[Feature] Support BiSeNetV1 (#851)

* First Commit * fix typos * fix typos * Fix assertion bug * Adding Assert * Adding Unittest * Fixing typo * Uploading models & logs * Fixing unittest error * changing README.md * changing README.md
2025-06-03 22:03:48 +08:00 · 2021-09-29 02:12:57 +08:00 · 2021-09-29 02:12:57 +08:00 · ab12009414
commit ab12009414
parent 2aa632ebe7
14 changed files with 767 additions and 1 deletions
--- a/README.md
+++ b/README.md
@ -75,6 +75,7 @@ Supported methods:
 - [x] [PSPNet (CVPR'2017)](configs/pspnet)
 - [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
 - [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16)
+- [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1)
 - [x] [PSANet (ECCV'2018)](configs/psanet)
 - [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
 - [x] [UPerNet (ECCV'2018)](configs/upernet)
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@ -74,6 +74,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [PSPNet (CVPR'2017)](configs/pspnet)
 - [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
 - [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16)
+- [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1)
 - [x] [PSANet (ECCV'2018)](configs/psanet)
 - [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
 - [x] [UPerNet (ECCV'2018)](configs/upernet)
--- a/configs/_base_/models/bisenetv1_r18-d32.py
+++ b/configs/_base_/models/bisenetv1_r18-d32.py
@ -0,0 +1,68 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    backbone=dict(
+        type='BiSeNetV1',
+        in_channels=3,
+        context_channels=(128, 256, 512),
+        spatial_channels=(64, 64, 64, 128),
+        out_indices=(0, 1, 2),
+        out_channels=256,
+        backbone_cfg=dict(
+            type='ResNet',
+            in_channels=3,
+            depth=18,
+            num_stages=4,
+            out_indices=(0, 1, 2, 3),
+            dilations=(1, 1, 1, 1),
+            strides=(1, 2, 2, 2),
+            norm_cfg=norm_cfg,
+            norm_eval=False,
+            style='pytorch',
+            contract_dilation=True),
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        init_cfg=None),
+    decode_head=dict(
+        type='FCNHead',
+        in_channels=256,
+        in_index=0,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=19,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    auxiliary_head=[
+        dict(
+            type='FCNHead',
+            in_channels=128,
+            channels=64,
+            num_convs=1,
+            num_classes=19,
+            in_index=1,
+            norm_cfg=norm_cfg,
+            concat_input=False,
+            align_corners=False,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+        dict(
+            type='FCNHead',
+            in_channels=128,
+            channels=64,
+            num_convs=1,
+            num_classes=19,
+            in_index=2,
+            norm_cfg=norm_cfg,
+            concat_input=False,
+            align_corners=False,
+            loss_decode=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    ],
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
--- a/configs/bisenetv1/README.md
+++ b/configs/bisenetv1/README.md
@ -0,0 +1,42 @@
+# BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+<a href="https://github.com/ycszen/TorchSeg/tree/master/model/bisenet">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="https://arxiv.org/abs/1808.00897">BiSeNetV1 (ECCV'2018)</a></summary>
+
+```latex
+@inproceedings{yu2018bisenet,
+  title={Bisenet: Bilateral segmentation network for real-time semantic segmentation},
+  author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
+  booktitle={Proceedings of the European conference on computer vision (ECCV)},
+  pages={325--341},
+  year={2018}
+}
+```
+
+</details>
+
+## Results and models
+
+### Cityscapes
+
+| Method    | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                  | download                                                                                                                                                                                                                                                       |
+| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| BiSeNetV1 (No Pretrain) | R-18-D32 | 1024x1024  | 160000 | 5.69 | 31.77 | 74.44 | 77.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239.log.json) |
+| BiSeNetV1| R-18-D32 | 1024x1024  | 160000 | 5.69 | 31.77 | 74.37 | 76.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251.log.json) |
+| BiSeNetV1 (4x8) | R-18-D32 | 1024x1024  | 160000 | 11.17 | 31.77 | 75.16 | 77.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322.log.json) |
+| BiSeNetV1 (No Pretrain) | R-50-D32 | 1024x1024  | 160000 | 3.3 | 7.71 | 76.92 | 78.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639.log.json) |
+| BiSeNetV1 | R-50-D32 | 1024x1024  | 160000 | 15.39 | 7.71 | 77.68 | 79.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628.log.json) |
+
+Note:
+
+- `4x8`: Using 4 GPUs with 8 samples per GPU in training.
