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type="CascadeRCNN",
backbone=dict(
type="SwinTransformer",
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(
type="Pretrained",
checkpoint="https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth",
),
),
neck=dict(
type="FPN", in_channels=[96, 192, 384, 768], 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="SmoothL1Loss", beta=0.1111111111111111, loss_weight=1.0),
),
roi_head=dict(
type="CascadeRoIHead_LGF",
num_stages=3,
stage_loss_weights=[1, 1, 0.5],
bbox_roi_extractor=dict(
type="SingleRoIExtractor",
roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
),
bbox_head=[
dict(
type="Shared3FCBBoxHead_with_BboxEncoding",
in_channels=256,
fc_out_channels=1024,
bbox_encoding_dim=512,
roi_feat_size=7,
num_classes=18,
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=True,
loss_cls=dict(type="FocalLoss"),
loss_bbox=dict(type="BalancedL1Loss", beta=1.0, loss_weight=1.0),
),
dict(
type="Shared3FCBBoxHead_with_BboxEncoding",
in_channels=256,
fc_out_channels=1024,
bbox_encoding_dim=512,
roi_feat_size=7,
num_classes=18,
bbox_coder=dict(
type="DeltaXYWHBBoxCoder",
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.05, 0.05, 0.1, 0.1],
),
reg_class_agnostic=True,
loss_cls=dict(type="FocalLoss"),
loss_bbox=dict(type="BalancedL1Loss", beta=1.0, loss_weight=1.0),
),
dict(
type="Shared3FCBBoxHead_with_BboxEncoding",
in_channels=256,
fc_out_channels=1024,
bbox_encoding_dim=512,
roi_feat_size=7,
num_classes=18,
bbox_coder=dict(
type="DeltaXYWHBBoxCoder",
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.033, 0.033, 0.067, 0.067],
),
reg_class_agnostic=True,
loss_cls=dict(type="FocalLoss"),
loss_bbox=dict(type="BalancedL1Loss", beta=1.0, loss_weight=1.0),
),
],
localglobal_fuser=dict(
type="LocalGlobal_Context_Fuser",
channels=256,
roi_size=7,
reduced_channels=256,
lg_merge_layer=dict(type="SELayer", channels=256),
),
lgf_shared=False,
bbox_encoder=dict(
type="BboxEncoder",
n_layer=4,
n_head=4,
n_embd=512,
bbox_cord_dim=4,
bbox_max_num=1024,
embd_pdrop=0.1,
attn_pdrop=0.1,
),
bbox_encoder_shared=False,
),
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=0,
pos_weight=-1,
debug=False,
),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
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,
),
dict(
assigner=dict(
type="MaxIoUAssigner",
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
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,
),
dict(
assigner=dict(
type="MaxIoUAssigner",
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
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=0.0, nms=dict(type="nms", iou_threshold=0.7), max_per_img=200
),
),
)
dataset_type = "CocoDataset"
data_root = "data/coco/"
img_norm_cfg = dict(
mean=[216.45, 212.36, 206.76], std=[55.82, 56.04, 55.56], to_rgb=True
)
train_pipeline = [
dict(type="LoadImageFromFile"),
dict(type="LoadAnnotations", with_bbox=True),
dict(
type="AutoAugment",
policies=[
[
{
"type": "Resize",
"img_scale": [
(480, 1333),
(512, 1333),
(544, 1333),
(576, 1333),
(608, 1333),
(640, 1333),
(672, 1333),
(704, 1333),
(736, 1333),
(768, 1333),
(800, 1333),
],
"multiscale_mode": "value",
"keep_ratio": True,
}
],
[
{
"type": "Resize",
"img_scale": [(400, 1333), (500, 1333), (600, 1333)],
"multiscale_mode": "value",
"keep_ratio": True,
},
{
"type": "RandomCrop",
"crop_type": "absolute_range",
"crop_size": (384, 600),
"allow_negative_crop": True,
},
{
"type": "Resize",
"img_scale": [
(480, 1333),
(512, 1333),
(544, 1333),
(576, 1333),
(608, 1333),
(640, 1333),
(672, 1333),
(704, 1333),
(736, 1333),
(768, 1333),
(800, 1333),
],
"multiscale_mode": "value",
"override": True,
"keep_ratio": True,
},
{
"type": "PhotoMetricDistortion",
"brightness_delta": 32,
"contrast_range": (0.5, 1.5),
"saturation_range": (0.5, 1.5),
"hue_delta": 18,
},
{
"type": "MinIoURandomCrop",
"min_ious": (0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
"min_crop_size": 0.3,
},
{
"type": "CutOut",
"n_holes": (5, 10),
"cutout_shape": [
(4, 4),
(4, 8),
(8, 4),
(8, 8),
(16, 32),
(32, 16),
(32, 32),
(32, 48),
(48, 32),
(48, 48),
],
},
],
],
),
dict(type="RandomFlip", flip_ratio=0.1),
dict(
type="Normalize",
mean=[216.45, 212.36, 206.76],
std=[55.82, 56.04, 55.56],
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", to_float32=True),
