[Feature] Support BiSeNetV2 (#804)

* BiSeNetV2 first commit * BiSeNetV2 unittest * remove pytest * add pytest module * fix ConvModule input name * fix pytest error * fix unittest * refactor * BiSeNetV2 Refactory * fix docstrings and add some small changes * use_sigmoid=False * fix potential bugs about upsampling * Use ConvModule instead * Use ConvModule instead * fix typos * fix typos * fix typos * discard nn.conv2d * discard nn.conv2d * discard nn.conv2d * delete **kwargs * uploading markdown and model * final commit * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * BiSeNetV2 adding Unittest for its modules * Fix README conflict * Fix unittest problem * Fix unittest problem * BiSeNetV2 * Fixing fps * Fixing typpos * bisenetv2
2025-06-03 22:03:48 +08:00 · 2021-09-26 18:52:16 +08:00 · 2021-09-26 18:52:16 +08:00 · f82e4d6fc9
commit f82e4d6fc9
parent 96b369bd55
15 changed files with 952 additions and 1 deletions
--- a/README.md
+++ b/README.md
@ -94,6 +94,7 @@ Supported methods:
 - [x] [PointRend (CVPR'2020)](configs/point_rend)
 - [x] [CGNet (TIP'2020)](configs/cgnet)
 - [x] [SETR (CVPR'2021)](configs/setr)
+- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
 - [x] [SegFormer (ArXiv'2021)](configs/segformer)

 Supported datasets:
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@ -93,6 +93,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [PointRend (CVPR'2020)](configs/point_rend)
 - [x] [CGNet (TIP'2020)](configs/cgnet)
 - [x] [SETR (CVPR'2021)](configs/setr)
+- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
 - [x] [SegFormer (ArXiv'2021)](configs/segformer)

 已支持的数据集：
--- a/configs/_base_/datasets/cityscapes_1024x1024.py
+++ b/configs/_base_/datasets/cityscapes_1024x1024.py
@ -0,0 +1,35 @@
+_base_ = './cityscapes.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (1024, 1024)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 1024),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
--- a/configs/_base_/models/bisenetv2.py
+++ b/configs/_base_/models/bisenetv2.py
@ -0,0 +1,80 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    pretrained=None,
+    backbone=dict(
+        type='BiSeNetV2',
+        detail_channels=(64, 64, 128),
+        semantic_channels=(16, 32, 64, 128),
+        semantic_expansion_ratio=6,
+        bga_channels=128,
+        out_indices=(0, 1, 2, 3, 4),
+        init_cfg=None,
+        align_corners=False),
+    decode_head=dict(
+        type='FCNHead',
+        in_channels=128,
+        in_index=0,
+        channels=1024,
+        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=16,
+            channels=16,
+            num_convs=2,
+            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=32,
+            channels=64,
+            num_convs=2,
+            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)),
+        dict(
+            type='FCNHead',
+            in_channels=64,
+            channels=256,
+            num_convs=2,
+            num_classes=19,
+            in_index=3,
+            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=1024,
+            num_convs=2,
+            num_classes=19,
+            in_index=4,
+            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/bisenetv2/README.md
+++ b/configs/bisenetv2/README.md
@ -0,0 +1,33 @@
+# Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```latex
+@article{yu2021bisenet,
+  title={Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation},
+  author={Yu, Changqian and Gao, Changxin and Wang, Jingbo and Yu, Gang and Shen, Chunhua and Sang, Nong},
+  journal={International Journal of Computer Vision},
+  pages={1--18},
+  year={2021},
+  publisher={Springer}
+}
+```
+
+## Results and models
+
+### Cityscapes
+
+| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                | download                                                                                                                                                                                                                                                                                                                               |
+| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| BiSeNetV2  | BiSeNetV2  | 1024x1024   |   160000 | 7.64     | 31.77         | 73.21 |         75.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551.log.json)         |
+| BiSeNetV2 (OHEM)  | BiSeNetV2 | 1024x1024   |   160000 | 7.64       | -          | 73.57 |         75.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947.log.json)     |
+| BiSeNetV2 (4x8) | BiSeNetV2 | 1024x1024   |  160000 | 15.05        | -              | 75.76 |         77.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032.log.json) |
+| BiSeNetV2 (FP16)  | BiSeNetV2  | 1024x1024   |  160000 | 5.77         | 36.65              | 73.07 |         75.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942.log.json)     |
+
+Note:
+
+- `OHEM` means Online Hard Example Mining (OHEM) is adopted in training.
