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htc_swin_template.py
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htc_swin_template.py
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cvpr_data_path = "/remote-home/admin/cvpr_project/raw_data"
dataset_name = "DATASET_NAME"
classes_dict = dict( Cable = ('break', 'thunderbolt'),
Capacitor = ('0',),
Casting = ('Inclusoes', 'Rechupe'),
Console = ('Collision', 'Dirty', 'Gap', 'Scratch'),
Cylinder = ('Chip', 'PistonMiss', 'Porosity', 'RCS'),
Electronics= ('damage',),
Groove = ('s_burr', 's_scratch'),
Hemisphere = ('Defect-A', 'Defect-B', 'Defect-C', 'Defect-D'),
Lens = ('Fiber', 'Flash Particle', 'Hole', 'Surface Damage', 'Tear'),
PCB_1 = ('missing_hole', 'mouse_bite', 'open_circuit', 'short', 'spur', 'spurious_copper'),
PCB_2 = ('defect1', 'defect2', 'defect3', 'defect4', 'defect5', 'defect6', 'defect7'),
Ring = ('t_contamination', 't_scratch', 'unfinished_surface'),
Screw = ('defect',),
Wood = ('impurities', 'pits'))
train_resize = [(1024, 1024), (1536,1536),(2048, 2048), (2560,2560),(3072, 3072),(3584,3584),(4096,4096)]
train_crop_size = (2048,2048)
test_resize = [(1024, 1024), (1536,1536),(2048, 2048), (2560,2560),(3072, 3072),(3584,3584),(4096,4096)]
samples_per_gpu = 2
train_repeat_times = 5
workers_per_gpu=4
save_interval = 1
log_interval = 10
eval_interval = 2
lr_init = 1e-4
max_epochs = 10
lr_step = [2,6,9]
train_subpath = "train_val"
val_subpath = "train_val"
test_subpath = "inference"
#########################################################################################
dataset_type = 'CocoDataset'
dataset_classes = classes_dict[dataset_name]
num_classes=len(dataset_classes)
train_path = f"{cvpr_data_path}/{dataset_name}/{train_subpath}/"
train_json = f"{cvpr_data_path}/{dataset_name}/{train_subpath}/_annotations.coco.json"
val_path = f"{cvpr_data_path}/{dataset_name}/{val_subpath}/"
val_json = f"{cvpr_data_path}/{dataset_name}/{val_subpath}/_annotations.coco.json"
test_path = f"{cvpr_data_path}/{dataset_name}/{test_subpath}/"
test_json = f"{cvpr_data_path}/{dataset_name}/{test_subpath}/_annotations.coco.json"
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
###################################################################################################
model = dict(
type='HybridTaskCascade',
pretrained=None,
backbone=dict(
type='CBSwinTransformer',
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.3,
ape=False,
patch_norm=True,
out_indices=(0, 1, 2, 3),
use_checkpoint=False,
frozen_stages=2),
neck=dict(
type='CBFPN',
in_channels=[128, 256, 512, 1024],
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],
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=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
type='HybridTaskCascadeRoIHead',
interleaved=True,
mask_info_flow=True,
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
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='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=num_classes,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
reg_decoded_bbox=True,
norm_cfg=dict(type='SyncBN', requires_grad=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
dict(
type='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=num_classes,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
reg_decoded_bbox=True,
norm_cfg=dict(type='SyncBN', requires_grad=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
dict(
type='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=num_classes,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
reg_decoded_bbox=True,
norm_cfg=dict(type='SyncBN', requires_grad=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=[
dict(
type='HTCMaskHead',
with_conv_res=False,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=num_classes,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=num_classes,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=num_classes,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
],
# semantic_roi_extractor=dict(
# type='SingleRoIExtractor',
# roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
# out_channels=256,
# featmap_strides=[8]),
# semantic_head=dict(
# type='FusedSemanticHead',
# num_ins=5,
# fusion_level=1,
# num_convs=4,
# in_channels=256,
# conv_out_channels=256,
# num_classes=183,
# ignore_label=255,
# loss_weight=0.2)
),
# model training and testing settings
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
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=0.4,
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
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,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
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=0.001,
nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.0001),
max_per_img=100,
mask_thr_binary=0.5))
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=train_resize, multiscale_mode="value", keep_ratio=True),
dict(type='RandomCrop', crop_type='absolute_range', crop_size=train_crop_size, allow_negative_crop=False),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=test_resize,
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=samples_per_gpu,
workers_per_gpu=workers_per_gpu,
train=dict(
type='RepeatDataset',
times=train_repeat_times,
dataset=dict(
type=dataset_type,
classes=dataset_classes,
img_prefix=train_path,
ann_file=train_json,
pipeline=train_pipeline)),
val=dict(
type=dataset_type,
classes=dataset_classes,
img_prefix=val_path,
ann_file=val_json,
pipeline=test_pipeline),
test=dict(
type=dataset_type,
classes=dataset_classes,
img_prefix=test_path,
ann_file=test_json,
pipeline=test_pipeline),
persistent_workers=True)
evaluation = dict(metric=['bbox', 'segm'],interval=eval_interval)
optimizer = dict(
type='AdamW',
lr=lr_init,
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,
type='Fp16OptimizerHook',
loss_scale='dynamic',
)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=100,
warmup_ratio=0.001,
step=lr_step)
runner = dict(type='EpochBasedRunner', max_epochs=max_epochs)
checkpoint_config = dict(interval=save_interval)
log_config = dict(interval=log_interval, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'coco_weight/htc_cbv2_swin_base22k_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_20e_coco.pth'
resume_from = None
workflow = [('train', 1)]
work_dir = f"result/0.weight/{dataset_name}"