201 lines
7.6 KiB
Markdown
201 lines
7.6 KiB
Markdown
# Prerequisites
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In this section we demonstrate how to prepare an environment with PyTorch.
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MMSegmentation works on Linux, Windows and macOS. It requires Python 3.6+, CUDA 9.2+ and PyTorch 1.3+.
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```{note}
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If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation.
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```
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**Step 0.** Download and install Miniconda from the [official website](https://docs.conda.io/en/latest/miniconda.html).
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**Step 1.** Create a conda environment and activate it.
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```shell
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conda create --name openmmlab python=3.8 -y
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conda activate openmmlab
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```
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**Step 2.** Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.
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On GPU platforms:
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```shell
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conda install pytorch torchvision -c pytorch
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```
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On CPU platforms:
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```shell
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conda install pytorch torchvision cpuonly -c pytorch
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```
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# Installation
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We recommend that users follow our best practices to install MMSegmentation. However, the whole process is highly customizable. See [Customize Installation](#customize-installation) section for more information.
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## Best Practices
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**Step 0.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
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```shell
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pip install -U openmim
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mim install mmcv-full
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```
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**Step 1.** Install MMSegmentation.
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Case a: If you develop and run mmseg directly, install it from source:
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```shell
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git clone https://github.com/open-mmlab/mmsegmentation.git
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cd mmsegmentation
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pip install -v -e .
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# "-v" means verbose, or more output
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# "-e" means installing a project in editable mode,
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# thus any local modifications made to the code will take effect without reinstallation.
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```
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Case b: If you use mmsegmentation as a dependency or third-party package, install it with pip:
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```shell
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pip install mmsegmentation
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```
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## Verify the installation
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To verify whether MMSegmentation is installed correctly, we provide some sample codes to run an inference demo.
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**Step 1.** We need to download config and checkpoint files.
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```shell
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mim download mmsegmentation --config pspnet_r50-d8_512x1024_40k_cityscapes --dest .
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```
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The downloading will take several seconds or more, depending on your network environment. When it is done, you will find two files `pspnet_r50-d8_512x1024_40k_cityscapes.py` and `pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth` in your current folder.
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**Step 2.** Verify the inference demo.
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Option (a). If you install mmsegmentation from source, just run the following command.
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```shell
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python demo/image_demo.py demo/demo.png configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth --device cuda:0 --out-file result.jpg
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```
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You will see a new image `result.jpg` on your current folder, where segmentation masks are covered on all objects.
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Option (b). If you install mmsegmentation with pip, open you python interpreter and copy&paste the following codes.
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```python
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from mmseg.apis import inference_segmentor, init_segmentor
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import mmcv
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config_file = 'pspnet_r50-d8_512x1024_40k_cityscapes.py'
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checkpoint_file = 'pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
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# build the model from a config file and a checkpoint file
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model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
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# test a single image and show the results
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img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once
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result = inference_segmentor(model, img)
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# visualize the results in a new window
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model.show_result(img, result, show=True)
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# or save the visualization results to image files
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# you can change the opacity of the painted segmentation map in (0, 1].
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model.show_result(img, result, out_file='result.jpg', opacity=0.5)
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# test a video and show the results
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video = mmcv.VideoReader('video.mp4')
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for frame in video:
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result = inference_segmentor(model, frame)
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model.show_result(frame, result, wait_time=1)
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```
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You can modify the code above to test a single image or a video, both of these options can verify that the installation was successful.
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## Customize Installation
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### CUDA versions
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When installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:
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- For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
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- For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.
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Please make sure the GPU driver satisfies the minimum version requirements. See [this table](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions) for more information.
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```{note}
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Installing CUDA runtime libraries is enough if you follow our best practices, because no CUDA code will be compiled locally. However if you hope to compile MMCV from source or develop other CUDA operators, you need to install the complete CUDA toolkit from NVIDIA's [website](https://developer.nvidia.com/cuda-downloads), and its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in `conda install` command.
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```
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### Install MMCV without MIM
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MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. MIM solves such dependencies automatically and makes the installation easier. However, it is not a must.
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To install MMCV with pip instead of MIM, please follow [MMCV installation guides](https://mmcv.readthedocs.io/en/latest/get_started/installation.html). This requires manually specifying a find-url based on PyTorch version and its CUDA version.
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For example, the following command install mmcv-full built for PyTorch 1.10.x and CUDA 11.3.
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```shell
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pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
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```
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### Install on CPU-only platforms
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MMSegmentation can be built for CPU only environment. In CPU mode you can train (requires MMCV version >= 1.4.4), test or inference a model.
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### Install on Google Colab
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[Google Colab](https://research.google.com/) usually has PyTorch installed,
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thus we only need to install MMCV and MMSegmentation with the following commands.
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**Step 1.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
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```shell
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!pip3 install openmim
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!mim install mmcv-full
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```
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**Step 2.** Install MMSegmentation from the source.
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```shell
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!git clone https://github.com/open-mmlab/mmsegmentation.git
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%cd mmsegmentation
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!pip install -e .
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```
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**Step 3.** Verification.
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```python
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import mmseg
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print(mmseg.__version__)
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# Example output: 0.24.1
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```
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```{note}
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Within Jupyter, the exclamation mark `!` is used to call external executables and `%cd` is a [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd) to change the current working directory of Python.
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```
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### Using MMSegmentation with Docker
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We provide a [Dockerfile](https://github.com/open-mmlab/mmsegmentation/blob/master/docker/Dockerfile) to build an image. Ensure that your [docker version](https://docs.docker.com/engine/install/) >=19.03.
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```shell
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# build an image with PyTorch 1.11, CUDA 11.3
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# If you prefer other versions, just modified the Dockerfile
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docker build -t mmsegmentation docker/
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```
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Run it with
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```shell
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docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmsegmentation/data mmsegmentation
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```
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## Trouble shooting
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If you have some issues during the installation, please first view the [FAQ](faq.md) page.
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You may [open an issue](https://github.com/open-mmlab/mmsegmentation/issues/new/choose) on GitHub if no solution is found.
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