+- `FP16` means Mixed Precision (FP16) is adopted in training.
+- `4x8` means 4 GPUs with 8 samples per GPU in training.
--- a/configs/bisenetv2/bisenetv2.yml
+++ b/configs/bisenetv2/bisenetv2.yml
@ -0,0 +1,80 @@
+Collections:
+- Metadata:
+    Training Data:
+    - Cityscapes
+  Name: bisenetv2
+Models:
+- Config: configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py
+  In Collection: bisenetv2
+  Metadata:
+    backbone: BiSeNetV2
+    crop size: (1024,1024)
+    inference time (ms/im):
+    - backend: PyTorch
+      batch size: 1
+      hardware: V100
+      mode: FP32
+      resolution: (1024,1024)
+      value: 31.48
+    lr schd: 160000
+    memory (GB): 7.64
+  Name: bisenetv2_fcn_4x4_1024x1024_160k_cityscapes
+  Results:
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.21
+      mIoU(ms+flip): 75.74
+    Task: Semantic Segmentation
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth
+- Config: configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py
+  In Collection: bisenetv2
+  Metadata:
+    backbone: BiSeNetV2
+    crop size: (1024,1024)
+    lr schd: 160000
+    memory (GB): 7.64
+  Name: bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes
+  Results:
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.57
+      mIoU(ms+flip): 75.8
+    Task: Semantic Segmentation
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth
+- Config: configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py
+  In Collection: bisenetv2
+  Metadata:
+    backbone: BiSeNetV2
+    crop size: (1024,1024)
+    lr schd: 160000
+    memory (GB): 15.05
+  Name: bisenetv2_fcn_4x8_1024x1024_160k_cityscapes
+  Results:
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.76
+      mIoU(ms+flip): 77.79
+    Task: Semantic Segmentation
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth
+- Config: configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py
+  In Collection: bisenetv2
+  Metadata:
+    backbone: BiSeNetV2
+    crop size: (1024,1024)
+    inference time (ms/im):
+    - backend: PyTorch
+      batch size: 1
+      hardware: V100
+      mode: FP32
+      resolution: (1024,1024)
+      value: 27.29
+    lr schd: 160000
+    memory (GB): 5.77
+  Name: bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes
+  Results:
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 73.07
+      mIoU(ms+flip): 75.13
+    Task: Semantic Segmentation
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth
--- a/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py
+++ b/configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py
@ -0,0 +1,11 @@
+_base_ = [
+    '../_base_/models/bisenetv2.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=0.05)
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+)
--- a/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py
+++ b/configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py
@ -0,0 +1,11 @@
+_base_ = [
+    '../_base_/models/bisenetv2.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=0.05)
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=8,
+)
--- a/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py
+++ b/configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py
@ -0,0 +1,5 @@
+_base_ = './bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py'
+# fp16 settings
+optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
+# fp16 placeholder
+fp16 = dict()
--- a/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py
+++ b/configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py
@ -0,0 +1,12 @@
+_base_ = [
+    '../_base_/models/bisenetv2.py',
+    '../_base_/datasets/cityscapes_1024x1024.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+sampler = dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)
+lr_config = dict(warmup='linear', warmup_iters=1000)
+optimizer = dict(lr=0.05)
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+)
--- a/mmseg/models/backbones/init.py
+++ b/mmseg/models/backbones/init.py
@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from .bisenetv2 import BiSeNetV2
 from .cgnet import CGNet
 from .fast_scnn import FastSCNN
 from .hrnet import HRNet
@ -15,5 +16,5 @@ from .vit import VisionTransformer
 __all__ = [
    'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN',
    'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3',
-    'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer'
+    'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer', 'BiSeNetV2'
 ]
--- a/mmseg/models/backbones/bisenetv2.py
+++ b/mmseg/models/backbones/bisenetv2.py
@ -0,0 +1,622 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule,
+                      build_activation_layer, build_norm_layer)
+from mmcv.runner import BaseModule
+
+from mmseg.ops import resize
+from ..builder import BACKBONES
+
+
+class DetailBranch(BaseModule):
+    """Detail Branch with wide channels and shallow layers to capture low-level
+    details and generate high-resolution feature representation.
