diff --git a/docker/release/Dockerfile b/docker/release/Dockerfile
index 493aa6d1625..1f10acc2bf6 100644
--- a/docker/release/Dockerfile
+++ b/docker/release/Dockerfile
@@ -16,5 +16,8 @@ RUN apt-get update && apt-get install -y libgl1 libglib2.0-0 \
&& rm -rf /var/lib/apt/lists/*
# Install mmcv-full
-ARG MMCV="1.5.1"
-RUN pip install openmim && mim install mmcv-full==${MMCV} && python -c 'import mmcv;print(mmcv.__version__)'
+ARG MMCV=""
+RUN if [ "${MMCV}" = "" ]; then pip install -U openmim && mim install mmcv-full; else pip install -U openmim && mim install mmcv-full==${MMCV}; fi
+
+# Verify the installation
+RUN python -c 'import mmcv;print(mmcv.__version__)'
diff --git a/docs/en/_static/version.json b/docs/en/_static/version.json
new file mode 100644
index 00000000000..a1643eedbb8
--- /dev/null
+++ b/docs/en/_static/version.json
@@ -0,0 +1,1931 @@
+{
+ "Linux": [
+ {
+ "cuda": "11.6",
+ "torch": "1.12.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0"
+ ]
+ },
+ {
+ "cuda": "11.5",
+ "torch": "1.11.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7"
+ ]
+ },
+ {
+ "cuda": "11.3",
+ "torch": "1.12.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0"
+ ]
+ },
+ {
+ "cuda": "11.3",
+ "torch": "1.11.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7"
+ ]
+ },
+ {
+ "cuda": "11.3",
+ "torch": "1.10.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7",
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+ "1.4.2",
+ "1.4.1",
+ "1.4.0",
+ "1.3.18",
+ "1.3.17",
+ "1.3.16"
+ ]
+ },
+ {
+ "cuda": "11.1",
+ "torch": "1.10.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
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+ "1.3.17",
+ "1.3.16"
+ ]
+ },
+ {
+ "cuda": "11.1",
+ "torch": "1.9.x",
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+ "1.6.1",
+ "1.6.0",
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+ "1.3.14",
+ "1.3.13",
+ "1.3.12",
+ "1.3.11",
+ "1.3.10"
+ ]
+ },
+ {
+ "cuda": "11.1",
+ "torch": "1.8.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
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+ "1.3.11",
+ "1.3.10",
+ "1.3.1",
+ "1.3.0"
+ ]
+ },
+ {
+ "cuda": "11.0",
+ "torch": "1.7.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
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+ "1.2.4",
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+ "1.2.0"
+ ]
+ },
+ {
+ "cuda": "10.2",
+ "torch": "1.12.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0"
+ ]
+ },
+ {
+ "cuda": "10.2",
+ "torch": "1.11.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
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+ "1.4.7"
+ ]
+ },
+ {
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+ "1.3.16"
+ ]
+ },
+ {
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+ "torch": "1.9.x",
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+ "1.6.2",
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+ "1.3.11",
+ "1.3.10"
+ ]
+ },
+ {
+ "cuda": "10.2",
+ "torch": "1.8.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
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+ "1.3.0"
+ ]
+ },
+ {
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+ ]
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+ {
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+ ]
+ },
+ {
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+ ]
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+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0"
+ ]
+ },
+ {
+ "cuda": "10.1",
+ "torch": "1.8.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0"
+ ]
+ },
+ {
+ "cuda": "10.1",
+ "torch": "1.7.x",
+ "mmcv": [
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0"
+ ]
+ },
+ {
+ "cuda": "10.1",
+ "torch": "1.6.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7",
+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0",
+ "1.1.5",
+ "1.1.3"
+ ]
+ },
+ {
+ "cuda": "10.1",
+ "torch": "1.5.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7",
+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0"
+ ]
+ },
+ {
+ "cuda": "cpu",
