[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 <mcmong@pku.edu.cn>pull/1218/head
Kingdrone
GitHub
parent
0f48c7605d
commit
b997a13e28
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@ -0,0 +1,54 @@
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# dataset settings
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dataset_type = 'PotsdamDataset'
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data_root = 'data/potsdam'
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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 = (512, 512)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', reduce_zero_label=True),
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dict(type='Resize', img_scale=(512, 512), 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='Normalize', **img_norm_cfg),
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
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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='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(512, 512),
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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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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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data_root=data_root,
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img_dir='img_dir/train',
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ann_dir='ann_dir/train',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='img_dir/val',
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ann_dir='ann_dir/val',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='img_dir/val',
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ann_dir='ann_dir/val',
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pipeline=test_pipeline))
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@ -104,6 +104,14 @@ Spatial pyramid pooling module or encode-decoder structure are used in deep neur
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| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 7.37 | 6.00 | 50.99 | 50.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442-f0720392.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_loveda/deeplabv3plus_r50-d8_512x512_80k_loveda_20211105_080442.log.json) |
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| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 10.84 | 4.33 | 51.47 | 51.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759.log.json) |
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### Potsdam
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| DeepLabV3+ | R-18-D8 | 512x512 | 80000 | 1.91 | 81.68 | 77.09 | 78.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601.log.json) |
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| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 7.36 | 26.44 | 78.33 | 79.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508.log.json) |
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| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 10.83 | 17.56 | 78.7 | 79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508.log.json) |
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Note:
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- `FP16` means Mixed Precision (FP16) is adopted in training.
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@ -8,6 +8,7 @@ Collections:
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- Pascal Context
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- Pascal Context 59
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- LoveDA
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- Potsdam
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Paper:
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URL: https://arxiv.org/abs/1802.02611
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Title: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
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@ -669,3 +670,69 @@ Models:
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mIoU(ms+flip): 51.32
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Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_loveda/deeplabv3plus_r101-d8_512x512_80k_loveda_20211105_110759-4c1f297e.pth
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- Name: deeplabv3plus_r18-d8_512x512_80k_potsdam
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In Collection: deeplabv3plus
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Metadata:
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backbone: R-18-D8
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crop size: (512,512)
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lr schd: 80000
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inference time (ms/im):
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- value: 12.24
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 1.91
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Results:
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- Task: Semantic Segmentation
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Dataset: Potsdam
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Metrics:
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mIoU: 77.09
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mIoU(ms+flip): 78.44
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Config: configs/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x512_80k_potsdam/deeplabv3plus_r18-d8_512x512_80k_potsdam_20211219_020601-75fd5bc3.pth
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- Name: deeplabv3plus_r50-d8_512x512_80k_potsdam
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In Collection: deeplabv3plus
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Metadata:
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backbone: R-50-D8
