Windows Support (Experimental) (open-mmlab#75)

* Windows basic support

* getting_started updated for Windows.

* add experimental

* install.md restructured to seperate Windows & Linux.

* fix problems in install.md

* fix mmcv version problem.

* Fix fastscnn resize problems. (open-mmlab#82)

* Fix fast_scnn resize problems

* Fix fast_scnn resize problems 1

* Fix fast_scnn resize problems 2

* test for pascal voc

* [Doc] Add annotaion format note (open-mmlab#77)

* update pytorch version to 1.6.0 in install.md

* del fastscnn_pascal config

* del create_symlink=True

* Merge instructions for Linux & Windows

* mmcv version updated

* redundant newline deleted

* Update docs/install.md

Co-authored-by: Jerry Jiarui XU <[email protected]>

* Update docs/install.md

Co-authored-by: Jerry Jiarui XU <[email protected]>

Co-authored-by: Jerry Jiarui XU <[email protected]>

Author: johnzja

docs/install.md

@@ -1,13 +1,11 @@
-## Installation
-
-### Requirements
+## Requirements
 
-- Linux (Windows is not officially supported)
+- Linux or Windows(Experimental)
 - Python 3.6+
 - PyTorch 1.3 or higher
 - [mmcv](https://github.com/open-mmlab/mmcv)
 
-### Install mmsegmentation
+## Installation
 
 a. Create a conda virtual environment and activate it.
 
@@ -17,22 +15,51 @@ conda activate open-mmlab
 ```
 
 b. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/).
-Here we use PyTorch 1.5.0 and CUDA 10.1.
+Here we use PyTorch 1.6.0 and CUDA 10.1.
 You may also switch to other version by specifying the version number.
 
 ```shell
-conda install pytorch=1.5.0 torchvision cudatoolkit=10.1 -c pytorch
+conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
 ```
 
 c. Install [MMCV](https://mmcv.readthedocs.io/en/latest/) following the [official instructions](https://mmcv.readthedocs.io/en/latest/#installation).
-Either `mmcv` or `mmcv-full` is compatible with MMSegmentation, but for methods like CCNet and PSANet, CUDA ops in `mmcv-full` is required
+Either `mmcv` or `mmcv-full` is compatible with MMSegmentation, but for methods like CCNet and PSANet, CUDA ops in `mmcv-full` is required.
+
+**Install mmcv for Linux:**
 
 The pre-build mmcv-full (with PyTorch 1.5 and CUDA 10.1) can be installed by running: (other available versions could be found [here](https://mmcv.readthedocs.io/en/latest/#install-with-pip))
 
 ```shell
 pip install mmcv-full==latest+torch1.5.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
 ```
 
+**Install mmcv for Windows (Experimental):**
+
+For Windows, the installation of MMCV requires native C++ compilers, such as cl.exe. Please add the compiler to %PATH%.
+
+A typical path for cl.exe looks like the following if you have Windows SDK and Visual Studio installed on your computer:
+
+```shell
+C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.26.28801\bin\Hostx86\x64
+```
+
+Or you should download the cl compiler from web and then set up the path.
+
+Then, clone mmcv from github and install mmcv via pip:
+
+```shell
+git clone https://github.com/open-mmlab/mmcv.git
+cd mmcv
+pip install -e .
+```
+
+Or simply:
+```shell
+pip install mmcv
+```
+
+Currently, mmcv-full is not supported on Windows.
+
 d. Install MMSegmentation.
 
 ```shell
@@ -47,37 +74,60 @@ pip install git+https://github.com/open-mmlab/mmsegmentation.git # install the m
 
 Instead, if you would like to install MMSegmentation in `dev` mode, run following
 ```shell
-git clone https://github.com/open-mmlab/mmsegmentation
+git clone https://github.com/open-mmlab/mmsegmentation.git
 cd mmsegmentation
 pip install -e .  # or "python setup.py develop"
 ```
 
 Note:
 
-1. The `version+git_hash` will also be saved in trained models meta, e.g. 0.5.0+c415a2e.
+1. When training or testing models on Windows, please ensure that all the '\\' in paths are replaced with '/'. Add .replace('\\', '/') to your python code wherever path strings occur.
+
+2. The `version+git_hash` will also be saved in trained models meta, e.g. 0.5.0+c415a2e.
 
-2. When MMsegmentation is installed on `dev` mode, any local modifications made to the code will take effect without the need to reinstall it.
+3. When MMsegmentation is installed on `dev` mode, any local modifications made to the code will take effect without the need to reinstall it.
 
-3. If you would like to use `opencv-python-headless` instead of `opencv-python`,
+4. If you would like to use `opencv-python-headless` instead of `opencv-python`,
 you can install it before installing MMCV.
 
-4. Some dependencies are optional. Simply running `pip install -e .` will only install the minimum runtime requirements.
+5. Some dependencies are optional. Simply running `pip install -e .` will only install the minimum runtime requirements.
 To use optional dependencies like `cityscapessripts`  either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`.
 
-### A from-scratch setup script
+
+## A from-scratch setup script
+### Linux
 
 Here is a full script for setting up mmsegmentation with conda and link the dataset path (supposing that your dataset path is $DATA_ROOT).
 
 ```shell
 conda create -n open-mmlab python=3.7 -y
 conda activate open-mmlab
 
-conda install pytorch=1.5.0 torchvision cudatoolkit=10.1 -c pytorch
+conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
 pip install mmcv-full==latest+torch1.5.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
-git clone https://github.com/open-mmlab/mmsegmentation
+git clone https://github.com/open-mmlab/mmsegmentation.git
 cd mmsegmentation
 pip install -e .  # or "python setup.py develop"
 
 mkdir data
 ln -s $DATA_ROOT data
 ```
+
+### Windows(Experimental)
+Here is a full script for setting up mmsegmentation with conda and link the dataset path (supposing that your dataset path is
+%DATA_ROOT%. Notice: It must be an absolute path).
+
+```shell
+conda create -n open-mmlab python=3.7 -y
+conda activate open-mmlab
+
+conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
+set PATH=full\path\to\your\cpp\compiler;%PATH%
+pip install mmcv
+
+git clone https://github.com/open-mmlab/mmsegmentation.git
+cd mmsegmentation
+pip install -e .  # or "python setup.py develop"
+
+mklink /D data %DATA_ROOT%
+```

mmseg/__init__.py

@@ -3,7 +3,7 @@
 from .version import __version__, version_info
 
 MMCV_MIN = '1.0.5'
-MMCV_MAX = '1.1.0'
+MMCV_MAX = '1.1.1'
 
 
 def digit_version(version_str):

mmseg/utils/collect_env.py

@@ -40,10 +40,12 @@ def collect_env():
             devices[torch.cuda.get_device_name(k)].append(str(k))
         for name, devids in devices.items():
             env_info['GPU ' + ','.join(devids)] = name
-
-    gcc = subprocess.check_output('gcc --version | head -n1', shell=True)
-    gcc = gcc.decode('utf-8').strip()
-    env_info['GCC'] = gcc
+    try:
+        gcc = subprocess.check_output('gcc --version | head -n1', shell=True)
+        gcc = gcc.decode('utf-8').strip()
+        env_info['GCC'] = gcc
+    except subprocess.CalledProcessError:
+        env_info['GCC'] = 'n/a'
 
     env_info['PyTorch'] = torch.__version__
     env_info['PyTorch compiling details'] = get_build_config()