[Feature] Support BiSeNetV1

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))

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'))

configs/bisenetv1/README.md

@@ -0,0 +1,29 @@
+# BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```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}
+}
+```
+
+## Results and models
+
+### Cityscapes
+
+| Method    | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                  | download                                                                                                                                                                                                                                                       |
+| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| BiSeNetV1 (4x4) | R-18-D32 | 1024x1024  | 160000 | 3.3 | 31.77 | 74.37 | 76.91 | [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/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853.log.json) |
+| BiSeNetV1 (4x8) | R-18-D32 | 1024x1024  | 160000 | 3.3 | - | 75.16 | 77.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_4x8_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853.log.json) |
+
+Note:
+
+- `4x4`: Using 4 GPUs with 4 samples per GPU in training.
+- `4x8`: Using 4 GPUs with 8 samples per GPU in training.

configs/bisenetv1/bisenetv1.yml

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+Collections:
+- Metadata:
+    Training Data:
+    - Cityscapes
+  Name: bisenetv1
+Models:
+- Config: configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py
+  In Collection: bisenetv1
+  Metadata:
+    backbone: R-18-D32
+    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): 3.3
+  Name: bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes
+  Results:
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 74.37
+      mIoU(ms+flip): 76.91
+    Task: Semantic Segmentation
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth
+- Config: configs/bisenetv1/bisenetv1_r18-d32_4x8_1024x1024_160k_cityscapes.py
+  In Collection: bisenetv1
+  Metadata:
+    backbone: R-18-D32
+    crop size: (1024,1024)
+    lr schd: 160000
+    memory (GB): 3.3
+  Name: bisenetv1_r18-d32_4x8_1024x1024_160k_cityscapes
+  Results:
+    Dataset: Cityscapes
+    Metrics:
+      mIoU: 75.16
+      mIoU(ms+flip): 77.24
+    Task: Semantic Segmentation
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_lr0.12_8x4_160k_cityscapes/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth

configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py

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+_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,
+)

configs/bisenetv1/bisenetv1_r18-d32_4x8_1024x1024_160k_cityscapes.py

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+_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=8,
+    workers_per_gpu=8,
+)

mmseg/models/backbones/__init__.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from .bisenetv1 import BiSeNetV1
 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', 'BiSeNetV1'
 ]