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crop size: (512,512)
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lr schd: 80000
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inference time (ms/im):
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- value: 37.82
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 7.36
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Results:
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- Task: Semantic Segmentation
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Dataset: Potsdam
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Metrics:
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mIoU: 78.33
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mIoU(ms+flip): 79.27
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Config: configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_potsdam/deeplabv3plus_r50-d8_512x512_80k_potsdam_20211219_031508-7e7a2b24.pth
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- Name: deeplabv3plus_r101-d8_512x512_80k_potsdam
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In Collection: deeplabv3plus
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Metadata:
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backbone: R-101-D8
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crop size: (512,512)
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lr schd: 80000
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inference time (ms/im):
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- value: 56.95
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 10.83
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Results:
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- Task: Semantic Segmentation
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Dataset: Potsdam
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Metrics:
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mIoU: 78.7
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mIoU(ms+flip): 79.47
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Config: configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_potsdam/deeplabv3plus_r101-d8_512x512_80k_potsdam_20211219_031508-8b112708.pth
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@ -0,0 +1,2 @@
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_base_ = './deeplabv3plus_r50-d8_512x512_80k_potsdam.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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@ -0,0 +1,11 @@
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_base_ = './deeplabv3plus_r50-d8_512x512_80k_potsdam.py'
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model = dict(
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pretrained='open-mmlab://resnet18_v1c',
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backbone=dict(depth=18),
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decode_head=dict(
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c1_in_channels=64,
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c1_channels=12,
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in_channels=512,
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channels=128,
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),
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auxiliary_head=dict(in_channels=256, channels=64))
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@ -0,0 +1,7 @@
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_base_ = [
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'../_base_/models/deeplabv3plus_r50-d8.py',
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'../_base_/datasets/potsdam.py', '../_base_/default_runtime.py',
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'../_base_/schedules/schedule_80k.py'
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]
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model = dict(
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decode_head=dict(num_classes=6), auxiliary_head=dict(num_classes=6))
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@ -92,3 +92,11 @@ High-resolution representations are essential for position-sensitive vision prob
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| FCN | HRNetV2p-W18-Small | 512x512 | 80000 | 1.59 | 24.87 | 49.28 | 49.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228-60a86a7a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_loveda/fcn_hr18s_512x512_80k_loveda_20211210_203228.log.json) |
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| FCN | HRNetV2p-W18 | 512x512 | 80000 | 2.76 | 12.92 | 50.81 | 50.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952-93d9c3b3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_loveda/fcn_hr18_512x512_80k_loveda_20211210_203952.log.json) |
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| FCN | HRNetV2p-W48 | 512x512 | 80000 | 6.20 | 9.61 | 51.42 | 51.64 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756.log.json) |
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### Potsdam
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| FCN | HRNetV2p-W18-Small | 512x512 | 80000 | 1.58 | 36.00 | 77.64 | 78.8 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517.log.json) |
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| FCN | HRNetV2p-W18 | 512x512 | 80000 | 2.76 | 19.25 | 78.26 | 79.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517.log.json) |
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| FCN | HRNetV2p-W48 | 512x512 | 80000 | 6.20 | 16.42 | 78.39 | 79.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601.log.json) |
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@ -0,0 +1,5 @@
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_base_ = [
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'../_base_/models/fcn_hr18.py', '../_base_/datasets/potsdam.py',
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'../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
