_ids = targets[b_i]["labels"] + b_out_prob = out_prob[b_i] + + cost_class = b_out_prob[:, b_tgt_ids] + + b_tgt_mask = targets[b_i]["match_masks"] + b_out_mask = out_mask[b_i] + + # Compute the dice coefficient cost between masks + # The 1 is a constant that doesn't change the matching as cost_class, thus omitted. + cost_dice = retry_if_cuda_oom(dice_coef)( + b_out_mask, b_tgt_mask + ).to(cost_class) + + # Final cost matrix + C = self.cost_dice * cost_dice + self.cost_class * cost_class + + indices.append(linear_sum_assignment(C.cpu(), maximize=True)) + + return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] diff --git a/Mask2Former/mask2former_video/ifc/models/misc.py b/Mask2Former/mask2former_video/ifc/models/misc.py new file mode 100644 index 0000000..5c48608 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/models/misc.py @@ -0,0 +1,18 @@ +import torch +import torch.nn.functional as F +from torch import nn + + +class MLP(nn.Module): + """ Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x diff --git a/Mask2Former/mask2former_video/ifc/models/position_encoding.py b/Mask2Former/mask2former_video/ifc/models/position_encoding.py new file mode 100644 index 0000000..3c785a6 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/models/position_encoding.py @@ -0,0 +1,89 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Various positional encodings for the transformer. +""" +import math +import torch +from torch import nn + +from ..util.misc import NestedTensor + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, tensor_list: NestedTensor): + # x = tensor_list.tensors + mask = tensor_list.mask + assert mask is not None + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=mask.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +class PositionEmbeddingLearned(nn.Module): + """ + Absolute pos embedding, learned. + """ + def __init__(self, num_pos_feats=256): + super().__init__() + self.row_embed = nn.Embedding(50, num_pos_feats) + self.col_embed = nn.Embedding(50, num_pos_feats) + self.reset_parameters() + + def reset_parameters(self): + nn.init.uniform_(self.row_embed.weight) + nn.init.uniform_(self.col_embed.weight) + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + h, w = x.shape[-2:] + i = torch.arange(w, device=x.device) + j = torch.arange(h, device=x.device) + x_emb = self.col_embed(i) + y_emb = self.row_embed(j) + pos = torch.cat([ + x_emb.unsqueeze(0).repeat(h, 1, 1), + y_emb.unsqueeze(1).repeat(1, w, 1), + ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) + return pos + + +def build_position_encoding(args): + N_steps = args.hidden_dim // 2 + if args.position_embedding in ('v2', 'sine'): + # TODO find a better way of exposing other arguments + position_embedding = PositionEmbeddingSine(N_steps, normalize=True) + elif args.position_embedding in ('v3', 'learned'): + position_embedding = PositionEmbeddingLearned(N_steps) + else: + raise ValueError(f"not supported {args.position_embedding}") + + return position_embedding diff --git a/Mask2Former/mask2former_video/ifc/models/segmentation.py b/Mask2Former/mask2former_video/ifc/models/segmentation.py new file mode 100644 index 0000000..e8ee122 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/models/segmentation.py @@ -0,0 +1,171 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +This file provides the definition of the convolutional heads used to predict masks, as well as the losses +""" +from typing import List + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from detectron2.structures import Instances +from detectron2.layers.blocks import DepthwiseSeparableConv2d + +from ..util.misc import interpolate +from .misc import MLP + + +class MaskHead(nn.Module): + def __init__(self, hidden_dim, fpn_dims, num_frames): + super().__init__() + self.num_frames = num_frames + + self.lay1 = torch.nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1) + self.gn1 = torch.nn.GroupNorm(32, hidden_dim) + self.lay2 = torch.nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1) + self.gn2 = torch.nn.GroupNorm(32, hidden_dim) + self.lay3 = torch.nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1) + self.gn3 = torch.nn.GroupNorm(32, hidden_dim) + self.out_lay = DepthwiseSeparableConv2d(hidden_dim, hidden_dim, 5, padding=2, activation1=F.relu, activation2=F.relu) + + self.adapter1 = torch.nn.Conv2d(fpn_dims[0], hidden_dim, 1) + self.adapter2 = torch.nn.Conv2d(fpn_dims[1], hidden_dim, 1) + + self.convert_to_weight = MLP(hidden_dim, hidden_dim, hidden_dim, 3) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_uniform_(m.weight, a=1) + nn.init.constant_(m.bias, 0) + + def forward(self, x: Tensor, fpns: List[Tensor], tq: Tensor): + x = self.lay1(x) #先经过spatial decoder(实际为一个FPN结构) + x = self.gn1(x) #[40, 256, 15, 27] + + cur_fpn = self.adapter1(fpns[0]) #backone后的res4先经过1*1卷积运算 为 [40, 256, 30, 54] + x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay2(x) + x = self.gn2(x) + + cur_fpn = self.adapter2(fpns[1]) #backone后的res3先经过1*1卷积运算 为 [40, 256, 60, 107] + x = cur_fpn + F.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay3(x) + x = self.gn3(x) + x = F.relu(x) #[40, 256, 60, 107] + + BT, C, H, W = x.shape + L, B, N, C = tq.shape #[3, 8, 100, 256] L表示经过了L次的trans decoder + T = BT // B + + x = self.out_lay(x) #[40, 256, 60, 107] + w = self.convert_to_weight(tq).permute(1,0,2,3) #[8, 3, 100, 256] + w = w.unsqueeze(1).repeat(1,T,1,1,1) #[8, 5, 3, 100, 256] + #B*T*L*N == 12000 下面就是组卷积操作 + mask_logits = F.conv2d(x.view(1, BT*C, H, W), w.reshape(B*T*L*N, C, 1, 1), groups=BT) #[1, 12000, 60, 107] + mask_logits = mask_logits.view(B, T, L, N, H, W).permute(2, 0, 3, 1, 4, 5) + + return mask_logits + + +def dice_coef(inputs, targets): + # inputs = inputs.float().sigmoid() + inputs = inputs.sigmoid() + inputs = inputs.flatten(1).unsqueeze(1) + targets = targets.flatten(1).unsqueeze(0) + numerator = 2 * (inputs * targets).sum(2) + denominator = inputs.sum(-1) + targets.sum(-1) + + # NOTE coef doesn't be subtracted to 1 as it is not necessary for computing costs + coef = (numerator + 1) / (denominator + 1) + return coef + + +def dice_loss(inputs, targets, num_boxes): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_boxes + + +def sigmoid_focal_coef(inputs, targets, alpha: float = 0.25, gamma: float = 2): + N, M = len(inputs), len(targets) + inputs = inputs.flatten(1).unsqueeze(1).expand(-1, M, -1) + targets = targets.flatten(1).unsqueeze(0).expand(N, -1, -1) + + prob = inputs.sigmoid() + ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + p_t = prob * targets + (1 - prob) * (1 - targets) + coef = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + coef = alpha_t * coef + + return coef.mean(2) + + +def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + alpha: (optional) Weighting factor in range (0,1) to balance + positive vs negative examples. Default = -1 (no weighting). + gamma: Exponent of the modulating factor (1 - p_t) to + balance easy vs hard examples. + Returns: + Loss tensor + """ + prob = inputs.sigmoid() + ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + p_t = prob * targets + (1 - prob) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + return loss.mean(1).sum() / num_boxes + + +def segmentation_postprocess(results, output_height, output_width, mask_threshold=0.5): + """ + For instance segmentation whose masks are size of batched output, + not regional sizes as from R-CNN based predictor. + """ + scale_x, scale_y = (float(output_width) / results.image_size[1], float(output_height) / results.image_size[0]) + results = Instances((output_height, output_width), **results.get_fields()) + + if results.has("pred_boxes"): + output_boxes = results.pred_boxes + elif results.has("proposal_boxes"): + output_boxes = results.proposal_boxes + + output_boxes.scale(scale_x, scale_y) + output_boxes.clip(results.image_size) + + if results.has("pred_masks"): + results.pred_masks = interpolate( + results.pred_masks.float().unsqueeze(1), size=(output_height, output_width), + mode='bilinear' + ).squeeze(1) > 0.5 + + results = results[output_boxes.nonempty()] + + return results diff --git a/Mask2Former/mask2former_video/ifc/models/transformer.py b/Mask2Former/mask2former_video/ifc/models/transformer.py new file mode 100644 index 0000000..acec3aa --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/models/transformer.py @@ -0,0 +1,342 @@ +""" +https://github.com/facebookresearch/detr +""" +import copy +from typing import Optional +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +import sys, os +sys.path.append(os.getcwd()) + +from mask2former_video.config import * + +# projects/VITA/Mask2Former/mask2former_video/config.py + +class IFCTransformer(nn.Module): + + def __init__(self, num_frames, d_model=256, nhead=8, + num_encoder_layers=3, num_decoder_layers=3, + num_memory_bus=8, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False, + return_intermediate_dec=False): + super().__init__() + self.num_frames = num_frames + self.num_memory_bus = num_memory_bus + encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward,dropout, activation, normalize_before) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = IFCEncoder(num_frames, encoder_layer, num_encoder_layers, encoder_norm) + decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before) + decoder_norm = nn.LayerNorm(d_model) + self.clip_decoder = IFCDecoder(decoder_layer, num_decoder_layers, num_frames, decoder_norm, return_intermediate=return_intermediate_dec) + self._reset_parameters() #默认初始化 + self.return_intermediate_dec = return_intermediate_dec + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def pad_zero(self, x, pad, dim=0): + if x is None: + return None + pad_shape = list(x.shape) + pad_shape[dim] = pad + return torch.cat((x, x.new_zeros(pad_shape)), dim=dim) + + def forward(self, window_tokens, query_embed, is_train): + # prepare for enc-dec + # bs = src.shape[0] // self.num_frames if is_train else 1 + # t = src.shape[0] // bs + # _, c, h, w = src.shape + + # encoder + # src = src.view(bs*t, c, h*w).permute(2, 0, 1) # HW, BT, C + # frame_pos = pos_embed.view(bs*t, c, h*w).permute(2, 0, 1) # HW, BT, C + # frame_mask = mask.view(bs*t, h*w) # BT, HW + + src = self.encoder(window_tokens, is_train=is_train) + + # decoder #*****此处与之前Mask2Former交接******** + # t = size_window + # bs = window_tokens.shape[1]//t + # c = window_tokens.shape[2] + # n = window_tokens.shape[0] + # dec_src = src.view(h*w, bs, t, c).permute(2, 0, 1, 3).flatten(0,1) # THW, B, C + query_embed = query_embed.unsqueeze(1).repeat(1, window_tokens.shape[1], 1) # N, B, C + tgt = torch.zeros_like(query_embed) + + # dec_pos = pos_embed.view(bs, t, c, h*w).permute(1, 3, 0, 2).flatten(0,1) # THW, B, C + # dec_mask = mask.view(bs, t*h*w) # B, THW + #经过三层transformer decoder + clip_hs = self.clip_decoder(tgt, src, query_pos=query_embed, is_train=is_train) #[3, 100, 8, 256] + + # ret_memory = src.permute(1,2,0).reshape(bs*t, c, h, w) + + return clip_hs + + +class IFCEncoder(nn.Module): + + def __init__(self, num_frames, encoder_layer, num_layers, norm=None): + super().__init__() + self.num_frames = num_frames + self.num_layers = num_layers + self.enc_layers = _get_clones(encoder_layer, num_layers) + self.bus_layers = _get_clones(encoder_layer, num_layers) + norm = [copy.deepcopy(norm) for i in range(2)] + self.out_norm, self.bus_norm = norm + + def pad_zero(self, x, pad, dim=0): + if x is None: + return None + pad_shape = list(x.shape) + pad_shape[dim] = pad + return torch.cat((x, x.new_zeros(pad_shape)), dim=dim) + + def forward(self, output, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + is_train: bool = True): + # bs = src.shape[1] // self.num_frames if is_train else 1 + # t = src.shape[1] // bs + # hw, _, c = src.shape + # M = len(memory_bus) + + # memory_bus = memory_bus[:, None, :].repeat(1, bs*t, 1) + # memory_pos = memory_pos[:, None, :].repeat(1, bs*t, 1) + + # pos = torch.cat((pos, memory_pos)) + # mask = self.pad_zero(mask, dim=1) + # src_key_padding_mask = self.pad_zero(src_key_padding_mask, dim=1) + + # output = src + + for layer_idx in range(self.num_layers): + # output = torch.cat((output, memory_bus)) + output = self.enc_layers[layer_idx](output, src_mask=mask, + src_key_padding_mask=src_key_padding_mask, pos=pos) + + # output, memory_bus = output[:hw, :, :], output[hw:, :, :] + + # memory_bus = memory_bus.view(M, bs, t, c).permute(2,1,0,3).flatten(1,2) # TxBMxC + # memory_bus = self.bus_layers[layer_idx](memory_bus) + # memory_bus = memory_bus.view(t, bs, M, c).permute(2,1,0,3).flatten(1,2) # MxBTxC + + if self.out_norm is not None: + output = self.out_norm(output) + # if self.bus_norm is not None: + # memory_bus = self.bus_norm(memory_bus) + + return output + + +class IFCDecoder(nn.Module): + + def __init__(self, decoder_layer, num_layers, num_frames, norm=None, return_intermediate=False): + super().__init__() + self.layers = _get_clones(decoder_layer, num_layers) + self.num_layers = num_layers + self.num_frames = num_frames + self.norm = norm + self.return_intermediate = return_intermediate + + def pad_zero(self, x, pad, dim=0): + if x is None: + return None + pad_shape = list(x.shape) + pad_shape[dim] = pad + return torch.cat((x, x.new_zeros(pad_shape)), dim=dim) + + def forward(self, tgt, dec_src, #tgt为object Decoder的输入video query，而dec_src为k与v + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + is_train: bool = True): + output = tgt #相当于 Object queries + return_intermediate = (self.return_intermediate and is_train) + intermediate = [] + + for layer in self.layers: + output = layer(output, dec_src, tgt_mask=tgt_mask, + memory_mask=memory_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + pos=pos, query_pos=query_pos) + if return_intermediate: + intermediate.append(self.norm(output)) + + if self.norm is not None: + output = self.norm(output) + if return_intermediate: + intermediate.pop() + intermediate.append(output) + + if return_intermediate: + return torch.stack(intermediate) + + return output.unsqueeze(0) + + +class TransformerEncoderLayer(nn.Module): + + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, src, src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(src, pos) + src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, + key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) #残差连接 + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src + + def forward_pre(self, src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + src2 = self.norm1(src) + q = k = self.with_pos_embed(src2, pos) + src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, + key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src2 = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) + src = src + self.dropout2(src2) + return src + + def forward(self, src, src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(src, src_mask, src_key_padding_mask, pos) + return self.forward_post(src, src_mask, src_key_padding_mask, pos) + + +class TransformerDecoderLayer(nn.Module): + + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + q = self.with_pos_embed(tgt, query_pos) #video queries + k = v = memory #object Tokens + tgt2 = self.self_attn(q, k, value=v)[0] + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout3(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward_pre(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout1(tgt2) + tgt2 = self.norm2(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + def forward(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, memory, tgt_mask, memory_mask, + tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) + return self.forward_post(tgt, memory, tgt_mask, memory_mask, + tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") diff --git a/Mask2Former/mask2former_video/ifc/structures/__pycache__/clip_output.