[Feature] Support K-Net

README.md

@@ -121,6 +121,7 @@ Supported methods:
 - [x] [DPT (ArXiv'2021)](configs/dpt)
 - [x] [Segmenter (ICCV'2021)](configs/segmenter)
 - [x] [SegFormer (NeurIPS'2021)](configs/segformer)
+- [x] [K-Net (NeurIPS'2021)](configs/knet)
 
 Supported datasets:
 

README_zh-CN.md

@@ -120,6 +120,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [DPT (ArXiv'2021)](configs/dpt)
 - [x] [Segmenter (ICCV'2021)](configs/segmenter)
 - [x] [SegFormer (NeurIPS'2021)](configs/segformer)
+- [x] [K-Net (NeurIPS'2021)](configs/knet)
 
 已支持的数据集：
 

configs/knet/README.md

@@ -0,0 +1,49 @@
+# K-Net
+
+[K-Net: Towards Unified Image Segmentation](https://arxiv.org/abs/2106.14855)
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+<a href="https://github.com/ZwwWayne/K-Net/">Official Repo</a>
+
+<a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392">Code Snippet</a>
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+Semantic, instance, and panoptic segmentations have been addressed using different and specialized frameworks despite their underlying connections. This paper presents a unified, simple, and effective framework for these essentially similar tasks. The framework, named K-Net, segments both instances and semantic categories consistently by a group of learnable kernels, where each kernel is responsible for generating a mask for either a potential instance or a stuff class. To remedy the difficulties of distinguishing various instances, we propose a kernel update strategy that enables each kernel dynamic and conditional on its meaningful group in the input image. K-Net can be trained in an end-to-end manner with bipartite matching, and its training and inference are naturally NMS-free and box-free. Without bells and whistles, K-Net surpasses all previous published state-of-the-art single-model results of panoptic segmentation on MS COCO test-dev split and semantic segmentation on ADE20K val split with 55.2% PQ and 54.3% mIoU, respectively. Its instance segmentation performance is also on par with Cascade Mask R-CNN on MS COCO with 60%-90% faster inference speeds. Code and models will be released at [this https URL](https://github.com/ZwwWayne/K-Net/).
+
+<!-- [IMAGE] -->
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24582831/157008300-9f40905c-b8e8-4a2a-9593-c1177fa35b2c.png" width="90%"/>
+</div>
+
+```bibtex
+@inproceedings{zhang2021knet,
+    title={{K-Net: Towards} Unified Image Segmentation},
+    author={Wenwei Zhang and Jiangmiao Pang and Kai Chen and Chen Change Loy},
+    year={2021},
+    booktitle={NeurIPS},
+}
+```
+
+## Results and models
+
+### ADE20K
+
+| Method          | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) | config | download |
+| --------------- | -------- | --------- | ------- | -------- | -------------- | ----- | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | ----- |
+| KNet + FCN      | R-50-D8  | 512x512   | 80000   | 7.01     | 19.24          | 43.60 | 45.12         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k.py)       | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751-abcab920.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751.log.json) |
+| KNet + PSPNet   | R-50-D8  | 512x512   | 80000   | 6.98     | 20.04          | 44.18 | 45.58         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k.py)    | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634-d2c72240.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634.log.json) |
+| KNet + DeepLabV3| R-50-D8  | 512x512   | 80000   | 7.42     | 12.10          | 45.06 | 46.11         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642-00c8fbeb.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642.log.json) |
+| KNet + UperNet  | R-50-D8  | 512x512   | 80000   | 7.34     | 17.11          | 43.45 | 44.07         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657.log.json)     |
+| KNet + UperNet  | Swin-T   | 512x512   | 80000   | 7.57     | 15.56          | 45.84 | 46.27         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059.log.json)     |
+| KNet + UperNet  | Swin-L   | 512x512   | 80000   | 13.5     | 8.29           | 52.05 | 53.24         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559.log.json)     |
+| KNet + UperNet  | Swin-L   | 640x640   | 80000   | 13.54    | 8.29           | 52.21 | 53.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747.log.json)     |
+
+Note:
+
+- All experiments of K-Net are implemented with 8 V100 (32G) GPUs with 2 samplers per GPU.