+ "torch": "1.12.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0"
+ ]
+ },
+ {
+ "cuda": "cpu",
+ "torch": "1.11.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7"
+ ]
+ },
+ {
+ "cuda": "cpu",
+ "torch": "1.10.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7",
+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0"
+ ]
+ },
+ {
+ "cuda": "cpu",
+ "torch": "1.9.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7",
+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0"
+ ]
+ },
+ {
+ "cuda": "cpu",
+ "torch": "1.8.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7",
+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0"
+ ]
+ },
+ {
+ "cuda": "cpu",
+ "torch": "1.7.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7",
+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0"
+ ]
+ },
+ {
+ "cuda": "cpu",
+ "torch": "1.6.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7",
+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0",
+ "1.1.5",
+ "1.1.3"
+ ]
+ },
+ {
+ "cuda": "cpu",
+ "torch": "1.5.x",
+ "mmcv": [
+ "1.6.2",
+ "1.6.1",
+ "1.6.0",
+ "1.5.3",
+ "1.5.2",
+ "1.5.1",
+ "1.5.0",
+ "1.4.8",
+ "1.4.7",
+ "1.4.6",
+ "1.4.5",
+ "1.4.4",
+ "1.4.3",
+ "1.4.2",
+ "1.4.1",
+ "1.4.0"
+ ]
+ },
+ {
+ "cuda": "cpu",
+ "torch": "1.4.x",
+ "mmcv": [
+ "1.1.5"
+ ]
+ }
+ ]
+}
diff --git a/docs/en/conf.py b/docs/en/conf.py
index e38dfab1d26..08f5f7eb7df 100644
--- a/docs/en/conf.py
+++ b/docs/en/conf.py
@@ -56,6 +56,8 @@
myst_heading_anchors = 4
+myst_enable_extensions = ['colon_fence']
+
autodoc_mock_imports = ['mmcv._ext', 'mmcv.utils.ext_loader', 'torchvision']
# Add any paths that contain templates here, relative to this directory.
diff --git a/docs/en/get_started/build.md b/docs/en/get_started/build.md
index d987c1e17e2..c03bea59125 100644
--- a/docs/en/get_started/build.md
+++ b/docs/en/get_started/build.md
@@ -1,58 +1,124 @@
## Build MMCV from source
-### Build on Linux or macOS
+### Build mmcv-full
-After cloning the repo with
+Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [PyTorch official installation guide](https://pytorch.org/get-started/locally/#start-locally). This can be verified using the following command
```bash
-git clone https://github.com/open-mmlab/mmcv.git
-cd mmcv
+python -c 'import torch;print(torch.__version__)'
```
-It is recommended to install `ninja` to speed up the compilation
+If version information is output, then PyTorch is installed.
-```bash
-pip install -r requirements/optional.txt
+```{note}
+- To compile ONNX Runtime custom operators, please refer to [How to build custom operators for ONNX Runtime](https://mmcv.readthedocs.io/en/latest/deployment/onnxruntime_op.html#how-to-build-custom-operators-for-onnx-runtime)
+- To compile TensorRT customization, please refer to [How to build TensorRT plugins in MMCV](https://mmcv.readthedocs.io/en/latest/deployment/tensorrt_plugin.html#how-to-build-tensorrt-plugins-in-mmcv)
```
-You can either
+```{note}
+If you would like to use `opencv-python-headless` instead of `opencv-python`,
+e.g., in a minimum container environment or servers without GUI,
+you can first install it before installing MMCV to skip the installation of `opencv-python`.
+```
-- install the lite version
+#### Build on Linux
- ```bash
- pip install -e .
- ```
+1. Clone the repo
-- install the full version
+ ```bash
+ git clone https://github.com/open-mmlab/mmcv.git
+ cd mmcv
+ ```
- ```bash
- MMCV_WITH_OPS=1 pip install -e .
- ```
+2. Install `ninja` and `psutil` to speed up the compilation
-If you are on macOS, add the following environment variables before the installing command.
+ ```bash
+ pip install -r requirements/optional.txt
+ ```
-```bash
-CC=clang CXX=clang++ CFLAGS='-stdlib=libc++'
-```
+3. Check the nvcc version (requires 9.2+. Skip if no GPU available.)
-e.g.,
+ ```bash
+ nvcc --version
+ ```
-```bash
-CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' MMCV_WITH_OPS=1 pip install -e .
-```
+ If the above command outputs the following message, it means that the nvcc setting is OK, otherwise you need to set CUDA_HOME.
