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## Motivation Based on the ImageNet dataset, we propose the ImageNet-S dataset has 1.2 million training images and 50k high-quality semantic segmentation annotations to support unsupervised/semi-supervised semantic segmentation on the ImageNet dataset. paper: Large-scale Unsupervised Semantic Segmentation (TPAMI 2022) [Paper link](https://arxiv.org/abs/2106.03149) ## Modification 1. Support imagenet-s dataset and its' configuration 2. Add the dataset preparation in the documentation
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
# dataset settings
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dataset_type = 'ImageNetSDataset'
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subset = 919
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data_root = 'data/ImageNetS/ImageNetS919'
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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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crop_size = (224, 224)
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train_pipeline = [
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dict(type='LoadImageNetSImageFromFile', downsample_large_image=True),
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dict(type='LoadImageNetSAnnotations', reduce_zero_label=False),
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dict(type='Resize', img_scale=(1024, 256), ratio_range=(0.5, 2.0)),
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dict(
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type='RandomCrop',
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crop_size=crop_size,
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cat_max_ratio=0.75,
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ignore_index=1000),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=1000),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_semantic_seg']),
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]
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test_pipeline = [
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dict(type='LoadImageNetSImageFromFile', downsample_large_image=True),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1024, 256),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=4,
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workers_per_gpu=4,
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train=dict(
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type=dataset_type,
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subset=subset,
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data_root=data_root,
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img_dir='train-semi',
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ann_dir='train-semi-segmentation',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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subset=subset,
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data_root=data_root,
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img_dir='validation',
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ann_dir='validation-segmentation',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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subset=subset,
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data_root=data_root,
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img_dir='validation',
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ann_dir='validation-segmentation',
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pipeline=test_pipeline))
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