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]
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model = dict(decode_head=dict(num_classes=6))
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_base_ = './fcn_hr18_512x512_80k_potsdam.py'
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model = dict(
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pretrained='open-mmlab://msra/hrnetv2_w18_small',
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backbone=dict(
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extra=dict(
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stage1=dict(num_blocks=(2, )),
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stage2=dict(num_blocks=(2, 2)),
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stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
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stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
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_base_ = './fcn_hr18_512x512_80k_potsdam.py'
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model = dict(
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pretrained='open-mmlab://msra/hrnetv2_w48',
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backbone=dict(
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extra=dict(
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stage2=dict(num_channels=(48, 96)),
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stage3=dict(num_channels=(48, 96, 192)),
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stage4=dict(num_channels=(48, 96, 192, 384)))),
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decode_head=dict(
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in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))
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@ -8,6 +8,7 @@ Collections:
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- Pascal Context
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- Pascal Context 59
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- LoveDA
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- Potsdam
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Paper:
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URL: https://arxiv.org/abs/1908.07919
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Title: Deep High-Resolution Representation Learning for Human Pose Estimation
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@ -514,3 +515,69 @@ Models:
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mIoU(ms+flip): 51.64
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Config: configs/hrnet/fcn_hr48_512x512_80k_loveda.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_loveda/fcn_hr48_512x512_80k_loveda_20211211_044756-67072f55.pth
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- Name: fcn_hr18s_512x512_80k_potsdam
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In Collection: hrnet
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Metadata:
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backbone: HRNetV2p-W18-Small
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crop size: (512,512)
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lr schd: 80000
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inference time (ms/im):
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- value: 27.78
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 1.58
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Results:
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- Task: Semantic Segmentation
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Dataset: Potsdam
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Metrics:
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mIoU: 77.64
|
||||
mIoU(ms+flip): 78.8
|
||||
Config: configs/hrnet/fcn_hr18s_512x512_80k_potsdam.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_potsdam/fcn_hr18s_512x512_80k_potsdam_20211218_205517-ba32af63.pth
|
||||
- Name: fcn_hr18_512x512_80k_potsdam
|
||||
In Collection: hrnet
|
||||
Metadata:
|
||||
backbone: HRNetV2p-W18
|
||||
crop size: (512,512)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 51.95
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 2.76
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: Potsdam
|
||||
Metrics:
|
||||
mIoU: 78.26
|
||||
mIoU(ms+flip): 79.24
|
||||
Config: configs/hrnet/fcn_hr18_512x512_80k_potsdam.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_potsdam/fcn_hr18_512x512_80k_potsdam_20211218_205517-5d0387ad.pth
|
||||
- Name: fcn_hr48_512x512_80k_potsdam
|
||||
In Collection: hrnet
|
||||
Metadata:
|
||||
backbone: HRNetV2p-W48
|
||||
crop size: (512,512)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 60.9
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 6.2
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: Potsdam
|
||||
Metrics:
|
||||
mIoU: 78.39
|
||||
mIoU(ms+flip): 79.34
|
||||
Config: configs/hrnet/fcn_hr48_512x512_80k_potsdam.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_potsdam/fcn_hr48_512x512_80k_potsdam_20211219_020601-97434c78.pth
|
||||
|
|
|
|||
|
|
@ -133,6 +133,14 @@ We support evaluation results on these two datasets using models above trained o
|
|||
| PSPNet | R-50-D8 | 512x512 | 80000 | 6.14 | 6.60 | 50.46 | 50.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728-88610f9f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_loveda/pspnet_r50-d8_512x512_80k_loveda_20211104_155728.log.json) |
|
||||
| PSPNet | R-101-D8 | 512x512 | 80000 | 9.61 | 4.58 | 51.86 | 51.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212.log.json) |
|
||||
|
||||
### Potsdam
|
||||
|
||||
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
|
||||
| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| PSPNet | R-18-D8 | 512x512 | 80000 | 1.50 | 85.12 | 77.09 | 78.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json) |
