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- # dataset settings
- dataset_type = 'iSAIDDataset'
- data_root = 'data/iSAID/'
- backend_args = None
- # Please see `projects/iSAID/README.md` for data preparation
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args=backend_args),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='Resize', scale=(800, 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=(800, 800), keep_ratio=True),
- 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=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='train/instancesonly_filtered_train.json',
- data_prefix=dict(img='train/images/'),
- 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='val/instancesonly_filtered_val.json',
- data_prefix=dict(img='val/images/'),
- test_mode=True,
- pipeline=test_pipeline,
- backend_args=backend_args))
- test_dataloader = val_dataloader
- val_evaluator = dict(
- type='CocoMetric',
- ann_file=data_root + 'val/instancesonly_filtered_val.json',
- metric=['bbox', 'segm'],
- format_only=False,
- backend_args=backend_args)
- test_evaluator = val_evaluator
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