mirror of https://github.com/open-mmlab/mmyolo.git
130 lines
3.8 KiB
Python
130 lines
3.8 KiB
Python
_base_ = '../_base_/default_runtime.py'
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# dataset settings
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data_root = 'data/coco/'
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dataset_type = 'YOLOv5CocoDataset'
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# parameters that often need to be modified
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img_scale = (640, 640) # height, width
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deepen_factor = 1.0
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widen_factor = 1.0
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max_epochs = 300
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save_epoch_intervals = 10
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train_batch_size_per_gpu = 16
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train_num_workers = 8
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val_batch_size_per_gpu = 1
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val_num_workers = 2
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# persistent_workers must be False if num_workers is 0.
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persistent_workers = True
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# only on Val
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batch_shapes_cfg = dict(
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type='BatchShapePolicy',
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batch_size=val_batch_size_per_gpu,
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img_size=img_scale[0],
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size_divisor=32,
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extra_pad_ratio=0.5)
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# different from yolov5
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anchors = [[(12, 16), (19, 36), (40, 28)], [(36, 75), (76, 55), (72, 146)],
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[(142, 110), (192, 243), (459, 401)]]
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strides = [8, 16, 32]
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# single-scale training is recommended to
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# be turned on, which can speed up training.
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env_cfg = dict(cudnn_benchmark=True)
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model = dict(
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type='YOLODetector',
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data_preprocessor=dict(
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type='YOLOv5DetDataPreprocessor',
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mean=[0., 0., 0.],
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std=[255., 255., 255.],
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bgr_to_rgb=True),
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backbone=dict(
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type='YOLOv7Backbone',
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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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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neck=dict(
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type='YOLOv7PAFPN',
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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upsample_feats_cat_first=False,
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in_channels=[512, 1024, 1024],
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out_channels=[128, 256, 512],
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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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bbox_head=dict(
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type='YOLOv7Head',
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head_module=dict(
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type='YOLOv5HeadModule',
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num_classes=80,
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in_channels=[256, 512, 1024],
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widen_factor=widen_factor,
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featmap_strides=strides,
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num_base_priors=3),
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prior_generator=dict(
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type='mmdet.YOLOAnchorGenerator',
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base_sizes=anchors,
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strides=strides)),
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test_cfg=dict(
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multi_label=True,
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nms_pre=30000,
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score_thr=0.001,
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nms=dict(type='nms', iou_threshold=0.65),
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max_per_img=300))
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test_pipeline = [
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dict(
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type='LoadImageFromFile',
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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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batch_size=val_batch_size_per_gpu,
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num_workers=val_num_workers,
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persistent_workers=persistent_workers,
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pin_memory=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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test_mode=True,
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data_prefix=dict(img='val2017/'),
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ann_file='annotations/instances_val2017.json',
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pipeline=test_pipeline,
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batch_shapes_cfg=batch_shapes_cfg))
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test_dataloader = val_dataloader
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val_evaluator = dict(
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type='mmdet.CocoMetric',
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proposal_nums=(100, 1, 10), # Can be accelerated
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ann_file=data_root + 'annotations/instances_val2017.json',
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metric='bbox')
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test_evaluator = val_evaluator
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# train_cfg = dict(
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# type='EpochBasedTrainLoop',
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# max_epochs=max_epochs,
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# val_interval=save_epoch_intervals)
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val_cfg = dict(type='ValLoop')
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test_cfg = dict(type='TestLoop')
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# randomness = dict(seed=1, deterministic=True)
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