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| Original file line number | Diff line number | Diff line change |
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| _base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py'] | ||
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|
||
| # ========================Frequently modified parameters====================== | ||
| # -----data related----- | ||
| data_root = 'data/coco/' # Root path of data | ||
| # Path of train annotation file | ||
| train_ann_file = 'annotations/instances_train2017.json' | ||
| train_data_prefix = 'train2017/' # Prefix of train image path | ||
| # Path of val annotation file | ||
| val_ann_file = 'annotations/instances_val2017.json' | ||
| val_data_prefix = 'val2017/' # Prefix of val image path | ||
|
|
||
| num_classes = 80 # Number of classes for classification | ||
| # Batch size of a single GPU during training | ||
| train_batch_size_per_gpu = 16 | ||
| # Worker to pre-fetch data for each single GPU during training | ||
| train_num_workers = 8 | ||
| # persistent_workers must be False if num_workers is 0 | ||
| persistent_workers = True | ||
|
|
||
| # -----model related----- | ||
| # Basic size of multi-scale prior box | ||
| anchors = [[(10, 14), (23, 27), (37, 58)], [(81, 82), (135, 169), (344, 319)]] | ||
|
|
||
| # -----train val related----- | ||
| # Base learning rate for optim_wrapper. Corresponding to 8xb16=128 bs | ||
| base_lr = 0.01 | ||
| max_epochs = 300 # Maximum training epochs | ||
|
|
||
| model_test_cfg = dict( | ||
| # The config of multi-label for multi-class prediction. | ||
| multi_label=True, | ||
| # The number of boxes before NMS | ||
| nms_pre=30000, | ||
| score_thr=0.001, # Threshold to filter out boxes. | ||
| nms=dict(type='nms', iou_threshold=0.65), # NMS type and threshold | ||
| max_per_img=300) # Max number of detections of each image | ||
|
|
||
| # ========================Possible modified parameters======================== | ||
| # -----data related----- | ||
| img_scale = (416, 416) # width, height | ||
| # Dataset type, this will be used to define the dataset | ||
| dataset_type = 'YOLOv5CocoDataset' | ||
| # Batch size of a single GPU during validation | ||
| val_batch_size_per_gpu = 1 | ||
| # Worker to pre-fetch data for each single GPU during validation | ||
| val_num_workers = 2 | ||
|
|
||
| # Config of batch shapes. Only on val. | ||
| # It means not used if batch_shapes_cfg is None. | ||
| batch_shapes_cfg = dict( | ||
| type='BatchShapePolicy', | ||
| batch_size=val_batch_size_per_gpu, | ||
| img_size=img_scale[0], | ||
| # The image scale of padding should be divided by pad_size_divisor | ||
| size_divisor=32, | ||
| # Additional paddings for pixel scale | ||
| extra_pad_ratio=0.5) | ||
|
|
||
| # -----model related----- | ||
| # The scaling factor that controls the depth of the network structure | ||
| deepen_factor = 1.0 | ||
| # The scaling factor that controls the width of the network structure | ||
| widen_factor = 1.0 | ||
| # Strides of multi-scale prior box | ||
| strides = [8, 16] | ||
| num_det_layers = 3 # The number of model output scales | ||
| norm_cfg = dict(type='BN', momentum=0.03, eps=0.001) # Normalization config | ||
|
|
||
| # -----train val related----- | ||
| affine_scale = 0.5 # YOLOv5RandomAffine scaling ratio | ||
| loss_cls_weight = 0.5 | ||
| loss_bbox_weight = 0.05 | ||
| loss_obj_weight = 1.0 | ||
| prior_match_thr = 4. # Priori box matching threshold | ||
| # The obj loss weights of the three output layers | ||
| obj_level_weights = [4., 1.] | ||
| lr_factor = 0.01 # Learning rate scaling factor | ||
| weight_decay = 0.0005 | ||
| # Save model checkpoint and validation intervals | ||
| save_checkpoint_intervals = 10 | ||
| # The maximum checkpoints to keep. | ||
| max_keep_ckpts = 3 | ||
| # Single-scale training is recommended to | ||
| # be turned on, which can speed up training. | ||
| env_cfg = dict(cudnn_benchmark=True) | ||
|
|
||
| #randomness = dict(seed=0) | ||
| # ===============================Unmodified in most cases==================== | ||
| model = dict( | ||
| type='YOLODetector', | ||
| data_preprocessor=dict( | ||
| type='mmdet.DetDataPreprocessor', | ||
| mean=[0., 0., 0.], | ||
