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fsaf_x101_64x4d_fpn_1x_coco.py
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fsaf_x101_64x4d_fpn_1x_coco.py
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model = dict(
type="FSAF",
backbone=dict(
type="ResNeXt",
depth=101,
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="open-mmlab://resnext101_64x4d"),
groups=64,
base_width=4,
),
neck=dict(
type="FPN",
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs="on_input",
num_outs=5,
),
bbox_head=dict(
type="FSAFHead",
num_classes=5,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type="AnchorGenerator",
octave_base_scale=1,
scales_per_octave=1,
ratios=[1.0],
strides=[8, 16, 32, 64, 128],
),
bbox_coder=dict(type="TBLRBBoxCoder", normalizer=4.0),
loss_cls=dict(
type="FocalLoss",
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0,
reduction="none",
),
loss_bbox=dict(type="IoULoss", eps=1e-06, loss_weight=1.0, reduction="none"),
reg_decoded_bbox=True,
),
train_cfg=dict(
assigner=dict(
type="CenterRegionAssigner", pos_scale=0.2, neg_scale=0.2, min_pos_iof=0.01
),
allowed_border=-1,
pos_weight=-1,
debug=False,
),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
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=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
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")