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## Motivation Support SOTA real-time semantic segmentation method in [Paper with code](https://paperswithcode.com/task/real-time-semantic-segmentation) Paper: https://arxiv.org/pdf/2206.02066.pdf Official repo: https://github.com/XuJiacong/PIDNet ## Current results **Cityscapes** |Model|Ref mIoU|mIoU (ours)| |---|---|---| |PIDNet-S|78.8|78.74| |PIDNet-M|79.9|80.22| |PIDNet-L|80.9|80.89| ## TODO - [x] Support inference with official weights - [x] Support training on Cityscapes - [x] Update docstring - [x] Add unit test
22 lines
909 B
Python
22 lines
909 B
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from .basic_block import BasicBlock, Bottleneck
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from .embed import PatchEmbed
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from .encoding import Encoding
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from .inverted_residual import InvertedResidual, InvertedResidualV3
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from .make_divisible import make_divisible
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from .ppm import DAPPM, PAPPM
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from .res_layer import ResLayer
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from .se_layer import SELayer
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from .self_attention_block import SelfAttentionBlock
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from .shape_convert import (nchw2nlc2nchw, nchw_to_nlc, nlc2nchw2nlc,
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nlc_to_nchw)
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from .up_conv_block import UpConvBlock
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from .wrappers import Upsample, resize
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__all__ = [
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'ResLayer', 'SelfAttentionBlock', 'make_divisible', 'InvertedResidual',
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'UpConvBlock', 'InvertedResidualV3', 'SELayer', 'PatchEmbed',
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'nchw_to_nlc', 'nlc_to_nchw', 'nchw2nlc2nchw', 'nlc2nchw2nlc', 'Encoding',
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'Upsample', 'resize', 'DAPPM', 'PAPPM', 'BasicBlock', 'Bottleneck'
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]
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