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/structures/__pycache__/clip_output.cpython-38.pyc new file mode 100644 index 0000000..41d6567 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/structures/__pycache__/clip_output.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/structures/clip_output.py b/Mask2Former/mask2former_video/ifc/structures/clip_output.py new file mode 100644 index 0000000..443ad97 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/structures/clip_output.py @@ -0,0 +1,144 @@ +from typing import List +import torch +from scipy.optimize import linear_sum_assignment + +from detectron2.structures import Instances +from detectron2.utils.memory import retry_if_cuda_oom + +from ..util.misc import interpolate + + +class Videos: + """ + This structure is to support Section 3.3: Clip-level instance tracking. + NOTE most errors occuring in this structure is due to + the number of predictions exceeding num_max_inst. + TODO make further GPU-memory friendly while maintaining speed, + and max number of instances be dynamically changed. + """ + def __init__(self, num_frames, video_length, num_classes, image_size, device): + self.num_frames = num_frames + self.video_length = video_length + self.device = device + + num_max_inst = 50 + self.match_threshold = 0.05 + + self.num_inst = 0 + self.num_clip = 0 + self.saved_idx_set = set() + + self.saved_logits = torch.zeros((video_length, num_max_inst, self.video_length, *image_size), dtype=torch.float, device=device) + self.saved_masks = torch.zeros((video_length, num_max_inst, self.video_length, *image_size), dtype=torch.float, device=device) + self.saved_valid = torch.zeros((video_length, num_max_inst, self.video_length), dtype=torch.bool, device=device) + self.saved_cls = torch.zeros((video_length, num_max_inst, num_classes+1), dtype=torch.float, device=device) + + def get_siou(self, input_masks, saved_masks, saved_valid): + # input_masks : N_i, T, H, W + # saved_masks : C, N_s, T, H, W + # saved_valid : C, N_s, T + + input_masks = input_masks.flatten(-2) # N_i, T, HW + saved_masks = saved_masks.flatten(-2) # C, N_s, T, HW + + input_masks = input_masks[None, None] # 1, 1, N_i, T, HW + saved_masks = saved_masks.unsqueeze(2) # C, N_s, 1, T, HW + saved_valid = saved_valid[:, :, None, :, None] # C, N_s, 1, T, 1 + + # C, N_s, N_i, T, HW + numerator = saved_masks * input_masks + denominator = saved_masks + input_masks - saved_masks * input_masks + + numerator = (numerator * saved_valid).sum(dim=(-1, -2)) + denominator = (denominator * saved_valid).sum(dim=(-1, -2)) + + siou = numerator / (denominator + 1e-6) # C, N_s, N_i + + # To divide only the frames that are being compared + num_valid_clip = (saved_valid.flatten(2).sum(dim=2) > 0).sum(dim=0) # N_s, + + siou = siou.sum(dim=0) / (num_valid_clip[..., None] + 1e-6) + + return siou + + def update(self, input_clip): + # gather intersection + inter_input_idx, inter_saved_idx = [], [] + for o_i, f_i in enumerate(input_clip.frame_idx): + if f_i in self.saved_idx_set: + inter_input_idx.append(o_i) + inter_saved_idx.append(f_i) + + # compute sIoU + i_masks = input_clip.mask_probs[:, inter_input_idx] + s_masks = self.saved_masks[ + max(self.num_clip-len(input_clip.frame_idx), 0) : self.num_clip, : self.num_inst, inter_saved_idx + ] + s_valid = self.saved_valid[ + max(self.num_clip-len(input_clip.frame_idx), 0) : self.num_clip, : self.num_inst, inter_saved_idx + ] + + scores = self.get_siou(i_masks, s_masks, s_valid) # N_s, N_i + + # bipartite match + above_thres = scores > self.match_threshold + scores = scores * above_thres.float() + + row_idx, col_idx = linear_sum_assignment(scores.cpu(), maximize=True) + + existed_idx = [] + for is_above, r, c in zip(above_thres[row_idx, col_idx], row_idx, col_idx): + if not is_above: + continue + + self.saved_logits[self.num_clip, r, input_clip.frame_idx] = input_clip.mask_logits[c] + self.saved_masks[self.num_clip, r, input_clip.frame_idx] = input_clip.mask_probs[c] + self.saved_valid[self.num_clip, r, input_clip.frame_idx] = True + self.saved_cls[self.num_clip, r] = input_clip.cls_probs[c] + existed_idx.append(c) + + left_idx = [i for i in range(input_clip.num_instance) if i not in existed_idx] + self.saved_logits[self.num_clip, + self.num_inst:self.num_inst+len(left_idx), + input_clip.frame_idx] = input_clip.mask_logits[left_idx] + self.saved_masks[self.num_clip, + self.num_inst:self.num_inst+len(left_idx), + input_clip.frame_idx] = input_clip.mask_probs[left_idx] + self.saved_valid[self.num_clip, + self.num_inst:self.num_inst+len(left_idx), + input_clip.frame_idx] = True + self.saved_cls[self.num_clip, self.num_inst:self.num_inst+len(left_idx)] = input_clip.cls_probs[left_idx] + + # Update status + self.saved_idx_set.update(input_clip.frame_set) + self.num_clip += 1 + self.num_inst += len(left_idx) + + def get_result(self, image_size): + _mask_logits = self.saved_logits[:self.num_clip, :self.num_inst] + _valid = self.saved_valid[:self.num_clip, :self.num_inst] + _cls = self.saved_cls[:self.num_clip, :self.num_inst] + + _mask_logits = _mask_logits.sum(dim=0) / _valid.sum(dim=0)[..., None, None] + _mask_logits = retry_if_cuda_oom(interpolate)( + _mask_logits, size=image_size, mode="bilinear", align_corners=False + ) + + out_cls = _cls.sum(dim=0) / (_valid.sum(dim=2) > 0).sum(dim=0)[..., None] + out_masks = retry_if_cuda_oom(lambda x: x > 0.0)(_mask_logits) + + return out_cls, out_masks + + +class Clips: + def __init__(self, frame_idx: List[int], results: List[Instances]): + self.frame_idx = frame_idx + self.frame_set = set(frame_idx) + + self.classes = results.pred_classes + self.scores = results.scores + self.cls_probs = results.cls_probs + self.mask_logits = results.pred_masks + self.mask_probs = results.pred_masks.sigmoid() + + self.num_instance = len(self.scores) diff --git a/Mask2Former/mask2former_video/ifc/util/__init__.py b/Mask2Former/mask2former_video/ifc/util/__init__.py new file mode 100644 index 0000000..168f997 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/util/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved diff --git a/Mask2Former/mask2former_video/ifc/util/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/util/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..0eb87a5 Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/util/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/util/__pycache__/misc.cpython-38.pyc b/Mask2Former/mask2former_video/ifc/util/__pycache__/misc.cpython-38.pyc new file mode 100644 index 0000000..9257f5b Binary files /dev/null and b/Mask2Former/mask2former_video/ifc/util/__pycache__/misc.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/ifc/util/box_ops.py b/Mask2Former/mask2former_video/ifc/util/box_ops.py new file mode 100644 index 0000000..475676b --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/util/box_ops.py @@ -0,0 +1,156 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Utilities for bounding box manipulation and GIoU. +""" +import torch +from torchvision.ops.boxes import box_area + + +def box_cxcywh_to_xyxy(x): + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), + (x_c + 0.5 * w), (y_c + 0.5 * h)] + return torch.stack(b, dim=-1) + + +def box_xyxy_to_cxcywh(x): + x0, y0, x1, y1 = x.unbind(-1) + b = [(x0 + x1) / 2, (y0 + y1) / 2, + (x1 - x0), (y1 - y0)] + return torch.stack(b, dim=-1) + + +# modified from torchvision to also return the union +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + The boxes should be in [x0, y0, x1, y1] format + + Returns a [N, M] pairwise matrix, where N = len(boxes1) + and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + iou, union = box_iou(boxes1, boxes2) + + lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,M,2] + area = wh[:, :, 0] * wh[:, :, 1] + + return iou - (area - union) / area + + +def video_box_l1(out_bbox, tgt_bbox, valid): + """ + out_bbox: [N, num_frames, 4] + tgt_bbox: [M, num_frames, 4] + valid: [M, num_frames] + + out: [N, M] + """ + # NOTE subtraction with abs leads to slight difference with torch.cdist. + # However, as it is very subtle (nearly 1e-7), believe this would not harm the performance + out_bbox = out_bbox.unsqueeze(1) # [N, 1, num_frames, 4] + tgt_bbox = tgt_bbox.unsqueeze(0) # [1, M, num_frames, 4] + + cost_bbox = torch.abs(out_bbox - tgt_bbox).sum(dim=-1) + cost_bbox[:, ~valid] = 0.0 + + return cost_bbox.sum(dim=-1) / valid.float().sum(dim=-1) + + +def video_box_iou(boxes1, boxes2): + """ + boxes1: [N, num_frames, 4] + boxes2: [M, num_frames, 4] + + out: [N, M, numframes] + """ + num_frames = boxes1.shape[1] + N, M = len(boxes1), len(boxes2) + + area1 = box_area(boxes1.flatten(0,1)).view(N, num_frames) # [N, num_frames] + area2 = box_area(boxes2.flatten(0,1)).view(M, num_frames) # [M, num_frames] + + lt = torch.max(boxes1[:, None, :, :2], boxes2[:, :, :2]) # [N, M, num_frames, 2] + rb = torch.min(boxes1[:, None, :, 2:], boxes2[:, :, 2:]) # [N, M, num_frames, 2] + + wh = (rb - lt).clamp(min=0) # [N, M, num_frames, 2] + inter = wh[:, :, :, 0] * wh[:, :, :, 1] # [N, M, num_frames] + + union = area1[:, None, :] + area2 - inter + + iou = inter / union + return iou, union + + +def video_generalized_box_iou(out_bbox, tgt_bbox, valid): + """ + out_bbox: [N, num_frames, 4] + tgt_bbox: [M, num_frames, 4] + valid: [M, num_frames] + + out: [N, M] + """ + assert (out_bbox[..., 2:] >= out_bbox[..., :2]).all(), out_bbox + assert (tgt_bbox[..., 2:] >= tgt_bbox[..., :2]).all(), tgt_bbox + iou, union = video_box_iou(out_bbox, tgt_bbox) # [N, M, num_frames] respectively + + lt = torch.min(out_bbox[:, None, :, :2], tgt_bbox[:, :, :2]) + rb = torch.max(out_bbox[:, None, :, 2:], tgt_bbox[:, :, 2:]) + + wh = (rb - lt).clamp(min=0) # [N, M, num_frames, 2] + area = wh[:, :, :, 0] * wh[:, :, :, 1] # [N, M, num_frames] + + giou = iou - (area - union) / area # [N, M, num_frames] + giou[:, ~valid] = 0.0 + + return giou.sum(dim=-1) / valid.float().sum(dim=-1) + + +def masks_to_boxes(masks): + """Compute the bounding boxes around the provided masks + + The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. + + Returns a [N, 4] tensors, with the boxes in xyxy format + """ + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device) + + h, w = masks.shape[-2:] + + y = torch.arange(0, h, dtype=torch.float) + x = torch.arange(0, w, dtype=torch.float) + y, x = torch.meshgrid(y, x) + + x_mask = (masks * x.unsqueeze(0)) + x_max = x_mask.flatten(1).max(-1)[0] + x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + y_mask = (masks * y.unsqueeze(0)) + y_max = y_mask.flatten(1).max(-1)[0] + y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + return torch.stack([x_min, y_min, x_max, y_max], 1) diff --git a/Mask2Former/mask2former_video/ifc/util/misc.py b/Mask2Former/mask2former_video/ifc/util/misc.py new file mode 100644 index 0000000..a6495e8 --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/util/misc.py @@ -0,0 +1,474 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +import os +import subprocess +import time +from collections import defaultdict, deque +import datetime +import pickle +from typing import Optional, List + +import torch +import torch.distributed as dist +from torch import Tensor + +# needed due to empty tensor bug in pytorch and torchvision 0.5 +import torchvision +if float(torchvision.__version__.split(".")[1]) < 7.0: + from torchvision.ops import _new_empty_tensor + from torchvision.ops.misc import _output_size + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device="cuda") + size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.4f}') + data_time = SmoothedValue(fmt='{avg:.4f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + if torch.cuda.is_available(): + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}', + 'max mem: {memory:.0f}' + ]) + else: + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ]) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('{} Total time: {} ({:.4f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() + sha = 'N/A' + diff = "clean" + branch = 'N/A' + try: + sha = _run(['git', 'rev-parse', 'HEAD']) + subprocess.check_output(['git', 'diff'], cwd=cwd) + diff = _run(['git', 'diff-index', 'HEAD']) + diff = "has uncommited changes" if diff else "clean" + branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def collate_fn(batch): + batch = list(zip(*batch)) + batch[0] = nested_tensor_from_tensor_list(batch[0]) + return tuple(batch) + + +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device): + # type: (Device) -> NestedTensor # noqa + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + def __getitem__(self, item) -> "NestedTensor": + if isinstance(item, int): + return NestedTensor(self.tensors[[item]], self.mask[[item]]) + t = self.tensors[item] + m = self.mask[item] + return NestedTensor(t, m) + + def __repr__(self): + return str(self.tensors) + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + # TODO make this more general + if tensor_list[0].ndim == 3: + if torchvision._is_tracing(): + # nested_tensor_from_tensor_list() does not export well to ONNX + # call _onnx_nested_tensor_from_tensor_list() instead + return _onnx_nested_tensor_from_tensor_list(tensor_list) + + # TODO make it support different-sized images + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], :img.shape[2]] = False + else: + raise ValueError('not supported') + return NestedTensor(tensor, mask) + + +# _onnx_nested_tensor_from_tensor_list() is an implementation of +# nested_tensor_from_tensor_list() that is supported by ONNX tracing. +@torch.jit.unused +def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: + max_size = [] + for i in range(tensor_list[0].dim()): + max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64) + max_size.append(max_size_i) + max_size = tuple(max_size) + + # work around for + # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + # m[: img.shape[1], :img.shape[2]] = False + # which is not yet supported in onnx + padded_imgs = [] + padded_masks = [] + for img in tensor_list: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) + padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) + padded_masks.append(padded_mask.to(torch.bool)) + + tensor = torch.stack(padded_imgs) + mask = torch.stack(padded_masks) + + return NestedTensor(tensor, mask=mask) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = int(os.environ['LOCAL_RANK']) + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.rank % torch.cuda.device_count() + else: + print('Not using distributed mode') + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = 'nccl' + print('| distributed init (rank {}): {}'.format( + args.rank, args.dist_url), flush=True) + torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + torch.distributed.barrier() + setup_for_distributed(args.rank == 0) + + +@torch.no_grad() +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + if target.numel() == 0: + return [torch.zeros([], device=output.device)] + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor + """ + Equivalent to nn.functional.interpolate, but with support for empty batch sizes. + This will eventually be supported natively by PyTorch, and this + class can go away. + """ + if float(torchvision.__version__.split(".")[1]) < 7.0: + if input.numel() > 0: + return torch.nn.functional.interpolate( + input, size, scale_factor, mode, align_corners + ) + + output_shape = _output_size(2, input, size, scale_factor) + output_shape = list(input.shape[:-2]) + list(output_shape) + return _new_empty_tensor(input, output_shape) + else: + return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/Mask2Former/mask2former_video/ifc/util/plot_utils.py b/Mask2Former/mask2former_video/ifc/util/plot_utils.py new file mode 100644 index 0000000..0f24bed --- /dev/null +++ b/Mask2Former/mask2former_video/ifc/util/plot_utils.py @@ -0,0 +1,107 @@ +""" +Plotting utilities to visualize training logs. +""" +import torch +import pandas as pd +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt + +from pathlib import Path, PurePath + + +def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'): + ''' + Function to plot specific fields from training log(s). Plots both training and test results. + + :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file + - fields = which results to plot from each log file - plots both training and test for each field. + - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots + - log_name = optional, name of log file if different than default 'log.txt'. + + :: Outputs - matplotlib plots of results in fields, color coded for each log file. + - solid lines are training results, dashed lines are test results. + + ''' + func_name = "plot_utils.py::plot_logs" + + # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path, + # convert single Path to list to avoid 'not iterable' error + + if not isinstance(logs, list): + if isinstance(logs, PurePath): + logs = [logs] + print(f"{func_name} info: logs param expects a list argument, converted to list[Path].") + else: + raise ValueError(f"{func_name} - invalid argument for logs parameter.\n \ + Expect list[Path] or single Path obj, received {type(logs)}") + + # Quality checks - verify valid dir(s), that every item in list is Path object, and that log_name exists in each dir + for i, dir in enumerate(logs): + if not isinstance(dir, PurePath): + raise ValueError(f"{func_name} - non-Path object in logs argument of {type(dir)}: \n{dir}") + if not dir.exists(): + raise ValueError(f"{func_name} - invalid directory in logs argument:\n{dir}") + # verify log_name exists + fn = Path(dir / log_name) + if not fn.exists(): + print(f"-> missing {log_name}. Have you gotten to Epoch 1 in training?") + print(f"--> full path of missing log file: {fn}") + return + + # load log file(s) and plot + dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs] + + fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5)) + + for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))): + for j, field in enumerate(fields): + if field == 'mAP': + coco_eval = pd.DataFrame( + np.stack(df.test_coco_eval_bbox.dropna().values)[:, 1] + ).ewm(com=ewm_col).mean() + axs[j].plot(coco_eval, c=color) + else: + df.interpolate().ewm(com=ewm_col).mean().plot( + y=[f'train_{field}', f'test_{field}'], + ax=axs[j], + color=[color] * 2, + style=['-', '--'] + ) + for ax, field in zip(axs, fields): + ax.legend([Path(p).name for p in logs]) + ax.set_title(field) + + +def plot_precision_recall(files, naming_scheme='iter'): + if naming_scheme == 'exp_id': + # name becomes exp_id + names = [f.parts[-3] for f in files] + elif naming_scheme == 'iter': + names = [f.stem for f in files] + else: + raise ValueError(f'not supported {naming_scheme}') + fig, axs = plt.subplots(ncols=2, figsize=(16, 5)) + for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names): + data = torch.load(f) + # precision is n_iou, n_points, n_cat, n_area, max_det + precision = data['precision'] + recall = data['params'].recThrs + scores = data['scores'] + # take precision for all classes, all areas and 100 detections + precision = precision[0, :, :, 0, -1].mean(1) + scores = scores[0, :, :, 0, -1].mean(1) + prec = precision.mean() + rec = data['recall'][0, :, 0, -1].mean() + print(f'{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, ' + + f'score={scores.mean():0.3f}, ' + + f'f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}' + ) + axs[0].plot(recall, precision, c=color) + axs[1].plot(recall, scores, c=color) + + axs[0].set_title('Precision / Recall') + axs[0].legend(names) + axs[1].set_title('Scores / Recall') + axs[1].legend(names) + return fig, axs diff --git a/Mask2Former/mask2former_video/modeling/__init__.py b/Mask2Former/mask2former_video/modeling/__init__.py new file mode 100644 index 0000000..9cc855f --- /dev/null +++ b/Mask2Former/mask2former_video/modeling/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .transformer_decoder.video_mask2former_transformer_decoder import VideoMultiScaleMaskedTransformerDecoder diff --git a/Mask2Former/mask2former_video/modeling/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/modeling/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..92e6b3e Binary files /dev/null and b/Mask2Former/mask2former_video/modeling/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/modeling/__pycache__/criterion.cpython-38.pyc b/Mask2Former/mask2former_video/modeling/__pycache__/criterion.cpython-38.pyc new file mode 100644 index 0000000..9615c8d Binary files /dev/null and b/Mask2Former/mask2former_video/modeling/__pycache__/criterion.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/modeling/__pycache__/matcher.cpython-38.pyc b/Mask2Former/mask2former_video/modeling/__pycache__/matcher.cpython-38.pyc new file mode 100644 index 0000000..6b79e3d Binary files /dev/null and b/Mask2Former/mask2former_video/modeling/__pycache__/matcher.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/modeling/criterion.py b/Mask2Former/mask2former_video/modeling/criterion.py new file mode 100644 index 0000000..176a55c --- /dev/null +++ b/Mask2Former/mask2former_video/modeling/criterion.py @@ -0,0 +1,259 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py +""" +MaskFormer criterion. +""" +import logging + +import torch +import torch.nn.functional as F +from torch import nn + +from detectron2.utils.comm import get_world_size +from detectron2.projects.point_rend.point_features import ( + get_uncertain_point_coords_with_randomness, + point_sample, +) + +from mask2former.utils.misc import is_dist_avail_and_initialized + + +def dice_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(-1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_masks + + +dice_loss_jit = torch.jit.script( + dice_loss +) # type: torch.jit.ScriptModule + + +def sigmoid_ce_loss( + inputs: torch.Tensor, + targets: torch.Tensor, + num_masks: float, + ): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + + return loss.mean(1).sum() / num_masks + + +sigmoid_ce_loss_jit = torch.jit.script( + sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +def calculate_uncertainty(logits): + """ + We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the + foreground class in `classes`. + Args: + logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or + class-agnostic, where R is the total number of predicted masks in all images and C is + the number of foreground classes. The values are logits. + Returns: + scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with + the most uncertain locations having the highest uncertainty score. + """ + assert logits.shape[1] == 1 + gt_class_logits = logits.clone() + return -(torch.abs(gt_class_logits)) + + +class VideoSetCriterion(nn.Module): + """This class computes the loss for DETR. + The process happens in two steps: + 1) we compute hungarian assignment between ground truth boxes and the outputs of the model + 2) we supervise each pair of matched ground-truth / prediction (supervise class and box) + """ + + def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses, + num_points, oversample_ratio, importance_sample_ratio): + """Create the criterion. + Parameters: + num_classes: number of object categories, omitting the special no-object category + matcher: module able to compute a matching between targets and proposals + weight_dict: dict containing as key the names of the losses and as values their relative weight. + eos_coef: relative classification weight applied to the no-object category + losses: list of all the losses to be applied. See get_loss for list of available losses. + """ + super().__init__() + self.num_classes = num_classes + self.matcher = matcher + self.weight_dict = weight_dict + self.eos_coef = eos_coef + self.losses = losses + empty_weight = torch.ones(self.num_classes + 1) + empty_weight[-1] = self.eos_coef + self.register_buffer("empty_weight", empty_weight) + + # pointwise mask loss parameters + self.num_points = num_points + self.oversample_ratio = oversample_ratio + self.importance_sample_ratio = importance_sample_ratio + + def loss_labels(self, outputs, targets, indices, num_masks): + """Classification loss (NLL) + targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] + """ + assert "pred_logits" in outputs + src_logits = outputs["pred_logits"].float() + + idx = self._get_src_permutation_idx(indices) + target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device + ) + target_classes[idx] = target_classes_o + + loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight) + losses = {"loss_ce": loss_ce} + return losses + + def loss_masks(self, outputs, targets, indices, num_masks): + """Compute the losses related to the masks: the focal loss and the dice loss. + targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] + """ + assert "pred_masks" in outputs + + src_idx = self._get_src_permutation_idx(indices) + src_masks = outputs["pred_masks"] + src_masks = src_masks[src_idx] + # Modified to handle video + target_masks = torch.cat([t['masks'][i] for t, (_, i) in zip(targets, indices)]).to(src_masks) + + # No need to upsample predictions as we are using normalized coordinates :) + # NT x 1 x H x W + src_masks = src_masks.flatten(0, 1)[:, None] + target_masks = target_masks.flatten(0, 1)[:, None] + + with torch.no_grad(): + # sample point_coords + point_coords = get_uncertain_point_coords_with_randomness( + src_masks, + lambda logits: calculate_uncertainty(logits), + self.num_points, + self.oversample_ratio, + self.importance_sample_ratio, + ) + # get gt labels + point_labels = point_sample( + target_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + point_logits = point_sample( + src_masks, + point_coords, + align_corners=False, + ).squeeze(1) + + losses = { + "loss_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks), + "loss_dice": dice_loss_jit(point_logits, point_labels, num_masks), + } + + del src_masks + del target_masks + return losses + + def _get_src_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + src_idx = torch.cat([src for (src, _) in indices]) + return batch_idx, src_idx + + def _get_tgt_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + tgt_idx = torch.cat([tgt for (_, tgt) in indices]) + return batch_idx, tgt_idx + + def get_loss(self, loss, outputs, targets, indices, num_masks): + loss_map = { + 'labels': self.loss_labels, + 'masks': self.loss_masks, + } + assert loss in loss_map, f"do you really want to compute {loss} loss?" + return loss_map[loss](outputs, targets, indices, num_masks) + + def forward(self, outputs, targets): + """This performs the loss computation. + Parameters: + outputs: dict of tensors, see the output specification of the model for the format + targets: list of dicts, such that len(targets) == batch_size. + The expected keys in each dict depends on the losses applied, see each loss' doc + """ + outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets) + + # Compute the average number of target boxes accross all nodes, for normalization purposes + num_masks = sum(len(t["labels"]) for t in targets) + num_masks = torch.as_tensor( + [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device + ) + if is_dist_avail_and_initialized(): + torch.distributed.all_reduce(num_masks) + num_masks = torch.clamp(num_masks / get_world_size(), min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + losses.update(self.get_loss(loss, outputs, targets, indices, num_masks)) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if "aux_outputs" in outputs: + for i, aux_outputs in enumerate(outputs["aux_outputs"]): + indices = self.matcher(aux_outputs, targets) + for loss in self.losses: + l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses + + def __repr__(self): + head = "Criterion " + self.__class__.__name__ + body = [ + "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)), + "losses: {}".format(self.losses), + "weight_dict: {}".format(self.weight_dict), + "num_classes: {}".format(self.num_classes), + "eos_coef: {}".format(self.eos_coef), + "num_points: {}".format(self.num_points), + "oversample_ratio: {}".format(self.oversample_ratio), + "importance_sample_ratio: {}".format(self.importance_sample_ratio), + ] + _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/Mask2Former/mask2former_video/modeling/matcher.py b/Mask2Former/mask2former_video/modeling/matcher.py new file mode 100644 index 0000000..642f360 --- /dev/null +++ b/Mask2Former/mask2former_video/modeling/matcher.py @@ -0,0 +1,189 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/matcher.py +""" +Modules to compute the matching cost and solve the corresponding LSAP. +""" +import torch +import torch.nn.functional as F +from scipy.optimize import linear_sum_assignment +from torch import nn +from torch.cuda.amp import autocast + +from detectron2.projects.point_rend.point_features import point_sample + + +def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor): + """ + Compute the DICE loss, similar to generalized IOU for masks + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets) + denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :] + loss = 1 - (numerator + 1) / (denominator + 1) + return loss + + +batch_dice_loss_jit = torch.jit.script( + batch_dice_loss +) # type: torch.jit.ScriptModule + + +def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor): + """ + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs + (0 for the negative class and 1 for the positive class). + Returns: + Loss tensor + """ + hw = inputs.shape[1] + + pos = F.binary_cross_entropy_with_logits( + inputs, torch.ones_like(inputs), reduction="none" + ) + neg = F.binary_cross_entropy_with_logits( + inputs, torch.zeros_like(inputs), reduction="none" + ) + + loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum( + "nc,mc->nm", neg, (1 - targets) + ) + + return loss / hw + + +batch_sigmoid_ce_loss_jit = torch.jit.script( + batch_sigmoid_ce_loss +) # type: torch.jit.ScriptModule + + +class VideoHungarianMatcher(nn.Module): + """This class computes an assignment between the targets and the predictions of the network + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, + there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, + while the others are un-matched (and thus treated as non-objects). + """ + + def __init__(self, cost_class: float = 1, cost_mask: float = 1, cost_dice: float = 1, num_points: int = 0): + """Creates the matcher + + Params: + cost_class: This is the relative weight of the classification error in the matching cost + cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost + cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost + """ + super().