[Feature] Support MaskFormer(NeurIPS'2021) in MMSeg 1.x

.circleci/test.yml

@@ -61,8 +61,9 @@ jobs:
           command: |
             pip install git+https://github.com/open-mmlab/mmengine.git@main
             pip install -U openmim
-            mim install 'mmcv>=2.0.0rc1'
+            mim install 'mmcv>=2.0.0rc3'
             pip install git+https://github.com/open-mmlab/mmclassification@dev-1.x
+            pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
             pip install -r requirements/tests.txt -r requirements/optional.txt
       - run:
           name: Build and install
@@ -96,18 +97,20 @@ jobs:
           command: |
             git clone -b main --depth 1 https://github.com/open-mmlab/mmengine.git /home/circleci/mmengine
             git clone -b dev-1.x --depth 1 https://github.com/open-mmlab/mmclassification.git /home/circleci/mmclassification
+            git clone -b dev-3.x --depth 1 https://github.com/open-mmlab/mmdetection.git /home/circleci/mmdetection
       - run:
           name: Build Docker image
           command: |
             docker build .circleci/docker -t mmseg:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
-            docker run --gpus all -t -d -v /home/circleci/project:/mmseg -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmclassification:/mmclassification -w /mmseg --name mmseg mmseg:gpu
+            docker run --gpus all -t -d -v /home/circleci/project:/mmseg -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmclassification:/mmclassification -v /home/circleci/mmdetection:/mmdetection -w /mmseg --name mmseg mmseg:gpu
       - run:
           name: Install mmseg dependencies
           command: |
             docker exec mmseg pip install -e /mmengine
             docker exec mmseg pip install -U openmim
-            docker exec mmseg mim install 'mmcv>=2.0.0rc1'
+            docker exec mmseg mim install 'mmcv>=2.0.0rc3'
             docker exec mmseg pip install -e /mmclassification
+            docker exec mmseg pip install -e /mmdetection
             docker exec mmseg pip install -r requirements/tests.txt -r requirements/optional.txt
       - run:
           name: Build and install

.github/workflows/merge_stage_test.yml

@@ -44,8 +44,9 @@ jobs:
           python -V
           pip install -U openmim
           pip install git+https://github.com/open-mmlab/mmengine.git
-          mim install 'mmcv>=2.0.0rc1'
+          mim install 'mmcv>=2.0.0rc3'
           pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
       - name: Install unittest dependencies
         run: pip install -r requirements/tests.txt -r requirements/optional.txt
       - name: Build and install
@@ -92,8 +93,9 @@ jobs:
           python -V
           pip install -U openmim
           pip install git+https://github.com/open-mmlab/mmengine.git
-          mim install 'mmcv>=2.0.0rc1'
+          mim install 'mmcv>=2.0.0rc3'
           pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
       - name: Install unittest dependencies
         run: pip install -r requirements/tests.txt -r requirements/optional.txt
       - name: Build and install
@@ -155,8 +157,9 @@ jobs:
           python -V
           pip install -U openmim
           pip install git+https://github.com/open-mmlab/mmengine.git
-          mim install 'mmcv>=2.0.0rc1'
+          mim install 'mmcv>=2.0.0rc3'
           pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
       - name: Install unittest dependencies
         run: pip install -r requirements/tests.txt -r requirements/optional.txt
       - name: Build and install
@@ -187,8 +190,9 @@ jobs:
           python -V
           pip install -U openmim
           pip install git+https://github.com/open-mmlab/mmengine.git
-          mim install 'mmcv>=2.0.0rc1'
+          mim install 'mmcv>=2.0.0rc3'
           pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
       - name: Install unittest dependencies
         run: pip install -r requirements/tests.txt -r requirements/optional.txt
       - name: Build and install

.github/workflows/pr_stage_test.yml

@@ -40,8 +40,9 @@ jobs:
         run: |
           pip install -U openmim
           pip install git+https://github.com/open-mmlab/mmengine.git
-          mim install 'mmcv>=2.0.0rc1'
+          mim install 'mmcv>=2.0.0rc3'
           pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
       - name: Install unittest dependencies
         run: pip install -r requirements/tests.txt -r requirements/optional.txt
       - name: Build and install
@@ -92,8 +93,9 @@ jobs:
           python -V
           pip install -U openmim
           pip install git+https://github.com/open-mmlab/mmengine.git
-          mim install 'mmcv>=2.0.0rc1'
+          mim install 'mmcv>=2.0.0rc3'
           pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
       - name: Install unittest dependencies
         run: pip install -r requirements/tests.txt -r requirements/optional.txt
       - name: Build and install
@@ -124,8 +126,9 @@ jobs:
           python -V
           pip install -U openmim
           pip install git+https://github.com/open-mmlab/mmengine.git
-          mim install 'mmcv>=2.0.0rc1'
+          mim install 'mmcv>=2.0.0rc3'
           pip install git+https://github.com/open-mmlab/mmclassification.git@dev-1.x
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
       - name: Install unittest dependencies
         run: pip install -r requirements/tests.txt -r requirements/optional.txt
       - name: Build and install

