55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
# dataset settings
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dataset_type = 'iSAIDDataset'
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data_root = 'data/iSAID'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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"""
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This crop_size setting is followed by the implementation of
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`PointFlow: Flowing Semantics Through Points for Aerial Image
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Segmentation <https://arxiv.org/pdf/2103.06564.pdf>`_.
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"""
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crop_size = (896, 896)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='RandomResize', scale=(896, 896), ratio_range=(0.5, 2.0)),
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Pad', size=crop_size),
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dict(type='PackSegInputs')
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', scale=(896, 896), keep_ratio=True),
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dict(type='PackSegInputs')
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]
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train_dataloader = dict(
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batch_size=4,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='InfiniteSampler', shuffle=True),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(
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img_path='img_dir/train', seg_map_path='ann_dir/train'),
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pipeline=train_pipeline))
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val_dataloader = dict(
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batch_size=4,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(img_path='img_dir/val', seg_map_path='ann_dir/val'),
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pipeline=test_pipeline))
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test_dataloader = val_dataloader
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val_evaluator = dict(type='IoUMetric', metrics=['mIoU'])
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test_evaluator = val_evaluator
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