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[Feature] Add detectors trained on humanart (#2724)
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,95 @@ | ||
| # dataset settings | ||
| dataset_type = 'CocoDataset' | ||
| data_root = 'data/' | ||
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| # Example to use different file client | ||
| # Method 1: simply set the data root and let the file I/O module | ||
| # automatically infer from prefix (not support LMDB and Memcache yet) | ||
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| # data_root = 's3://openmmlab/datasets/detection/coco/' | ||
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| # Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6 | ||
| # backend_args = dict( | ||
| # backend='petrel', | ||
| # path_mapping=dict({ | ||
| # './data/': 's3://openmmlab/datasets/detection/', | ||
| # 'data/': 's3://openmmlab/datasets/detection/' | ||
| # })) | ||
| backend_args = None | ||
|
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| 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')) | ||
| ] | ||
| 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='HumanArt/annotations/training_humanart_coco.json', | ||
| data_prefix=dict(img=''), | ||
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | ||
| pipeline=train_pipeline, | ||
| backend_args=backend_args)) | ||
| 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='HumanArt/annotations/validation_humanart_coco.json', | ||
| data_prefix=dict(img=''), | ||
| test_mode=True, | ||
| pipeline=test_pipeline, | ||
| backend_args=backend_args)) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| val_evaluator = dict( | ||
| type='CocoMetric', | ||
| ann_file=data_root + 'HumanArt/annotations/validation_humanart_coco.json', | ||
| metric='bbox', | ||
| format_only=False, | ||
| backend_args=backend_args) | ||
| test_evaluator = val_evaluator | ||
|
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| # inference on test dataset and | ||
| # format the output results for submission. | ||
| # test_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=data_root + 'annotations/image_info_test-dev2017.json', | ||
| # data_prefix=dict(img='test2017/'), | ||
| # test_mode=True, | ||
| # pipeline=test_pipeline)) | ||
| # test_evaluator = dict( | ||
| # type='CocoMetric', | ||
| # metric='bbox', | ||
| # format_only=True, | ||
| # ann_file=data_root + 'annotations/image_info_test-dev2017.json', | ||
| # outfile_prefix='./work_dirs/coco_detection/test') |
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