+
+    Args:
+        detail_channels (Tuple[int]): Size of channel numbers of each stage
+            in Detail Branch, in paper it has 3 stages.
+            Default: (64, 64, 128).
+        in_channels (int): Number of channels of input image. Default: 3.
+        conv_cfg (dict | None): Config of conv layers.
+            Default: None.
+        norm_cfg (dict | None): Config of norm layers.
+            Default: dict(type='BN').
+        act_cfg (dict): Config of activation layers.
+            Default: dict(type='ReLU').
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    Returns:
+        x (torch.Tensor): Feature map of Detail Branch.
+    """
+
+    def __init__(self,
+                 detail_channels=(64, 64, 128),
+                 in_channels=3,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super(DetailBranch, self).__init__(init_cfg=init_cfg)
+        detail_branch = []
+        for i in range(len(detail_channels)):
+            if i == 0:
+                detail_branch.append(
+                    nn.Sequential(
+                        ConvModule(
+                            in_channels=in_channels,
+                            out_channels=detail_channels[i],
+                            kernel_size=3,
+                            stride=2,
+                            padding=1,
+                            conv_cfg=conv_cfg,
+                            norm_cfg=norm_cfg,
+                            act_cfg=act_cfg),
+                        ConvModule(
+                            in_channels=detail_channels[i],
+                            out_channels=detail_channels[i],
+                            kernel_size=3,
+                            stride=1,
+                            padding=1,
+                            conv_cfg=conv_cfg,
+                            norm_cfg=norm_cfg,
+                            act_cfg=act_cfg)))
+            else:
+                detail_branch.append(
+                    nn.Sequential(
+                        ConvModule(
+                            in_channels=detail_channels[i - 1],
+                            out_channels=detail_channels[i],
+                            kernel_size=3,
+                            stride=2,
+                            padding=1,
+                            conv_cfg=conv_cfg,
+                            norm_cfg=norm_cfg,
+                            act_cfg=act_cfg),
+                        ConvModule(
+                            in_channels=detail_channels[i],
+                            out_channels=detail_channels[i],
+                            kernel_size=3,
+                            stride=1,
+                            padding=1,
+                            conv_cfg=conv_cfg,
+                            norm_cfg=norm_cfg,
+                            act_cfg=act_cfg),
+                        ConvModule(
+                            in_channels=detail_channels[i],
+                            out_channels=detail_channels[i],
+                            kernel_size=3,
+                            stride=1,
+                            padding=1,
+                            conv_cfg=conv_cfg,
+                            norm_cfg=norm_cfg,
+                            act_cfg=act_cfg)))
+        self.detail_branch = nn.ModuleList(detail_branch)
+
+    def forward(self, x):
+        for stage in self.detail_branch:
+            x = stage(x)
+        return x
+
+
+class StemBlock(BaseModule):
+    """Stem Block at the beginning of Semantic Branch.
+
+    Args:
+        in_channels (int): Number of input channels.
+            Default: 3.
+        out_channels (int): Number of output channels.
+            Default: 16.
+        conv_cfg (dict | None): Config of conv layers.
+            Default: None.
+        norm_cfg (dict | None): Config of norm layers.
+            Default: dict(type='BN').
+        act_cfg (dict): Config of activation layers.
+            Default: dict(type='ReLU').
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    Returns:
+        x (torch.Tensor): First feature map in Semantic Branch.