|
||||
| PSPNet | R-50-D8 | 512x512 | 80000 | 6.14 | 30.21 | 78.12 | 78.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541.log.json) |
|
||||
| PSPNet | R-101-D8 | 512x512 | 80000 | 9.61 | 19.40 | 78.62 | 79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612.log.json) |
|
||||
|
||||
Note:
|
||||
|
||||
- `FP16` means Mixed Precision (FP16) is adopted in training.
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ Collections:
|
|||
- COCO-Stuff 10k
|
||||
- COCO-Stuff 164k
|
||||
- LoveDA
|
||||
- Potsdam
|
||||
Paper:
|
||||
URL: https://arxiv.org/abs/1612.01105
|
||||
Title: Pyramid Scene Parsing Network
|
||||
|
|
@ -808,3 +809,69 @@ Models:
|
|||
mIoU(ms+flip): 51.34
|
||||
Config: configs/pspnet/pspnet_r101-d8_512x512_80k_loveda.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_loveda/pspnet_r101-d8_512x512_80k_loveda_20211104_153212-1c06c6a8.pth
|
||||
- Name: pspnet_r18-d8_4x4_512x512_80k_potsdam
|
||||
In Collection: pspnet
|
||||
Metadata:
|
||||
backbone: R-18-D8
|
||||
crop size: (512,512)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 11.75
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 1.5
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: Potsdam
|
||||
Metrics:
|
||||
mIoU: 77.09
|
||||
mIoU(ms+flip): 78.3
|
||||
Config: configs/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_4x4_512x512_80k_potsdam/pspnet_r18-d8_4x4_512x512_80k_potsdam_20211220_125612-7cd046e1.pth
|
||||
- Name: pspnet_r50-d8_4x4_512x512_80k_potsdam
|
||||
In Collection: pspnet
|
||||
Metadata:
|
||||
backbone: R-50-D8
|
||||
crop size: (512,512)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 33.1
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 6.14
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: Potsdam
|
||||
Metrics:
|
||||
mIoU: 78.12
|
||||
mIoU(ms+flip): 78.98
|
||||
Config: configs/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_4x4_512x512_80k_potsdam/pspnet_r50-d8_4x4_512x512_80k_potsdam_20211219_043541-2dd5fe67.pth
|
||||
- Name: pspnet_r101-d8_4x4_512x512_80k_potsdam
|
||||
In Collection: pspnet
|
||||
Metadata:
|
||||
backbone: R-101-D8
|
||||
crop size: (512,512)
|
||||
lr schd: 80000
|
||||
inference time (ms/im):
|
||||
- value: 51.55
|
||||
hardware: V100
|
||||
backend: PyTorch
|
||||
batch size: 1
|
||||
mode: FP32
|
||||
resolution: (512,512)
|
||||
Training Memory (GB): 9.61
|
||||
Results:
|
||||
- Task: Semantic Segmentation
|
||||
Dataset: Potsdam
|
||||
Metrics:
|
||||
mIoU: 78.62
|
||||
mIoU(ms+flip): 79.47
|
||||
Config: configs/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam.py
|
||||
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_4x4_512x512_80k_potsdam/pspnet_r101-d8_4x4_512x512_80k_potsdam_20211220_125612-aed036c4.pth
|
||||
|
|
|
|||
|
|
@ -0,0 +1,2 @@
|
|||
_base_ = './pspnet_r50-d8_4x4_512x512_80k_potsdam.py'
|
||||
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
_base_ = './pspnet_r50-d8_4x4_512x512_80k_potsdam.py'
|
||||
model = dict(
|
||||
pretrained='open-mmlab://resnet18_v1c',
|
||||
backbone=dict(depth=18),
|
||||
decode_head=dict(
|
||||
in_channels=512,
|
||||
channels=128,
|
||||
),
|
||||
auxiliary_head=dict(in_channels=256, channels=64))
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
_base_ = [
|
||||
'../_base_/models/pspnet_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))
|
||||
|
|
@ -116,6 +116,13 @@ mmsegmentation
|
|||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ ├── potsdam
|
||||
│ │ ├── img_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
```
|
||||
|
||||
### Cityscapes
|
||||
|
|
@ -286,3 +293,19 @@ python tools/convert_datasets/loveda.py /path/to/loveDA
|
|||
Using trained model to predict test set of LoveDA and submit it to server can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/inference.md).
|
||||
|
||||
More details about LoveDA can be found [here](https://github.com/Junjue-Wang/LoveDA).
|
||||
|
||||
### ISPRS Potsdam
|
||||
|
||||
The [Potsdam](https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-potsdam/)
|
||||
dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Potsdam.
|
||||
|
||||
The dataset can be requested at the challenge [homepage](https://www2.isprs.org/commissions/comm2/wg4/benchmark/data-request-form/).
|
||||
The '2_Ortho_RGB.zip' and '5_Labels_all_noBoundary.zip' are required.
|
||||
|
||||
For Potsdam dataset, please run the following command to download and re-organize the dataset.
|
||||
|
||||
```shell
|
||||
python tools/convert_datasets/potsdam.py /path/to/potsdam
|
||||
```
|
||||
|
||||
In our default setting, it will generate 3456 images for training and 2016 images for validation.
|
||||
|
|
|
|||
|
|
@ -97,6 +97,13 @@ mmsegmentation
|
|||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ ├── potsdam
|
||||
│ │ ├── img_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
│ │ ├── ann_dir
|
||||
│ │ │ ├── train
|
||||
│ │ │ ├── val
|
||||
```
|
||||
|
||||
### Cityscapes
|
||||
|
|
@ -228,3 +235,18 @@ python tools/convert_datasets/loveda.py /path/to/loveDA
|
|||
请参照 [这里](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/inference.md) 来使用训练好的模型去预测 LoveDA 测试集并且提交到官网。
|
||||
|
||||
关于 LoveDA 的更多细节可以在[这里](https://github.com/Junjue-Wang/LoveDA) 找到。
|
||||
|
||||
### ISPRS Potsdam
|
||||
|
||||
[Potsdam](https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-potsdam/)
|
||||
数据集是一个有着2D 语义分割内容标注的城市遥感数据集。
|
||||
数据集可以从挑战[主页](https://www2.isprs.org/commissions/comm2/wg4/benchmark/data-request-form/) 获得。
|
||||
需要其中的 '2_Ortho_RGB.zip' 和 '5_Labels_all_noBoundary.zip'。
|
||||
|
||||
对于 Potsdam 数据集，请运行以下命令下载并重新组织数据集
|
||||
|
||||
```shell
|
||||
python tools/convert_datasets/potsdam.py /path/to/potsdam
|
||||
```
|
||||
|
||||
使用我们默认的配置， 将生成 3456 张图片的训练集和 2016 张图片的验证集。
|
||||
|
|
|
|||
|
|
@ -52,6 +52,22 @@ def voc_classes():
|
|||
]
|
||||
|
||||
|
||||
def loveda_classes():
|
||||
"""LoveDA class names for external use."""
