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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 = 'CityscapesDataset' | |
| data_root = 'data/cityscapes/' | |
| # 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/segmentation/cityscapes/' | |
| # 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, with_mask=True), | |
| dict( | |
| type='RandomResize', | |
| scale=[(2048, 800), (2048, 1024)], | |
| 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=(2048, 1024), keep_ratio=True), | |
| # If you don't have a gt annotation, delete the pipeline | |
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ] | |
| train_dataloader = dict( | |
| batch_size=1, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| batch_sampler=dict(type='AspectRatioBatchSampler'), | |
| dataset=dict( | |
| type='RepeatDataset', | |
| times=8, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/instancesonly_filtered_gtFine_train.json', | |
| data_prefix=dict(img='leftImg8bit/train/'), | |
| 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/instancesonly_filtered_gtFine_val.json', | |
| data_prefix=dict(img='leftImg8bit/val/'), | |
| test_mode=True, | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=test_pipeline, | |
| backend_args=backend_args)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = [ | |
| dict( | |
| type='CocoMetric', | |
| ann_file=data_root + | |
| 'annotations/instancesonly_filtered_gtFine_val.json', | |
| metric=['bbox', 'segm'], | |
| backend_args=backend_args), | |
| dict( | |
| type='CityScapesMetric', | |
| seg_prefix=data_root + 'gtFine/val', | |
| outfile_prefix='./work_dirs/cityscapes_metric/instance', | |
| 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='annotations/instancesonly_filtered_gtFine_test.json', | |
| # data_prefix=dict(img='leftImg8bit/test/'), | |
| # test_mode=True, | |
| # filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| # pipeline=test_pipeline)) | |
| # test_evaluator = dict( | |
| # type='CityScapesMetric', | |
| # format_only=True, | |
| # outfile_prefix='./work_dirs/cityscapes_metric/test') | |