[Feature] Support SegNeXt in MMSegmentation 2.0

README.md

@@ -95,7 +95,7 @@ There are also [advanced tutorials](https://mmsegmentation.readthedocs.io/en/dev
 
 A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/1.x/demo/MMSegmentation_Tutorial.ipynb) on Colab.
 
-To migrate from MMSegmentation 1.x, please refer to [migration](docs/en/migration.md).
+To migrate from MMSegmentation 1.x, please refer to [migration](docs/en/migration).
 
 ## Benchmark and model zoo
 
@@ -117,6 +117,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 - [x] [ConvNeXt (CVPR'2022)](configs/convnext)
 - [x] [MAE (CVPR'2022)](configs/mae)
 - [x] [PoolFormer (CVPR'2022)](configs/poolformer)
+- [x] [SegNeXt (NeurIPS'2022)](configs/segnext)
 
 </details>
 

README_zh-CN.md

@@ -98,6 +98,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [ConvNeXt (CVPR'2022)](configs/convnext)
 - [x] [MAE (CVPR'2022)](configs/mae)
 - [x] [PoolFormer (CVPR'2022)](configs/poolformer)
+- [x] [SegNeXt (NeurIPS'2022)](configs/segnext)
 
 </details>
 

configs/segnext/README.md

@@ -0,0 +1,61 @@
+# SegNeXt
+
+> [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://arxiv.org/abs/2209.08575)
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+<a href="https://github.com/visual-attention-network/segnext">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/mscan.py#L328">Code Snippet</a>
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information. In this paper, we show that convolutional attention is a more efficient and effective way to encode contextual information than the self-attention mechanism in transformers. By re-examining the characteristics owned by successful segmentation models, we discover several key components leading to the performance improvement of segmentation models. This motivates us to design a novel convolutional attention network that uses cheap convolutional operations. Without bells and whistles, our SegNeXt significantly improves the performance of previous state-of-the-art methods on popular benchmarks, including ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal Context, and iSAID. Notably, SegNeXt outperforms EfficientNet-L2 w/ NAS-FPN and achieves 90.6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10 parameters of it. On average, SegNeXt achieves about 2.0% mIoU improvements compared to the state-of-the-art methods on the ADE20K datasets with the same or fewer computations. Code is available at [this https URL](https://github.com/uyzhang/JSeg) (Jittor) and [this https URL](https://github.com/Visual-Attention-Network/SegNeXt) (Pytorch).
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24582831/215688018-5d4c8366-7793-4fdf-9397-960a09fac951.png" width="70%"/>
+</div>
+
+```bibtex
+@article{guo2022segnext,
+  title={SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation},
+  author={Guo, Meng-Hao and Lu, Cheng-Ze and Hou, Qibin and Liu, Zhengning and Cheng, Ming-Ming and Hu, Shi-Min},
+  journal={arXiv preprint arXiv:2209.08575},
+  year={2022}
+}
+```
+
+## Results and models
+
+### ADE20K
+
+| Method  | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                   |
+| ------- | -------- | --------- | ------- | -------- | -------------- | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| SegNeXt | MSCAN-T  | 512x512   | 160000  | 17.88    | 52.38          | 41.50 | 42.59         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244.log.json) |
+| SegNeXt | MSCAN-S  | 512x512   | 160000  | 21.47    | 42.27          | 44.16 | 45.81         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014-43013668.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014.log.json) |
+| SegNeXt | MSCAN-B  | 512x512   | 160000  | 31.03    | 35.15          | 48.03 | 49.68         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053-b6f6c70c.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053.log.json) |
+| SegNeXt | MSCAN-L  | 512x512   | 160000  | 43.32    | 22.91          | 50.99 | 52.10         | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/segnext/segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055-19b14b63.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055.log.json) |
+
+Note:
+
+- When we integrated SegNeXt into MMSegmentation, we modified some layers' names to make them more precise and concise without changing the model architecture. Therefore, the keys of pre-trained weights are different from the [original weights](https://cloud.tsinghua.edu.cn/d/c15b25a6745946618462/), but don't worry about these changes. we have converted them and uploaded the checkpoints, you might find URL of pre-trained checkpoints in config files and can use them directly for training.
+
+- The total batch size is 16. We trained for SegNeXt with a single GPU as the performance degrades significantly when using`SyncBN` (mainly in `OverlapPatchEmbed` modules of `MSCAN`) of PyTorch 1.9.
+
+- There will be subtle differences when model testing as Non-negative Matrix Factorization (NMF) in `LightHamHead` will be initialized randomly. To control this randomness, please set the random seed when model testing. You can modify [`./tools/test.py`](https://github.com/open-mmlab/mmsegmentation/blob/master/tools/test.py) like:
+
+```python
+def main():
+    from mmseg.apis import set_random_seed
+    random_seed = xxx # set random seed recorded in training log
+    set_random_seed(random_seed, deterministic=False)
+    ...
+```
+
+- This model performance is sensitive to the seed values used, please refer to the log file for the specific settings of the seed. If you choose a different seed, the results might differ from the table results. Take SegNeXt Large for example, its results range from 49.60 to 51.0.

