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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 = 'VOCDataset' | |
| data_root = 'data/VOCdevkit/' | |
| # 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/segmentation/VOCdevkit/' | |
| # 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/segmentation/', | |
| # 'data/': 's3://openmmlab/datasets/segmentation/' | |
| # })) | |
| backend_args = None | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=backend_args), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(type='Resize', scale=(1000, 600), 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=(1000, 600), keep_ratio=True), | |
| # avoid bboxes being resized | |
| 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='RepeatDataset', | |
| times=3, | |
| dataset=dict( | |
| type='ConcatDataset', | |
| # VOCDataset will add different `dataset_type` in dataset.metainfo, | |
| # which will get error if using ConcatDataset. Adding | |
| # `ignore_keys` can avoid this error. | |
| ignore_keys=['dataset_type'], | |
| datasets=[ | |
| dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='VOC2007/ImageSets/Main/trainval.txt', | |
| data_prefix=dict(sub_data_root='VOC2007/'), | |
| filter_cfg=dict( | |
| filter_empty_gt=True, min_size=32, bbox_min_size=32), | |
| pipeline=train_pipeline, | |
| backend_args=backend_args), | |
| dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='VOC2012/ImageSets/Main/trainval.txt', | |
| data_prefix=dict(sub_data_root='VOC2012/'), | |
| filter_cfg=dict( | |
| filter_empty_gt=True, min_size=32, bbox_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='VOC2007/ImageSets/Main/test.txt', | |
| data_prefix=dict(sub_data_root='VOC2007/'), | |
| test_mode=True, | |
| pipeline=test_pipeline, | |
| backend_args=backend_args)) | |
| test_dataloader = val_dataloader | |
| # Pascal VOC2007 uses `11points` as default evaluate mode, while PASCAL | |
| # VOC2012 defaults to use 'area'. | |
| val_evaluator = dict(type='VOCMetric', metric='mAP', eval_mode='11points') | |
| test_evaluator = val_evaluator | |