open-mmlab · Junjun2016 · Sep 26, 2021 · Aug 20, 2021 · Aug 20, 2021 · Aug 20, 2021
diff --git 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:

diff --git 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：

diff --git 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))
diff --git 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'))
diff --git 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.
diff --git 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
diff --git 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,
+)
diff --git 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,
+)
diff --git 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()
diff --git 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,
+)
diff --git 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 @@
 __all__ = [
     'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN',
     'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3',
-    'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer'
+    'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer', 'BiSeNetV2'
 ]