|
||||
return [
|
||||
'background', 'building', 'road', 'water', 'barren', 'forest',
|
||||
'agricultural'
|
||||
]
|
||||
|
||||
|
||||
def potsdam_classes():
|
||||
"""Potsdam class names for external use."""
|
||||
return [
|
||||
'impervious_surface', 'building', 'low_vegetation', 'tree', 'car',
|
||||
'clutter'
|
||||
]
|
||||
|
||||
|
||||
def cityscapes_palette():
|
||||
"""Cityscapes palette for external use."""
|
||||
return [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
|
||||
|
|
@ -112,10 +128,24 @@ def voc_palette():
|
|||
[128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
|
||||
|
||||
|
||||
def loveda_palette():
|
||||
"""LoveDA palette for external use."""
|
||||
return [[255, 255, 255], [255, 0, 0], [255, 255, 0], [0, 0, 255],
|
||||
[159, 129, 183], [0, 255, 0], [255, 195, 128]]
|
||||
|
||||
|
||||
def potsdam_palette():
|
||||
"""Potsdam palette for external use."""
|
||||
return [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0],
|
||||
[255, 255, 0], [255, 0, 0]]
|
||||
|
||||
|
||||
dataset_aliases = {
|
||||
'cityscapes': ['cityscapes'],
|
||||
'ade': ['ade', 'ade20k'],
|
||||
'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug']
|
||||
'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug'],
|
||||
'loveda': ['loveda'],
|
||||
'potsdam': ['potsdam']
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -13,6 +13,7 @@ from .hrf import HRFDataset
|
|||
from .loveda import LoveDADataset
|
||||
from .night_driving import NightDrivingDataset
|
||||
from .pascal_context import PascalContextDataset, PascalContextDataset59
|
||||
from .potsdam import PotsdamDataset
|
||||
from .stare import STAREDataset
|
||||
from .voc import PascalVOCDataset
|
||||
|
||||
|
|
@ -22,5 +23,6 @@ __all__ = [
|
|||
'PascalVOCDataset', 'ADE20KDataset', 'PascalContextDataset',
|
||||
'PascalContextDataset59', 'ChaseDB1Dataset', 'DRIVEDataset', 'HRFDataset',
|
||||
'STAREDataset', 'DarkZurichDataset', 'NightDrivingDataset',
|
||||
'COCOStuffDataset', 'LoveDADataset', 'MultiImageMixDataset'
|
||||
'COCOStuffDataset', 'LoveDADataset', 'MultiImageMixDataset',
|
||||
'PotsdamDataset'
|
||||
]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,25 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
from .builder import DATASETS
|
||||
from .custom import CustomDataset
|
||||
|
||||
|
||||
@DATASETS.register_module()
|
||||
class PotsdamDataset(CustomDataset):
|
||||
"""ISPRS Potsdam dataset.
|
||||
|
||||
In segmentation map annotation for Potsdam dataset, 0 is the ignore index.
|
||||
``reduce_zero_label`` should be set to True. The ``img_suffix`` and
|
||||
``seg_map_suffix`` are both fixed to '.png'.