+- Default setting is 4 GPUs with 4 samples per GPU in training.
+- `No Pretrain` means the model is trained from scratch.
--- a/configs/bisenetv1/bisenetv1.yml
+++ b/configs/bisenetv1/bisenetv1.yml
@ -0,0 +1,125 @@
+Collections:
+- Name: bisenetv1
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    URL: https://arxiv.org/abs/1808.00897
+    Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
+  README: configs/bisenetv1/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
+    Version: v0.18.0
+  Converted From:
+    Code: https://github.com/ycszen/TorchSeg/tree/master/model/bisenet
+Models:
+- Name: bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes
+  In Collection: bisenetv1
+  Metadata:
+    backbone: R-18-D32
+    crop size: (1024,1024)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 31.48
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (1024,1024)
+    memory (GB): 5.69
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.44
+      mIoU(ms+flip): 77.05
+  Config: configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth
+- Name: bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
+  In Collection: bisenetv1
+  Metadata:
+    backbone: R-18-D32
+    crop size: (1024,1024)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 31.48
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (1024,1024)
+    memory (GB): 5.69
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.37
+      mIoU(ms+flip): 76.91
+  Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth
+- Name: bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes
+  In Collection: bisenetv1
+  Metadata:
+    backbone: R-18-D32
+    crop size: (1024,1024)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 31.48
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (1024,1024)
+    memory (GB): 11.17
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.16
+      mIoU(ms+flip): 77.24
+  Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth
+- Name: bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes
+  In Collection: bisenetv1
+  Metadata:
+    backbone: R-50-D32
+    crop size: (1024,1024)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 129.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (1024,1024)
+    memory (GB): 3.3
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 76.92
+      mIoU(ms+flip): 78.87
+  Config: configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth
+- Name: bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
+  In Collection: bisenetv1
+  Metadata:
+    backbone: R-50-D32
+    crop size: (1024,1024)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 129.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (1024,1024)
+    memory (GB): 15.39
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 77.68
+      mIoU(ms+flip): 79.57
+  Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth
--- a/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py
+++ b/configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py
@ -0,0 +1,11 @@
+_base_ = [
+    '../_base_/models/bisenetv1_r18-d32.py',
+    '../_base_/datasets/cityscapes_1024x1024.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+lr_config = dict(warmup='linear', warmup_iters=1000)
+optimizer = dict(lr=0.025)
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+)
--- a/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
+++ b/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
@ -0,0 +1,16 @@
+_base_ = [
+    '../_base_/models/bisenetv1_r18-d32.py',
+    '../_base_/datasets/cityscapes_1024x1024.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+model = dict(
+    backbone=dict(
+        backbone_cfg=dict(
+            init_cfg=dict(
+                type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))))
+lr_config = dict(warmup='linear', warmup_iters=1000)
+optimizer = dict(lr=0.025)
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+)
--- a/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py
+++ b/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py
@ -0,0 +1,5 @@
+_base_ = './bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py'
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=8,
+)
--- a/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py
+++ b/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py
@ -0,0 +1,46 @@
+_base_ = [
+    '../_base_/models/bisenetv1_r18-d32.py',
+    '../_base_/datasets/cityscapes_1024x1024.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    backbone=dict(
+        type='BiSeNetV1',
+        context_channels=(512, 1024, 2048),
+        spatial_channels=(256, 256, 256, 512),
+        out_channels=1024,
+        backbone_cfg=dict(
+            init_cfg=dict(
+                type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'),
+            type='ResNet',