README.md

@@ -139,6 +139,7 @@ Supported methods:
 - [x] [Segmenter (ICCV'2021)](configs/segmenter)
 - [x] [SegFormer (NeurIPS'2021)](configs/segformer)
 - [x] [K-Net (NeurIPS'2021)](configs/knet)
+- [x] [MaskFormer (NeurIPS'2021)](configs/maskformer)
 
 Supported datasets:
 

README_zh-CN.md

@@ -134,6 +134,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [Segmenter (ICCV'2021)](configs/segmenter)
 - [x] [SegFormer (NeurIPS'2021)](configs/segformer)
 - [x] [K-Net (NeurIPS'2021)](configs/knet)
+- [x] [MaskFormer (NeurIPS'2021)](configs/maskformer)
 
 已支持的数据集：
 

configs/maskformer/README.md

@@ -0,0 +1,60 @@
+# MaskFormer
+
+[MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278)
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+<a href="https://github.com/facebookresearch/MaskFormer/">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/models/dense_heads/maskformer_head.py#L21">Code Snippet</a>
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification. Our key insight: mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure. Following this observation, we propose MaskFormer, a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction. Overall, the proposed mask classification-based method simplifies the landscape of effective approaches to semantic and panoptic segmentation tasks and shows excellent empirical results. In particular, we observe that MaskFormer outperforms per-pixel classification baselines when the number of classes is large. Our mask classification-based method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24582831/199215459-ea507126-aafe-4823-8eb1-ae6487509d5c.png" width="90%"/>
+</div>
+
+```bibtex
+@article{cheng2021per,
+  title={Per-pixel classification is not all you need for semantic segmentation},
+  author={Cheng, Bowen and Schwing, Alex and Kirillov, Alexander},
+  journal={Advances in Neural Information Processing Systems},
+  volume={34},
+  pages={17864--17875},
+  year={2021}
+}
+```
+
+### Usage
+
+- MaskFormer model needs to install [MMDetection](https://github.com/open-mmlab/mmdetection) first.
+
+```shell
+pip install "mmdet>=3.0.0rc4"
+```
+
+## Results and models
+
+### ADE20K
+
+| Method     | Backbone  | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config                                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                                                                                     |
+| ---------- | --------- | --------- | ------- | -------- | -------------- | ----- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| MaskFormer | R-50-D32  | 512x512   | 160000  | 3.29     | 42.20          | 44.29 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py)        | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724-cbd39cc1.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724.json)                             |
+| MaskFormer | R-101-D32 | 512x512   | 160000  | 4.12     | 34.90          | 45.11 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053-c8e0931d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053.json)                         |
+| MaskFormer | Swin-T    | 512x512   | 160000  | 3.73     | 40.53          | 46.69 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-03550716.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813.json) |
+| MaskFormer | Swin-S    | 512x512   | 160000  | 5.33     | 26.98          | 49.36 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710-5ab67e58.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710.json) |
+
+Note:
+
+- All experiments of MaskFormer are implemented with 8 V100 (32G) GPUs with 2 samplers per GPU.
+- The results of MaskFormer are relatively not stable.  The accuracy (mIoU) of model with `R-101-D32` is from 44.7 to 46.0, and with `Swin-S` is from 49.0 to 49.8.
+- The ResNet backbones utilized in MaskFormer models are standard `ResNet` rather than `ResNetV1c`.
+- Test time augmentation is not supported in MMSegmentation 1.x version yet, we would add "ms+flip" results as soon as possible.

configs/maskformer/maskformer.yml

@@ -0,0 +1,101 @@
+Collections:
+- Name: MaskFormer
+  Metadata:
+    Training Data:
+    - Usage
+    - ADE20K
+  Paper:
+    URL: https://arxiv.org/abs/2107.06278
+    Title: 'MaskFormer: Per-Pixel Classification is Not All You Need for Semantic
+      Segmentation'
+  README: configs/maskformer/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmdetection/blob/dev-3.x/mmdet/models/dense_heads/maskformer_head.py#L21
+    Version: dev-3.x
+  Converted From:
+    Code: https://github.com/facebookresearch/MaskFormer/
+Models:
+- Name: maskformer_r50-d32_8xb2-160k_ade20k-512x512
+  In Collection: MaskFormer
+  Metadata:
+    backbone: R-50-D32
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 23.7
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 3.29
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.29
+  Config: configs/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724-cbd39cc1.pth
+- Name: maskformer_r101-d32_8xb2-160k_ade20k-512x512
+  In Collection: MaskFormer
+  Metadata:
+    backbone: R-101-D32
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 28.65
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 4.12
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.11
+  Config: configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512/maskformer_r101-d32_8xb2-160k_ade20k-512x512_20221031_223053-c8e0931d.pth
+- Name: maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512
+  In Collection: MaskFormer
+  Metadata:
+    backbone: Swin-T
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 24.67
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 3.73
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 46.69
+  Config: configs/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-03550716.pth
+- Name: maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512
+  In Collection: MaskFormer
+  Metadata:
+    backbone: Swin-S
+    crop size: (512,512)
+    lr schd: 160000
+    inference time (ms/im):
+    - value: 37.06
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 5.33
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 49.36
+  Config: configs/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512_20221115_114710-5ab67e58.pth

configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py

@@ -0,0 +1,7 @@
+_base_ = './maskformer_r50-d32_8xb2-160k_ade20k-512x512.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))