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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,94 @@ | ||
| # dataset settings | ||
| dataset_type = 'DOTADataset' | ||
| data_root = 'data/split_ms_dota/' | ||
| file_client_args = dict(backend='disk') | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'), | ||
| dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')), | ||
| dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True), | ||
| dict( | ||
| type='mmdet.RandomFlip', | ||
| prob=0.75, | ||
| direction=['horizontal', 'vertical', 'diagonal']), | ||
| dict( | ||
| type='RandomRotate', | ||
| prob=0.5, | ||
| angle_range=180, | ||
| rect_obj_labels=[9, 11]), | ||
| dict(type='mmdet.PackDetInputs') | ||
| ] | ||
| val_pipeline = [ | ||
| dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True), | ||
| # avoid bboxes being resized | ||
| dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'), | ||
| dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')), | ||
| dict( | ||
| type='mmdet.PackDetInputs', | ||
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | ||
| 'scale_factor')) | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True), | ||
| # avoid bboxes being resized | ||
| dict( | ||
| type='mmdet.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=None, | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file='trainval/annfiles/', | ||
| data_prefix=dict(img_path='trainval/images/'), | ||
| img_shape=(1024, 1024), | ||
| filter_cfg=dict(filter_empty_gt=True), | ||
| pipeline=train_pipeline)) | ||
| 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='trainval/annfiles/', | ||
| data_prefix=dict(img_path='trainval/images/'), | ||
| img_shape=(1024, 1024), | ||
| test_mode=True, | ||
| pipeline=val_pipeline)) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| val_evaluator = dict(type='DOTAMetric', metric='mAP') | ||
| test_evaluator = val_evaluator | ||
|
|
||
| # inference on test dataset and format the output results | ||
| # for submission. Note: the test set has no annotation. | ||
| # 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, | ||
| # data_prefix=dict(img_path='test/images/'), | ||
| # img_shape=(1024, 1024), | ||
| # test_mode=True, | ||
| # pipeline=test_pipeline)) | ||
| # test_evaluator = dict( | ||
| # type='DOTAMetric', | ||
| # format_only=True, | ||
| # merge_patches=True, | ||
| # outfile_prefix='./work_dirs/dota/Task1') |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,56 +1,66 @@ | ||
| # dataset settings | ||
| dataset_type = 'HRSCDataset' | ||
| data_root = 'data/hrsc/' | ||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
| file_client_args = dict(backend='disk') | ||
|
|
||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict(type='RResize', img_scale=(800, 800)), | ||
| dict(type='RRandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) | ||
| dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'), | ||
| dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')), | ||
| dict(type='mmdet.Resize', scale=(800, 512), keep_ratio=True), | ||
| dict( | ||
| type='mmdet.RandomFlip', | ||
| prob=0.75, | ||
| direction=['horizontal', 'vertical', 'diagonal']), | ||
| dict(type='mmdet.PackDetInputs') | ||
| ] | ||
| val_pipeline = [ | ||
| dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='mmdet.Resize', scale=(800, 512), keep_ratio=True), | ||
| # avoid bboxes being resized | ||
| dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'), | ||
| dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')), | ||
| dict( | ||
| type='mmdet.PackDetInputs', | ||
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | ||
| 'scale_factor')) | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args), | ||
| dict(type='mmdet.Resize', scale=(800, 512), keep_ratio=True), | ||
| # avoid bboxes being resized | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(800, 800), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='RResize'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img']) | ||
| ]) | ||
| type='mmdet.PackDetInputs', | ||
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | ||
| 'scale_factor')) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| classwise=False, | ||
| ann_file=data_root + 'ImageSets/trainval.txt', | ||
| ann_subdir=data_root + 'FullDataSet/Annotations/', | ||
| img_subdir=data_root + 'FullDataSet/AllImages/', | ||
| img_prefix=data_root + 'FullDataSet/AllImages/', | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| train_dataloader = dict( | ||
| batch_size=2, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| batch_sampler=None, | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| classwise=False, | ||
| ann_file=data_root + 'ImageSets/test.txt', | ||
| ann_subdir=data_root + 'FullDataSet/Annotations/', | ||
| img_subdir=data_root + 'FullDataSet/AllImages/', | ||
| img_prefix=data_root + 'FullDataSet/AllImages/', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| data_root=data_root, | ||
| ann_file='ImageSets/trainval.txt', | ||
| data_prefix=dict(sub_data_root='FullDataSet/'), | ||
| filter_cfg=dict(filter_empty_gt=True), | ||
| pipeline=train_pipeline)) | ||
| 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, | ||
| classwise=False, | ||
| ann_file=data_root + 'ImageSets/test.txt', | ||
| ann_subdir=data_root + 'FullDataSet/Annotations/', | ||
| img_subdir=data_root + 'FullDataSet/AllImages/', | ||
| img_prefix=data_root + 'FullDataSet/AllImages/', | ||
| pipeline=test_pipeline)) | ||
| data_root=data_root, | ||
| ann_file='ImageSets/test.txt', | ||
| data_prefix=dict(sub_data_root='FullDataSet/'), | ||
| test_mode=True, | ||
| pipeline=val_pipeline)) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| val_evaluator = dict(type='DOTAMetric', metric='mAP') | ||
| test_evaluator = val_evaluator |
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