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dh_faster_rcnn_r50_fpn_1x_coco.py
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dh_faster_rcnn_r50_fpn_1x_coco.py
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model = dict(
type="FasterRCNN",
backbone=dict(
type="ResNet",
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type="BN", requires_grad=True),
norm_eval=True,
style="pytorch",
init_cfg=dict(type="Pretrained", checkpoint="torchvision://resnet50"),
),
neck=dict(
type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5
),
rpn_head=dict(
type="RPNHead",
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type="AnchorGenerator",
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64],
),
bbox_coder=dict(
type="DeltaXYWHBBoxCoder",
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0],
),
loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type="L1Loss", loss_weight=1.0),
),
roi_head=dict(
type="StandardRoIHead",
bbox_roi_extractor=dict(
type="SingleRoIExtractor",
roi_layer=dict(
type="ModulatedDeformRoIPoolPack", output_size=7, output_channels=256
),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
),
bbox_head=dict(
type="Shared2FCBBoxHead",
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=5,
bbox_coder=dict(
type="DeltaXYWHBBoxCoder",
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2],
),
reg_class_agnostic=False,
loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type="L1Loss", loss_weight=1.0),
),
),
train_cfg=dict(
rpn=dict(
assigner=dict(
type="MaxIoUAssigner",
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1,
),
sampler=dict(
type="RandomSampler",
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False,
),
allowed_border=-1,
pos_weight=-1,
debug=False,
),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type="nms", iou_threshold=0.7),
min_bbox_size=0,
),
rcnn=dict(
assigner=dict(
type="MaxIoUAssigner",
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1,
),
sampler=dict(
type="RandomSampler",
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True,
),
pos_weight=-1,
debug=False,
),
),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type="nms", iou_threshold=0.7),
min_bbox_size=0,
),
rcnn=dict(
score_thr=0.05, nms=dict(type="nms", iou_threshold=0.5), max_per_img=100
),
),
)
dataset_type = "COCODataset"
data_root = "data/coco/"
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
train_pipeline = [
dict(type="LoadImageFromFile"),
dict(type="LoadAnnotations", with_bbox=True),
dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
dict(type="RandomFlip", flip_ratio=0.5),
dict(
type="Normalize",
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
),
dict(type="Pad", size_divisor=32),
dict(type="DefaultFormatBundle"),
dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
dict(type="LoadImageFromFile"),
dict(
type="MultiScaleFlipAug",
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type="Resize", keep_ratio=True),
dict(type="RandomFlip"),
dict(
type="Normalize",
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
),
dict(type="Pad", size_divisor=32),
dict(type="ImageToTensor", keys=["img"]),
dict(type="Collect", keys=["img"]),
],
),
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type="CocoDataset",
ann_file="data/coco/train.json",
img_prefix="data/images/",
pipeline=[
dict(type="LoadImageFromFile"),
dict(type="LoadAnnotations", with_bbox=True),
dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
dict(type="RandomFlip", flip_ratio=0.5),
dict(
type="Normalize",
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
),
dict(type="Pad", size_divisor=32),
dict(type="DefaultFormatBundle"),
dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
],
classes=("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"),
),
val=dict(
type="CocoDataset",
ann_file="data/coco/test.json",
img_prefix="data/images/",
pipeline=[
dict(type="LoadImageFromFile"),
dict(
type="MultiScaleFlipAug",
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type="Resize", keep_ratio=True),
dict(type="RandomFlip"),
dict(
type="Normalize",
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
),
dict(type="Pad", size_divisor=32),
dict(type="ImageToTensor", keys=["img"]),
dict(type="Collect", keys=["img"]),
],
),
],
classes=("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"),
),
test=dict(
type="CocoDataset",
ann_file="data/coco/test.json",
img_prefix="data/images/",
pipeline=[
dict(type="LoadImageFromFile"),
dict(
type="MultiScaleFlipAug",
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type="Resize", keep_ratio=True),
dict(type="RandomFlip"),
dict(
type="Normalize",
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
),
dict(type="Pad", size_divisor=32),
dict(type="ImageToTensor", keys=["img"]),
dict(type="Collect", keys=["img"]),
],
),
],
classes=("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"),
),
)
evaluation = dict(interval=1, metric="bbox")
optimizer = dict(type="SGD", lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy="step", warmup="linear", warmup_iters=500, warmup_ratio=0.001, step=[8, 11]
)
runner = dict(type="EpochBasedRunner", max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50, hooks=[dict(type="TensorboardLoggerHook"), dict(type="TextLoggerHook")]
)
custom_hooks = [dict(type="NumClassCheckHook")]
dist_params = dict(backend="nccl")
log_level = "INFO"
load_from = None
resume_from = None
workflow = [("train", 1)]
classes = ("Coverall", "Face_Shield", "Gloves", "Goggles", "Mask")