configs/knet/knet.yml

@@ -0,0 +1,169 @@
+Collections:
+- Name: KNet
+  Metadata:
+    Training Data:
+    - ADE20K
+  Paper:
+    URL: https://arxiv.org/abs/2106.14855
+    Title: 'K-Net: Towards Unified Image Segmentation'
+  README: configs/knet/README.md
+  Code:
+    URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
+    Version: v0.23.0
+  Converted From:
+    Code: https://github.com/ZwwWayne/K-Net/
+Models:
+- Name: knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k
+  In Collection: KNet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 51.98
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 7.01
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.6
+      mIoU(ms+flip): 45.12
+  Config: configs/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751-abcab920.pth
+- Name: knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k
+  In Collection: KNet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 49.9
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 6.98
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 44.18
+      mIoU(ms+flip): 45.58
+  Config: configs/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634-d2c72240.pth
+- Name: knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k
+  In Collection: KNet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 82.64
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 7.42
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.06
+      mIoU(ms+flip): 46.11
+  Config: configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642-00c8fbeb.pth
+- Name: knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k
+  In Collection: KNet
+  Metadata:
+    backbone: R-50-D8
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 58.45
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 7.34
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 43.45
+      mIoU(ms+flip): 44.07
+  Config: configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth
+- Name: knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k
+  In Collection: KNet
+  Metadata:
+    backbone: Swin-T
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 64.27
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 7.57
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 45.84
+      mIoU(ms+flip): 46.27
+  Config: configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth
+- Name: knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k
+  In Collection: KNet
+  Metadata:
+    backbone: Swin-L
+    crop size: (512,512)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 120.63
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (512,512)
+    Training Memory (GB): 13.5
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 52.05
+      mIoU(ms+flip): 53.24
+  Config: configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth
+- Name: knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k
+  In Collection: KNet
+  Metadata:
+    backbone: Swin-L
+    crop size: (640,640)
+    lr schd: 80000
+    inference time (ms/im):
+    - value: 120.63
+      hardware: V100
+      backend: PyTorch
+      batch size: 1
+      mode: FP32
+      resolution: (640,640)
+    Training Memory (GB): 13.54
+  Results:
+  - Task: Semantic Segmentation
+    Dataset: ADE20K
+    Metrics:
+      mIoU: 52.21
+      mIoU(ms+flip): 53.34
+  Config: configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth

configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.py

@@ -0,0 +1,93 @@
+_base_ = [
+    '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_80k.py'
+]
+
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+num_stages = 3
+conv_kernel_size = 1
+model = dict(
+    type='EncoderDecoder',
+    pretrained='open-mmlab://resnet50_v1c',
+    backbone=dict(
+        type='ResNetV1c',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        dilations=(1, 1, 2, 4),
+        strides=(1, 2, 1, 1),
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        contract_dilation=True),
+    decode_head=dict(
+        type='IterativeDecodeHead',
+        num_stages=num_stages,
+        kernel_update_head=[
+            dict(
+                type='KernelUpdateHead',
+                num_classes=150,
+                num_ffn_fcs=2,
+                num_heads=8,
+                num_mask_fcs=1,
+                feedforward_channels=2048,
+                in_channels=512,
+                out_channels=512,
+                dropout=0.0,
+                conv_kernel_size=conv_kernel_size,
+                ffn_act_cfg=dict(type='ReLU', inplace=True),
+                with_ffn=True,
+                feat_transform_cfg=dict(
+                    conv_cfg=dict(type='Conv2d'), act_cfg=None),
+                kernel_updator_cfg=dict(
+                    type='KernelUpdator',
+                    in_channels=256,
+                    feat_channels=256,
+                    out_channels=256,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN'))) for _ in range(num_stages)
+        ],
+        kernel_generate_head=dict(
+            type='ASPPHead',
+            in_channels=2048,
+            in_index=3,
+            channels=512,
+            dilations=(1, 12, 24, 36),
+            dropout_ratio=0.1,
+            num_classes=150,
+            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=1024,
+        in_index=2,
+        channels=256,
+        num_convs=1,
+        concat_input=False,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=norm_cfg,
+        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'))
+
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0005)
+optimizer_config = dict(grad_clip=dict(max_norm=1, norm_type=2))
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.001,
+    step=[60000, 72000],
+    by_epoch=False)
+# In K-Net implementation we use batch size 2 per GPU as default
+data = dict(samples_per_gpu=2, workers_per_gpu=2)