|
||||
"""
|
||||
CLASSES = ('impervious_surface', 'building', 'low_vegetation', 'tree',
|
||||
'car', 'clutter')
|
||||
|
||||
PALETTE = [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0],
|
||||
[255, 255, 0], [255, 0, 0]]
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(PotsdamDataset, self).__init__(
|
||||
img_suffix='.png',
|
||||
seg_map_suffix='.png',
|
||||
reduce_zero_label=True,
|
||||
**kwargs)
|
||||
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|
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|
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|
|
@ -15,7 +15,7 @@ from mmseg.core.evaluation import get_classes, get_palette
|
|||
from mmseg.datasets import (DATASETS, ADE20KDataset, CityscapesDataset,
|
||||
ConcatDataset, CustomDataset, LoveDADataset,
|
||||
MultiImageMixDataset, PascalVOCDataset,
|
||||
RepeatDataset, build_dataset)
|
||||
PotsdamDataset, RepeatDataset, build_dataset)
|
||||
|
||||
|
||||
def test_classes():
|
||||
|
|
@ -24,6 +24,8 @@ def test_classes():
|
|||
'pascal_voc')
|
||||
assert list(
|
||||
ADE20KDataset.CLASSES) == get_classes('ade') == get_classes('ade20k')
|
||||
assert list(LoveDADataset.CLASSES) == get_classes('loveda')
|
||||
assert list(PotsdamDataset.CLASSES) == get_classes('potsdam')
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
get_classes('unsupported')
|
||||
|
|
@ -65,6 +67,8 @@ def test_palette():
|
|||
assert PascalVOCDataset.PALETTE == get_palette('voc') == get_palette(
|
||||
'pascal_voc')
|
||||
assert ADE20KDataset.PALETTE == get_palette('ade') == get_palette('ade20k')
|
||||
assert LoveDADataset.PALETTE == get_palette('loveda')
|
||||
assert PotsdamDataset.PALETTE == get_palette('potsdam')
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
get_palette('unsupported')
|
||||
|
|
@ -709,6 +713,16 @@ def test_loveda():
|
|||
shutil.rmtree('.format_loveda')
|
||||
|
||||
|
||||
def test_potsdam():
|
||||
test_dataset = PotsdamDataset(
|
||||
pipeline=[],
|
||||
img_dir=osp.join(
|
||||
osp.dirname(__file__), '../data/pseudo_potsdam_dataset/img_dir'),
|
||||
ann_dir=osp.join(
|
||||
osp.dirname(__file__), '../data/pseudo_potsdam_dataset/ann_dir'))
|
||||
assert len(test_dataset) == 1
|
||||
|
||||
|
||||
@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
|
||||
@patch('mmseg.datasets.CustomDataset.__getitem__',
|
||||
MagicMock(side_effect=lambda idx: idx))
|
||||
|
|
|
|||
|
|
@ -0,0 +1,157 @@
|
|||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import argparse
|
||||
import glob
|
||||
import math
|
||||
import os
|
||||
import os.path as osp
|
||||
import tempfile
|
||||
import zipfile
|
||||
|
||||
import mmcv
|
||||
import numpy as np
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Convert potsdam dataset to mmsegmentation format')
|
||||
parser.add_argument('dataset_path', help='potsdam folder path')
|
||||
parser.add_argument('--tmp_dir', help='path of the temporary directory')
|
||||
parser.add_argument('-o', '--out_dir', help='output path')
|
||||
parser.add_argument(
|
||||
'--clip_size',
|
||||
type=int,
|
||||
help='clipped size of image after preparation',
|
||||
default=512)
|
||||
parser.add_argument(
|
||||
'--stride_size',
|
||||
type=int,
|
||||
help='stride of clipping original images',
|
||||
default=256)
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def clip_big_image(image_path, clip_save_dir, args, to_label=False):
