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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # dataset settings | |
| dataset_type = 'OpenImagesDataset' | |
| data_root = 'data/OpenImages/' | |
| # 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) | |
| # data_root = 's3://openmmlab/datasets/detection/coco/' | |
| # 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 | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=backend_args), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(type='Resize', scale=(1024, 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=(1024, 800), keep_ratio=True), | |
| # avoid bboxes being resized | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| # TODO: find a better way to collect image_level_labels | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor', 'instances', 'image_level_labels')) | |
| ] | |
| train_dataloader = dict( | |
| batch_size=2, | |
| num_workers=0, # workers_per_gpu > 0 may occur out of memory | |
| persistent_workers=False, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| batch_sampler=dict(type='AspectRatioBatchSampler'), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/oidv6-train-annotations-bbox.csv', | |
| data_prefix=dict(img='OpenImages/train/'), | |
| label_file='annotations/class-descriptions-boxable.csv', | |
| hierarchy_file='annotations/bbox_labels_600_hierarchy.json', | |
| meta_file='annotations/train-image-metas.pkl', | |
| pipeline=train_pipeline, | |
| backend_args=backend_args)) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=0, | |
| persistent_workers=False, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/validation-annotations-bbox.csv', | |
| data_prefix=dict(img='OpenImages/validation/'), | |
| label_file='annotations/class-descriptions-boxable.csv', | |
| hierarchy_file='annotations/bbox_labels_600_hierarchy.json', | |
| meta_file='annotations/validation-image-metas.pkl', | |
| image_level_ann_file='annotations/validation-' | |
| 'annotations-human-imagelabels-boxable.csv', | |
| pipeline=test_pipeline, | |
| backend_args=backend_args)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict( | |
| type='OpenImagesMetric', | |
| iou_thrs=0.5, | |
| ioa_thrs=0.5, | |
| use_group_of=True, | |
| get_supercategory=True) | |
| test_evaluator = val_evaluator | |