Merge a5aaabe into 1db3967

.gitignore

@@ -27,6 +27,8 @@ wheels/
 .installed.cfg
 *.egg
 MANIFEST
+mlu-ops/
+mlu-ops.*
 
 # PyInstaller
 #  Usually these files are written by a python script from a template

docs/en/get_started/build.md

@@ -290,3 +290,60 @@ If you need to use PyTorch-related modules, make sure PyTorch has been successfu
    ```bash
    python -c 'import mmcv;print(mmcv.__version__)'
    ```
+
+### Build mmcv-full on Cambricon MLU Devices
+
+#### Install torch_mlu
+
+##### Option1: Install mmcv-full based on Cambricon docker image
+
+Firstly, install and pull Cambricon docker image (please email service@cambricon.com for the latest release docker):
+
+```bash
+docker pull ${docker image}
+```
+
+Run and attach to the docker, [Install mmcv-full on MLU device](#install-mmcv\-full-on-cambricon-mlu-device) and [make sure you've installed mmcv-full on MLU device successfully](#test-code)
+
+##### Option2: Install mmcv-full from compiling Cambricon PyTorch source code
+
+Please email service@cambricon.com or contact with Cambricon engineers for a suitable version of CATCH package. After you get the suitable version of CATCH package, please follow the steps in ${CATCH-path}/CONTRIBUTING.md to install Cambricon PyTorch.
+
+#### Install mmcv-full on Cambricon MLU device
+
+Clone the repo
+
+```bash
+git clone https://github.com/open-mmlab/mmcv.git
+```
+
+The mlu-ops library will be downloaded to the default directory (mmcv/mlu-ops) while building MMCV. You can also set `MMCV_MLU_OPS_PATH` to an existing mlu-ops library before building as follows:
+
+```bash
+export MMCV_MLU_OPS_PATH=/xxx/xxx/mlu-ops
+```
+
+Install mmcv-full
+
+```bash
+cd mmcv
+export MMCV_WITH_OPS=1
+export FORCE_MLU=1
+python setup.py install
+```
+
+#### Test Code
+
+After finishing previous steps, you can run the following python code to make sure that you've installed mmcv-full on MLU device successfully
+
+```python
+import torch
+import torch_mlu
+from mmcv.ops import sigmoid_focal_loss
+x = torch.randn(3, 10).mlu()
+x.requires_grad = True
+y = torch.tensor([1, 5, 3]).mlu()
+w = torch.ones(10).float().mlu()
+output = sigmoid_focal_loss(x, y, 2.0, 0.25, w, 'none')
+print(output)
+```

docs/en/understand_mmcv/ops.md

@@ -6,7 +6,7 @@ We implement common ops used in detection, segmentation, etc.
 | ---------------------------- | --- | ---- | --- | --- | ------ |
 | ActiveRotatedFilter          | √   | √    |     |     |        |
 | AssignScoreWithK             |     | √    |     |     |        |
-| BallQuery                    |     | √    |     |     |        |
+| BallQuery                    |     | √    | √   |     |        |
 | BBoxOverlaps                 |     | √    | √   | √   | √      |
 | BorderAlign                  |     | √    |     |     |        |
 | BoxIouRotated                | √   | √    |     |     |        |
@@ -35,7 +35,7 @@ We implement common ops used in detection, segmentation, etc.
 | ModulatedDeformConv2d        | √   | √    |     |     | √      |
 | MultiScaleDeformableAttn     |     | √    | √   |     |        |
 | NMS                          | √   | √    | √   |     | √      |
-| NMSRotated                   | √   | √    |     |     | √      |
+| NMSRotated                   | √   | √    | √   |     | √      |
 | NMSQuadri                    | √   | √    |     |     |        |
 | PixelGroup                   | √   |      |     |     |        |
 | PointsInBoxes                | √   | √    |     |     |        |
@@ -52,13 +52,13 @@ We implement common ops used in detection, segmentation, etc.
 | SigmoidFocalLoss             |     | √    | √   |     | √      |
 | SoftmaxFocalLoss             |     | √    |     |     | √      |
 | SoftNMS                      |     | √    |     |     |        |
-| Sparse Convolution           |     | √    |     |     |        |
+| Sparse Convolution           |     | √    | √   |     |        |
 | Synchronized BatchNorm       |     | √    |     |     |        |
 | ThreeInterpolate             |     | √    |     |     |        |
 | ThreeNN                      |     | √    | √   |     |        |
 | TINShift                     |     | √    | √   |     |        |
 | UpFirDn2d                    |     | √    |     |     |        |
-| Voxelization                 | √   | √    |     |     | √      |
+| Voxelization                 | √   | √    | √   |     | √      |
 | PrRoIPool                    |     | √    |     |     |        |
 | BezierAlign                  | √   | √    |     |     |        |
 | BiasAct                      |     | √    |     |     |        |

