mirror of https://github.com/open-mmlab/mmyolo.git
183 lines
6.1 KiB
Python
183 lines
6.1 KiB
Python
_base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py'
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# ========================modified parameters========================
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# -----data related-----
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img_scale = (1280, 1280) # height, width
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num_classes = 80 # Number of classes for classification
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# Config of batch shapes. Only on val
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# It means not used if batch_shapes_cfg is None.
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batch_shapes_cfg = dict(
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img_size=img_scale[
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0], # The image scale of padding should be divided by pad_size_divisor
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size_divisor=64) # Additional paddings for pixel scale
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tta_img_scales = [(1280, 1280), (1024, 1024), (1536, 1536)]
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# -----model related-----
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# Basic size of multi-scale prior box
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anchors = [
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[(19, 27), (44, 40), (38, 94)], # P3/8
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[(96, 68), (86, 152), (180, 137)], # P4/16
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[(140, 301), (303, 264), (238, 542)], # P5/32
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[(436, 615), (739, 380), (925, 792)] # P6/64
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]
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strides = [8, 16, 32, 64] # Strides of multi-scale prior box
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num_det_layers = 4 # # The number of model output scales
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norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
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# Data augmentation
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max_translate_ratio = 0.2 # YOLOv5RandomAffine
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scaling_ratio_range = (0.1, 2.0) # YOLOv5RandomAffine
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mixup_prob = 0.15 # YOLOv5MixUp
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randchoice_mosaic_prob = [0.8, 0.2]
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mixup_alpha = 8.0 # YOLOv5MixUp
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mixup_beta = 8.0 # YOLOv5MixUp
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# -----train val related-----
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loss_cls_weight = 0.3
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loss_bbox_weight = 0.05
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loss_obj_weight = 0.7
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obj_level_weights = [4.0, 1.0, 0.25, 0.06]
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simota_candidate_topk = 20
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# The only difference between P6 and P5 in terms of
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# hyperparameters is lr_factor
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lr_factor = 0.2
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# ===============================Unmodified in most cases====================
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pre_transform = _base_.pre_transform
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model = dict(
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backbone=dict(arch='W', out_indices=(2, 3, 4, 5)),
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neck=dict(
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in_channels=[256, 512, 768, 1024],
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out_channels=[128, 256, 384, 512],
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use_maxpool_in_downsample=False,
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use_repconv_outs=False),
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bbox_head=dict(
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head_module=dict(
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type='YOLOv7p6HeadModule',
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in_channels=[128, 256, 384, 512],
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featmap_strides=strides,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='SiLU', inplace=True)),
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prior_generator=dict(base_sizes=anchors, strides=strides),
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simota_candidate_topk=simota_candidate_topk, # note
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# scaled based on number of detection layers
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loss_cls=dict(loss_weight=loss_cls_weight *
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(num_classes / 80 * 3 / num_det_layers)),
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loss_bbox=dict(loss_weight=loss_bbox_weight * (3 / num_det_layers)),
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loss_obj=dict(loss_weight=loss_obj_weight *
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((img_scale[0] / 640)**2 * 3 / num_det_layers)),
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obj_level_weights=obj_level_weights))
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mosiac4_pipeline = [
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dict(
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type='Mosaic',
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img_scale=img_scale,
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pad_val=114.0,
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pre_transform=pre_transform),
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dict(
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type='YOLOv5RandomAffine',
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max_rotate_degree=0.0,
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max_shear_degree=0.0,
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max_translate_ratio=max_translate_ratio, # note
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scaling_ratio_range=scaling_ratio_range, # note
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# img_scale is (width, height)
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border=(-img_scale[0] // 2, -img_scale[1] // 2),
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border_val=(114, 114, 114)),
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]
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mosiac9_pipeline = [
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dict(
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type='Mosaic9',
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img_scale=img_scale,
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pad_val=114.0,
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pre_transform=pre_transform),
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dict(
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type='YOLOv5RandomAffine',
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max_rotate_degree=0.0,
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max_shear_degree=0.0,
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max_translate_ratio=max_translate_ratio, # note
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scaling_ratio_range=scaling_ratio_range, # note
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# img_scale is (width, height)
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border=(-img_scale[0] // 2, -img_scale[1] // 2),
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border_val=(114, 114, 114)),
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]
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randchoice_mosaic_pipeline = dict(
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type='RandomChoice',
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transforms=[mosiac4_pipeline, mosiac9_pipeline],
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prob=randchoice_mosaic_prob)
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train_pipeline = [
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*pre_transform,
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randchoice_mosaic_pipeline,
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dict(
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type='YOLOv5MixUp',
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alpha=mixup_alpha, # note
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beta=mixup_beta, # note
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prob=mixup_prob,
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pre_transform=[*pre_transform, randchoice_mosaic_pipeline]),
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dict(type='YOLOv5HSVRandomAug'),
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dict(type='mmdet.RandomFlip', prob=0.5),
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dict(
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type='mmdet.PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
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'flip_direction'))
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]
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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test_pipeline = [
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dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
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dict(type='YOLOv5KeepRatioResize', scale=img_scale),
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dict(
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type='LetterResize',
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scale=img_scale,
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allow_scale_up=False,
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pad_val=dict(img=114)),
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dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
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dict(
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type='mmdet.PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor', 'pad_param'))
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]
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val_dataloader = dict(
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dataset=dict(pipeline=test_pipeline, batch_shapes_cfg=batch_shapes_cfg))
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test_dataloader = val_dataloader
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default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))
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# Config for Test Time Augmentation. (TTA)
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_multiscale_resize_transforms = [
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dict(
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type='Compose',
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transforms=[
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dict(type='YOLOv5KeepRatioResize', scale=s),
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dict(
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type='LetterResize',
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scale=s,
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allow_scale_up=False,
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pad_val=dict(img=114))
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]) for s in tta_img_scales
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]
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tta_pipeline = [
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dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
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dict(
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type='TestTimeAug',
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transforms=[
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_multiscale_resize_transforms,
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[
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dict(type='mmdet.RandomFlip', prob=1.),
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dict(type='mmdet.RandomFlip', prob=0.)
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], [dict(type='mmdet.LoadAnnotations', with_bbox=True)],
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[
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dict(
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type='mmdet.PackDetInputs',
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meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
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'scale_factor', 'pad_param', 'flip',
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'flip_direction'))
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]
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])
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]
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