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| _base_ = 'yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py' | |
| # ========================modified parameters====================== | |
| img_scale = (1280, 1280) # width, height | |
| num_classes = 80 # Number of classes for classification | |
| # Config of batch shapes. Only on val. | |
| # It means not used if batch_shapes_cfg is None. | |
| batch_shapes_cfg = dict( | |
| img_size=img_scale[0], | |
| # The image scale of padding should be divided by pad_size_divisor | |
| size_divisor=64) | |
| # Basic size of multi-scale prior box | |
| anchors = [ | |
| [(19, 27), (44, 40), (38, 94)], # P3/8 | |
| [(96, 68), (86, 152), (180, 137)], # P4/16 | |
| [(140, 301), (303, 264), (238, 542)], # P5/32 | |
| [(436, 615), (739, 380), (925, 792)] # P6/64 | |
| ] | |
| # Strides of multi-scale prior box | |
| strides = [8, 16, 32, 64] | |
| num_det_layers = 4 # The number of model output scales | |
| loss_cls_weight = 0.5 | |
| loss_bbox_weight = 0.05 | |
| loss_obj_weight = 1.0 | |
| # The obj loss weights of the three output layers | |
| obj_level_weights = [4.0, 1.0, 0.25, 0.06] | |
| affine_scale = 0.5 # YOLOv5RandomAffine scaling ratio | |
| tta_img_scales = [(1280, 1280), (1024, 1024), (1536, 1536)] | |
| # =======================Unmodified in most cases================== | |
| model = dict( | |
| backbone=dict(arch='P6', out_indices=(2, 3, 4, 5)), | |
| neck=dict( | |
| in_channels=[256, 512, 768, 1024], out_channels=[256, 512, 768, 1024]), | |
| bbox_head=dict( | |
| head_module=dict( | |
| in_channels=[256, 512, 768, 1024], featmap_strides=strides), | |
| prior_generator=dict(base_sizes=anchors, strides=strides), | |
| # scaled based on number of detection layers | |
| loss_cls=dict(loss_weight=loss_cls_weight * | |
| (num_classes / 80 * 3 / num_det_layers)), | |
| loss_bbox=dict(loss_weight=loss_bbox_weight * (3 / num_det_layers)), | |
| loss_obj=dict(loss_weight=loss_obj_weight * | |
| ((img_scale[0] / 640)**2 * 3 / num_det_layers)), | |
| obj_level_weights=obj_level_weights)) | |
| pre_transform = _base_.pre_transform | |
| albu_train_transforms = _base_.albu_train_transforms | |
| train_pipeline = [ | |
| *pre_transform, | |
| dict( | |
| type='Mosaic', | |
| img_scale=img_scale, | |
| pad_val=114.0, | |
| pre_transform=pre_transform), | |
| dict( | |
| type='YOLOv5RandomAffine', | |
| max_rotate_degree=0.0, | |
| max_shear_degree=0.0, | |
| scaling_ratio_range=(1 - affine_scale, 1 + affine_scale), | |
| # img_scale is (width, height) | |
| border=(-img_scale[0] // 2, -img_scale[1] // 2), | |
| border_val=(114, 114, 114)), | |
| dict( | |
| type='mmdet.Albu', | |
| transforms=albu_train_transforms, | |
| bbox_params=dict( | |
| type='BboxParams', | |
| format='pascal_voc', | |
| label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), | |
| keymap={ | |
| 'img': 'image', | |
| 'gt_bboxes': 'bboxes' | |
| }), | |
| dict(type='YOLOv5HSVRandomAug'), | |
| dict(type='mmdet.RandomFlip', prob=0.5), | |
| dict( | |
| type='mmdet.PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', | |
| 'flip_direction')) | |
| ] | |
| train_dataloader = dict(dataset=dict(pipeline=train_pipeline)) | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
| dict(type='YOLOv5KeepRatioResize', scale=img_scale), | |
| dict( | |
| type='LetterResize', | |
| scale=img_scale, | |
| allow_scale_up=False, | |
| pad_val=dict(img=114)), | |
| dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), | |
| dict( | |
| type='mmdet.PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor', 'pad_param')) | |
| ] | |
| val_dataloader = dict( | |
| dataset=dict(pipeline=test_pipeline, batch_shapes_cfg=batch_shapes_cfg)) | |
| test_dataloader = val_dataloader | |
| # Config for Test Time Augmentation. (TTA) | |
| _multiscale_resize_transforms = [ | |
| dict( | |
| type='Compose', | |
| transforms=[ | |
| dict(type='YOLOv5KeepRatioResize', scale=s), | |
| dict( | |
| type='LetterResize', | |
| scale=s, | |
| allow_scale_up=False, | |
| pad_val=dict(img=114)) | |
| ]) for s in tta_img_scales | |
| ] | |
| tta_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
| dict( | |
| type='TestTimeAug', | |
| transforms=[ | |
| _multiscale_resize_transforms, | |
| [ | |
| dict(type='mmdet.RandomFlip', prob=1.), | |
| dict(type='mmdet.RandomFlip', prob=0.) | |
| ], [dict(type='mmdet.LoadAnnotations', with_bbox=True)], | |
| [ | |
| dict( | |
| type='mmdet.PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor', 'pad_param', 'flip', | |
| 'flip_direction')) | |
| ] | |
| ]) | |
| ] | |