docs/zh_cn/get_started/build.md

@@ -298,3 +298,59 @@ mmcv 有两个版本：
    ```bash
    python -c 'import mmcv;print(mmcv.__version__)'
    ```
+
+### 在寒武纪 MLU 机器编译 mmcv-full
+
+#### 安装 torch_mlu
+
+##### 选项1: 基于寒武纪 docker image 安装
+
+首先请下载并且拉取寒武纪 docker (请向 service@cambricon.com 发邮件以获得最新的寒武纪 pytorch 发布 docker)。
+
+```
+docker pull ${docker image}
+```
+
+进入 docker, [编译 MMCV MLU](#编译mmcv-mlu) 并[进行验证](#验证是否成功安装)。
+
+##### 选项2：基于 cambricon pytorch 源码编译安装
+
+请向 service@cambricon.com 发送邮件或联系 Cambricon 工程师以获取合适版本的 CATCH 软件包，在您获得合适版本的 CATCH 软件包后，请参照 ${CATCH-path}/CONTRIBUTING.md 中的步骤安装 CATCH。
+
+#### 编译 MMCV
+
+克隆代码仓库
+
+```bash
+git clone https://github.com/open-mmlab/mmcv.git
+```
+
+算子库 mlu-ops 在编译 MMCV 时自动下载到默认路径(mmcv/mlu-ops)，你也可以在编译前设置环境变量 MMCV_MLU_OPS_PATH 指向已经存在的 mlu-ops 算子库路径。
+
+```bash
+export MMCV_MLU_OPS_PATH=/xxx/xxx/mlu-ops
+```
+
+开始编译
+
+```bash
+cd mmcv
+export MMCV_WITH_OPS=1
+export FORCE_MLU=1
+python setup.py install
+```
+
+#### 验证是否成功安装
+
+完成上述安装步骤之后，您可以尝试运行下面的 Python 代码以测试您是否成功在 MLU 设备上安装了 mmcv-full
+
+```python
+import torch
+import torch_mlu
+from mmcv.ops import sigmoid_focal_loss
+x = torch.randn(3, 10).mlu()
+x.requires_grad = True
+y = torch.tensor([1, 5, 3]).mlu()
+w = torch.ones(10).float().mlu()
+output = sigmoid_focal_loss(x, y, 2.0, 0.25, w, 'none')
+```

docs/zh_cn/understand_mmcv/ops.md

@@ -6,7 +6,7 @@ MMCV 提供了检测、分割等任务中常用的算子
 | ---------------------------- | --- | ---- | --- | --- | ------ |
 | ActiveRotatedFilter          | √   | √    |     |     |        |
 | AssignScoreWithK             |     | √    |     |     |        |
-| BallQuery                    |     | √    |     |     |        |
+| BallQuery                    |     | √    | √   |     |        |
 | BBoxOverlaps                 |     | √    | √   | √   | √      |
 | BorderAlign                  |     | √    |     |     |        |
 | BoxIouRotated                | √   | √    |     |     |        |
@@ -35,7 +35,7 @@ MMCV 提供了检测、分割等任务中常用的算子
 | ModulatedDeformConv2d        | √   | √    |     |     | √      |
 | MultiScaleDeformableAttn     |     | √    | √   |     |        |
 | NMS                          | √   | √    | √   |     | √      |
-| NMSRotated                   | √   | √    |     |     | √      |
+| NMSRotated                   | √   | √    | √   |     | √      |
 | NMSQuadri                    | √   | √    |     |     |        |
 | PixelGroup                   | √   |      |     |     |        |
 | PointsInBoxes                | √   | √    |     |     |        |
@@ -52,13 +52,13 @@ MMCV 提供了检测、分割等任务中常用的算子
 | SigmoidFocalLoss             |     | √    | √   |     | √      |
 | SoftmaxFocalLoss             |     | √    |     |     | √      |
 | SoftNMS                      |     | √    |     |     |        |
-| Sparse Convolution           |     | √    |     |     |        |
+| Sparse Convolution           |     | √    | √   |     |        |
 | Synchronized BatchNorm       |     | √    |     |     |        |
 | ThreeInterpolate             |     | √    |     |     |        |
 | ThreeNN                      |     | √    | √   |     |        |
 | TINShift                     |     | √    | √   |     |        |
 | UpFirDn2d                    |     | √    |     |     |        |
-| Voxelization                 | √   | √    |     |     | √      |
+| Voxelization                 | √   | √    | √   |     | √      |
 | PrRoIPool                    |     | √    |     |     |        |
 | BezierAlign                  | √   | √    |     |     |        |
 | BiasAct                      |     | √    |     |     |        |