+
+ ```
+ nvcc: NVIDIA (R) Cuda compiler driver
+ Copyright (c) 2005-2020 NVIDIA Corporation
+ Built on Mon_Nov_30_19:08:53_PST_2020
+ Cuda compilation tools, release 11.2, V11.2.67
+ Build cuda_11.2.r11.2/compiler.29373293_0
+ ```
+
+ :::{note}
+ If you want to support ROCm, you can refer to [AMD ROCm](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html) to install ROCm.
+ :::
+
+4. Check the gcc version (requires 5.4+)
+
+ ```bash
+ gcc --version
+ ```
+
+5. Start building (takes 10+ min)
+
+ ```bash
+ MMCV_WITH_OPS=1 pip install -e . -v
+ ```
+
+6. Validate the installation
+
+ ```bash
+ python .dev_scripts/check_installation.py
+ ```
+
+ If no error is reported by the above command, the installation is successful. If there is an error reported, please check [Frequently Asked Questions](../faq.md) to see if there is already a solution.
+
+ If no solution is found, please feel free to open an [issue](https://github.com/open-mmlab/mmcv/issues).
+
+#### Build on macOS
```{note}
-If you would like to use `opencv-python-headless` instead of `opencv-python`,
-e.g., in a minimum container environment or servers without GUI,
-you can first install it before installing MMCV to skip the installation of `opencv-python`.
+If you are using a mac with an M1 chip, install the nightly version of PyTorch, otherwise you will encounter the problem in [issues#2218](https://github.com/open-mmlab/mmcv/issues/2218).
```
-### Build on Windows
+1. Clone the repo
+
+ ```bash
+ git clone https://github.com/open-mmlab/mmcv.git
+ cd mmcv
+ ```
+
+2. Install `ninja` and `psutil` to speed up the compilation
+
+ ```bash
+ pip install -r requirements/optional.txt
+ ```
+
+3. Start building
+
+ ```bash
+ MMCV_WITH_OPS=1 pip install -e .
+ ```
+
+4. Validate the installation
+
+ ```bash
+ python .dev_scripts/check_installation.py
+ ```
+
+ If no error is reported by the above command, the installation is successful. If there is an error reported, please check [Frequently Asked Questions](../faq.md) to see if there is already a solution.
+
+ If no solution is found, please feel free to open an [issue](https://github.com/open-mmlab/mmcv/issues).
+
+#### Build on Windows
Building MMCV on Windows is a bit more complicated than that on Linux.
The following instructions show how to get this accomplished.
-#### Prerequisite
+##### Prerequisite
The following software is required for building MMCV on windows.
Install them first.
@@ -71,7 +137,7 @@ Install them first.
You should know how to set up environment variables, especially `Path`, on Windows. The following instruction relies heavily on this skill.
```
-#### Setup Python Environment
+##### Common steps
1. Launch Anaconda prompt from Windows Start menu
@@ -79,62 +145,45 @@ You should know how to set up environment variables, especially `Path`, on Windo
2. Create a new conda environment
- ```shell
- conda create --name mmcv python=3.7 # 3.6, 3.7, 3.8 should work too as tested
- conda activate mmcv # make sure to activate environment before any operation
+ ```powershell
+ (base) PS C:\Users\xxx> conda create --name mmcv python=3.7
+ (base) PS C:\Users\xxx> conda activate mmcv # make sure to activate environment before any operation
```
3. Install PyTorch. Choose a version based on your need.
- ```shell
- conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
+ ```powershell
+ # CUDA version
+ (mmcv) PS C:\Users\xxx> conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
+ # CPU version
+ (mmcv) PS C:\Users\xxx> conda install install pytorch torchvision cpuonly -c pytorch
```
- We only tested PyTorch version >= 1.6.0.