+            depth=50)),
+    decode_head=dict(
+        type='FCNHead', in_channels=1024, in_index=0, channels=1024),
+    auxiliary_head=[
+        dict(
+            type='FCNHead',
+            in_channels=512,
+            channels=256,
+            num_convs=1,
+            num_classes=19,
+            in_index=1,
+            norm_cfg=norm_cfg,
+            concat_input=False),
+        dict(
+            type='FCNHead',
+            in_channels=512,
+            channels=256,
+            num_convs=1,
+            num_classes=19,
+            in_index=2,
+            norm_cfg=norm_cfg,
+            concat_input=False),
+    ])
+lr_config = dict(warmup='linear', warmup_iters=1000)
+optimizer = dict(lr=0.05)
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+)
--- a/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
+++ b/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
@ -0,0 +1,7 @@
+_base_ = './bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py'
+model = dict(
+    type='EncoderDecoder',
+    backbone=dict(
+        backbone_cfg=dict(
+            init_cfg=dict(
+                type='Pretrained', checkpoint='open-mmlab://resnet50_v1c'))))
--- a/mmseg/models/backbones/init.py
+++ b/mmseg/models/backbones/init.py
@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from .bisenetv1 import BiSeNetV1
 from .bisenetv2 import BiSeNetV2
 from .cgnet import CGNet
 from .fast_scnn import FastSCNN
@ -16,5 +17,6 @@ from .vit import VisionTransformer
 __all__ = [
    'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN',
    'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3',
-    'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer', 'BiSeNetV2'
+    'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer',
+    'BiSeNetV1', 'BiSeNetV2'
 ]
--- a/mmseg/models/backbones/bisenetv1.py
+++ b/mmseg/models/backbones/bisenetv1.py
@ -0,0 +1,332 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+
+from mmseg.ops import resize
+from ..builder import BACKBONES, build_backbone
+
+
+class SpatialPath(BaseModule):
+    """Spatial Path to preserve the spatial size of the original input image
+    and encode affluent spatial information.
+
+    Args:
+        in_channels(int): The number of channels of input
+            image. Default: 3.
+        num_channels (Tuple[int]): The number of channels of
+            each layers in Spatial Path.
+            Default: (64, 64, 64, 128).
+    Returns:
+        x (torch.Tensor): Feature map for Feature Fusion Module.
+    """
+
+    def __init__(self,
+                 in_channels=3,
+                 num_channels=(64, 64, 64, 128),
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super(SpatialPath, self).__init__(init_cfg=init_cfg)
+        assert len(num_channels) == 4, 'Length of input channels \
+                                        of Spatial Path must be 4!'
+
+        self.layers = []
+        for i in range(len(num_channels)):
+            layer_name = f'layer{i + 1}'
+            self.layers.append(layer_name)
+            if i == 0:
+                self.add_module(
+                    layer_name,
+                    ConvModule(
+                        in_channels=in_channels,
+                        out_channels=num_channels[i],
+                        kernel_size=7,
+                        stride=2,
+                        padding=3,
+                        conv_cfg=conv_cfg,
+                        norm_cfg=norm_cfg,
+                        act_cfg=act_cfg))
+            elif i == len(num_channels) - 1:
+                self.add_module(
+                    layer_name,
+                    ConvModule(
+                        in_channels=num_channels[i - 1],
+                        out_channels=num_channels[i],
+                        kernel_size=1,
+                        stride=1,
+                        padding=0,
+                        conv_cfg=conv_cfg,
+                        norm_cfg=norm_cfg,
+                        act_cfg=act_cfg))
+            else:
+                self.add_module(
+                    layer_name,
+                    ConvModule(
+                        in_channels=num_channels[i - 1],
+                        out_channels=num_channels[i],
+                        kernel_size=3,
+                        stride=2,
+                        padding=1,
+                        conv_cfg=conv_cfg,
+                        norm_cfg=norm_cfg,
+                        act_cfg=act_cfg))
+
+    def forward(self, x):
+        for i, layer_name in enumerate(self.layers):
+            layer_stage = getattr(self, layer_name)
+            x = layer_stage(x)
+        return x
+
+
+class AttentionRefinementModule(BaseModule):
+    """Attention Refinement Module (ARM) to refine the features of each stage.
