mirror of https://github.com/open-mmlab/mmyolo.git
121 lines
3.8 KiB
Python
121 lines
3.8 KiB
Python
_base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py'
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img_scale = (1280, 1280) # height, width
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num_classes = 80
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# only on Val
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batch_shapes_cfg = dict(img_size=img_scale[0], size_divisor=64)
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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]
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num_det_layers = 4
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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=dict(type='BN', momentum=0.03, eps=0.001),
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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=20, # note
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# scaled based on number of detection layers
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loss_cls=dict(loss_weight=0.3 *
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(num_classes / 80 * 3 / num_det_layers)),
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loss_bbox=dict(loss_weight=0.05 * (3 / num_det_layers)),
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loss_obj=dict(loss_weight=0.7 *
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((img_scale[0] / 640)**2 * 3 / num_det_layers)),
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obj_level_weights=[4.0, 1.0, 0.25, 0.06]))
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pre_transform = _base_.pre_transform
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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=0.2, # note
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scaling_ratio_range=(0.1, 2.0), # 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=0.2, # note
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scaling_ratio_range=(0.1, 2.0), # 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=[0.8, 0.2])
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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=8.0, # note
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beta=8.0, # note
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prob=0.15,
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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', file_client_args=_base_.file_client_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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# The only difference between P6 and P5 in terms of
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# hyperparameters is lr_factor
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default_hooks = dict(param_scheduler=dict(lr_factor=0.2))
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