__init__() + self.cost_class = cost_class + self.cost_mask = cost_mask + self.cost_dice = cost_dice + + assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0" + + self.num_points = num_points + + @torch.no_grad() + def memory_efficient_forward(self, outputs, targets): + """More memory-friendly matching""" + bs, num_queries = outputs["pred_logits"].shape[:2] + + indices = [] + + # Iterate through batch size + for b in range(bs): + + out_prob = outputs["pred_logits"][b].softmax(-1) # [num_queries, num_classes] + tgt_ids = targets[b]["labels"] + + # Compute the classification cost. Contrary to the loss, we don't use the NLL, + # but approximate it in 1 - proba[target class]. + # The 1 is a constant that doesn't change the matching, it can be ommitted. + cost_class = -out_prob[:, tgt_ids] + + out_mask = outputs["pred_masks"][b] # [num_queries, T, H_pred, W_pred] + # gt masks are already padded when preparing target + tgt_mask = targets[b]["masks"].to(out_mask) # [num_gts, T, H_pred, W_pred] + + # out_mask = out_mask[:, None] + # tgt_mask = tgt_mask[:, None] + # all masks share the same set of points for efficient matching! + point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device) + # get gt labels + tgt_mask = point_sample( + tgt_mask, + point_coords.repeat(tgt_mask.shape[0], 1, 1), + align_corners=False, + ).flatten(1) + + out_mask = point_sample( + out_mask, + point_coords.repeat(out_mask.shape[0], 1, 1), + align_corners=False, + ).flatten(1) + + with autocast(enabled=False): + out_mask = out_mask.float() + tgt_mask = tgt_mask.float() + # Compute the focal loss between masks + cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask) + + # Compute the dice loss betwen masks + cost_dice = batch_dice_loss_jit(out_mask, tgt_mask) + + # Final cost matrix + C = ( + self.cost_mask * cost_mask + + self.cost_class * cost_class + + self.cost_dice * cost_dice + ) + C = C.reshape(num_queries, -1).cpu() + + indices.append(linear_sum_assignment(C)) + + return [ + (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) + for i, j in indices + ] + + @torch.no_grad() + def forward(self, outputs, targets): + """Performs the matching + + Params: + outputs: This is a dict that contains at least these entries: + "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + "pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks + + targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing: + "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth + objects in the target) containing the class labels + "masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks + + Returns: + A list of size batch_size, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: + len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + return self.memory_efficient_forward(outputs, targets) + + def __repr__(self, _repr_indent=4): + head = "Matcher " + self.__class__.__name__ + body = [ + "cost_class: {}".format(self.cost_class), + "cost_mask: {}".format(self.cost_mask), + "cost_dice: {}".format(self.cost_dice), + ] + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) diff --git a/Mask2Former/mask2former_video/modeling/transformer_decoder/__init__.py b/Mask2Former/mask2former_video/modeling/transformer_decoder/__init__.py new file mode 100644 index 0000000..1fe7ecf --- /dev/null +++ b/Mask2Former/mask2former_video/modeling/transformer_decoder/__init__.py @@ -0,0 +1,2 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +from .video_mask2former_transformer_decoder import VideoMultiScaleMaskedTransformerDecoder \ No newline at end of file diff --git a/Mask2Former/mask2former_video/modeling/transformer_decoder/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/modeling/transformer_decoder/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..f7aefa4 Binary files /dev/null and b/Mask2Former/mask2former_video/modeling/transformer_decoder/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/modeling/transformer_decoder/__pycache__/position_encoding.cpython-38.pyc b/Mask2Former/mask2former_video/modeling/transformer_decoder/__pycache__/position_encoding.cpython-38.pyc new file mode 100644 index 0000000..9322191 Binary files /dev/null and b/Mask2Former/mask2former_video/modeling/transformer_decoder/__pycache__/position_encoding.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/modeling/transformer_decoder/__pycache__/video_mask2former_transformer_decoder.cpython-38.pyc b/Mask2Former/mask2former_video/modeling/transformer_decoder/__pycache__/video_mask2former_transformer_decoder.cpython-38.pyc new file mode 100644 index 0000000..f86cb0d Binary files /dev/null and b/Mask2Former/mask2former_video/modeling/transformer_decoder/__pycache__/video_mask2former_transformer_decoder.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/modeling/transformer_decoder/position_encoding.py b/Mask2Former/mask2former_video/modeling/transformer_decoder/position_encoding.py new file mode 100644 index 0000000..8711011 --- /dev/null +++ b/Mask2Former/mask2former_video/modeling/transformer_decoder/position_encoding.py @@ -0,0 +1,110 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py +""" +Various positional encodings for the transformer. +""" +import math +import torch +from torch import nn + + +class PositionEmbeddingSine3D(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + # b, t, c, h, w + assert x.dim() == 5, f"{x.shape} should be a 5-dimensional Tensor, got {x.dim()}-dimensional Tensor instead" + if mask is None: + mask = torch.zeros((x.size(0), x.size(1), x.size(3), x.size(4)), device=x.device, dtype=torch.bool) + not_mask = ~mask + z_embed = not_mask.cumsum(1, dtype=torch.float32) + y_embed = not_mask.cumsum(2, dtype=torch.float32) + x_embed = not_mask.cumsum(3, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + z_embed = z_embed / (z_embed[:, -1:, :, :] + eps) * self.scale + y_embed = y_embed / (y_embed[:, :, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + dim_t_z = torch.arange((self.num_pos_feats * 2), dtype=torch.float32, device=x.device) + dim_t_z = self.temperature ** (2 * (dim_t_z // 2) / (self.num_pos_feats * 2)) + + pos_x = x_embed[:, :, :, :, None] / dim_t + pos_y = y_embed[:, :, :, :, None] / dim_t + pos_z = z_embed[:, :, :, :, None] / dim_t_z + pos_x = torch.stack((pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()), dim=5).flatten(4) + pos_y = torch.stack((pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()), dim=5).flatten(4) + pos_z = torch.stack((pos_z[:, :, :, :, 0::2].sin(), pos_z[:, :, :, :, 1::2].cos()), dim=5).flatten(4) + pos = (torch.cat((pos_y, pos_x), dim=4) + pos_z).permute(0, 1, 4, 2, 3) # b, t, c, h, w + return pos + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + if mask is None: + mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + def __repr__(self, _repr_indent=4): + head = "Positional encoding " + self.__class__.__name__ + body = [ + "num_pos_feats: {}".format(self.num_pos_feats), + "temperature: {}".format(self.temperature), + "normalize: {}".format(self.normalize), + "scale: {}".format(self.scale), + ] + # _repr_indent = 4 + lines = [head] + [" " * _repr_indent + line for line in body] + return "\n".join(lines) \ No newline at end of file diff --git a/Mask2Former/mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py b/Mask2Former/mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py new file mode 100644 index 0000000..baa3e8f --- /dev/null +++ b/Mask2Former/mask2former_video/modeling/transformer_decoder/video_mask2former_transformer_decoder.py @@ -0,0 +1,508 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py +import logging +import fvcore.nn.weight_init as weight_init +from typing import Optional +import torch +from torch import nn, Tensor +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.layers import Conv2d + +from mask2former.modeling.transformer_decoder.maskformer_transformer_decoder import TRANSFORMER_DECODER_REGISTRY + +from .position_encoding import PositionEmbeddingSine3D +from .position_encoding import PositionEmbeddingSine + + +class SelfAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + return self.forward_post(tgt, tgt_mask, + tgt_key_padding_mask, query_pos) + + +class CrossAttentionLayer(nn.Module): + + def __init__(self, d_model, nhead, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + + return tgt + + def forward_pre(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout(tgt2) + + return tgt + + def forward(self, tgt, memory, + memory_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + return self.forward_post(tgt, memory, memory_mask, + memory_key_padding_mask, pos, query_pos) + + +class FFNLayer(nn.Module): + + def __init__(self, d_model, dim_feedforward=2048, dropout=0.0, + activation="relu", normalize_before=False): + super().__init__() + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm = nn.LayerNorm(d_model) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt): + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout(tgt2) + tgt = self.norm(tgt) + return tgt + + def forward_pre(self, tgt): + tgt2 = self.norm(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout(tgt2) + return tgt + + def forward(self, tgt): + if self.normalize_before: + return self.forward_pre(tgt) + return self.forward_post(tgt) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") + + +class MLP(nn.Module): + """ Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +@TRANSFORMER_DECODER_REGISTRY.register() +class VideoMultiScaleMaskedTransformerDecoder(nn.Module): + + _version = 2 + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + version = local_metadata.get("version", None) + if version is None or version < 2: + # Do not warn if train from scratch + scratch = True + logger = logging.getLogger(__name__) + for k in list(state_dict.keys()): + newk = k + if "static_query" in k: + newk = k.replace("static_query", "query_feat") + if newk != k: + state_dict[newk] = state_dict[k] + del state_dict[k] + scratch = False + + if not scratch: + logger.warning( + f"Weight format of {self.__class__.__name__} have changed! " + "Please upgrade your models. Applying automatic conversion now ..." + ) + + @configurable + def __init__( + self, + in_channels, + mask_classification=True, + *, + num_classes: int, + hidden_dim: int, + num_queries: int, + nheads: int, + dim_feedforward: int, + dec_layers: int, + pre_norm: bool, + mask_dim: int, + enforce_input_project: bool, + # video related + num_frames, + ): + """ + NOTE: this interface is experimental. + Args: + in_channels: channels of the input features + mask_classification: whether to add mask classifier or not + num_classes: number of classes + hidden_dim: Transformer feature dimension + num_queries: number of queries + nheads: number of heads + dim_feedforward: feature dimension in feedforward network + enc_layers: number of Transformer encoder layers + dec_layers: number of Transformer decoder layers + pre_norm: whether to use pre-LayerNorm or not + mask_dim: mask feature dimension + enforce_input_project: add input project 1x1 conv even if input + channels and hidden dim is identical + """ + super().__init__() + + assert mask_classification, "Only support mask classification model" + self.mask_classification = mask_classification + + self.num_frames = num_frames + + # positional encoding + N_steps = hidden_dim // 2 + # self.pe_layer = PositionEmbeddingSine3D(N_steps, normalize=True) + self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True) #改为2D + + # define Transformer decoder here + self.num_heads = nheads + self.num_layers = dec_layers + self.transformer_self_attention_layers = nn.ModuleList() + self.transformer_cross_attention_layers = nn.ModuleList() + self.transformer_ffn_layers = nn.ModuleList() + + for _ in range(self.num_layers): + self.transformer_self_attention_layers.append( + SelfAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_cross_attention_layers.append( + CrossAttentionLayer( + d_model=hidden_dim, + nhead=nheads, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.transformer_ffn_layers.append( + FFNLayer( + d_model=hidden_dim, + dim_feedforward=dim_feedforward, + dropout=0.0, + normalize_before=pre_norm, + ) + ) + + self.decoder_norm = nn.LayerNorm(hidden_dim) + + self.num_queries = num_queries + # learnable query features + self.query_feat = nn.Embedding(num_queries, hidden_dim) + # learnable query p.e. + self.query_embed = nn.Embedding(num_queries, hidden_dim) + + # level embedding (we always use 3 scales) + self.num_feature_levels = 3 + self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim) + self.input_proj = nn.ModuleList() + for _ in range(self.num_feature_levels): + if in_channels != hidden_dim or enforce_input_project: + self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1)) + weight_init.c2_xavier_fill(self.input_proj[-1]) + else: + self.input_proj.append(nn.Sequential()) + + # output FFNs + if self.mask_classification: + self.class_embed = nn.Linear(hidden_dim, num_classes + 1) + self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3) + + @classmethod + def from_config(cls, cfg, in_channels, mask_classification): + ret = {} + ret["in_channels"] = in_channels + ret["mask_classification"] = mask_classification + + ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES + ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM + ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES + # Transformer parameters: + ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS + ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD + + # NOTE: because we add learnable query features which requires supervision, + # we add minus 1 to decoder layers to be consistent with our loss + # implementation: that is, number of auxiliary losses is always + # equal to number of decoder layers. With learnable query features, the number of + # auxiliary losses equals number of decoders plus 1. + assert cfg.MODEL.MASK_FORMER.DEC_LAYERS >= 1 + ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS - 1 + ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM + ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ + + ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM + + ret["num_frames"] = cfg.INPUT.SAMPLING_FRAME_NUM + + return ret + + def forward(self, x, mask_features, mask = None): + bt, c_m, h_m, w_m = mask_features.shape + bs = bt // self.num_frames if self.training else 1 + t = bt // bs + mask_features = mask_features.view(bs, t, c_m, h_m, w_m) + + # x is a list of multi-scale feature + assert len(x) == self.num_feature_levels + src = [] + pos = [] + size_list = [] + + # disable mask, it does not affect performance + del mask + + for i in