mirror of https://github.com/open-mmlab/mmyolo.git
99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
_base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py'
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# ========================modified parameters========================
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# -----model related-----
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# Data augmentation
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max_translate_ratio = 0.1 # YOLOv5RandomAffine
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scaling_ratio_range = (0.5, 1.6) # YOLOv5RandomAffine
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mixup_prob = 0.05 # 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 = 0.5
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loss_obj_weight = 1.0
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lr_factor = 0.01 # Learning rate scaling factor
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# ===============================Unmodified in most cases====================
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num_classes = _base_.num_classes
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num_det_layers = _base_.num_det_layers
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img_scale = _base_.img_scale
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pre_transform = _base_.pre_transform
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model = dict(
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backbone=dict(
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arch='Tiny', act_cfg=dict(type='LeakyReLU', negative_slope=0.1)),
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neck=dict(
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is_tiny_version=True,
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in_channels=[128, 256, 512],
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out_channels=[64, 128, 256],
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block_cfg=dict(
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_delete_=True, type='TinyDownSampleBlock', middle_ratio=0.25),
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act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
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use_repconv_outs=False),
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bbox_head=dict(
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head_module=dict(in_channels=[128, 256, 512]),
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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_obj=dict(loss_weight=loss_obj_weight *
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((img_scale[0] / 640)**2 * 3 / num_det_layers))))
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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, # change
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scaling_ratio_range=scaling_ratio_range, # change
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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, # change
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scaling_ratio_range=scaling_ratio_range, # change
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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,
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beta=mixup_beta,
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prob=mixup_prob, # change
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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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default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))
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