|
||||
# Original image of Potsdam dataset is very large, thus pre-processing
|
||||
# of them is adopted. Given fixed clip size and stride size to generate
|
||||
# clipped image, the intersection of width and height is determined.
|
||||
# For example, given one 5120 x 5120 original image, the clip size is
|
||||
# 512 and stride size is 256, thus it would generate 20x20 = 400 images
|
||||
# whose size are all 512x512.
|
||||
image = mmcv.imread(image_path)
|
||||
|
||||
h, w, c = image.shape
|
||||
clip_size = args.clip_size
|
||||
stride_size = args.stride_size
|
||||
|
||||
num_rows = math.ceil((h - clip_size) / stride_size) if math.ceil(
|
||||
(h - clip_size) /
|
||||
stride_size) * stride_size + clip_size >= h else math.ceil(
|
||||
(h - clip_size) / stride_size) + 1
|
||||
num_cols = math.ceil((w - clip_size) / stride_size) if math.ceil(
|
||||
(w - clip_size) /
|
||||
stride_size) * stride_size + clip_size >= w else math.ceil(
|
||||
(w - clip_size) / stride_size) + 1
|
||||
|
||||
x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1))
|
||||
xmin = x * clip_size
|
||||
ymin = y * clip_size
|
||||
|
||||
xmin = xmin.ravel()
|
||||
ymin = ymin.ravel()
|
||||
xmin_offset = np.where(xmin + clip_size > w, w - xmin - clip_size,
|
||||
np.zeros_like(xmin))
|
||||
ymin_offset = np.where(ymin + clip_size > h, h - ymin - clip_size,
|
||||
np.zeros_like(ymin))
|
||||
boxes = np.stack([
|
||||
xmin + xmin_offset, ymin + ymin_offset,
|
||||
np.minimum(xmin + clip_size, w),
|
||||
np.minimum(ymin + clip_size, h)
|
||||
],
|
||||
axis=1)
|
||||
|
||||
if to_label:
|
||||
color_map = np.array([[0, 0, 0], [255, 255, 255], [255, 0, 0],
|
||||
[255, 255, 0], [0, 255, 0], [0, 255, 255],
|
||||
[0, 0, 255]])
|
||||
flatten_v = np.matmul(
|
||||
image.reshape(-1, c),
|
||||
np.array([2, 3, 4]).reshape(3, 1))
|
||||
out = np.zeros_like(flatten_v)
|
||||
for idx, class_color in enumerate(color_map):
|
||||
value_idx = np.matmul(class_color,
|
||||
np.array([2, 3, 4]).reshape(3, 1))
|
||||
out[flatten_v == value_idx] = idx
|
||||
image = out.reshape(h, w)
|
||||
|
||||
for box in boxes:
|
||||
start_x, start_y, end_x, end_y = box
|
||||
clipped_image = image[start_y:end_y,
|
||||
start_x:end_x] if to_label else image[
|
||||
start_y:end_y, start_x:end_x, :]
|
||||
idx_i, idx_j = osp.basename(image_path).split('_')[2:4]
|
||||
mmcv.imwrite(
|
||||
clipped_image.astype(np.uint8),
|
||||
osp.join(
|
||||
clip_save_dir,
|
||||
f'{idx_i}_{idx_j}_{start_x}_{start_y}_{end_x}_{end_y}.png'))
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
splits = {
|
||||
'train': [
|
||||
'2_10', '2_11', '2_12', '3_10', '3_11', '3_12', '4_10', '4_11',
|
||||
'4_12', '5_10', '5_11', '5_12', '6_10', '6_11', '6_12', '6_7',
|
||||
'6_8', '6_9', '7_10', '7_11', '7_12', '7_7', '7_8', '7_9'
|
||||
],
|
||||
'val': [
|
||||
'5_15', '6_15', '6_13', '3_13', '4_14', '6_14', '5_14', '2_13',
|
||||
'4_15', '2_14', '5_13', '4_13', '3_14', '7_13'
|
||||
]
|
||||
}
|
||||
|
||||
dataset_path = args.dataset_path
|
||||
if args.out_dir is None:
|
||||
out_dir = osp.join('data', 'potsdam')
|
||||
else:
|
||||
out_dir = args.out_dir
|
||||
|
||||
print('Making directories...')
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
|
||||
mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
|
||||
|
||||
zipp_list = glob.glob(os.path.join(dataset_path, '*.zip'))
|
||||
print('Find the data', zipp_list)
|
||||
|
||||
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
|
||||
for zipp in zipp_list:
|
||||
zip_file = zipfile.ZipFile(zipp)
|
||||
zip_file.extractall(tmp_dir)
|
||||
src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
|
||||
if not len(src_path_list):
|
||||
sub_tmp_dir = os.path.join(tmp_dir, os.listdir(tmp_dir)[0])
|
||||
src_path_list = glob.glob(os.path.join(sub_tmp_dir, '*.tif'))
|
||||
|
||||
prog_bar = mmcv.ProgressBar(len(src_path_list))
|
||||
for i, src_path in enumerate(src_path_list):
|
||||
idx_i, idx_j = osp.basename(src_path).split('_')[2:4]
|
||||
data_type = 'train' if f'{idx_i}_{idx_j}' in splits[
|
||||
'train'] else 'val'
|
||||
if 'label' in src_path:
|
||||
dst_dir = osp.join(out_dir, 'ann_dir', data_type)
|
||||
clip_big_image(src_path, dst_dir, args, to_label=True)
|
||||
else:
|
||||
dst_dir = osp.join(out_dir, 'img_dir', data_type)
|
||||
clip_big_image(src_path, dst_dir, args, to_label=False)
|
||||
prog_bar.update()
|
||||
|
||||
print('Removing the temporary files...')
|
||||
|
||||
print('Done!')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Loading…
Reference in New Issue