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[Fix] Update ddrnet readme (#3198)
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‎configs/ddrnet/README.md

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@@ -24,10 +24,10 @@ Semantic segmentation is a key technology for autonomous vehicles to understand
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### Cityscapes
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| Method | Backbone | Crop Size | Lr schd | Mem(GB) | Inf time(fps) | Device | mIoU | mIoU(ms+flip) | config | download |
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| ------ | ------------- | --------- | ------- | ------- | ------------- | ------ | ----- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| DDRNet | DDRNet23-slim | 1024x1024 | 120000 | 1.70 | 85.85 | A100 | 77.84 | 80.15 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312.json) |
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| DDRNet | DDRNet23 | 1024x1024 | 120000 | 7.26 | 33.41 | A100 | 79.99 | 81.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230425_162633-81601db0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230425_162633.json) |
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
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| ------ | ------------- | --------- | ------- | -------- | -------------- | ------ | ----- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| DDRNet | DDRNet23-slim | 1024x1024 | 120000 | 1.70 | 85.85 | A100 | 77.84 | 80.15 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312.json) |
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| DDRNet | DDRNet23 | 1024x1024 | 120000 | 7.26 | 33.41 | A100 | 79.99 | 81.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/main/configs/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230425_162633-81601db0.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230425_162633.json) |
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## Notes
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‎configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py

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1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529,
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1.0507
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]
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checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa
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crop_size = (1024, 1024)
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data_preprocessor = dict(
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type='SegDataPreProcessor',
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ppm_channels=128,
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norm_cfg=norm_cfg,
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align_corners=False,
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init_cfg=dict(
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type='Pretrained',
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checkpoint='pretrained/ddrnet23s_in1k_mmseg.pth')),
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init_cfg=dict(type='Pretrained', checkpoint=checkpoint)),
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decode_head=dict(
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type='DDRHead',
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in_channels=32 * 4,

‎configs/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py

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1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529,
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1.0507
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]
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checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23-in1kpre_3rdparty-9ca29f62.pth' # noqa
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crop_size = (1024, 1024)
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data_preprocessor = dict(
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type='SegDataPreProcessor',
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ppm_channels=128,
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norm_cfg=norm_cfg,
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align_corners=False,
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init_cfg=dict(
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type='Pretrained',
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checkpoint='pretrained/ddrnet23_in1k_mmseg.pth')),
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init_cfg=dict(type='Pretrained', checkpoint=checkpoint)),
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decode_head=dict(
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type='DDRHead',
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in_channels=64 * 4,

‎configs/ddrnet/metafile.yaml

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Collections:
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- Name: ''
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- Name: DDRNet
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License: Apache License 2.0
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Metadata:
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Training Data:
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README: configs/ddrnet/README.md
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Frameworks:
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- PyTorch
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Models: []
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Models:
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- Name: ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024
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In Collection: DDRNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 77.84
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mIoU(ms+flip): 80.15
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Config: configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 12
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Architecture:
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- DDRNet23-slim
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- DDRNet
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Training Resources: 2x A100 GPUS
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Memory (GB): 1.7
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312.json
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Paper:
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Title: Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation
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of Road Scenes
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URL: http://arxiv.org/abs/2101.06085
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Code: ''
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Framework: PyTorch
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- Name: ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024
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In Collection: DDRNet
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Results:
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Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.99
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mIoU(ms+flip): 81.71
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Config: configs/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py
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Metadata:
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Training Data: Cityscapes
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Batch Size: 12
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Architecture:
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- DDRNet23
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- DDRNet
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Training Resources: 2x A100 GPUS
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Memory (GB): 7.26
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230425_162633-81601db0.pth
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Training log: https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230425_162633.json
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Paper:
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Title: Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation
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of Road Scenes
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URL: http://arxiv.org/abs/2101.06085
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Code: ''
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Framework: PyTorch

‎tools/misc/publish_model.py

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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import subprocess
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from hashlib import sha256
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import torch
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BLOCK_SIZE = 128 * 1024
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def parse_args():
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parser = argparse.ArgumentParser(
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return args
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def sha256sum(filename: str) -> str:
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"""Compute SHA256 message digest from a file."""
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hash_func = sha256()
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byte_array = bytearray(BLOCK_SIZE)
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memory_view = memoryview(byte_array)
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with open(filename, 'rb', buffering=0) as file:
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for block in iter(lambda: file.readinto(memory_view), 0):
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hash_func.update(memory_view[:block])
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return hash_func.hexdigest()
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def process_checkpoint(in_file, out_file):
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checkpoint = torch.load(in_file, map_location='cpu')
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# remove optimizer for smaller file size
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# if it is necessary to remove some sensitive data in checkpoint['meta'],
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# add the code here.
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torch.save(checkpoint, out_file)
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sha = subprocess.check_output(['sha256sum', out_file]).decode()
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sha = sha256sum(in_file)
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final_file = out_file.rstrip('.pth') + f'-{sha[:8]}.pth'
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subprocess.Popen(['mv', out_file, final_file])
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