configs/segnext/segnext.yml

@@ -0,0 +1,103 @@
+Collections:
+- Name: SegNeXt
+  Metadata:
+    Training Data:
+    - ADE20K
+  Paper:
+    URL: https://arxiv.org/abs/2209.08575
+    Title: 'SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation'
+  README: configs/segnext/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/models/backbones/mscan.py#L328
+    Version: dev-1.x
+  Converted From:
+    Code: https://github.com/visual-attention-network/segnext
+Models:
+- Name: segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512
+  In Collection: SegNeXt
+  Metadata:
+    backbone: MSCAN-T
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 19.09
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 17.88
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 41.5
+      mIoU(ms+flip): 42.59
+  Config: configs/segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth
+- Name: segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512
+  In Collection: SegNeXt
+  Metadata:
+    backbone: MSCAN-S
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 23.66
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 21.47
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.16
+      mIoU(ms+flip): 45.81
+  Config: configs/segnext/segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k/segnext_mscan-s_1x16_512x512_adamw_160k_ade20k_20230214_113014-43013668.pth
+- Name: segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512
+  In Collection: SegNeXt
+  Metadata:
+    backbone: MSCAN-B
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 28.45
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 31.03
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 48.03
+      mIoU(ms+flip): 49.68
+  Config: configs/segnext/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k/segnext_mscan-b_1x16_512x512_adamw_160k_ade20k_20230209_172053-b6f6c70c.pth
+- Name: segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512
+  In Collection: SegNeXt
+  Metadata:
+    backbone: MSCAN-L
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 43.65
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 43.32
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 50.99
+      mIoU(ms+flip): 52.1
+  Config: configs/segnext/segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k/segnext_mscan-l_1x16_512x512_adamw_160k_ade20k_20230209_172055-19b14b63.pth

configs/segnext/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py

@@ -0,0 +1,28 @@
+_base_ = './segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py'
+
+# model settings
+checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_b_20230227-3ab7d230.pth'  # noqa
+ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    backbone=dict(
+        embed_dims=[64, 128, 320, 512],
+        depths=[3, 3, 12, 3],
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
+        drop_path_rate=0.1,
+        norm_cfg=dict(type='BN', requires_grad=True)),
+    decode_head=dict(
+        type='LightHamHead',
+        in_channels=[128, 320, 512],
+        in_index=[1, 2, 3],
+        channels=512,
+        ham_channels=512,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=ham_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/segnext/segnext_mscan-l_1xb16-adamw-160k_ade20k-512x512.py

@@ -0,0 +1,27 @@
+_base_ = './segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py'
+# model settings
+checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_l_20230227-cef260d4.pth'  # noqa
+ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    backbone=dict(
+        embed_dims=[64, 128, 320, 512],
+        depths=[3, 5, 27, 3],
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
+        drop_path_rate=0.3,
+        norm_cfg=dict(type='BN', requires_grad=True)),
+    decode_head=dict(
+        type='LightHamHead',
+        in_channels=[128, 320, 512],
+        in_index=[1, 2, 3],
+        channels=1024,
+        ham_channels=1024,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=ham_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/segnext/segnext_mscan-s_1xb16-adamw-160k_ade20k-512x512.py