-
-4. Prepare MMCV source code
+4. Clone the repo
- ```shell
- git clone https://github.com/open-mmlab/mmcv.git
- cd mmcv
+ ```powershell
+ (mmcv) PS C:\Users\xxx> git clone https://github.com/open-mmlab/mmcv.git
+ (mmcv) PS C:\Users\xxx\mmcv> cd mmcv
```
-5. Install required Python packages
+5. Install `ninja` and `psutil` to speed up the compilation
- ```shell
- pip3 install -r requirements/runtime.txt
+ ```powershell
+ (mmcv) PS C:\Users\xxx\mmcv> pip install -r requirements/optional.txt
```
-6. It is recommended to install `ninja` to speed up the compilation
+6. Install required Python packages
- ```bash
- pip install -r requirements/optional.txt
+ ```shell
+ (mmcv) PS C:\Users\xxx\mmcv> pip install -r requirements/runtime.txt
```
-#### Build and install MMCV
-
-MMCV can be built in three ways:
-
-1. Lite version (without ops)
-
- In this way, no custom ops are compiled and mmcv is a pure python package.
-
-2. Full version (CPU ops)
-
- Module `ops` will be compiled as a pytorch extension, but only x86 code will be compiled. The compiled ops can be executed on CPU only.
-
-3. Full version (CUDA ops)
-
- Both x86 and CUDA codes of `ops` module will be compiled. The compiled version can be run on both CPU and CUDA-enabled GPU (if implemented).
-
-##### Common steps
-
-1. Set up MSVC compiler
+7. Set up MSVC compiler
Set Environment variable, add `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\Hostx86\x64` to `PATH`, so that `cl.exe` will be available in prompt, as shown below.
- ```none
- (base) PS C:\Users\xxx> cl
+ ```powershell
+ (mmcv) PS C:\Users\xxx\mmcv> cl
Microsoft (R) C/C++ Optimizing Compiler Version 19.27.29111 for x64
Copyright (C) Microsoft Corporation. All rights reserved.
@@ -145,124 +194,135 @@ MMCV can be built in three ways:
You may want to change the system language to English because pytorch will parse text output from `cl.exe` to check its version. However only utf-8 is recognized. Navigate to Control Panel -> Region -> Administrative -> Language for Non-Unicode programs and change it to English.
-##### Option 1: Build MMCV (lite version)
+##### Build and install MMCV
-After finishing above common steps, launch Anaconda shell from Start menu and issue the following commands:
+mmcv-full can be built in two ways:
-```shell
-# activate environment
-conda activate mmcv
-# change directory
-cd mmcv
-# install
-python setup.py develop
-# check
-pip list
-```
+1. Full version (CPU ops)
-##### Option 2: Build MMCV (full version with CPU)
+ Module `ops` will be compiled as a pytorch extension, but only x86 code will be compiled. The compiled ops can be executed on CPU only.
-1. Finish above common steps
+2. Full version (CUDA ops)
-2. Set up environment variables
+ Both x86 and CUDA codes of `ops` module will be compiled. The compiled version can be run on both CPU and CUDA-enabled GPU (if implemented).
- ```shell
- $env:MMCV_WITH_OPS = 1
- $env:MAX_JOBS = 8 # based on your available number of CPU cores and amount of memory
- ```
+###### CPU version
-3. Following build steps of the lite version
+1. Set up environment variables
- ```shell
- # activate environment
- conda activate mmcv
- # change directory
- cd mmcv
- # build
- python setup.py build_ext # if success, cl will be launched to compile ops
- # install
- python setup.py develop
- # check
- pip list
+ ```powershell
+ (mmcv) PS C:\Users\xxx\mmcv> $env:MMCV_WITH_OPS = 1
```
-##### Option 3: Build MMCV (full version with CUDA)
+2. Build and install
-1. Finish above common steps
+ ```powershell
+ (mmcv) PS C:\Users\xxx\mmcv> python setup.py build_ext
+ (mmcv) PS C:\Users\xxx\mmcv> python setup.py develop
+ ```
+
+###### GPU version
2. Make sure `CUDA_PATH` or `CUDA_HOME` is already set in `envs` via `ls env:`, desired output is shown as below:
- ```none
- (base) PS C:\Users\WRH> ls env:
+ ```powershell
+ (mmcv) PS C:\Users\xxx\mmcv> ls env:
Name Value
---- -----
- <... omit some lines ...>
CUDA_PATH C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
CUDA_PATH_V10_1 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1
CUDA_PATH_V10_2 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2
- <... omit some lines ...>
```
This should already be done by CUDA installer. If not, or you have multiple version of CUDA toolkit installed, set it with
- ```shell
- $env:CUDA_HOME = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2"
+ ```powershell
+ (mmcv) PS C:\Users\xxx\mmcv> $env:CUDA_HOME = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2"
# OR
- $env:CUDA_HOME = $env:CUDA_PATH_V10_2 # if CUDA_PATH_V10_2 is in envs:
+ (mmcv) PS C:\Users\xxx\mmcv> $env:CUDA_HOME = $env:CUDA_PATH_V10_2 # if CUDA_PATH_V10_2 is in envs:
```
3. Set CUDA target arch
```shell
- # Suppose you are using GTX 1080, which is of capability 6.1
- $env:TORCH_CUDA_ARCH_LIST="6.1"
- # OR build all supported arch, will be slow
- $env:TORCH_CUDA_ARCH_LIST="3.5 3.7 5.0 5.2 6.0 6.1 7.0 7.5"
+ # Here you need to change to the target architecture corresponding to your GPU
+ (mmcv) PS C:\Users\xxx\mmcv> $env:TORCH_CUDA_ARCH_LIST="7.5"
```
-```{note}
-Check your the compute capability of your GPU from [here](https://developer.nvidia.com/cuda-gpus).