range(self.num_feature_levels): + size_list.append(x[i].shape[-2:]) + #下面原始为3D位置编码 + # pos.append(self.pe_layer(x[i].view(bs, t, -1, size_list[-1][0], size_list[-1][1]), None).flatten(3)) + pos.append(self.pe_layer(x[i]).flatten(2)) + + # src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None]) + src.append(self.input_proj[i](x[i]).flatten(2)+ self.level_embed.weight[i][None, :, None]) + + # NTxCxHW => NxTxCxHW => (TxHW)xNxC + bt, c, hw = src[-1].shape + # pos[-1] = pos[-1].view(bs, t, c, hw).permute(1, 3, 0, 2).flatten(0, 1) + # src[-1] = src[-1].view(bs, t, c, hw).permute(1, 3, 0, 2).flatten(0, 1) + pos[-1] = pos[-1].permute(2, 0, 1) + src[-1] = src[-1].permute(2, 0, 1) + + # QxNxC + # query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1) + query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs*t, 1) + # output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1) + output = self.query_feat.weight.unsqueeze(1).repeat(1, bs*t, 1) + + predictions_class = [] + predictions_mask = [] + # prediction heads on learnable query features + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0]) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + for i in range(self.num_layers-5): + level_index = i % self.num_feature_levels #sum(-1)和sum(1)求数组每一行的和,sum(0)求数组每一列的和 + attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False + # attention: cross-attention first + output = self.transformer_cross_attention_layers[i]( + output, src[level_index], + memory_mask=attn_mask, + memory_key_padding_mask=None, # here we do not apply masking on padded region + pos=pos[level_index], query_pos=query_embed + ) + + output = self.transformer_self_attention_layers[i]( + output, tgt_mask=None, + tgt_key_padding_mask=None, + query_pos=query_embed + ) + + # FFN + output = self.transformer_ffn_layers[i]( + output #[100, 8*5, 256] + ) + outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels]) + predictions_class.append(outputs_class) + predictions_mask.append(outputs_mask) + + assert len(predictions_class) == self.num_layers-5 + 1 #因为显存不足，所以减少5层 + + out = { + 'object_tokens': output, + 'pred_logits': predictions_class[-1], + 'pred_masks': predictions_mask[-1], + 'aux_outputs': self._set_aux_loss( + predictions_class if self.mask_classification else None, predictions_mask + ) + } + return out + + def forward_prediction_heads(self, output, mask_features, attn_mask_target_size): + decoder_output = self.decoder_norm(output) + decoder_output = decoder_output.transpose(0, 1) #[16, 100, 256] + + b, t, _, _, _ = mask_features.shape + tem_output = [] + for i in range(t): + indices = torch.tensor([j for j in range(i, b*t, t)]).to(decoder_output.device) + tem_output.append(torch.index_select(decoder_output, 0, indices)) + tem_output = sum(tem_output)/t + + + outputs_class = self.class_embed(tem_output) #[40,100,41] + b, t, c, h, w = mask_features.shape #[8, 5, 256, 120, 216] + + mask_embed = self.mask_embed(decoder_output) #[40,100,256] + q = mask_embed.shape[1] + mask_embed = mask_embed.view(b, t, q, c) ##[8,5,100,256] + + # mask_features = mask_features.flatten(0, 1) #[8, 5, 256, 120, 216] + # outputs_mask = torch.einsum("bqc,btchw->bqthw", mask_embed, mask_features) + outputs_mask = torch.einsum("btqc,btchw->btqhw", mask_embed, mask_features)#[b, t, q, h, w] + outputs_mask = outputs_mask.permute(0, 2, 1, 3, 4) #[b, q, t, h, w] + + # outputs_mask = outputs_mask.view(b, t, q, h, w).permute(0, 2, 1, 3, 4) #bqthw + # b, q, t, _, _ = outputs_mask.shape + + # NOTE: prediction is of higher-resolution + # attn_mask = F.interpolate(outputs_mask.flatten(0, 1), size=attn_mask_target_size, mode="bilinear", align_corners=False).view( + # b, q, t, attn_mask_target_size[0], attn_mask_target_size[1]) #[8, 100, 5, 15, 27] + attn_mask = F.interpolate(outputs_mask.flatten(0, 1), size=attn_mask_target_size, mode="bilinear", align_corners=False).view( + b, q, t, attn_mask_target_size[0], attn_mask_target_size[1]) #[8, 100, 2, 15, 27] + attn_mask = attn_mask.permute(0, 2, 1, 3, 4) + # must use bool type + attn_mask = attn_mask.sigmoid().flatten(3) + attn_mask = attn_mask.flatten(0, 1) + attn_mask = attn_mask.unsqueeze(1) + attn_mask = attn_mask.repeat(1, self.num_heads,1,1) + attn_mask = attn_mask.flatten(0, 1) + attn_mask = (attn_mask < 0.5).bool() + attn_mask = attn_mask.detach() #[64, 100, 810] + + return outputs_class, outputs_mask, attn_mask + + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_seg_masks): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + if self.mask_classification: + return [ + {"pred_logits": a, "pred_masks": b} + for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1]) + ] + else: + return [{"pred_masks": b} for b in outputs_seg_masks[:-1]] #VITA只取后三层计算loss[:-1]改[-4:-1] diff --git a/Mask2Former/mask2former_video/utils/__init__.py b/Mask2Former/mask2former_video/utils/__init__.py new file mode 100644 index 0000000..9020c2d --- /dev/null +++ b/Mask2Former/mask2former_video/utils/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. diff --git a/Mask2Former/mask2former_video/utils/__pycache__/__init__.cpython-38.pyc b/Mask2Former/mask2former_video/utils/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..fc3fe05 Binary files /dev/null and b/Mask2Former/mask2former_video/utils/__pycache__/__init__.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/utils/__pycache__/memory.cpython-38.pyc b/Mask2Former/mask2former_video/utils/__pycache__/memory.cpython-38.pyc new file mode 100644 index 0000000..f45e282 Binary files /dev/null and b/Mask2Former/mask2former_video/utils/__pycache__/memory.cpython-38.pyc differ diff --git a/Mask2Former/mask2former_video/utils/memory.py b/Mask2Former/mask2former_video/utils/memory.py new file mode 100644 index 0000000..7ee5f15 --- /dev/null +++ b/Mask2Former/mask2former_video/utils/memory.py @@ -0,0 +1,80 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +import logging +from contextlib import contextmanager +from functools import wraps +import torch +from torch.cuda.amp import autocast + +__all__ = ["retry_if_cuda_oom"] + + +@contextmanager +def _ignore_torch_cuda_oom(): + """ + A context which ignores CUDA OOM exception from pytorch. + """ + try: + yield + except RuntimeError as e: + # NOTE: the string may change? + if "CUDA out of memory. " in str(e): + pass + else: + raise + + +def retry_if_cuda_oom(func): + """ + Makes a function retry itself after encountering + pytorch's CUDA OOM error. + It will first retry after calling `torch.cuda.empty_cache()`. + If that still fails, it will then retry by trying to convert inputs to CPUs. + In this case, it expects the function to dispatch to CPU implementation. + The return values may become CPU tensors as well and it's user's + responsibility to convert it back to CUDA tensor if needed. + Args: + func: a stateless callable that takes tensor-like objects as arguments + Returns: + a callable which retries `func` if OOM is encountered. + Examples: + :: + output = retry_if_cuda_oom(some_torch_function)(input1, input2) + # output may be on CPU even if inputs are on GPU + Note: + 1. When converting inputs to CPU, it will only look at each argument and check + if it has `.device` and `.to` for conversion. Nested structures of tensors + are not supported. + 2. Since the function might be called more than once, it has to be + stateless. + """ + + def maybe_to_cpu(x): + try: + like_gpu_tensor = x.device.type == "cuda" and hasattr(x, "to") + except AttributeError: + like_gpu_tensor = False + if like_gpu_tensor: + return x.to(device="cpu").to(torch.float32) + else: + return x + + @wraps(func) + def wrapped(*args, **kwargs): + with _ignore_torch_cuda_oom(): + return func(*args, **kwargs) + + # Clear cache and retry + torch.cuda.empty_cache() + with _ignore_torch_cuda_oom(): + return func(*args, **kwargs) + + # Try on CPU. This slows down the code significantly, therefore print a notice. + logger = logging.getLogger(__name__) + logger.info("Attempting to copy inputs to CPU due to CUDA OOM") + new_args = (maybe_to_cpu(x) for x in args) + new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()} + with autocast(enabled=False): + return func(*new_args, **new_kwargs) + + return wrapped diff --git a/Mask2Former/mask2former_video/video_maskformer_model.py b/Mask2Former/mask2former_video/video_maskformer_model.py new file mode 100644 index 0000000..53750cf --- /dev/null +++ b/Mask2Former/mask2former_video/video_maskformer_model.py @@ -0,0 +1,529 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import logging +import math +import numpy as np +from typing import Tuple + +import torch +from torch import device, nn +from torch.nn import functional as F + +from detectron2.config import configurable +from detectron2.data import MetadataCatalog +from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head +from detectron2.modeling.backbone import Backbone +from detectron2.modeling.postprocessing import sem_seg_postprocess +from detectron2.structures import Boxes, ImageList, Instances, BitMasks + +from .modeling.criterion import VideoSetCriterion +from .modeling.matcher import VideoHungarianMatcher +from .utils.memory import retry_if_cuda_oom + +logger = logging.getLogger(__name__) + +import sys,os +sys.path.append(os.path.dirname(__file__) + os.sep + '../') +####新增IFC模块 +from .ifc.models.backbone import Joiner +from .ifc.models.ifc import IFC, SetCriterion +from .ifc.models.matcher import HungarianMatcher +from .ifc.models.position_encoding import PositionEmbeddingSine +from .ifc.models.segmentation import MaskHead, segmentation_postprocess +from .ifc.models.transformer import IFCTransformer +from .ifc.structures.clip_output import Videos, Clips +from .ifc.util.misc import NestedTensor, _max_by_axis, interpolate + +from .ifc.models.misc import MLP +from .ifc.ifc import Ifc + +####新增IFC的MaskedBackbone +class MaskedBackbone(nn.Module): + """ This is a thin wrapper around D2's backbone to provide padding masking""" + + def __init__(self, cfg): + super().__init__() + # self.backbone = build_backbone(cfg) + # backbone_shape = self.backbone.output_shape() + # self.feature_strides = [backbone_shape[f].stride for f in backbone_shape.keys()] + self.feature_strides = None + # self.num_channels = backbone_shape[list(backbone_shape.keys())[-1]].channels + + def forward(self, images, features): + # features = self.backbone(images.tensor) + self.feature_strides = features['feature_strides'] + features = dict([(key, features[key]) for key in ['res2','res3','res4','res5']]) + masks = self.mask_out_padding( + [features_per_level.shape for features_per_level in features.values()], + images.image_sizes, + images.tensor.device, + ) + assert len(features) == len(masks) + for i, k in enumerate(features.keys()): + features[k] = NestedTensor(features[k], masks[i]) + return features + + def mask_out_padding(self, feature_shapes, image_sizes, device): + masks = [] + assert len(feature_shapes) == len(self.feature_strides) + for idx, shape in enumerate(feature_shapes): + N, _, H, W = shape + masks_per_feature_level = torch.ones((N, H, W), dtype=torch.bool, device=device) + for img_idx, (h, w) in enumerate(image_sizes): + masks_per_feature_level[ + img_idx, + : int(np.ceil(float(h) / self.feature_strides[idx])), + : int(np.ceil(float(w) / self.feature_strides[idx])), + ] = 0 + masks.append(masks_per_feature_level) + return masks + +@META_ARCH_REGISTRY.register() +class VideoMaskFormer(nn.Module): + """ + Main class for mask classification semantic segmentation architectures. + """ + @configurable + def __init__(self, + *, + backbone: Backbone, + sem_seg_head: nn.Module, + criterion: nn.Module, + num_queries: int, + object_mask_threshold: float, + overlap_threshold: float, + metadata, + size_divisibility: int, + sem_seg_postprocess_before_inference: bool, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + # video + num_frames, + cfg, #IFC模块 + ): + super().__init__() + self.backbone = backbone + backbone_shape = self.backbone.output_shape() + self.feature_strides = [backbone_shape[f].stride for f in backbone_shape.keys()] + + self.sem_seg_head = sem_seg_head + self.criterion = criterion + # self.num_queries = num_queries + self.overlap_threshold = overlap_threshold + self.object_mask_threshold = object_mask_threshold + self.metadata = metadata + if size_divisibility < 0: + # use backbone size_divisibility if not set + size_divisibility = self.backbone.size_divisibility + self.size_divisibility = size_divisibility + self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference + self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False) + self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) + self.num_frames = num_frames + + #IFC模块下的 + # cfg = self.cfg + self.num_frames = cfg.INPUT.SAMPLING_FRAME_NUM + + device = torch.device(cfg.MODEL.DEVICE) + self.clip_stride = cfg.MODEL.IFC.CLIP_STRIDE + self.merge_on_cpu = cfg.MODEL.IFC.MERGE_ON_CPU + self.is_multi_cls = cfg.MODEL.IFC.MULTI_CLS_ON + self.apply_cls_thres = cfg.MODEL.IFC.APPLY_CLS_THRES + + self.is_coco = cfg.DATASETS.TEST[0].startswith("coco") + self.num_classes = cfg.MODEL.IFC.NUM_CLASSES + self.mask_stride = cfg.MODEL.IFC.MASK_STRIDE + self.match_stride = cfg.MODEL.IFC.MATCH_STRIDE + self.mask_on = cfg.MODEL.MASK_ON + hidden_dim = cfg.MODEL.IFC.HIDDEN_DIM + num_queries = cfg.MODEL.IFC.NUM_OBJECT_QUERIES + self.num_queries = num_queries + + # Transformer parameters: + nheads = cfg.MODEL.IFC.NHEADS + dropout = cfg.MODEL.IFC.DROPOUT + dim_feedforward = cfg.MODEL.IFC.DIM_FEEDFORWARD + enc_layers = cfg.MODEL.IFC.ENC_LAYERS + dec_layers_ifc = cfg.MODEL.IFC.DEC_LAYERS + pre_norm = cfg.MODEL.IFC.PRE_NORM + num_memory_bus = cfg.MODEL.IFC.NUM_MEMORY_BUS + + # Loss parameters: + mask_weight_ifc = cfg.MODEL.IFC.MASK_WEIGHT + dice_weight_ifc = cfg.MODEL.IFC.DICE_WEIGHT + deep_supervision_ifc = cfg.MODEL.IFC.DEEP_SUPERVISION + no_object_weight_ifc = cfg.MODEL.IFC.NO_OBJECT_WEIGHT + + N_steps = hidden_dim // 2 + d2_backbone = MaskedBackbone(cfg) + backbone_ifc = Joiner(d2_backbone, PositionEmbeddingSine(N_steps, normalize=True)) + # backbone_ifc.num_channels = d2_backbone.num_channels + + transformer = IFCTransformer(num_frames=self.num_frames,d_model=hidden_dim,dropout=dropout,nhead=nheads, + num_memory_bus=num_memory_bus,dim_feedforward=dim_feedforward,num_encoder_layers=enc_layers, + num_decoder_layers=dec_layers_ifc,normalize_before=pre_norm,return_intermediate_dec=deep_supervision_ifc,) + mask_head = MaskHead(hidden_dim, [1024, 512], self.num_frames) + self.detr = IFC(backbone_ifc, transformer, mask_head,num_classes=self.num_classes, num_queries=num_queries, + num_frames=self.num_frames, aux_loss=deep_supervision_ifc) + self.detr.to(device) + # building criterion + matcher_ifc = HungarianMatcher(cost_class=1,cost_dice=dice_weight_ifc,num_classes=self.num_classes,) + weight_dict_ifc = {"loss_ce": 1, "loss_mask": mask_weight_ifc, "loss_dice": dice_weight_ifc} + if deep_supervision_ifc: + aux_weight_dict = {} + for i in range(dec_layers_ifc - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict_ifc.items()}) + weight_dict_ifc.update(aux_weight_dict) + losses = ["labels", "masks", "cardinality"] + self.criterion_ifc = SetCriterion(self.num_classes, matcher=matcher_ifc, weight_dict=weight_dict_ifc, + eos_coef=no_object_weight_ifc, losses=losses,num_frames=self.num_frames) + self.criterion_ifc.to(device) + self.normalizer = lambda x: (x - pixel_mean) / pixel_std + self.to(device) + self.merge_device = "cpu" if self.merge_on_cpu else device + + self.size_window = cfg.MODEL.IFC.size_window + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape()) + + # Loss parameters: + deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION #Transformer Decoder每一层是否都计算loss + no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT + + # loss weights + class_weight = cfg.MODEL.MASK_FORMER.CLASS_WEIGHT + dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT + mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT + + # building criterion + matcher = VideoHungarianMatcher( + cost_class=class_weight, + cost_mask=mask_weight, + cost_dice=dice_weight, + num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, + ) + + weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, "loss_dice": dice_weight} + + if deep_supervision: + dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS + aux_weight_dict = {} + for i in range(dec_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + + losses = ["labels", "masks"] + + criterion = VideoSetCriterion( + sem_seg_head.num_classes, + matcher=matcher, + weight_dict=weight_dict, + eos_coef=no_object_weight, + losses=losses, + num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, + oversample_ratio=cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO, + importance_sample_ratio=cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO, + ) + + return { + "backbone": backbone, + "sem_seg_head": sem_seg_head, + "criterion": criterion, + "num_queries": cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES, + "object_mask_threshold": cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD, + "overlap_threshold": cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD, + "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), + "size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY, + "sem_seg_postprocess_before_inference": True, + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + # video + "num_frames": cfg.INPUT.SAMPLING_FRAME_NUM, + "cfg": cfg + } + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs): + images = [] + #视频预处理 + for video in batched_inputs: + for frame in video["image"]: + images.append(frame.to(self.device)) + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.size_divisibility) + + #Frame-Level模型前向推理 + features = self.backbone(images.tensor) #[res2,res3,res4,res5] + outputs = self.sem_seg_head(features) + outputs_frame_level = dict([(key,outputs[key]) for key in ['pred_logits','pred_masks','aux_outputs']]) + + #shift-window预处理 + object_tokens = outputs['object_tokens'] #[100, 8*5, 256] [N, B*T, C] + del outputs + b = len(batched_inputs) + t = self.num_frames + + t_o = object_tokens.shape[1] #用于测试时的判断 + if b < 2 : + t = t_o + + n = self.num_queries + c = object_tokens.shape[2] + if b < 2 : + object_tokens = object_tokens.unsqueeze(1) + else: + object_tokens = object_tokens.view(n, b, t, c) + b = b if self.training else 1 + + # object_tokens = object_tokens.permute(1, 2, 0, 3).flatten(2,3) # THW, B, C + # features['feature_strides'] = self.feature_strides + out = [] + for i in range(0, self.size_window-1, self.size_window//2): + for j in range(i, t-1, self.size_window): + if t-j < self.size_window: + # window_tokens = object_tokens[:,:,j:,:] #[100, 8, 3, 256] + # mlp = MLP(256*(t-j), 256, 256, 2).to(self.device) + break + elif t < self.num_frames : + window_tokens = object_tokens[:,:,j:,:] #[100, 8, 3, 256] + mlp = MLP(256*(t-j), 256, 256, 2).to(self.device) + else: + window_tokens = object_tokens[:,:,j:j+self.size_window,:] #[100, 8, 3, 256] + mlp = MLP(256*(self.size_window), 256, 256, 2).to(self.device) + window_tokens = window_tokens.flatten(2) + + window_tokens = mlp(window_tokens) + #VITA前向推理 + # out_vita = self.detr(images, window_tokens, features) + out_vita = self.detr(window_tokens, features) + out.append(out_vita) + del out_vita, window_tokens + + pred_logits = [] + pred_masks = [] + aux_outputs_pred_logits_0 = [] + aux_outputs_pred_masks_0 = [] + aux_outputs_pred_logits_1 = [] + aux_outputs_pred_masks_1 = [] + n = len(out) + for i in range(n): + pred_logits.append(out[i]['pred_logits']) + pred_masks.append(out[i]['pred_masks']) + aux_outputs_pred_logits_0.append(out[i]['aux_outputs'][0]['pred_logits']) + aux_outputs_pred_masks_0.append(out[i]['aux_outputs'][0]['pred_masks']) + aux_outputs_pred_logits_1.append(out[i]['aux_outputs'][1]['pred_logits']) + aux_outputs_pred_masks_1.append(out[i]['aux_outputs'][1]['pred_masks']) + del out + out_vita = {'pred_logits':sum(pred_logits)/n, 'pred_masks':sum(pred_masks)/n, + 'aux_outputs':[ + {'pred_logits':sum(aux_outputs_pred_logits_0)/2, + 'pred_masks':sum(aux_outputs_pred_masks_0)/2}, + {'pred_logits':sum(aux_outputs_pred_logits_1)/2, + 'pred_masks':sum(aux_outputs_pred_masks_1)/2} + ] + } + del pred_logits,pred_masks,aux_outputs_pred_logits_0,aux_outputs_pred_masks_0, + aux_outputs_pred_logits_1,aux_outputs_pred_masks_1 + + if self.training: + # Frame-Level模块 loss处理 + # mask classification target + targets = self.prepare_targets(batched_inputs, images) + # bipartite matching-based loss + losses = self.criterion(outputs_frame_level, targets) + for k in list(losses.keys()): + if k in self.criterion.weight_dict: + losses[k] *= self.criterion.weight_dict[k] + else: + # remove this loss if not specified in `weight_dict` + losses.pop(k) + + # IFC模块 loss处理 + targets_ifc = self.prepare_targets_ifc(batched_inputs) + loss_dict = self.criterion_ifc(out_vita, targets_ifc) + weight_dict_ifc = self.criterion_ifc.weight_dict + for k in list(loss_dict.keys()): + if k in weight_dict_ifc: + loss_dict[k] *= weight_dict_ifc[k] + else: + loss_dict.pop(k) + + return {**losses, **loss_dict} #前后模块loss相加 + + else: + mask_cls_results = out_vita["pred_logits"] + mask_pred_results = out_vita["pred_masks"] + + mask_cls_result = mask_cls_results[0] + # upsample masks + mask_pred_result = retry_if_cuda_oom(F.interpolate)( #尝试解决显存不足问题 + mask_pred_results[0], + size=(images.tensor.shape[-2], images.tensor.shape[-1]), + mode="bilinear", + align_corners=False, + ) + + del out_vita + + input_per_image = batched_inputs[0] + image_size = images.image_sizes[0] # image size without padding after data augmentation + + height = input_per_image.get("height", image_size[0]) # raw image size before data augmentation + width = input_per_image.get("width", image_size[1]) + + return retry_if_cuda_oom(self.inference_video)(mask_cls_result, mask_pred_result, image_size, height, width) + + + + def prepare_targets(self, targets, images): + h_pad, w_pad = images.tensor.shape[-2:] + gt_instances = [] + for targets_per_video in targets: + _num_instance = len(targets_per_video["instances"][0]) + mask_shape = [_num_instance, self.num_frames, h_pad, w_pad] + gt_masks_per_video = torch.zeros(mask_shape, dtype=torch.bool, device=self.device) + + gt_ids_per_video = [] + for f_i, targets_per_frame in enumerate(targets_per_video["instances"]): + targets_per_frame = targets_per_frame.to(self.device) + h, w = targets_per_frame.image_size + + gt_ids_per_video.append(targets_per_frame.gt_ids[:, None]) + gt_masks_per_video[:, f_i, :h, :w] = targets_per_frame.gt_masks.tensor + + gt_ids_per_video = torch.cat(gt_ids_per_video, dim=1) + valid_idx = (gt_ids_per_video != -1).any(dim=-1) + + gt_classes_per_video = targets_per_frame.gt_classes[valid_idx] # N, + gt_ids_per_video = gt_ids_per_video[valid_idx] # N, num_frames + + gt_instances.append({"labels": gt_classes_per_video, "ids": gt_ids_per_video}) + gt_masks_per_video = gt_masks_per_video[valid_idx].float() # N, num_frames, H, W + gt_instances[-1].update({"masks": gt_masks_per_video}) + + return gt_instances + + def prepare_targets_ifc(self, targets): + gt_instances = [] + + _sizes = [] + for targets_per_video in targets: + _sizes += [list(t.image_size) for t in targets_per_video["instances"]] + max_size = _max_by_axis(_sizes) + + for targets_per_video in targets: + _num_instance = len(targets_per_video["instances"][0]) + mask_shape = [_num_instance, self.num_frames] + max_size + gt_masks_per_video = torch.zeros(mask_shape, dtype=torch.bool, device=self.device) + + gt_ids_per_video = [] + gt_classes_per_video = torch.full((_num_instance,), self.num_classes, device=self.device) + for f_i, targets_per_frame in enumerate(targets_per_video["instances"]): + targets_per_frame = targets_per_frame.to(self.device) + h, w = targets_per_frame.image_size + + valid_cls_idx = (targets_per_frame.gt_classes != self.num_classes) + gt_classes_per_video[valid_cls_idx] = targets_per_frame.gt_classes[valid_cls_idx] + gt_ids_per_video.append(targets_per_frame.gt_ids[:, None]) + gt_masks_per_video[:, f_i, :h, :w] = targets_per_frame.gt_masks.tensor + + gt_ids_per_video = torch.cat(gt_ids_per_video, dim=1) + valid_idx = (gt_ids_per_video != -1).any(dim=-1) + + gt_classes_per_video = gt_classes_per_video[valid_idx] # N, + gt_ids_per_video = gt_ids_per_video[valid_idx] # N, num_frames + + gt_instances.append({"labels": gt_classes_per_video, "ids": gt_ids_per_video}) + gt_masks_per_video = gt_masks_per_video[valid_idx].float() # N, num_frames, H, W + o_h, o_w = gt_masks_per_video.shape[-2:] + l_h, l_w = math.ceil(o_h/self.mask_stride), math.ceil(o_w/self.mask_stride) + m_h, m_w = math.ceil(o_h/self.match_stride), math.ceil(o_w/self.match_stride) + + gt_masks_for_loss = interpolate(gt_masks_per_video, size=(l_h, l_w), mode="bilinear", align_corners=False) + gt_masks_for_match = interpolate(gt_masks_per_video, size=(m_h, m_w), mode="bilinear", align_corners=False) + gt_instances[-1].update({"masks": gt_masks_for_loss, "match_masks": gt_masks_for_match}) + + return gt_instances + + def prepare_targets_ifc(self, targets): + gt_instances = [] + + _sizes = [] + for targets_per_video in targets: + _sizes += [list(t.image_size) for t in targets_per_video["instances"]] + max_size = _max_by_axis(_sizes) + + for targets_per_video in targets: + _num_instance = len(targets_per_video["instances"][0]) + mask_shape = [_num_instance, self.num_frames] + max_size + gt_masks_per_video = torch.zeros(mask_shape, dtype=torch.bool, device=self.device) + + gt_ids_per_video = [] + gt_classes_per_video = torch.full((_num_instance,), self.num_classes, device=self.device) + for f_i, targets_per_frame in enumerate(targets_per_video["instances"]): + targets_per_frame = targets_per_frame.to(self.device) + h, w = targets_per_frame.image_size + + valid_cls_idx = (targets_per_frame.gt_classes != self.num_classes) + gt_classes_per_video[valid_cls_idx] = targets_per_frame.gt_classes[valid_cls_idx] + gt_ids_per_video.append(targets_per_frame.gt_ids[:, None]) + gt_masks_per_video[:, f_i, :h, :w] = targets_per_frame.gt_masks.tensor + + gt_ids_per_video = torch.cat(gt_ids_per_video, dim=1) + valid_idx = (gt_ids_per_video != -1).any(dim=-1) + + gt_classes_per_video = gt_classes_per_video[valid_idx] # N, + gt_ids_per_video = gt_ids_per_video[valid_idx] # N, num_frames + + gt_instances.append({"labels": gt_classes_per_video, "ids": gt_ids_per_video}) + gt_masks_per_video = gt_masks_per_video[valid_idx].float() # N, num_frames, H, W + o_h, o_w = gt_masks_per_video.shape[-2:] + l_h, l_w = math.ceil(o_h/self.mask_stride), math.ceil(o_w/self.mask_stride) + m_h, m_w = math.ceil(o_h/self.match_stride), math.ceil(o_w/self.match_stride) + + gt_masks_for_loss = interpolate(gt_masks_per_video, size=(l_h, l_w), mode="bilinear", align_corners=False) + gt_masks_for_match = interpolate(gt_masks_per_video, size=(m_h, m_w), mode="bilinear", align_corners=False) + gt_instances[-1].update({"masks": gt_masks_for_loss, "match_masks": gt_masks_for_match}) + + return gt_instances + + + def inference_video(self, pred_cls, pred_masks, img_size, output_height, output_width): + if len(pred_cls) > 0: + scores = F.softmax(pred_cls, dim=-1)[:, :-1] + labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1) + # keep top-10 predictions + scores_per_image, topk_indices = scores.flatten(0, 1).topk(10, sorted=False) + labels_per_image = labels[topk_indices] + topk_indices = topk_indices // self.sem_seg_head.num_classes + pred_masks = pred_masks[topk_indices] + + pred_masks = pred_masks[:, :, : img_size[0], : img_size[1]] + pred_masks = F.interpolate( + pred_masks, size=(output_height, output_width), mode="bilinear", align_corners=False + ) + + masks = pred_masks > 0. + + out_scores = scores_per_image.tolist() + out_labels = labels_per_image.tolist() + out_masks = [m for m in masks.cpu()] + else: + out_scores = [] + out_labels = [] + out_masks = [] + + video_output = { + "image_size": (output_height, output_width), + "pred_scores": out_scores, + "pred_labels": out_labels, + "pred_masks": out_masks, + } + + return video_output diff --git a/Mask2Former/predict.py b/Mask2Former/predict.py new file mode 100644 index 0000000..453479c --- /dev/null +++ b/Mask2Former/predict.py @@ -0,0 +1,54 @@ +import sys +sys.path.insert(0, "Mask2Former") +import tempfile +from pathlib import Path +import numpy as np +import cv2 +import cog + +# import some common detectron2 utilities +from detectron2.config import CfgNode as CN +from detectron2.engine import DefaultPredictor +from detectron2.config import get_cfg +from detectron2.utils.visualizer import Visualizer, ColorMode +from detectron2.data import MetadataCatalog +from detectron2.projects.deeplab import add_deeplab_config + +# import Mask2Former project +from mask2former import add_maskformer2_config + + +class Predictor(cog.Predictor): + def setup(self): + cfg = get_cfg() + add_deeplab_config(cfg) + add_maskformer2_config(cfg) + cfg.merge_from_file("Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml") + cfg.MODEL.WEIGHTS = 'model_final_f07440.pkl' + cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True + cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = True + cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = True + self.predictor = DefaultPredictor(cfg) + self.coco_metadata = MetadataCatalog.get("coco_2017_val_panoptic") + + + @cog.input( + "image", + type=Path, + help="Input image for segmentation. Output will be the concatenation of Panoptic segmentation (top), " + "instance segmentation (middle), and semantic segmentation (bottom).", + ) + def predict(self, image): + im = cv2.imread(str(image)) + outputs = self.predictor(im) + v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW) + panoptic_result = v.draw_panoptic_seg(outputs["panoptic_seg"][0].to("cpu"), + outputs["panoptic_seg"][1]).get_image() + v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW) + instance_result = v.draw_instance_predictions(outputs["instances"].to("cpu")).get_image() + v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW) + semantic_result = v.draw_sem_seg(outputs["sem_seg"].argmax(0).to("cpu")).get_image() + result = np.concatenate((panoptic_result, instance_result, semantic_result), axis=0)[:, :, ::-1] + out_path = Path(tempfile.mkdtemp()) / "out.png" + cv2.imwrite(str(out_path), result) + return out_path diff --git a/Mask2Former/requirements.txt b/Mask2Former/requirements.txt new file mode 100644 index 0000000..cceed66 --- /dev/null +++ b/Mask2Former/requirements.txt @@ -0,0 +1,7 @@ +cython +scipy +shapely +timm +h5py +submitit +scikit-image diff --git a/Mask2Former/tools/README.md b/Mask2Former/tools/README.md new file mode 100644 index 0000000..309f767 --- /dev/null +++ b/Mask2Former/tools/README.md @@ -0,0 +1,79 @@ +This directory contains few tools for MaskFormer. + +* `convert-torchvision-to-d2.py` + +Tool to convert torchvision pre-trained weights for D2. + +``` +wget https://download.pytorch.org/models/resnet101-63fe2227.pth +python tools/convert-torchvision-to-d2.py resnet101-63fe2227.pth R-101.pkl +``` + +* `convert-pretrained-swin-model-to-d2.py` + +Tool to convert Swin Transformer pre-trained weights for D2. + +``` +pip install timm + +wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth +python tools/convert-pretrained-swin-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl + +wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth +python tools/convert-pretrained-swin-model-to-d2.py swin_small_patch4_window7_224.pth swin_small_patch4_window7_224.pkl + +wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth +python tools/convert-pretrained-swin-model-to-d2.py swin_base_patch4_window12_384_22k.pth swin_base_patch4_window12_384_22k.pkl + +wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth +python tools/convert-pretrained-swin-model-to-d2.py swin_large_patch4_window12_384_22k.pth swin_large_patch4_window12_384_22k.pkl +``` + +* `evaluate_pq_for_semantic_segmentation.py` + +Tool to evaluate PQ (PQ-stuff) for semantic segmentation predictions. + +Usage: + +``` +python tools/evaluate_pq_for_semantic_segmentation.py --dataset-name ade20k_sem_seg_val --json-file OUTPUT_DIR/inference/sem_seg_predictions.json +``` + +where `OUTPUT_DIR` is set in the config file. + +* `evaluate_coco_boundary_ap.py` + +Tool to evaluate Boundary AP for instance segmentation predictions. + +Usage: + +``` +python tools/coco_instance_evaluation.py --gt-json-file COCO_GT_JSON --dt-json-file COCO_DT_JSON +``` + +To install Boundary IoU API, run: + +``` +pip install git+https://github.com/bowenc0221/boundary-iou-api.git +``` + +* `analyze_model.py` + +Tool to analyze model parameters and flops. + +Usage for semantic segmentation (ADE20K only, use with caution!): + +``` +python tools/analyze_model.py --num-inputs 1 --tasks flop --use-fixed-input-size --config-file CONFIG_FILE +``` + +Note that, for semantic segmentation (ADE20K only), we use a dummy image with fixed size that equals to `cfg.INPUT.CROP.SIZE[0] x cfg.INPUT.CROP.SIZE[0]`. +Please do not use `--use-fixed-input-size` for calculating FLOPs on other datasets like Cityscapes! + +Usage for panoptic and instance segmentation: + +``` +python tools/analyze_model.py --num-inputs 100 --tasks flop --config-file CONFIG_FILE +``` + +Note that, for panoptic and instance segmentation, we compute the average flops over 100 real validation images. diff --git a/Mask2Former/tools/analyze_model.py b/Mask2Former/tools/analyze_model.py new file mode 100644 index 0000000..a92227e --- /dev/null +++ b/Mask2Former/tools/analyze_model.py @@ -0,0 +1,177 @@ +# -*- coding: utf-8 -*- +# Copyright (c) Facebook, Inc. and its affiliates. +# Modified by Bowen Cheng from https://github.com/facebookresearch/detectron2/blob/main/tools/analyze_model.py + +import logging +import numpy as np +from collections import Counter +import tqdm +from fvcore.nn import flop_count_table # can also try flop_count_str + +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate +from detectron2.data import build_detection_test_loader +from detectron2.engine import default_argument_parser +from detectron2.modeling import build_model +from detectron2.projects.deeplab import add_deeplab_config +from detectron2.utils.analysis import ( + FlopCountAnalysis, + activation_count_operators, + parameter_count_table, +) +from detectron2.utils.logger import setup_logger + +# fmt: off +import os +import sys +sys.path.insert(1, os.path.join(sys.path[0], '..')) +# fmt: on + +from mask2former import add_maskformer2_config + +logger = logging.getLogger("detectron2") + + +def setup(args): + if args.config_file.endswith(".yaml"): + cfg = get_cfg() + add_deeplab_config(cfg) + add_maskformer2_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.DATALOADER.NUM_WORKERS = 0 + cfg.merge_from_list(args.opts) + cfg.freeze() + else: + cfg = LazyConfig.load(args.config_file) + cfg = LazyConfig.apply_overrides(cfg, args.opts) + setup_logger(name="fvcore") + setup_logger() + return cfg + + +def do_flop(cfg): + if isinstance(cfg, CfgNode): + data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0]) + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + data_loader = instantiate(cfg.dataloader.test) + model = instantiate(cfg.model) + model.to(cfg.train.device) + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + model.eval() + + counts = Counter() + total_flops = [] + for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa + if args.use_fixed_input_size and isinstance(cfg, CfgNode): + import torch + crop_size = cfg.INPUT.CROP.SIZE[0] + data[0]["image"] = torch.zeros((3, crop_size, crop_size)) + flops = FlopCountAnalysis(model, data) + if idx > 0: + flops.unsupported_ops_warnings(False).uncalled_modules_warnings(False) + counts += flops.by_operator() + total_flops.append(flops.total()) + + logger.info("Flops table computed from only one input sample:\n" + flop_count_table(flops)) + logger.info( + "Average GFlops for each type of operators:\n" + + str([(k, v / (idx + 1) / 1e9) for k, v in counts.items()]) + ) + logger.info( + "Total GFlops: {:.1f}±{:.1f}".format(np.mean(total_flops) / 1e9, np.std(total_flops) / 1e9) + ) + + +def do_activation(cfg): + if isinstance(cfg, CfgNode): + data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0]) + model = build_model(cfg) + DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS) + else: + data_loader = instantiate(cfg.dataloader.test) + model = instantiate(cfg.model) + model.to(cfg.train.device) + DetectionCheckpointer(model).load(cfg.train.init_checkpoint) + model.eval() + + counts = Counter() + total_activations = [] + for idx, data in zip(tqdm.trange(args.num_inputs), data_loader): # noqa + count = activation_count_operators(model, data) + counts += count + total_activations.append(sum(count.values())) + logger.info( + "(Million) Activations for Each Type of Operators:\n" + + str([(k, v / idx) for k, v in counts.items()]) + ) + logger.info( + "Total (Million) Activations: {}±{}".format( + np.mean(total_activations), np.std(total_activations) + ) + ) + + +def do_parameter(cfg): + if isinstance(cfg, CfgNode): + model = build_model(cfg) + else: + model = instantiate(cfg.model) + logger.info("Parameter Count:\n" + parameter_count_table(model, max_depth=5)) + + +def do_structure(cfg): + if isinstance(cfg, CfgNode): + model = build_model(cfg) + else: + model = instantiate(cfg.model) + logger.info("Model Structure:\n" + str(model)) + + +if __name__ == "__main__": + parser = default_argument_parser( + epilog=""" +Examples: +To show parameters of a model: +$ ./analyze_model.py --tasks parameter \\ + --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml +Flops and activations are data-dependent, therefore inputs and model weights +are needed to count them: +$ ./analyze_model.py --num-inputs 100 --tasks flop \\ + --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \\ + MODEL.WEIGHTS /path/to/model.pkl +""" + ) + parser.add_argument( + "--tasks", + choices=["flop", "activation", "parameter", "structure"], + required=True, + nargs="+", + ) + parser.add_argument( + "-n", + "--num-inputs", + default=100, + type=int, + help="number of inputs used to compute statistics for flops/activations, " + "both are data dependent.", + ) + parser.add_argument( + "--use-fixed-input-size", + action="store_true", + help="use fixed input size when calculating flops", + ) + args = parser.parse_args() + assert not args.eval_only + assert args.num_gpus == 1 + + cfg = setup(args) + + for task in args.tasks: + { + "flop": do_flop, + "activation": do_activation, + "parameter": do_parameter, + "structure": do_structure, + }[task](cfg) diff --git a/Mask2Former/tools/convert-pretrained-swin-model-to-d2.py b/Mask2Former/tools/convert-pretrained-swin-model-to-d2.py new file mode 100644 index 0000000..8fbaeab --- /dev/null +++ b/Mask2Former/tools/convert-pretrained-swin-model-to-d2.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved + +import pickle as pkl +import sys + +import torch + +""" +Usage: + # download pretrained swin model: + wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth + # run the conversion + ./convert-pretrained-model-to-d2.py swin_tiny_patch4_window7_224.pth swin_tiny_patch4_window7_224.pkl + # Then, use swin_tiny_patch4_window7_224.pkl with the following changes in config: +MODEL: + WEIGHTS: "/path/to/swin_tiny_patch4_window7_224.pkl" +INPUT: + FORMAT: "RGB" +""" + +if __name__ == "__main__": + input = sys.argv[1] + + obj = torch.load(input, map_location="cpu")["model"] + + res = {"model": obj, "__author__": "third_party", "matching_heuristics": True} + + with open(sys.argv[2], "wb") as f: + pkl.dump(res, f) diff --git a/Mask2Former/tools/convert-torchvision-to-d2.py b/Mask2Former/tools/convert-torchvision-to-d2.py new file mode 100644 index 0000000..060390a --- /dev/null +++ b/Mask2Former/tools/convert-torchvision-to-d2.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. + +import pickle as pkl +import sys + +import torch + +""" +Usage: + # download one of the ResNet{18,34,50,101,152} models from torchvision: + wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth + # run the conversion + ./convert-torchvision-to-d2.py r50.pth r50.pkl + # Then, use r50.pkl with the following changes in config: +MODEL: + WEIGHTS: "/path/to/r50.pkl" + PIXEL_MEAN: [123.675, 116.280, 103.530] + PIXEL_STD: [58.395, 57.120, 57.375] + RESNETS: + DEPTH: 50 + STRIDE_IN_1X1: False +INPUT: + FORMAT: "RGB" +""" + +if __name__ == "__main__": + input = sys.argv[1] + + obj = torch.load(input, map_location="cpu") + + newmodel = {} + for k in list(obj.keys()): + old_k = k + if "layer" not in k: + k = "stem." + k + for t in [1, 2, 3, 4]: + k = k.replace("layer{}".format(t), "res{}".format(t + 1)) + for t in [1, 2, 3]: + k = k.replace("bn{}".format(t), "conv{}.norm".format(t)) + k = k.replace("downsample.0", "shortcut") + k = k.replace("downsample.1", "shortcut.norm") + print(old_k, "->", k) + newmodel[k] = obj.pop(old_k).detach().numpy() + + res = {"model": newmodel, "__author__": "torchvision", "matching_heuristics": True} + + with open(sys.argv[2], "wb") as f: + pkl.dump(res, f) + if obj: + print("Unconverted keys:", obj.keys()) diff --git a/Mask2Former/tools/evaluate_coco_boundary_ap.py b/Mask2Former/tools/evaluate_coco_boundary_ap.py new file mode 100644 index 0000000..1e96b5d --- /dev/null +++ b/Mask2Former/tools/evaluate_coco_boundary_ap.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +# Modified by Bowen Cheng from: https://github.com/bowenc0221/boundary-iou-api/blob/master/tools/coco_instance_evaluation.py + +""" +Evaluation for COCO val2017: +python ./tools/coco_instance_evaluation.py \ + --gt-json-file COCO_GT_JSON \ + --dt-json-file COCO_DT_JSON +""" +import argparse +import json + +from boundary_iou.coco_instance_api.coco import COCO +from boundary_iou.coco_instance_api.cocoeval import COCOeval + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--gt-json-file", default="") + parser.add_argument("--dt-json-file", default="") + parser.add_argument("--iou-type", default="boundary") + parser.add_argument("--dilation-ratio", default="0.020", type=float) + args = parser.parse_args() + print(args) + + annFile = args.gt_json_file + resFile = args.dt_json_file + dilation_ratio = args.dilation_ratio + if args.iou_type == "boundary": + get_boundary = True + else: + get_boundary = False + cocoGt = COCO(annFile, get_boundary=get_boundary, dilation_ratio=dilation_ratio) + + # remove box predictions + resFile = json.load(open(resFile)) + for c in resFile: + c.pop("bbox", None) + + cocoDt = cocoGt.loadRes(resFile) + cocoEval = COCOeval(cocoGt, cocoDt, iouType=args.iou_type, dilation_ratio=dilation_ratio) + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + +if __name__ == '__main__': + main() diff --git a/Mask2Former/tools/evaluate_pq_for_semantic_segmentation.py b/Mask2Former/tools/evaluate_pq_for_semantic_segmentation.py new file mode 100644 index 0000000..d22d735 --- /dev/null +++ b/Mask2Former/tools/evaluate_pq_for_semantic_segmentation.py @@ -0,0 +1,245 @@ +#!/usr/bin/env python +# Copyright (c) Facebook, Inc. and its affiliates. + +import argparse +import json +import os +from collections import defaultdict +from tqdm import tqdm + +import numpy as np +import torch + +from detectron2.data import MetadataCatalog +from detectron2.data.detection_utils import read_image +from detectron2.utils.file_io import PathManager +from pycocotools import mask as maskUtils + +from panopticapi.evaluation import PQStat + + +def default_argument_parser(): + """ + Creates a parser with some common arguments used by analysis tools. + Returns: + argparse.ArgumentParser: + """ + parser = argparse.ArgumentParser(description="Evaluate PQ metric for semantic segmentation.") + # NOTE: currently does not support Cityscapes, you need to convert + # Cityscapes prediction format to Detectron2 prediction format. + parser.add_argument( + "--dataset-name", + default="ade20k_sem_seg_val", + choices=["ade20k_sem_seg_val", "coco_2017_test_stuff_10k_sem_seg", "ade20k_full_sem_seg_val"], + help="dataset name you want to evaluate") + parser.add_argument("--json-file", default="", help="path to detection json file") + + return parser + + +# Modified from the official panoptic api: https://github.com/cocodataset/panopticapi/blob/master/panopticapi/evaluation.py +def pq_compute_single_image(segm_gt, segm_dt, categories, ignore_label): + pq_stat = PQStat() + VOID = ignore_label + OFFSET = 256 * 256 * 256 + + pan_gt = segm_gt + pan_pred = segm_dt + + gt_ann = {'segments_info': []} + labels, labels_cnt = np.unique(segm_gt, return_counts=True) + for cat_id, cnt in zip(labels, labels_cnt): + if cat_id == VOID: + continue + gt_ann['segments_info'].append( + {"id": cat_id, "category_id": cat_id, "area": cnt, "iscrowd": 0} + ) + + pred_ann = {'segments_info': []} + for cat_id in np.unique(segm_dt): + pred_ann['segments_info'].append({"id": cat_id, "category_id": cat_id}) + + gt_segms = {el['id']: el for el in gt_ann['segments_info']} + pred_segms = {el['id']: el for el in pred_ann['segments_info']} + + # predicted segments area calculation + prediction sanity checks + pred_labels_set = set(el['id'] for el in pred_ann['segments_info']) + labels, labels_cnt = np.unique(pan_pred, return_counts=True) + for label, label_cnt in zip(labels, labels_cnt): + if label not in pred_segms: + if label == VOID: + continue + raise KeyError('In the image with ID {} segment with ID {} is presented in PNG and not presented in JSON.'.format(image_id, label)) + pred_segms[label]['area'] = label_cnt + pred_labels_set.remove(label) + if pred_segms[label]['category_id'] not in categories: + raise KeyError('In the image with ID {} segment with ID {} has unknown category_id {}.'