| std=[255., 255., 255.], | ||
| bgr_to_rgb=True), | ||
| backbone=dict( | ||
| type='YOLOv4TinyBackbone', | ||
| norm_cfg=norm_cfg, | ||
| act_cfg=dict(type='LeakyReLU', inplace=True)), | ||
| neck=dict( | ||
| type='V4TinyUpSampleBlock', | ||
| in_channels=[512, 256], | ||
| out_channels=[256, 128], | ||
| norm_cfg=norm_cfg, | ||
| act_cfg=dict(type='LeakyReLU', inplace=True)), | ||
| bbox_head=dict( | ||
| type='YOLOv3Head', | ||
| head_module=dict( | ||
| type='YOLOv3HeadModule', | ||
| num_classes=num_classes, | ||
| in_channels=[256, 384], | ||
| featmap_strides=strides, | ||
| num_base_priors=3), | ||
| prior_generator=dict( | ||
| type='mmdet.YOLOAnchorGenerator', | ||
| base_sizes=anchors, | ||
| strides=strides), | ||
| # scaled based on number of detection layers | ||
| loss_cls=dict( | ||
| type='mmdet.CrossEntropyLoss', | ||
| use_sigmoid=True, | ||
| reduction='mean', | ||
| loss_weight=loss_cls_weight * | ||
| (num_classes / 80 * 3 / num_det_layers)), | ||
| loss_bbox=dict( | ||
| type='IoULoss', | ||
| iou_mode='ciou', | ||
| bbox_format='xywh', | ||
| eps=1e-7, | ||
| reduction='mean', | ||
| loss_weight=loss_bbox_weight * (3 / num_det_layers), | ||
| return_iou=True), | ||
| loss_obj=dict( | ||
| type='mmdet.CrossEntropyLoss', | ||
| use_sigmoid=True, | ||
| reduction='mean', | ||
| loss_weight=loss_obj_weight * | ||
| ((img_scale[0] / 640)**2 * 3 / num_det_layers)), | ||
| prior_match_thr=prior_match_thr, | ||
| obj_level_weights=obj_level_weights), | ||
| test_cfg=model_test_cfg) | ||
|
|
||
| albu_train_transforms = [ | ||
| dict(type='Blur', p=0.01), | ||
| dict(type='MedianBlur', p=0.01), | ||
| dict(type='ToGray', p=0.01), | ||
| dict(type='CLAHE', p=0.01) | ||
| ] | ||
|
|
||
| pre_transform = [ | ||
| dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | ||
| dict(type='LoadAnnotations', with_bbox=True) | ||
| ] | ||
|
|
||
| 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( | ||
| batch_size=train_batch_size_per_gpu, | ||
| num_workers=train_num_workers, | ||
| persistent_workers=persistent_workers, | ||
| pin_memory=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file=train_ann_file, | ||
| data_prefix=dict(img=train_data_prefix), | ||
| filter_cfg=dict(filter_empty_gt=False, min_size=32), | ||
| 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( | ||
| batch_size=val_batch_size_per_gpu, | ||
| num_workers=val_num_workers, | ||
| persistent_workers=persistent_workers, | ||
| pin_memory=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| test_mode=True, | ||
| data_prefix=dict(img=val_data_prefix), | ||
| ann_file=val_ann_file, | ||
| pipeline=test_pipeline, | ||
| batch_shapes_cfg=batch_shapes_cfg)) | ||
|
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||
| test_dataloader = val_dataloader | ||
|
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||
| param_scheduler = None | ||
| optim_wrapper = dict( | ||
| type='OptimWrapper', | ||
| optimizer=dict( | ||
| type='SGD', | ||
| lr=base_lr, | ||
| momentum=0.937, | ||
| weight_decay=weight_decay, | ||
| nesterov=True, | ||
| batch_size_per_gpu=train_batch_size_per_gpu), | ||
| constructor='YOLOv5OptimizerConstructor') | ||
|
|
||
| default_hooks = dict( | ||
| param_scheduler=dict( | ||
| type='YOLOv5ParamSchedulerHook', | ||
| scheduler_type='linear', | ||
| lr_factor=lr_factor, | ||
| max_epochs=max_epochs), | ||
| checkpoint=dict( | ||
| type='CheckpointHook', | ||
| interval=save_checkpoint_intervals, | ||
| save_best='auto', | ||
| max_keep_ckpts=max_keep_ckpts)) | ||
|
|
||
| custom_hooks = [ | ||
| dict( | ||
| type='EMAHook', | ||
| ema_type='ExpMomentumEMA', | ||
| momentum=0.0001, | ||
| update_buffers=True, | ||
| strict_load=False, | ||
| priority=49) | ||
| ] | ||
|
|
||
| val_evaluator = dict( | ||
| type='mmdet.CocoMetric', | ||
| proposal_nums=(100, 1, 10), | ||
| ann_file=data_root + val_ann_file, | ||
| metric='bbox') | ||
| test_evaluator = val_evaluator | ||
|
|
||
| train_cfg = dict( | ||
| type='EpochBasedTrainLoop', | ||
| max_epochs=max_epochs, | ||
| val_interval=save_checkpoint_intervals) | ||
| val_cfg = dict(type='ValLoop') | ||
| test_cfg = dict(type='TestLoop') |
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