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- dataset_type = 'DSDLDetDataset'
- data_root = 'path to dataset folder'
- train_ann = 'path to train yaml file'
- val_ann = 'path to val yaml file'
- backend_args = None
- # backend_args = dict(
- # backend='petrel',
- # path_mapping=dict({
- # './data/': "s3://open_data/",
- # 'data/': "s3://open_data/"
- # }))
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args=backend_args),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PackDetInputs')
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile', backend_args=backend_args),
- dict(type='Resize', scale=(1333, 800), keep_ratio=True),
- # If you don't have a gt annotation, delete the pipeline
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor', 'instances'))
- ]
- train_dataloader = dict(
- batch_size=2,
- num_workers=2,
- persistent_workers=True,
- sampler=dict(type='DefaultSampler', shuffle=True),
- batch_sampler=dict(type='AspectRatioBatchSampler'),
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file=train_ann,
- filter_cfg=dict(filter_empty_gt=True, min_size=32, bbox_min_size=32),
- pipeline=train_pipeline))
- val_dataloader = dict(
- batch_size=1,
- num_workers=2,
- persistent_workers=True,
- drop_last=False,
- sampler=dict(type='DefaultSampler', shuffle=False),
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file=val_ann,
- test_mode=True,
- pipeline=test_pipeline))
- test_dataloader = val_dataloader
- val_evaluator = dict(type='CocoMetric', metric='bbox')
- # val_evaluator = dict(type='VOCMetric', metric='mAP', eval_mode='11points')
- test_evaluator = val_evaluator
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