.format(image_id, label, pred_segms[label]['category_id'])) + if len(pred_labels_set) != 0: + raise KeyError('In the image with ID {} the following segment IDs {} are presented in JSON and not presented in PNG.'.format(image_id, list(pred_labels_set))) + + # confusion matrix calculation + pan_gt_pred = pan_gt.astype(np.uint64) * OFFSET + pan_pred.astype(np.uint64) + gt_pred_map = {} + labels, labels_cnt = np.unique(pan_gt_pred, return_counts=True) + for label, intersection in zip(labels, labels_cnt): + gt_id = label // OFFSET + pred_id = label % OFFSET + gt_pred_map[(gt_id, pred_id)] = intersection + + # count all matched pairs + gt_matched = set() + pred_matched = set() + for label_tuple, intersection in gt_pred_map.items(): + gt_label, pred_label = label_tuple + if gt_label not in gt_segms: + continue + if pred_label not in pred_segms: + continue + if gt_segms[gt_label]['iscrowd'] == 1: + continue + if gt_segms[gt_label]['category_id'] != pred_segms[pred_label]['category_id']: + continue + + union = pred_segms[pred_label]['area'] + gt_segms[gt_label]['area'] - intersection - gt_pred_map.get((VOID, pred_label), 0) + iou = intersection / union + if iou > 0.5: + pq_stat[gt_segms[gt_label]['category_id']].tp += 1 + pq_stat[gt_segms[gt_label]['category_id']].iou += iou + gt_matched.add(gt_label) + pred_matched.add(pred_label) + + # count false positives + crowd_labels_dict = {} + for gt_label, gt_info in gt_segms.items(): + if gt_label in gt_matched: + continue + # crowd segments are ignored + if gt_info['iscrowd'] == 1: + crowd_labels_dict[gt_info['category_id']] = gt_label + continue + pq_stat[gt_info['category_id']].fn += 1 + + # count false positives + for pred_label, pred_info in pred_segms.items(): + if pred_label in pred_matched: + continue + # intersection of the segment with VOID + intersection = gt_pred_map.get((VOID, pred_label), 0) + # plus intersection with corresponding CROWD region if it exists + if pred_info['category_id'] in crowd_labels_dict: + intersection += gt_pred_map.get((crowd_labels_dict[pred_info['category_id']], pred_label), 0) + # predicted segment is ignored if more than half of the segment correspond to VOID and CROWD regions + if intersection / pred_info['area'] > 0.5: + continue + pq_stat[pred_info['category_id']].fp += 1 + + return pq_stat + + +def main(): + parser = default_argument_parser() + args = parser.parse_args() + + _root = os.getenv("DETECTRON2_DATASETS", "datasets") + json_file = args.json_file + + with open(json_file) as f: + predictions = json.load(f) + + imgToAnns = defaultdict(list) + for pred in predictions: + image_id = os.path.basename(pred["file_name"]).split(".")[0] + imgToAnns[image_id].append( + {"category_id" : pred["category_id"], "segmentation" : pred["segmentation"]} + ) + + image_ids = list(imgToAnns.keys()) + + meta = MetadataCatalog.get(args.dataset_name) + class_names = meta.stuff_classes + num_classes = len(meta.stuff_classes) + ignore_label = meta.ignore_label + conf_matrix = np.zeros((num_classes + 1, num_classes + 1), dtype=np.int64) + + categories = {} + for i in range(num_classes): + categories[i] = {"id": i, "name": class_names[i], "isthing": 0} + + pq_stat = PQStat() + + for image_id in tqdm(image_ids): + if args.dataset_name == "ade20k_sem_seg_val": + gt_dir = os.path.join(_root, "ADEChallengeData2016", "annotations_detectron2", "validation") + segm_gt = read_image(os.path.join(gt_dir, image_id + ".png")).copy().astype(np.int64) + elif args.dataset_name == "coco_2017_test_stuff_10k_sem_seg": + gt_dir = os.path.join(_root, "coco", "coco_stuff_10k", "annotations_detectron2", "test") + segm_gt = read_image(os.path.join(gt_dir, image_id + ".png")).copy().astype(np.int64) + elif args.dataset_name == "ade20k_full_sem_seg_val": + gt_dir = os.path.join(_root, "ADE20K_2021_17_01", "annotations_detectron2", "validation") + segm_gt = read_image(os.path.join(gt_dir, image_id + ".tif")).copy().astype(np.int64) + else: + raise ValueError(f"Unsupported dataset {args.dataset_name}") + + # get predictions + segm_dt = np.zeros_like(segm_gt) + anns = imgToAnns[image_id] + for ann in anns: + # map back category_id + if hasattr(meta, "stuff_dataset_id_to_contiguous_id"): + if ann["category_id"] in meta.stuff_dataset_id_to_contiguous_id: + category_id = meta.stuff_dataset_id_to_contiguous_id[ann["category_id"]] + else: + category_id = ann["category_id"] + mask = maskUtils.decode(ann["segmentation"]) + segm_dt[mask > 0] = category_id + + # miou + gt = segm_gt.copy() + pred = segm_dt.copy() + gt[gt == ignore_label] = num_classes + conf_matrix += np.bincount( + (num_classes + 1) * pred.reshape(-1) + gt.reshape(-1), + minlength=conf_matrix.size, + ).reshape(conf_matrix.shape) + + # pq + pq_stat_single = pq_compute_single_image(segm_gt, segm_dt, categories, meta.ignore_label) + pq_stat += pq_stat_single + + metrics = [("All", None), ("Stuff", False)] + results = {} + for name, isthing in metrics: + results[name], per_class_results = pq_stat.pq_average(categories, isthing=isthing) + if name == 'All': + results['per_class'] = per_class_results + print("{:10s}| {:>5s} {:>5s} {:>5s} {:>5s}".format("", "PQ", "SQ", "RQ", "N")) + print("-" * (10 + 7 * 4)) + + for name, _isthing in metrics: + print("{:10s}| {:5.1f} {:5.1f} {:5.1f} {:5d}".format( + name, + 100 * results[name]['pq'], + 100 * results[name]['sq'], + 100 * results[name]['rq'], + results[name]['n']) + ) + + # calculate miou + acc = np.full(num_classes, np.nan, dtype=np.float64) + iou = np.full(num_classes, np.nan, dtype=np.float64) + tp = conf_matrix.diagonal()[:-1].astype(np.float64) + pos_gt = np.sum(conf_matrix[:-1, :-1], axis=0).astype(np.float64) + pos_pred = np.sum(conf_matrix[:-1, :-1], axis=1).astype(np.float64) + acc_valid = pos_gt > 0 + acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid] + iou_valid = (pos_gt + pos_pred) > 0 + union = pos_gt + pos_pred - tp + iou[acc_valid] = tp[acc_valid] / union[acc_valid] + miou = np.sum(iou[acc_valid]) / np.sum(iou_valid) + + print("") + print(f"mIoU: {miou}") + + +if __name__ == '__main__': + main() diff --git a/Mask2Former/train_net_video.py b/Mask2Former/train_net_video.py new file mode 100644 index 0000000..4966d8e --- /dev/null +++ b/Mask2Former/train_net_video.py @@ -0,0 +1,293 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +MaskFormer Training Script. + +This script is a simplified version of the training script in detectron2/tools. +""" +try: + # ignore ShapelyDeprecationWarning from fvcore + from shapely.errors import ShapelyDeprecationWarning + import warnings + warnings.filterwarnings('ignore', category=ShapelyDeprecationWarning) +except: + pass + +import copy +import itertools +import logging +import os + +from collections import OrderedDict +from typing import Any, Dict, List, Set + +import torch + +import detectron2.utils.comm as comm +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.config import get_cfg +from detectron2.data import MetadataCatalog +from detectron2.engine import (DefaultTrainer, default_argument_parser, default_setup, launch,) +from detectron2.evaluation import ( + DatasetEvaluator, + inference_on_dataset, + print_csv_format, + verify_results, + ) +from detectron2.projects.deeplab import add_deeplab_config, build_lr_scheduler +from detectron2.solver.build import maybe_add_gradient_clipping +from detectron2.utils.logger import setup_logger + +# MaskFormer +from mask2former import add_maskformer2_config +from mask2former_video import ( + YTVISDatasetMapper, + YTVISEvaluator, + add_maskformer2_video_config, + build_detection_train_loader, + build_detection_test_loader, + get_detection_dataset_dicts, +) + +import warnings +warnings.filterwarnings("ignore", category=UserWarning) #pytorch忽略user warning + +class Trainer(DefaultTrainer): + """ + Extension of the Trainer class adapted to MaskFormer. + """ + + @classmethod + def build_evaluator(cls, cfg, dataset_name, output_folder=None): + """ + Create evaluator(s) for a given dataset. + This uses the special metadata "evaluator_type" associated with each builtin dataset. + For your own dataset, you can simply create an evaluator manually in your + script and do not have to worry about the hacky if-else logic here. + """ + if output_folder is None: + output_folder = os.path.join(cfg.OUTPUT_DIR, "inference") + os.makedirs(output_folder, exist_ok=True) + + return YTVISEvaluator(dataset_name, cfg, True, output_folder) + + @classmethod + def build_train_loader(cls, cfg): + dataset_name = cfg.DATASETS.TRAIN[0] + mapper = YTVISDatasetMapper(cfg, is_train=True) + + dataset_dict = get_detection_dataset_dicts( + dataset_name, + filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS, + proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None, + ) + + return build_detection_train_loader(cfg, mapper=mapper, dataset=dataset_dict) + + @classmethod + def build_test_loader(cls, cfg, dataset_name): + dataset_name = cfg.DATASETS.TEST[0] + mapper = YTVISDatasetMapper(cfg, is_train=False) + return build_detection_test_loader(cfg, dataset_name, mapper=mapper) + + @classmethod + def build_lr_scheduler(cls, cfg, optimizer): + """ + It now calls :func:`detectron2.solver.build_lr_scheduler`. + Overwrite it if you'd like a different scheduler. + """ + return build_lr_scheduler(cfg, optimizer) + + @classmethod + def build_optimizer(cls, cfg, model): + weight_decay_norm = cfg.SOLVER.WEIGHT_DECAY_NORM + weight_decay_embed = cfg.SOLVER.WEIGHT_DECAY_EMBED + + defaults = {} + defaults["lr"] = cfg.SOLVER.BASE_LR + defaults["weight_decay"] = cfg.SOLVER.WEIGHT_DECAY + + norm_module_types = ( + torch.nn.BatchNorm1d, + torch.nn.BatchNorm2d, + torch.nn.BatchNorm3d, + torch.nn.SyncBatchNorm, + # NaiveSyncBatchNorm inherits from BatchNorm2d + torch.nn.GroupNorm, + torch.nn.InstanceNorm1d, + torch.nn.InstanceNorm2d, + torch.nn.InstanceNorm3d, + torch.nn.LayerNorm, + torch.nn.LocalResponseNorm, + ) + + params: List[Dict[str, Any]] = [] + memo: Set[torch.nn.parameter.Parameter] = set() + for module_name, module in model.named_modules(): + for module_param_name, value in module.named_parameters(recurse=False): + if not value.requires_grad: + continue + # Avoid duplicating parameters + if value in memo: + continue + memo.add(value) + + hyperparams = copy.copy(defaults) + if "backbone" in module_name: + hyperparams["lr"] = hyperparams["lr"] * cfg.SOLVER.BACKBONE_MULTIPLIER + if ( + "relative_position_bias_table" in module_param_name + or "absolute_pos_embed" in module_param_name + ): + print(module_param_name) + hyperparams["weight_decay"] = 0.0 + if isinstance(module, norm_module_types): + hyperparams["weight_decay"] = weight_decay_norm + if isinstance(module, torch.nn.Embedding): + hyperparams["weight_decay"] = weight_decay_embed + params.append({"params": [value], **hyperparams}) + + def maybe_add_full_model_gradient_clipping(optim): + # detectron2 doesn't have full model gradient clipping now + clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE + enable = ( + cfg.SOLVER.CLIP_GRADIENTS.ENABLED + and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model" + and clip_norm_val > 0.0 + ) + + class FullModelGradientClippingOptimizer(optim): + def step(self, closure=None): + all_params = itertools.chain(*[x["params"] for x in self.param_groups]) + torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val) + super().step(closure=closure) + + return FullModelGradientClippingOptimizer if enable else optim + + optimizer_type = cfg.SOLVER.OPTIMIZER + if optimizer_type == "SGD": + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)( + params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM + ) + elif optimizer_type == "ADAMW": + optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)( + params, cfg.SOLVER.BASE_LR + ) + else: + raise NotImplementedError(f"no optimizer type {optimizer_type}") + if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model": + optimizer = maybe_add_gradient_clipping(cfg, optimizer) + return optimizer + + @classmethod + def test(cls, cfg, model, evaluators=None): + """ + Evaluate the given model. The given model is expected to already contain + weights to evaluate. + Args: + cfg (CfgNode): + model (nn.Module): + evaluators (list[DatasetEvaluator] or None): if None, will call + :meth:`build_evaluator`. Otherwise, must have the same length as + ``cfg.DATASETS.TEST``. + Returns: + dict: a dict of result metrics + """ + from torch.cuda.amp import autocast + logger = logging.getLogger(__name__) + if isinstance(evaluators, DatasetEvaluator): + evaluators = [evaluators] + if evaluators is not None: + assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format( + len(cfg.DATASETS.TEST), len(evaluators) + ) + + results = OrderedDict() + for idx, dataset_name in enumerate(cfg.DATASETS.TEST): + data_loader = cls.build_test_loader(cfg, dataset_name) + # When evaluators are passed in as arguments, + # implicitly assume that evaluators can be created before data_loader. + if evaluators is not None: + evaluator = evaluators[idx] + else: + try: + evaluator = cls.build_evaluator(cfg, dataset_name) + except NotImplementedError: + logger.warn( + "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, " + "or implement its `build_evaluator` method." + ) + results[dataset_name] = {} + continue + with autocast(): + results_i = inference_on_dataset(model, data_loader, evaluator) + results[dataset_name] = results_i + if comm.is_main_process(): + assert isinstance( + results_i, dict + ), "Evaluator must return a dict on the main process. Got {} instead.".format( + results_i + ) + logger.info("Evaluation results for {} in csv format:".format(dataset_name)) + print_csv_format(results_i) + + if len(results) == 1: + results = list(results.values())[0] + return results + +def setup(args): + """ + Create configs and perform basic setups. + """ + cfg = get_cfg() + # for poly lr schedule + add_deeplab_config(cfg) + add_maskformer2_config(cfg) + add_maskformer2_video_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + cfg.MODEL.WEIGHTS = '/root/detectron2/projects/VITA/Mask2Former/weights/video_maskformer2_R50_bs16_8ep.pkl' + cfg.freeze() + default_setup(cfg, args) + # Setup logger for "mask_former" module + setup_logger(name="mask2former") + setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="mask2former_video") + return cfg + + +def main(args): + cfg = setup(args) + + if args.eval_only: + model = Trainer.build_model(cfg) + DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( + cfg.MODEL.WEIGHTS, resume=args.resume + ) + res = Trainer.test(cfg, model) + if cfg.TEST.AUG.ENABLED: + raise NotImplementedError + if comm.is_main_process(): + verify_results(cfg, res) + return res + + trainer = Trainer(cfg) + trainer.resume_or_load(resume=args.resume) + return trainer.train() + + +if __name__ == "__main__": + args = default_argument_parser().parse_args() + args.config_file = "/root/detectron2/projects/VITA/Mask2Former/configs/youtubevis_2019/video_maskformer2_R50_bs16_8ep.yaml" + args.checkpoint ='/root/detectron2/projects/VITA/Mask2Former/weights/video_maskformer2_R50_bs16_8ep.pkl' + # args.num_gpus = 2 #使用多gpu + # args.confidence_threshold = 0.5 + # args.opts = ['SOLVER.IMS_PER_BATCH',8] + # args.eval_only = True #用于评估测试集 + print("Command Line Args:", args) + launch( + main, + args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + args=(args,), + )