+    """
+
+    def __init__(self,
+                 in_channels=3,
+                 out_channels=16,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super(StemBlock, self).__init__(init_cfg=init_cfg)
+
+        self.conv_first = ConvModule(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.convs = nn.Sequential(
+            ConvModule(
+                in_channels=out_channels,
+                out_channels=out_channels // 2,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg),
+            ConvModule(
+                in_channels=out_channels // 2,
+                out_channels=out_channels,
+                kernel_size=3,
+                stride=2,
+                padding=1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg))
+        self.pool = nn.MaxPool2d(
+            kernel_size=3, stride=2, padding=1, ceil_mode=False)
+        self.fuse_last = ConvModule(
+            in_channels=out_channels * 2,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+    def forward(self, x):
+        x = self.conv_first(x)
+        x_left = self.convs(x)
+        x_right = self.pool(x)
+        x = self.fuse_last(torch.cat([x_left, x_right], dim=1))
+        return x
+
+
+class GELayer(BaseModule):
+    """Gather-and-Expansion Layer.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        exp_ratio (int): Expansion ratio for middle channels.
+            Default: 6.
+        stride (int): Stride of GELayer. Default: 1
+        conv_cfg (dict | None): Config of conv layers.
+            Default: None.
+        norm_cfg (dict | None): Config of norm layers.
+            Default: dict(type='BN').
+        act_cfg (dict): Config of activation layers.
+            Default: dict(type='ReLU').
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    Returns:
+        x (torch.Tensor): Intermidiate feature map in
+            Semantic Branch.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 exp_ratio=6,
+                 stride=1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super(GELayer, self).__init__(init_cfg=init_cfg)
+        mid_channel = in_channels * exp_ratio
+        self.conv1 = ConvModule(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        if stride == 1:
+            self.dwconv = nn.Sequential(
+                # ReLU in ConvModule not shown in paper
+                ConvModule(
+                    in_channels=in_channels,
+                    out_channels=mid_channel,
+                    kernel_size=3,
+                    stride=stride,
+                    padding=1,
+                    groups=in_channels,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+            self.shortcut = None
+        else:
+            self.dwconv = nn.Sequential(
+                ConvModule(
+                    in_channels=in_channels,
+                    out_channels=mid_channel,
+                    kernel_size=3,
+                    stride=stride,
+                    padding=1,
+                    groups=in_channels,
+                    bias=False,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=None),
+                # ReLU in ConvModule not shown in paper
+                ConvModule(
+                    in_channels=mid_channel,
+                    out_channels=mid_channel,
+                    kernel_size=3,
+                    stride=1,
+                    padding=1,
+                    groups=mid_channel,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg),
+            )
+            self.shortcut = nn.Sequential(
+                DepthwiseSeparableConvModule(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    kernel_size=3,
+                    stride=stride,
+                    padding=1,
+                    dw_norm_cfg=norm_cfg,
+                    dw_act_cfg=None,
+                    pw_norm_cfg=norm_cfg,
+                    pw_act_cfg=None,
+                ))
+
+        self.conv2 = nn.Sequential(
+            ConvModule(
+                in_channels=mid_channel,
+                out_channels=out_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=False,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=None,
+            ))
+
+        self.act = build_activation_layer(act_cfg)
+
+    def forward(self, x):
+        identity = x
+        x = self.conv1(x)
+        x = self.dwconv(x)
+        x = self.conv2(x)
+        if self.shortcut is not None:
+            shortcut = self.shortcut(identity)
+            x = x + shortcut
+        else:
+            x = x + identity
+        x = self.act(x)
+        return x
+
+
+class CEBlock(BaseModule):
+    """Context Embedding Block for large receptive filed in Semantic Branch.
+
+    Args:
+        in_channels (int): Number of input channels.
+            Default: 3.
+        out_channels (int): Number of output channels.
+            Default: 16.
+        conv_cfg (dict | None): Config of conv layers.
+            Default: None.
+        norm_cfg (dict | None): Config of norm layers.
+            Default: dict(type='BN').
+        act_cfg (dict): Config of activation layers.
+            Default: dict(type='ReLU').
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    Returns:
+        x (torch.Tensor): Last feature map in Semantic Branch.
