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[Feature] Support ISPRS Potsdam Dataset. (#1097)
* add isprs potsdam dataset * add isprs dataset configs * fix lint error * fix potsdam conversion bug * fix error in potsdam class * fix error in potsdam class * add vaihingen dataset * add vaihingen dataset * add vaihingen dataset * fix some description errors. * fix some description errors. * fix some description errors. * upload models & logs of Potsdam * remove vaihingen and add unit test * add chinese readme * add pseudodataset * use mmcv and add class_names * use f-string * add new dataset unittest * add docstring and remove global variables args * fix metafile error in PSPNet * fix pretrained value * Add dataset info * fix typo Co-authored-by: MengzhangLI <[email protected]>
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| # dataset settings | ||
| dataset_type = 'PotsdamDataset' | ||
| data_root = 'data/potsdam' | ||
| img_norm_cfg = dict( | ||
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | ||
| crop_size = (512, 512) | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations', reduce_zero_label=True), | ||
| dict(type='Resize', img_scale=(512, 512), ratio_range=(0.5, 2.0)), | ||
| dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), | ||
| dict(type='RandomFlip', prob=0.5), | ||
| dict(type='PhotoMetricDistortion'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_semantic_seg']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(512, 512), | ||
| # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75], | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=4, | ||
| workers_per_gpu=4, | ||
| train=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| img_dir='img_dir/train', | ||
| ann_dir='ann_dir/train', | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| img_dir='img_dir/val', | ||
| ann_dir='ann_dir/val', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| img_dir='img_dir/val', | ||
| ann_dir='ann_dir/val', | ||
| pipeline=test_pipeline)) |
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configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py
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| _base_ = './deeplabv3plus_r50-d8_512x512_80k_potsdam.py' | ||
| model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) |
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configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py
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| _base_ = './deeplabv3plus_r50-d8_512x512_80k_potsdam.py' | ||
| model = dict( | ||
| pretrained='open-mmlab://resnet18_v1c', | ||
| backbone=dict(depth=18), | ||
| decode_head=dict( | ||
| c1_in_channels=64, | ||
| c1_channels=12, | ||
| in_channels=512, | ||
| channels=128, | ||
| ), | ||
| auxiliary_head=dict(in_channels=256, channels=64)) |
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configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py
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| _base_ = [ | ||
| '../_base_/models/deeplabv3plus_r50-d8.py', | ||
| '../_base_/datasets/potsdam.py', '../_base_/default_runtime.py', | ||
| '../_base_/schedules/schedule_80k.py' | ||
| ] | ||
| model = dict( | ||
| decode_head=dict(num_classes=6), auxiliary_head=dict(num_classes=6)) |
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| _base_ = [ | ||
| '../_base_/models/fcn_hr18.py', '../_base_/datasets/potsdam.py', | ||
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' | ||
| ] | ||
| model = dict(decode_head=dict(num_classes=6)) |
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| _base_ = './fcn_hr18_512x512_80k_potsdam.py' | ||
| model = dict( | ||
| pretrained='open-mmlab://msra/hrnetv2_w18_small', | ||
| backbone=dict( | ||
| extra=dict( | ||
| stage1=dict(num_blocks=(2, )), | ||
| stage2=dict(num_blocks=(2, 2)), | ||
| stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), | ||
| stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) |
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| _base_ = './fcn_hr18_512x512_80k_potsdam.py' | ||
| model = dict( | ||
| pretrained='open-mmlab://msra/hrnetv2_w48', | ||
| backbone=dict( | ||
| extra=dict( | ||
| stage2=dict(num_channels=(48, 96)), | ||
| stage3=dict(num_channels=(48, 96, 192)), | ||
| stage4=dict(num_channels=(48, 96, 192, 384)))), | ||
| decode_head=dict( | ||
| in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) |
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