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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from mmcv.transforms import LoadImageFromFile | |
| from mmengine.dataset.sampler import DefaultSampler | |
| from mmdet.datasets import AspectRatioBatchSampler, CocoDataset | |
| from mmdet.datasets.transforms import (LoadAnnotations, PackDetInputs, | |
| RandomFlip, Resize) | |
| from mmdet.evaluation import CocoMetric | |
| # dataset settings | |
| dataset_type = CocoDataset | |
| data_root = 'data/coco/' | |
| # 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=(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='annotations/instances_train2017.json', | |
| data_prefix=dict(img='train2017/'), | |
| 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='annotations/instances_val2017.json', | |
| data_prefix=dict(img='val2017/'), | |
| test_mode=True, | |
| pipeline=test_pipeline, | |
| backend_args=backend_args)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict( | |
| type=CocoMetric, | |
| ann_file=data_root + 'annotations/instances_val2017.json', | |
| metric='bbox', | |
| format_only=False, | |
| backend_args=backend_args) | |
| test_evaluator = val_evaluator | |
| # 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') | |