+    """
+
+    def __init__(self,
+                 in_channels=3,
+                 out_channels=16,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super(CEBlock, self).__init__(init_cfg=init_cfg)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.gap = nn.Sequential(
+            nn.AdaptiveAvgPool2d((1, 1)),
+            build_norm_layer(norm_cfg, self.in_channels)[1])
+        self.conv_gap = ConvModule(
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        # Note: in paper here is naive conv2d, no bn-relu
+        self.conv_last = ConvModule(
+            in_channels=self.out_channels,
+            out_channels=self.out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+    def forward(self, x):
+        identity = x
+        x = self.gap(x)
+        x = self.conv_gap(x)
+        x = identity + x
+        x = self.conv_last(x)
+        return x
+
+
+class SemanticBranch(BaseModule):
+    """Semantic Branch which is lightweight with narrow channels and deep
+    layers to obtain　high-level semantic context.
+
+    Args:
+        semantic_channels(Tuple[int]): Size of channel numbers of
+            various stages in Semantic Branch.
+            Default: (16, 32, 64, 128).
+        in_channels (int): Number of channels of input image. Default: 3.
+        exp_ratio (int): Expansion ratio for middle channels.
+            Default: 6.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    Returns:
+        semantic_outs (List[torch.Tensor]): List of several feature maps
+            for auxiliary heads (Booster) and Bilateral
+            Guided Aggregation Layer.
+    """
+
+    def __init__(self,
+                 semantic_channels=(16, 32, 64, 128),
+                 in_channels=3,
+                 exp_ratio=6,
+                 init_cfg=None):
+        super(SemanticBranch, self).__init__(init_cfg=init_cfg)
+        self.in_channels = in_channels
+        self.semantic_channels = semantic_channels
+        self.semantic_stages = []
+        for i in range(len(semantic_channels)):
+            stage_name = f'stage{i + 1}'
+            self.semantic_stages.append(stage_name)
+            if i == 0:
+                self.add_module(
+                    stage_name,
+                    StemBlock(self.in_channels, semantic_channels[i]))
+            elif i == (len(semantic_channels) - 1):
+                self.add_module(
+                    stage_name,
+                    nn.Sequential(
+                        GELayer(semantic_channels[i - 1], semantic_channels[i],
+                                exp_ratio, 2),
+                        GELayer(semantic_channels[i], semantic_channels[i],
+                                exp_ratio, 1),
+                        GELayer(semantic_channels[i], semantic_channels[i],
+                                exp_ratio, 1),
+                        GELayer(semantic_channels[i], semantic_channels[i],
+                                exp_ratio, 1)))
+            else:
+                self.add_module(
+                    stage_name,
+                    nn.Sequential(
+                        GELayer(semantic_channels[i - 1], semantic_channels[i],
+                                exp_ratio, 2),
+                        GELayer(semantic_channels[i], semantic_channels[i],
+                                exp_ratio, 1)))
+
+        self.add_module(f'stage{len(semantic_channels)}_CEBlock',
+                        CEBlock(semantic_channels[-1], semantic_channels[-1]))
+        self.semantic_stages.append(f'stage{len(semantic_channels)}_CEBlock')
+
+    def forward(self, x):
+        semantic_outs = []
+        for stage_name in self.semantic_stages:
+            semantic_stage = getattr(self, stage_name)
+            x = semantic_stage(x)
+            semantic_outs.append(x)
+        return semantic_outs
+
+
+class BGALayer(BaseModule):
+    """Bilateral Guided Aggregation Layer to fuse the complementary information
+    from both Detail Branch and Semantic Branch.
+
+    Args:
+        out_channels (int): Number of output channels.
+            Default: 128.
+        align_corners (bool): align_corners argument of F.interpolate.
+            Default: False.
+        conv_cfg (dict | None): Config of conv layers.
+            Default: None.
+        norm_cfg (dict | None): Config of norm layers.
+            Default: dict(type='BN').
+        act_cfg (dict): Config of activation layers.
+            Default: dict(type='ReLU').
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    Returns:
+        output (torch.Tensor): Output feature map for Segment heads.