-```
+ :::{note}
+ Check your the compute capability of your GPU from [here](https://developer.nvidia.com/cuda-gpus).
-4. Launch compiling the same way as CPU
+ ```powershell
+ (mmcv) PS C:\Users\xxx\mmcv> &"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\extras\demo_suite\deviceQuery.exe"
+ Device 0: "NVIDIA GeForce GTX 1660 SUPER"
+ CUDA Driver Version / Runtime Version 11.7 / 11.1
+ CUDA Capability Major/Minor version number: 7.5
+ ```
- ```shell
- $env:MMCV_WITH_OPS = 1
- $env:MAX_JOBS = 8 # based on available number of CPU cores and amount of memory
- # activate environment
- conda activate mmcv
- # change directory
- cd mmcv
+ The 7.5 above indicates the target architecture. Note: You need to replace v10.2 with your CUDA version in the above command.
+ :::
+
+4. Build and install
+
+ ```powershell
# build
python setup.py build_ext # if success, cl will be launched to compile ops
# install
python setup.py develop
- # check
- pip list
```
-```{note}
-If you are compiling against PyTorch 1.6.0, you might meet some errors from PyTorch as described in [this issue](https://github.com/pytorch/pytorch/issues/42467). Follow [this pull request](https://github.com/pytorch/pytorch/pull/43380/files) to modify the source code in your local PyTorch installation.
+ ```{note}
+ If you are compiling against PyTorch 1.6.0, you might meet some errors from PyTorch as described in [this issue](https://github.com/pytorch/pytorch/issues/42467). Follow [this pull request](https://github.com/pytorch/pytorch/pull/43380/files) to modify the source code in your local PyTorch installation.
+ ```
+
+##### Validate installation
+
+```powershell
+(mmcv) PS C:\Users\xxx\mmcv> python .dev_scripts/check_installation.py
```
-If you meet issues when running or compiling mmcv, we list some common issues in [Frequently Asked Question](../faq.html).
+If no error is reported by the above command, the installation is successful. If there is an error reported, please check [Frequently Asked Questions](../faq.md) to see if there is already a solution.
+If no solution is found, please feel free to open an [issue](https://github.com/open-mmlab/mmcv/issues).
+
+### Build mmcv
+
+If you need to use PyTorch-related modules, make sure PyTorch has been successfully installed in your environment by referring to the [PyTorch official installation guide](https://github.com/pytorch/pytorch#installation).
+
+1. Clone the repo
-## \[Optional\] Build MMCV on IPU machine
+ ```bash
+ git clone https://github.com/open-mmlab/mmcv.git
+ cd mmcv
+ ```
+
+2. Start building
+
+ ```bash
+ pip install -e . -v
+ ```
+
+3. Validate installation
+
+ ```bash
+ python -c 'import mmcv;print(mmcv.__version__)'
+ ```
+
+### Build mmcv-full on IPU machine
Firstly, you need to apply for an IPU cloud machine, see [here](https://www.graphcore.ai/ipus-in-the-cloud).