+
+    Args:
+        in_channels (int): The number of input channels.
+        out_channels (int): The number of output channels.
+    Returns:
+        x_out (torch.Tensor): Feature map of Attention Refinement Module.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channel,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super(AttentionRefinementModule, self).__init__(init_cfg=init_cfg)
+        self.conv_layer = ConvModule(
+            in_channels=in_channels,
+            out_channels=out_channel,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.atten_conv_layer = nn.Sequential(
+            nn.AdaptiveAvgPool2d((1, 1)),
+            ConvModule(
+                in_channels=out_channel,
+                out_channels=out_channel,
+                kernel_size=1,
+                bias=False,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=None), nn.Sigmoid())
+
+    def forward(self, x):
+        x = self.conv_layer(x)
+        x_atten = self.atten_conv_layer(x)
+        x_out = x * x_atten
+        return x_out
+
+
+class ContextPath(BaseModule):
+    """Context Path to provide sufficient receptive field.
+
+    Args:
+        backbone_cfg:(dict): Config of backbone of
+            Context Path.
+        context_channels (Tuple[int]): The number of channel numbers
+            of various modules in Context Path.
+            Default: (128, 256, 512).
+        align_corners (bool, optional): The align_corners argument of
+            resize operation. Default: False.
+    Returns:
+        x_16_up, x_32_up (torch.Tensor, torch.Tensor): Two feature maps
+            undergoing upsampling from 1/16 and 1/32 downsampling
+            feature maps. These two feature maps are used for Feature
+            Fusion Module and Auxiliary Head.
+    """
+
+    def __init__(self,
+                 backbone_cfg,
+                 context_channels=(128, 256, 512),
+                 align_corners=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super(ContextPath, self).__init__(init_cfg=init_cfg)
+        assert len(context_channels) == 3, 'Length of input channels \
+                                           of Context Path must be 3!'
+
+        self.backbone = build_backbone(backbone_cfg)
+
+        self.align_corners = align_corners
+        self.arm16 = AttentionRefinementModule(context_channels[1],
+                                               context_channels[0])
+        self.arm32 = AttentionRefinementModule(context_channels[2],
+                                               context_channels[0])
+        self.conv_head32 = ConvModule(
+            in_channels=context_channels[0],
+            out_channels=context_channels[0],
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.conv_head16 = ConvModule(
+            in_channels=context_channels[0],
+            out_channels=context_channels[0],
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.gap_conv = nn.Sequential(
+            nn.AdaptiveAvgPool2d((1, 1)),
+            ConvModule(
+                in_channels=context_channels[2],
+                out_channels=context_channels[0],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg))
+
+    def forward(self, x):
+        x_4, x_8, x_16, x_32 = self.backbone(x)
+        x_gap = self.gap_conv(x_32)
+
+        x_32_arm = self.arm32(x_32)
+        x_32_sum = x_32_arm + x_gap
+        x_32_up = resize(input=x_32_sum, size=x_16.shape[2:], mode='nearest')
+        x_32_up = self.conv_head32(x_32_up)
+
+        x_16_arm = self.arm16(x_16)
+        x_16_sum = x_16_arm + x_32_up
+        x_16_up = resize(input=x_16_sum, size=x_8.shape[2:], mode='nearest')
+        x_16_up = self.conv_head16(x_16_up)
+
+        return x_16_up, x_32_up
+
+
+class FeatureFusionModule(BaseModule):
+    """Feature Fusion Module to fuse low level output feature of Spatial Path
+    and high level output feature of Context Path.
