mirror of https://github.com/open-mmlab/mmyolo.git
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
_base_ = 'yolov5_s-v61_fast_1xb12-40e_cat.py'
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# This configuration is used to provide non-square training examples
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# Must be a multiple of 32
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img_scale = (608, 352) # w h
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anchors = [
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[(65, 35), (159, 45), (119, 80)], # P3/8
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[(215, 77), (224, 116), (170, 166)], # P4/16
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[(376, 108), (339, 176), (483, 190)] # P5/32
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]
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# ===============================Unmodified in most cases====================
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_base_.model.bbox_head.loss_obj.loss_weight = 1.0 * ((img_scale[1] / 640)**2)
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_base_.model.bbox_head.prior_generator.base_sizes = anchors
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train_pipeline = [
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*_base_.pre_transform,
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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=_base_.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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scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
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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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dict(
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type='mmdet.Albu',
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transforms=_base_.albu_train_transforms,
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bbox_params=dict(
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type='BboxParams',
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format='pascal_voc',
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label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
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keymap={
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'img': 'image',
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'gt_bboxes': 'bboxes'
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}),
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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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_base_.train_dataloader.dataset.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='mmdet.LoadAnnotations', with_bbox=True),
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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=None))
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test_dataloader = val_dataloader
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