@@ -0,0 +1,27 @@
+_base_ = './segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py'
+# model settings
+checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_s_20230227-f33ccdf2.pth'  # noqa
+ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    type='EncoderDecoder',
+    backbone=dict(
+        embed_dims=[64, 128, 320, 512],
+        depths=[2, 2, 4, 2],
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
+        norm_cfg=dict(type='BN', requires_grad=True)),
+    decode_head=dict(
+        type='LightHamHead',
+        in_channels=[128, 320, 512],
+        in_index=[1, 2, 3],
+        channels=256,
+        ham_channels=256,
+        ham_kwargs=dict(MD_R=16),
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=ham_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/segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py

@@ -0,0 +1,84 @@
+_base_ = [
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py',
+    '../_base_/datasets/ade20k.py'
+]
+# model settings
+checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth'  # noqa
+ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+crop_size = (512, 512)
+data_preprocessor = dict(
+    type='SegDataPreProcessor',
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    bgr_to_rgb=True,
+    pad_val=0,
+    seg_pad_val=255,
+    size=(512, 512),
+    test_cfg=dict(size_divisor=32))
+model = dict(
+    type='EncoderDecoder',
+    data_preprocessor=data_preprocessor,
+    pretrained=None,
+    backbone=dict(
+        type='MSCAN',
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
+        embed_dims=[32, 64, 160, 256],
+        mlp_ratios=[8, 8, 4, 4],
+        drop_rate=0.0,
+        drop_path_rate=0.1,
+        depths=[3, 3, 5, 2],
+        attention_kernel_sizes=[5, [1, 7], [1, 11], [1, 21]],
+        attention_kernel_paddings=[2, [0, 3], [0, 5], [0, 10]],
+        act_cfg=dict(type='GELU'),
+        norm_cfg=dict(type='BN', requires_grad=True)),
+    decode_head=dict(
+        type='LightHamHead',
+        in_channels=[64, 160, 256],
+        in_index=[1, 2, 3],
+        channels=256,
+        ham_channels=256,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=ham_norm_cfg,
+        align_corners=False,
+        loss_decode=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+        ham_kwargs=dict(
+            MD_S=1,
+            MD_R=16,
+            train_steps=6,
+            eval_steps=7,
+            inv_t=100,
+            rand_init=True)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
+
+# dataset settings
+train_dataloader = dict(batch_size=16)
+
+# optimizer
+optim_wrapper = dict(
+    _delete_=True,
+    type='OptimWrapper',
+    optimizer=dict(
+        type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01),
+    paramwise_cfg=dict(
+        custom_keys={
+            'pos_block': dict(decay_mult=0.),
+            'norm': dict(decay_mult=0.),
+            'head': dict(lr_mult=10.)
+        }))
+
+param_scheduler = [
+    dict(
+        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
+    dict(
+        type='PolyLR',
+        power=1.0,
+        begin=1500,
+        end=160000,
+        eta_min=0.0,
+        by_epoch=False,
+    )
+]

mmseg/models/backbones/__init__.py

@@ -11,6 +11,7 @@
 from .mit import MixVisionTransformer
 from .mobilenet_v2 import MobileNetV2
 from .mobilenet_v3 import MobileNetV3
+from .mscan import MSCAN
 from .resnest import ResNeSt
 from .resnet import ResNet, ResNetV1c, ResNetV1d
 from .resnext import ResNeXt
@@ -26,5 +27,5 @@
     'ResNeSt', 'MobileNetV2', 'UNet', 'CGNet', 'MobileNetV3',
     'VisionTransformer', 'SwinTransformer', 'MixVisionTransformer',
     'BiSeNetV1', 'BiSeNetV2', 'ICNet', 'TIMMBackbone', 'ERFNet', 'PCPVT',
-    'SVT', 'STDCNet', 'STDCContextPathNet', 'BEiT', 'MAE'
+    'SVT', 'STDCNet', 'STDCContextPathNet', 'BEiT', 'MAE', 'MSCAN'
 ]