+
+    Args:
+        in_channels (int): The number of input channels.
+        out_channels (int): The number of output channels.
+    Returns:
+        x_out (torch.Tensor): Feature map of Feature Fusion Module.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super(FeatureFusionModule, self).__init__(init_cfg=init_cfg)
+        self.conv1 = ConvModule(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.gap = nn.AdaptiveAvgPool2d((1, 1))
+        self.conv_atten = nn.Sequential(
+            ConvModule(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=False,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg), nn.Sigmoid())
+
+    def forward(self, x_sp, x_cp):
+        x_concat = torch.cat([x_sp, x_cp], dim=1)
+        x_fuse = self.conv1(x_concat)
+        x_atten = self.gap(x_fuse)
+        # Note: No BN and more 1x1 conv in paper.
+        x_atten = self.conv_atten(x_atten)
+        x_atten = x_fuse * x_atten
+        x_out = x_atten + x_fuse
+        return x_out
+
+
+@BACKBONES.register_module()
+class BiSeNetV1(BaseModule):
+    """BiSeNetV1 backbone.
+
+    This backbone is the implementation of `BiSeNet: Bilateral
+    Segmentation Network for Real-time Semantic
+    Segmentation <https://arxiv.org/abs/1808.00897>`_.
+
+    Args:
+        backbone_cfg:(dict): Config of backbone of
+            Context Path.
+        in_channels (int): The number of channels of input
+            image. Default: 3.
+        spatial_channels (Tuple[int]): Size of channel numbers of
+            various layers in Spatial Path.
+            Default: (64, 64, 64, 128).
+        context_channels (Tuple[int]): Size of channel numbers of
+            various modules in Context Path.
+            Default: (128, 256, 512).
+        out_indices (Tuple[int] | int, optional): Output from which stages.
+            Default: (0, 1, 2).
+        align_corners (bool, optional): The align_corners argument of
+            resize operation in Bilateral Guided Aggregation Layer.
+            Default: False.
+        out_channels(int): The number of channels of output.
+            It must be the same with `in_channels` of decode_head.
+            Default: 256.
+    """
+
+    def __init__(self,
+                 backbone_cfg,
+                 in_channels=3,
+                 spatial_channels=(64, 64, 64, 128),
+                 context_channels=(128, 256, 512),
+                 out_indices=(0, 1, 2),
+                 align_corners=False,
+                 out_channels=256,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+
+        super(BiSeNetV1, self).__init__(init_cfg=init_cfg)
+        assert len(spatial_channels) == 4, 'Length of input channels \
+                                           of Spatial Path must be 4!'
+
+        assert len(context_channels) == 3, 'Length of input channels \
+                                           of Context Path must be 3!'
+
+        self.out_indices = out_indices
+        self.align_corners = align_corners
+        self.context_path = ContextPath(backbone_cfg, context_channels,
+                                        self.align_corners)
+        self.spatial_path = SpatialPath(in_channels, spatial_channels)
+        self.ffm = FeatureFusionModule(context_channels[1], out_channels)
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+
+    def forward(self, x):
+        # stole refactoring code from Coin Cheung, thanks
+        x_context8, x_context16 = self.context_path(x)
+        x_spatial = self.spatial_path(x)
+        x_fuse = self.ffm(x_spatial, x_context8)
+
+        outs = [x_fuse, x_context8, x_context16]
+        outs = [outs[i] for i in self.out_indices]
+        return tuple(outs)
--- a/model-index.yml