-### Option 1: Docker
+#### Option 1: Docker
1. Pull docker
-```shell
- docker pull graphcore/pytorch
-```
+ ```bash
+ docker pull graphcore/pytorch
+ ```
2. Build MMCV under same python environment
-### Option 2: Install from SDK
+#### Option 2: Install from SDK
1. Build MMCV
diff --git a/docs/en/get_started/installation.md b/docs/en/get_started/installation.md
index d9fd1b33607..ea1a095aae5 100644
--- a/docs/en/get_started/installation.md
+++ b/docs/en/get_started/installation.md
@@ -2,183 +2,374 @@
There are two versions of MMCV:
-- **mmcv-full**: comprehensive, with full features and various CUDA ops out of box. It takes longer time to build.
-- **mmcv**: lite, without CUDA ops but all other features, similar to mmcv\<1.0.0. It is useful when you do not need those CUDA ops.
+- **mmcv-full**: comprehensive, with full features and various CPU and CUDA ops out of box. It takes longer time to build.
+- **mmcv**: lite, without CPU and CUDA ops but all other features, similar to mmcv\<1.0.0. It is useful when you do not need those CUDA ops.
```{warning}
Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other. `Installing the full version is highly recommended if CUDA is avaliable`.
```
-a. Install the full version.
+### Install mmcv-full
-Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/).
+```{note}
+- The following installation steps are for Linux and Windows platforms only. To install mmcv-full on macOS platform, please refer to [build mmcv-full from source](https://mmcv.readthedocs.io/en/latest/get_started/build.html#macos-mmcv-full).
+- To compile ONNX Runtime custom operators, please refer to [How to build custom operators for ONNX Runtime](https://mmcv.readthedocs.io/en/latest/deployment/onnxruntime_op.html#how-to-build-custom-operators-for-onnx-runtime)
+- To compile TensorRT customization, please refer to [How to build TensorRT plugins in MMCV](https://mmcv.readthedocs.io/en/latest/deployment/tensorrt_plugin.html#how-to-build-tensorrt-plugins-in-mmcv)
+```
-We provide pre-built mmcv packages (recommended) with different PyTorch and CUDA versions to simplify the building for **Linux and Windows systems**. In addition, you can run [check_installation.py](.dev_scripts/check_installation.py) to check the installation of mmcv-full after running the installation commands.
+Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [PyTorch official installation guide](https://pytorch.org/get-started/locally/#start-locally). This can be verified using the following command
-i. Install the latest version.
+```bash
+python -c 'import torch;print(torch.__version__)'
+```
-The rule for installing the latest `mmcv-full` is as follows:
+If version information is output, then PyTorch is installed.
-```shell
-pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
-```
+#### Install with mim (recommended)
-Please replace `{cu_version}` and `{torch_version}` in the url to your desired one. For example,
-to install the latest `mmcv-full` with `CUDA 11.1` and `PyTorch 1.9.0`, use the following command:
+[mim](https://github.com/open-mmlab/mim) is the package management tool for the OpenMMLab projects, which makes it easy to install mmcv-full
-```shell
-pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
+```bash
+pip install -U openmim
+mim install mmcv-full
```
-For more details, please refer the the following tables and delete `=={mmcv_version}`.
+If you find that the above installation command does not use a pre-built package ending with `.whl` but a source package ending with `.tar.gz`, you may not have a pre-build package corresponding to the PyTorch or CUDA or mmcv-full version, in which case you can [build mmcv-full from source](build.md).
+
+
+
+Collecting mmcv-full
+Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv_full-1.6.1-cp38-cp38-manylinux1_x86_64.whl + +
+
+Installation log using pre-built packages
+ +Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html+Collecting mmcv-full
+Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv_full-1.6.1-cp38-cp38-manylinux1_x86_64.whl + +
+
+Collecting mmcv-full==1.6.0
+Downloading mmcv-full-1.6.0.tar.gz -ii. Install a specified version. +
-The rule for installing a specified `mmcv-full` is as follows:
+To install a specific version of mmcv-full, for example, mmcv-full version 1.6.0, you can use the following command
-```shell
-pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+```bash
+mim install mmcv-full==1.6.0
```
-First of all, please refer to the Releases and replace `{mmcv_version}` a specified one. e.g. `1.3.9`.