+    """
+
+    def __init__(self,
+                 out_channels=128,
+                 align_corners=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super(BGALayer, self).__init__(init_cfg=init_cfg)
+        self.out_channels = out_channels
+        self.align_corners = align_corners
+        self.detail_dwconv = nn.Sequential(
+            DepthwiseSeparableConvModule(
+                in_channels=self.out_channels,
+                out_channels=self.out_channels,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                dw_norm_cfg=norm_cfg,
+                dw_act_cfg=None,
+                pw_norm_cfg=None,
+                pw_act_cfg=None,
+            ))
+        self.detail_down = nn.Sequential(
+            ConvModule(
+                in_channels=self.out_channels,
+                out_channels=self.out_channels,
+                kernel_size=3,
+                stride=2,
+                padding=1,
+                bias=False,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=None),
+            nn.AvgPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False))
+        self.semantic_conv = nn.Sequential(
+            ConvModule(
+                in_channels=self.out_channels,
+                out_channels=self.out_channels,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                bias=False,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=None))
+        self.semantic_dwconv = nn.Sequential(
+            DepthwiseSeparableConvModule(
+                in_channels=self.out_channels,
+                out_channels=self.out_channels,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                dw_norm_cfg=norm_cfg,
+                dw_act_cfg=None,
+                pw_norm_cfg=None,
+                pw_act_cfg=None,
+            ))
+        self.conv = ConvModule(
+            in_channels=self.out_channels,
+            out_channels=self.out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            inplace=True,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg,
+        )
+
+    def forward(self, x_d, x_s):
+        detail_dwconv = self.detail_dwconv(x_d)
+        detail_down = self.detail_down(x_d)
+        semantic_conv = self.semantic_conv(x_s)
+        semantic_dwconv = self.semantic_dwconv(x_s)
+        semantic_conv = resize(
+            input=semantic_conv,
+            size=detail_dwconv.shape[2:],
+            mode='bilinear',
+            align_corners=self.align_corners)
+        fuse_1 = detail_dwconv * torch.sigmoid(semantic_conv)
+        fuse_2 = detail_down * torch.sigmoid(semantic_dwconv)
+        fuse_2 = resize(
+            input=fuse_2,
+            size=fuse_1.shape[2:],
+            mode='bilinear',
+            align_corners=self.align_corners)
+        output = self.conv(fuse_1 + fuse_2)
+        return output
+
+
+@BACKBONES.register_module()
+class BiSeNetV2(BaseModule):
+    """BiSeNetV2: Bilateral Network with Guided Aggregation for
+    Real-time Semantic Segmentation.
+
+    This backbone is the implementation of
+    `BiSeNetV2 <https://arxiv.org/abs/2004.02147>`_.
+
+    Args:
+        in_channels (int): Number of channel of input image. Default: 3.
+        detail_channels (Tuple[int], optional): Channels of each stage
+            in Detail Branch. Default: (64, 64, 128).
+        semantic_channels (Tuple[int], optional): Channels of each stage
+            in Semantic Branch. Default: (16, 32, 64, 128).
+            See Table 1 and Figure 3 of paper for more details.
+        semantic_expansion_ratio (int, optional): The expansion factor
+            expanding channel number of middle channels in Semantic Branch.
+            Default: 6.
+        bga_channels (int, optional): Number of middle channels in
+            Bilateral Guided Aggregation Layer. Default: 128.
+        out_indices (Tuple[int] | int, optional): Output from which stages.
+            Default: (0, 1, 2, 3, 4).
+        align_corners (bool, optional): The align_corners argument of
+            resize operation in Bilateral Guided Aggregation Layer.
+            Default: False.
+        conv_cfg (dict | None): Config of conv layers.
+            Default: None.
+        norm_cfg (dict | None): Config of norm layers.
+            Default: dict(type='BN').
+        act_cfg (dict): Config of activation layers.
+            Default: dict(type='ReLU').
