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| # dataset settings | |
| dataset_type = 'CocoDataset' | |
| data_root = 'data/coco/' | |
| # file_client_args = dict( | |
| # backend='petrel', | |
| # path_mapping=dict({ | |
| # './data/': 's3://openmmlab/datasets/detection/', | |
| # 'data/': 's3://openmmlab/datasets/detection/' | |
| # })) | |
| file_client_args = dict(backend='disk') | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', file_client_args=file_client_args), | |
| dict( | |
| type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), | |
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PackDetInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', file_client_args=file_client_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, with_mask=True, with_seg=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ] | |
| 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='annotations/instances_train2017.json', | |
| data_prefix=dict(img='train2017/', seg='stuffthingmaps/train2017/'), | |
| filter_cfg=dict(filter_empty_gt=True, 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='annotations/instances_val2017.json', | |
| data_prefix=dict(img='val2017/'), | |
| test_mode=True, | |
| pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict( | |
| type='CocoMetric', | |
| ann_file=data_root + 'annotations/instances_val2017.json', | |
| metric=['bbox', 'segm'], | |
| format_only=False) | |
| test_evaluator = val_evaluator | |