+++ b/model-index.yml
@ -1,6 +1,7 @@
 Import:
 - configs/ann/ann.yml
 - configs/apcnet/apcnet.yml
+- configs/bisenetv1/bisenetv1.yml
 - configs/bisenetv2/bisenetv2.yml
 - configs/ccnet/ccnet.yml
 - configs/cgnet/cgnet.yml
--- a/tests/test_models/test_backbones/test_bisenetv1.py
+++ b/tests/test_models/test_backbones/test_bisenetv1.py
@ -0,0 +1,109 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmseg.models.backbones import BiSeNetV1
+from mmseg.models.backbones.bisenetv1 import (AttentionRefinementModule,
+                                              ContextPath, FeatureFusionModule,
+                                              SpatialPath)
+
+
+def test_bisenetv1_backbone():
+    # Test BiSeNetV1 Standard Forward
+    backbone_cfg = dict(
+        type='ResNet',
+        in_channels=3,
+        depth=18,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 1, 1),
+        strides=(1, 2, 2, 2),
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True)
+    model = BiSeNetV1(in_channels=3, backbone_cfg=backbone_cfg)
+    model.init_weights()
+    model.train()
+    batch_size = 2
+    imgs = torch.randn(batch_size, 3, 256, 512)
+    feat = model(imgs)
+
+    assert len(feat) == 3
+    # output for segment Head
+    assert feat[0].shape == torch.Size([batch_size, 256, 32, 64])
+    # for auxiliary head 1
+    assert feat[1].shape == torch.Size([batch_size, 128, 32, 64])
+    # for auxiliary head 2
+    assert feat[2].shape == torch.Size([batch_size, 128, 16, 32])
+
+    # Test input with rare shape
+    batch_size = 2
+    imgs = torch.randn(batch_size, 3, 527, 279)
+    feat = model(imgs)
+    assert len(feat) == 3
+
+    with pytest.raises(AssertionError):
+        # BiSeNetV1 spatial path channel constraints.
+        BiSeNetV1(
+            backbone_cfg=backbone_cfg,
+            in_channels=3,
+            spatial_channels=(64, 64, 64))
+
+    with pytest.raises(AssertionError):
+        # BiSeNetV1 context path constraints.
+        BiSeNetV1(
+            backbone_cfg=backbone_cfg,
+            in_channels=3,
+            context_channels=(128, 256, 512, 1024))
+
+
+def test_bisenetv1_spatial_path():
+    with pytest.raises(AssertionError):
+        # BiSeNetV1 spatial path channel constraints.
+        SpatialPath(num_channels=(64, 64, 64), in_channels=3)
+
+
+def test_bisenetv1_context_path():
+    backbone_cfg = dict(
+        type='ResNet',
+        in_channels=3,
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 1, 1),
+        strides=(1, 2, 2, 2),
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True)
+
+    with pytest.raises(AssertionError):
+        # BiSeNetV1 context path constraints.
+        ContextPath(
+            backbone_cfg=backbone_cfg, context_channels=(128, 256, 512, 1024))
+
+
+def test_bisenetv1_attention_refinement_module():
+    x_arm = AttentionRefinementModule(256, 64)
+    assert x_arm.conv_layer.in_channels == 256
+    assert x_arm.conv_layer.out_channels == 64
+    assert x_arm.conv_layer.kernel_size == (3, 3)
+    x = torch.randn(2, 256, 32, 64)
+    x_out = x_arm(x)
+    assert x_out.shape == torch.Size([2, 64, 32, 64])
+
+
+def test_bisenetv1_feature_fusion_module():
+    ffm = FeatureFusionModule(128, 256)
+    assert ffm.conv1.in_channels == 128
+    assert ffm.conv1.out_channels == 256
+    assert ffm.conv1.kernel_size == (1, 1)
+    assert ffm.gap.output_size == (1, 1)
+    assert ffm.conv_atten[0].in_channels == 256
+    assert ffm.conv_atten[0].out_channels == 256
+    assert ffm.conv_atten[0].kernel_size == (1, 1)
+
+    ffm = FeatureFusionModule(128, 128)
+    x1 = torch.randn(2, 64, 64, 128)
+    x2 = torch.randn(2, 64, 64, 128)
+    x_out = ffm(x1, x2)
+    assert x_out.shape == torch.Size([2, 128, 64, 128])