dict(
type="MultiScaleFlipAug",
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type="Resize", keep_ratio=True),
dict(type="RandomFlip", flip_ratio=0.0),
dict(
type="Normalize",
mean=[216.45, 212.36, 206.76],
std=[55.82, 56.04, 55.56],
to_rgb=True,
),
dict(type="Pad", size_divisor=32),
dict(type="DefaultFormatBundle"),
dict(type="Collect", keys=["img"]),
],
),
]
data = dict(
samples_per_gpu=3,
workers_per_gpu=4,
train=dict(
type="CocoDataset",
ann_file="./data/pmc_2022/pmc_coco/element_detection/train.json",
img_prefix="./data/pmc_2022/pmc_coco/element_detection/train/",
pipeline=[
dict(type="LoadImageFromFile"),
dict(type="LoadAnnotations", with_bbox=True),
dict(
type="AutoAugment",
policies=[
[
{
"type": "Resize",
"img_scale": [
(480, 1333),
(512, 1333),
(544, 1333),
(576, 1333),
(608, 1333),
(640, 1333),
(672, 1333),
(704, 1333),
(736, 1333),
(768, 1333),
(800, 1333),
],
"multiscale_mode": "value",
"keep_ratio": True,
}
],
[
{
"type": "Resize",
"img_scale": [(400, 1333), (500, 1333), (600, 1333)],
"multiscale_mode": "value",
"keep_ratio": True,
},
{
"type": "RandomCrop",
"crop_type": "absolute_range",
"crop_size": (384, 600),
"allow_negative_crop": True,
},
{
"type": "Resize",
"img_scale": [
(480, 1333),
(512, 1333),
(544, 1333),
(576, 1333),
(608, 1333),
(640, 1333),
(672, 1333),
(704, 1333),
(736, 1333),
(768, 1333),
(800, 1333),
],
"multiscale_mode": "value",
"override": True,
"keep_ratio": True,
},
{
"type": "PhotoMetricDistortion",
"brightness_delta": 32,
"contrast_range": (0.5, 1.5),
"saturation_range": (0.5, 1.5),
"hue_delta": 18,
},
{
"type": "MinIoURandomCrop",
"min_ious": (0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
"min_crop_size": 0.3,
},
{
"type": "CutOut",
"n_holes": (5, 10),
"cutout_shape": [
(4, 4),
(4, 8),
(8, 4),
(8, 8),
(16, 32),
(32, 16),
(32, 32),
(32, 48),
(48, 32),
(48, 48),
],
},
],
],
),
dict(type="RandomFlip", flip_ratio=0.1),
dict(
type="Normalize",
mean=[216.45, 212.36, 206.76],
std=[55.82, 56.04, 55.56],
to_rgb=True,
),
dict(type="Pad", size_divisor=32),
dict(type="DefaultFormatBundle"),
dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
],
classes=[
"x_title",
"y_title",
"plot_area",
"other",
"xlabel",
"ylabel",
"chart_title",
"x_tick",
"y_tick",
"legend_patch",
"legend_label",
"legend_title",
"legend_area",
"mark_label",
"value_label",
"y_axis_area",
"x_axis_area",
"tick_grouping",
],
),
val=dict(
type="CocoDataset",
ann_file="./data/pmc_2022/pmc_coco/element_detection/val.json",
img_prefix="./data/pmc_2022/pmc_coco/element_detection/val/",
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=[
"x_title",
"y_title",
"plot_area",
"other",
"xlabel",
"ylabel",
"chart_title",
"x_tick",
"y_tick",
"legend_patch",
"legend_label",
"legend_title",
"legend_area",
"mark_label",
"value_label",
"y_axis_area",
"x_axis_area",
"tick_grouping",
],
),
test=dict(
type="CocoDataset",
ann_file="./data/pmc_2022/pmc_coco/element_detection/split3_test.json",
img_prefix="./data/pmc_2022/pmc_coco/element_detection/split3_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"]),
],
),
],
classes=[
"x_title",
"y_title",
"plot_area",
"other",
"xlabel",
"ylabel",
"chart_title",
"x_tick",
"y_tick",
"legend_patch",
"legend_label",
"legend_title",
"legend_area",
"mark_label",
"value_label",
"y_axis_area",
"x_axis_area",
"tick_grouping",
],
),
)
evaluation = dict(interval=1, metric=["bbox"])
optimizer = dict(
type="AdamW",
lr=0.0002,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys=dict(
absolute_pos_embed=dict(decay_mult=0.0),
relative_position_bias_table=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0),
)
),
)
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=150)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[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)]
opencv_num_threads = 0
mp_start_method = "fork"
auto_scale_lr = dict(enable=False, base_batch_size=16)
pretrained = "https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth"
classes = [
"x_title",
"y_title",
"plot_area",
"other",
"xlabel",
"ylabel",
"chart_title",
"x_tick",
"y_tick",
"legend_patch",
"legend_label",
"legend_title",
"legend_area",
"mark_label",
"value_label",
"y_axis_area",
"x_axis_area",
"tick_grouping",
]
auto_resume = False
gpu_ids = range(0, 4)
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