-Then replace `{cu_version}` and `{torch_version}` in the url to your desired versions. For example,
-to install `mmcv-full==1.3.9` with `CUDA 11.1` and `PyTorch 1.9.0`, use the following command:
+:::{note}
+If you would like to use `opencv-python-headless` instead of `opencv-python`,
+e.g., in a minimum container environment or servers without GUI,
+you can first install it before installing MMCV to skip the installation of `opencv-python`.
-```shell
-pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
+Alternatively, if it takes too long to install a dependency library, you can specify the pypi source
+
+```bash
+mim install mmcv-full -i https://pypi.tuna.tsinghua.edu.cn/simple
```
-```{note}
+:::
+
+You can run [check_installation.py](https://github.com/open-mmlab/mmcv/.dev_scripts/check_installation.py) to check the installation of mmcv-full after running the installation commands.
+
+#### Install with pip
+
+Use the following command to check the version of CUDA and PyTorch
+
+```bash
+python -c 'import torch;print(torch.__version__);print(torch.version.cuda)'
+```
+
+Select the appropriate installation command depending on the type of system, CUDA version, PyTorch version, and MMCV version
+
+
+
+
+ Installation log using source packages
+ +Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html+Collecting mmcv-full==1.6.0
+Downloading mmcv-full-1.6.0.tar.gz -ii. Install a specified version. +
+
+
+
+
+
+
+
+
+
+
+If you do not find a corresponding version in the dropdown box above, you probably do not have a pre-built package corresponding to the PyTorch or CUDA or mmcv-full version, at which point you can [build mmcv-full from source](build.md).
+
+:::{note}
mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility
usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you
can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well.
-For example, if your PyTorch version is 1.8.1 and CUDA version is 11.1, you
-can use the following command to install mmcv-full.
+For example, if your PyTorch version is 1.8.1, you can feel free to choose 1.8.x.
+:::
+
+:::{note}
+If you would like to use `opencv-python-headless` instead of `opencv-python`,
+e.g., in a minimum container environment or servers without GUI,
+you can first install it before installing MMCV to skip the installation of `opencv-python`.
-`pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html`
+Alternatively, if it takes too long to install a dependency library, you can specify the pypi source
+
+```bash
+mim install mmcv-full -i https://pypi.tuna.tsinghua.edu.cn/simple
```
-For more details, please refer the the following tables.
-
-| CUDA | -torch 1.11 | -torch 1.10 | -torch 1.9 | -torch 1.8 | -torch 1.7 | -torch 1.6 | -torch 1.5 | -
|---|---|---|---|---|---|---|---|
| 11.5 | -install |
- - | - | - | - | - | - |
| 11.3 | -install |
- install |
- - | - | - | - | - |
| 11.1 | -- | install |
- install |
- install |
- - | - | - |
| 11.0 | -- | - | - | - | install |
- - | - |
| 10.2 | -install |
- install |
- install |
- install |
- install |
- install |
- install |
-
| 10.1 | -- | - | - | install |
- install |
- install |
- install |
-
| 9.2 | -- | - | - | - | install |
- install |
- install |
-
| cpu | -install |
- install |
- install |
- install |
- install |