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    """
+
+    def __init__(self,
+                 in_channels=3,
+                 detail_channels=(64, 64, 128),
+                 semantic_channels=(16, 32, 64, 128),
+                 semantic_expansion_ratio=6,
+                 bga_channels=128,
+                 out_indices=(0, 1, 2, 3, 4),
+                 align_corners=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        if init_cfg is None:
+            init_cfg = [
+                dict(type='Kaiming', layer='Conv2d'),
+                dict(
+                    type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])
+            ]
+        super(BiSeNetV2, self).__init__(init_cfg=init_cfg)
+        self.in_channels = in_channels
+        self.out_indices = out_indices
+        self.detail_channels = detail_channels
+        self.semantic_channels = semantic_channels
+        self.semantic_expansion_ratio = semantic_expansion_ratio
+        self.bga_channels = bga_channels
+        self.align_corners = align_corners
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+
+        self.detail = DetailBranch(self.detail_channels, self.in_channels)
+        self.semantic = SemanticBranch(self.semantic_channels,
+                                       self.in_channels,
+                                       self.semantic_expansion_ratio)
+        self.bga = BGALayer(self.bga_channels, self.align_corners)
+
+    def forward(self, x):
+        #  stole refactoring code from Coin Cheung, thanks
+        x_detail = self.detail(x)
+        x_semantic_lst = self.semantic(x)
+        x_head = self.bga(x_detail, x_semantic_lst[-1])
+        outs = [x_head] + x_semantic_lst[:-1]
+        outs = [outs[i] for i in self.out_indices]
+        return tuple(outs)
--- a/mmseg/models/backbones/swin.py
+++ b/mmseg/models/backbones/swin.py
@ -25,6 +25,7 @@ class PatchMerging(BaseModule):

    This layer use nn.Unfold to group feature map by kernel_size, and use norm
    and linear layer to embed grouped feature map.
+
    Args:
        in_channels (int): The num of input channels.
        out_channels (int): The num of output channels.
--- a/model-index.yml
+++ b/model-index.yml
@ -1,6 +1,7 @@
 Import:
 - configs/ann/ann.yml
 - configs/apcnet/apcnet.yml
+- configs/bisenetv2/bisenetv2.yml
 - configs/ccnet/ccnet.yml
 - configs/cgnet/cgnet.yml
 - configs/danet/danet.yml
--- a/tests/test_models/test_backbones/test_bisenetv2.py
+++ b/tests/test_models/test_backbones/test_bisenetv2.py
@ -0,0 +1,57 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.cnn import ConvModule
+
+from mmseg.models.backbones import BiSeNetV2
+from mmseg.models.backbones.bisenetv2 import (BGALayer, DetailBranch,
+                                              SemanticBranch)
+
+
+def test_bisenetv2_backbone():
+    # Test BiSeNetV2 Standard Forward
+    model = BiSeNetV2()
+    model.init_weights()
+    model.train()
+    batch_size = 2
+    imgs = torch.randn(batch_size, 3, 512, 1024)
+    feat = model(imgs)
+
+    assert len(feat) == 5
+    # output for segment Head
+    assert feat[0].shape == torch.Size([batch_size, 128, 64, 128])
+    # for auxiliary head 1
+    assert feat[1].shape == torch.Size([batch_size, 16, 128, 256])
+    # for auxiliary head 2
+    assert feat[2].shape == torch.Size([batch_size, 32, 64, 128])
+    # for auxiliary head 3
+    assert feat[3].shape == torch.Size([batch_size, 64, 32, 64])
+    # for auxiliary head 4
+    assert feat[4].shape == torch.Size([batch_size, 128, 16, 32])
+
+    # Test input with rare shape
+    batch_size = 2
+    imgs = torch.randn(batch_size, 3, 527, 952)
+    feat = model(imgs)
+    assert len(feat) == 5
+
+
+def test_bisenetv2_DetailBranch():
+    x = torch.randn(1, 3, 512, 1024)
+    detail_branch = DetailBranch(detail_channels=(64, 64, 128))
+    assert isinstance(detail_branch.detail_branch[0][0], ConvModule)
+    x_out = detail_branch(x)
+    assert x_out.shape == torch.Size([1, 128, 64, 128])
+
+
+def test_bisenetv2_SemanticBranch():
+    semantic_branch = SemanticBranch(semantic_channels=(16, 32, 64, 128))
+    assert semantic_branch.stage1.pool.stride == 2
+
+
+def test_bisenetv2_BGALayer():
+    x_a = torch.randn(1, 128, 64, 128)
+    x_b = torch.randn(1, 128, 16, 32)
+    bga = BGALayer()
+    assert isinstance(bga.conv, ConvModule)
+    x_out = bga(x_a, x_b)
+    assert x_out.shape == torch.Size([1, 128, 64, 128])