Fast-SCNN implemented

configs/_base_/models/fast_scnn.py

@@ -0,0 +1,58 @@
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
+model = dict(
+    type='EncoderDecoder',
+    backbone=dict(
+        type='FastSCNN',
+        downsample_dw_channels=(32, 48),
+        global_in_channels=64,
+        global_block_channels=(64, 96, 128),
+        global_block_strides=(2, 2, 1),
+        global_out_channels=128,
+        higher_in_channels=64,
+        lower_in_channels=128,
+        fusion_out_channels=128,
+        out_indices=(0, 1, 2),
+        norm_cfg=norm_cfg,
+        align_corners=False),
+    decode_head=dict(
+        type='DepthwiseSeparableFCNHead',
+        in_channels=128,
+        channels=128,
+        concat_input=False,
+        num_classes=19,
+        in_index=-1,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.)),
+    auxiliary_head=[
+        dict(
+            type='FCNHead',
+            in_channels=128,
+            channels=32,
+            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=0.4)),
+        dict(
+            type='FCNHead',
+            in_channels=64,
+            channels=32,
+            num_convs=1,
+            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=0.4)),
+    ])
+
+# model training and testing settings
+train_cfg = dict()
+test_cfg = dict(mode='whole')

configs/fastscnn/README.md

@@ -0,0 +1,27 @@
+# Fast-SCNN for Semantic Segmentation
+
+## Introduction
+```
+@article{DBLP:journals/corr/abs-1902-04502,
+  author    = {Rudra P. K. Poudel and
+               Stephan Liwicki and
+               Roberto Cipolla},
+  title     = {Fast-SCNN: Fast Semantic Segmentation Network},
+  journal   = {CoRR},
+  volume    = {abs/1902.04502},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1902.04502},
+  archivePrefix = {arXiv},
+  eprint    = {1902.04502},
+  timestamp = {Tue, 21 May 2019 18:03:38 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1902-04502.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+## Results and models
+
+### Cityscapes
+|   Method   | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                                                                                              download                                                                                              |
+|------------|-----------|-----------|--------:|----------|----------------|------:|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Fast-SCNN  | Fast-SCNN | 512x1024  |   80000 |      8.4 |          63.61 | 69.06 | -             | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-cae6c46a.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-20200807_165744.log.json)     |

configs/fastscnn/fast_scnn_4x8_80k_lr0.12_cityscapes.py

@@ -0,0 +1,10 @@
+_base_ = [
+    '../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
+]
+
+# Re-config the data sampler.
+data = dict(samples_per_gpu=8, workers_per_gpu=4)
+
+# Re-config the optimizer.
+optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5)

docs/model_zoo.md

@@ -81,6 +81,9 @@ Please refer to [ANN](https://github.com/open-mmlab/mmsegmentation/blob/master/c
 
 Please refer to [OCRNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet) for details.
 
+### Fast-SCNN
+
+Please refer to [Fast-SCNN](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastscnn) for details.
 
 ### Mixed Precision (FP16) Training
 

mmseg/models/backbones/__init__.py

@@ -1,5 +1,6 @@
+from .fast_scnn import FastSCNN
 from .hrnet import HRNet
 from .resnet import ResNet, ResNetV1c, ResNetV1d
 from .resnext import ResNeXt
 
-__all__ = ['ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet']
+__all__ = ['ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN']