99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
# dataset settings
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dataset_type = "CocoPanopticDataset"
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data_root = 'data/coco/'
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# Example to use different file client
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# Method 1: simply set the data root and let the file I/O module
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# automatically infer from prefix (not support LMDB and Memcache yet)
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# data_root = "s3://openmmlab/datasets/detection/coco/"
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# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
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# backend_args = dict(
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# backend='petrel',
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# path_mapping=dict({
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# './data/': 's3://openmmlab/datasets/detection/',
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# 'data/': 's3://openmmlab/datasets/detection/'
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# }))
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backend_args = None
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train_pipeline = [
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dict(type="LoadImageFromFile", backend_args=backend_args),
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dict(type="LoadPanopticAnnotations", backend_args=backend_args),
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dict(type="Resize", scale=(1333, 800), keep_ratio=True),
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dict(type="RandomFlip", prob=0.5),
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dict(type="PackDetInputs"),
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]
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test_pipeline = [
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dict(type="LoadImageFromFile", backend_args=backend_args),
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dict(type="Resize", scale=(1333, 800), keep_ratio=True),
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dict(type="LoadPanopticAnnotations", backend_args=backend_args),
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dict(
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type="PackDetInputs",
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meta_keys=("img_id", "img_path", "ori_shape", "img_shape", "scale_factor"),
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),
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]
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train_dataloader = dict(
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batch_size=2,
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num_workers=2,
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persistent_workers=True,
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sampler=dict(type="DefaultSampler", shuffle=True),
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batch_sampler=dict(type="AspectRatioBatchSampler"),
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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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ann_file="annotations/panoptic_train2017.json",
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data_prefix=dict(img="train2017/", seg="annotations/panoptic_train2017/"),
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filter_cfg=dict(filter_empty_gt=True, min_size=32),
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pipeline=train_pipeline,
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backend_args=backend_args,
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),
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)
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val_dataloader = dict(
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batch_size=1,
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num_workers=2,
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persistent_workers=True,
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drop_last=False,
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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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ann_file="annotations/panoptic_val2017.json",
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data_prefix=dict(img="val2017/", seg="annotations/panoptic_val2017/"),
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test_mode=True,
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pipeline=test_pipeline,
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backend_args=backend_args,
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),
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)
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test_dataloader = val_dataloader
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val_evaluator = dict(
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type="CocoPanopticMetric",
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ann_file=data_root + "annotations/panoptic_val2017.json",
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seg_prefix=data_root + "annotations/panoptic_val2017/",
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backend_args=backend_args,
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)
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test_evaluator = val_evaluator
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# inference on test dataset and
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# format the output results for submission.
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# test_dataloader = dict(
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# batch_size=1,
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# num_workers=1,
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# persistent_workers=True,
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# drop_last=False,
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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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# ann_file='annotations/panoptic_image_info_test-dev2017.json',
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# data_prefix=dict(img='test2017/'),
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# test_mode=True,
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# pipeline=test_pipeline))
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# test_evaluator = dict(
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# type='CocoPanopticMetric',
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# format_only=True,
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# ann_file=data_root + 'annotations/panoptic_image_info_test-dev2017.json',
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# outfile_prefix='./work_dirs/coco_panoptic/test')
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