[Feature] Support Real-time model ERFNet

configs/_base_/models/fcn_erfnet.py

@@ -0,0 +1,32 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    pretrained=None,
+    backbone=dict(
+        type='ERFNet',
+        in_channels=3,
+        enc_downsample_channels=(16, 64, 128),
+        enc_num_stages_non_bottleneck=(5, 8),
+        enc_non_bottleneck_dilations=(2, 4, 8, 16),
+        enc_non_bottleneck_channels=(64, 128),
+        dec_upsample_channels=(64, 16),
+        dec_num_stages_non_bottleneck=(2, 2),
+        dec_non_bottleneck_channels=(64, 16),
+        dropout_ratio=0.1,
+        init_cfg=None),
+    decode_head=dict(
+        type='FCNHead',
+        in_channels=16,
+        channels=512,
+        num_convs=2,
+        concat_input=True,
+        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)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))

configs/erfnet/README.md

@@ -0,0 +1,38 @@
+# ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+<a href="https://github.com/Eromera/erfnet_pytorch">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/erfnet.py#L321">Code Snippet</a>
+
+<details>
+<summary align="right"><a href="http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf">ERFNet (T-ITS)</a></summary>
+
+```latex
+@article{romera2017erfnet,
+  title={Erfnet: Efficient residual factorized convnet for real-time semantic segmentation},
+  author={Romera, Eduardo and Alvarez, Jos{\'e} M and Bergasa, Luis M and Arroyo, Roberto},
+  journal={IEEE Transactions on Intelligent Transportation Systems},
+  volume={19},
+  number={1},
+  pages={263--272},
+  year={2017},
+  publisher={IEEE}
+}
+```
+
+</details>
+
+## Results and models
+
+### Cityscapes
+
+| Method    | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                  | download                                                                                                                                                                                                                                                       |
+| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| FCN | ERFNet | 512x1024  | 160000 | 16.40 | 2.16 | 71.4 | 72.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/erfnet/fcn_erfnet_4x4_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/fcn_erfnet_4x4_512x1024_160k_cityscapes/fcn_erfnet_4x4_512x1024_160k_cityscapes_20211103_011334-8f691334.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/fcn_erfnet_4x4_512x1024_160k_cityscapes/fcn_erfnet_4x4_512x1024_160k_cityscapes_20211103_011334.log.json) |
+Note:
+
+- Last deconvolution layer in original paper is replaced by normal `FCN` decoder head and upsampling operation.

configs/erfnet/erfnet.yml

@@ -0,0 +1,37 @@
+Collections:
+- Name: erfnet
+  Metadata:
+    Training Data:
+    - Cityscapes
+  Paper:
+    URL: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
+    Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
+  README: configs/erfnet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/erfnet.py#L321
+    Version: v0.20.0
+  Converted From:
+    Code: https://github.com/Eromera/erfnet_pytorch
+Models:
+- Name: fcn_erfnet_4x4_512x1024_160k_cityscapes
+  In Collection: erfnet
+  Metadata:
+    backbone: ERFNet
+    crop size: (512,1024)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 462.96
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,1024)
+    memory (GB): 16.4
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 71.4
+      mIoU(ms+flip): 72.96
+  Config: configs/erfnet/fcn_erfnet_4x4_512x1024_160k_cityscapes.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/fcn_erfnet_4x4_512x1024_160k_cityscapes/fcn_erfnet_4x4_512x1024_160k_cityscapes_20211103_011334-8f691334.pth

configs/erfnet/fcn_erfnet_4x4_512x1024_160k_cityscapes.py

@@ -0,0 +1,9 @@
+_base_ = [
+    '../_base_/models/fcn_erfnet.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
+]
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+)

mmseg/models/backbones/__init__.py

@@ -2,6 +2,7 @@
 from .bisenetv1 import BiSeNetV1
 from .bisenetv2 import BiSeNetV2
 from .cgnet import CGNet
+from .erfnet import ERFNet
 from .fast_scnn import FastSCNN
 from .hrnet import HRNet
 from .icnet import ICNet
@@ -19,5 +20,5 @@
     'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN',
     'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3',
     'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer',
-    'BiSeNetV1', 'BiSeNetV2', 'ICNet'
+    'BiSeNetV1', 'BiSeNetV2', 'ICNet', 'ERFNet'
 ]