- install |
- install |
-
+
+Collecting mmcv-full
+Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv_full-1.6.1-cp38-cp38-manylinux1_x86_64.whl + +
+
+使用预编译包的安装日志
+ +Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html+Collecting mmcv-full
+Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv_full-1.6.1-cp38-cp38-manylinux1_x86_64.whl + +
+
+Collecting mmcv-full==1.6.0
+Downloading mmcv-full-1.6.0.tar.gz -```shell -pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html +
+
+如需安装指定版本的 mmcv-full，例如安装 1.6.0 版本的 mmcv-full，可使用以下命令
+
+```bash
+mim install mmcv-full==1.6.0
```
-```{note}
-PyTorch 在 1.x.0 和 1.x.1 之间通常是兼容的，故 mmcv-full 只提供 1.x.0 的编译包。如果你
-的 PyTorch 版本是 1.x.1，你可以放心地安装在 1.x.0 版本编译的 mmcv-full。例如，如果你的
-PyTorch 版本是 1.8.1、CUDA 版本是 11.1，你可以使用以下命令安装 mmcv-full。
+:::{note}
+如果你打算使用 `opencv-python-headless` 而不是 `opencv-python`，例如在一个很小的容器环境或者没有图形用户界面的服务器中，你可以先安装 `opencv-python-headless`，这样在安装 mmcv 依赖的过程中会跳过 `opencv-python`。
-`pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html`
+另外，如果安装依赖库的时间过长，可以指定 pypi 源
+
+```bash
+mim install mmcv-full -i https://pypi.tuna.tsinghua.edu.cn/simple
```
-如果想知道更多 CUDA 和 PyTorch 版本的命令，可以参考下面的表格，将链接中的 `=={mmcv_version}` 删去即可。
+:::
+
+安装完成后可以运行 [check_installation.py](https://github.com/open-mmlab/mmcv/.dev_scripts/check_installation.py) 脚本检查 mmcv-full 是否安装成功。
-ii. 安装特定的版本
+#### 使用 pip 安装
-如下是安装特定版本 `mmcv-full` 的命令
+使用以下命令查看 CUDA 和 PyTorch 的版本
-```shell
-pip install mmcv-full=={mmcv_version} -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+```bash
+python -c 'import torch;print(torch.__version__);print(torch.version.cuda)'
```
-首先请参考版本发布信息找到想要安装的版本号，将 `{mmcv_version}` 替换成该版本号，例如 `1.3.9`。
-然后将链接中的 `{cu_version}` 和 `{torch_version}` 根据自身需求替换成实际的版本号，例如想安装和 `CUDA 11.1`、`PyTorch 1.9.0` 兼容的 `mmcv-full` 1.3.9 版本，使用如下替换过的命令
+根据系统的类型、CUDA 版本、PyTorch 版本以及 MMCV 版本选择相应的安装命令
+
+
+
+
+ 使用源码包的安装日志
+ +Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html+Collecting mmcv-full==1.6.0
+Downloading mmcv-full-1.6.0.tar.gz -```shell -pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html +
+
+
+
+
+
+
+
+
+
+
+如果在上面的下拉框中没有找到对应的版本，则可能是没有对应 PyTorch 或者 CUDA 或者 mmcv-full 版本的预编译包，此时，你可以[源码安装 mmcv-full](../faq.md)。
-```shell
-pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
+:::{note}
+PyTorch 在 1.x.0 和 1.x.1 之间通常是兼容的，故 mmcv-full 只提供 1.x.0 的编译包。如果你
+的 PyTorch 版本是 1.x.1，你可以放心地安装在 1.x.0 版本编译的 mmcv-full。例如，如果你的
+PyTorch 版本是 1.8.1，你可以放心选择 1.8.x。
+:::
+
+:::{note}
+如果你打算使用 `opencv-python-headless` 而不是 `opencv-python`，例如在一个很小的容器环境或者没有图形用户界面的服务器中，你可以先安装 `opencv-python-headless`，这样在安装 mmcv 依赖的过程中会跳过 `opencv-python`。
+
+另外，如果安装依赖库的时间过长，可以指定 pypi 源
+
+```bash
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html -i https://pypi.tuna.tsinghua.edu.cn/simple
```
-对于更多的 PyTorch 和 CUDA 版本组合，请参考下表：
-
-| CUDA | -torch 1.11 | -torch 1.10 | -torch 1.9 | -torch 1.8 | -torch 1.7 | -torch 1.6 | -torch 1.5 | -
|---|---|---|---|---|---|---|---|
| 11.5 | -安装 |
- - | - | - | - | - | - |
| 11.3 | -安装 |
- 安装 |
- - | - | - | - | - |
| 11.1 | -- | 安装 |
- 安装 |
- 安装 |
- - | - | - |
| 11.0 | -- | - | - | - | 安装 |
- - | - |
| 10.2 | -安装 |
- 安装 |
- 安装 |
- 安装 |
- 安装 |
- 安装 |
- 安装 |
-
| 10.1 | -- | - | - | 安装 |
- 安装 |
- 安装 |
- 安装 |
-
| 9.2 | -- | - | - | - | 安装 |
- 安装 |
- 安装 |
-
| cpu | -安装 |
- 安装 |
- 安装 |
- 安装 |
- 安装 |
- 安装 |
- 安装 |
-