MMSegmentation Installation Guide On Ubuntu 22.04
This comprehensive guide walks you through the process of installing MMSegmentation on Ubuntu 22.04. MMSegmentation is a powerful open-source semantic segmentation toolbox based on PyTorch. This guide provides step-by-step instructions, along with troubleshooting tips for common issues encountered during installation.
Step 1: Creating and Activating a Conda Environment
To begin your MMSegmentation installation, it's highly recommended to create an isolated environment using Conda. This practice ensures that the dependencies for MMSegmentation do not conflict with other Python projects on your system. Conda is a powerful package, dependency, and environment management system that simplifies the process of managing different software environments.
To create a Conda environment, use the following command:
conda create --name openmmlab python=3.8 -y
This command creates a new Conda environment named "openmmlab" with Python 3.8. The -y flag automatically answers "yes" to any prompts during the creation process. Selecting a specific Python version like 3.8 is often crucial for compatibility with certain libraries and frameworks, especially in the deep learning domain where different versions of PyTorch and other dependencies may require a particular Python version.
Once the environment is created, activate it using the following command:
conda activate openmmlab
Activating the environment ensures that any subsequent installations or commands will be performed within the isolated environment, preventing conflicts with your global Python installation or other Conda environments. This step is vital for maintaining a clean and organized workspace, especially when working on projects with specific dependency requirements. By isolating the project's dependencies, you reduce the risk of encountering compatibility issues and ensure the project remains consistent and reproducible across different systems. Creating a dedicated Conda environment is considered a best practice in Python development and particularly crucial when setting up complex deep learning frameworks like MMSegmentation.
Step 2: Installing PyTorch
PyTorch, a widely used open-source machine learning framework, is a fundamental dependency for MMSegmentation. Installing the correct version of PyTorch, compatible with your hardware and software configuration, is crucial for a successful installation. Specifically, the version of PyTorch should be compatible with the CUDA toolkit installed on your system, especially when leveraging GPUs for accelerated computation. For users with NVIDIA 4090 GPUs, installing a lower version of PyTorch is essential to avoid potential compatibility issues, given that these GPUs typically require a CUDA Toolkit version of 11.8 or higher. This compatibility requirement stems from the fact that PyTorch relies on CUDA for GPU acceleration, and different PyTorch versions are built against specific CUDA versions.
To install a compatible PyTorch version, use the following command:
conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.7 -c pytorch -c nvidia
This command installs PyTorch 1.13.0 along with torchvision 0.14.0 and torchaudio 0.13.0. The pytorch-cuda=11.7 specification is critical as it ensures the installation of the PyTorch build that is compatible with CUDA 11.7. Even if your system has a higher CUDA version (e.g., 11.8 or later), PyTorch versions built with CUDA 11.7 can often be backward compatible, making this a safe choice for many configurations. The -c pytorch -c nvidia flags specify the channels from which Conda should fetch the packages, ensuring you are getting the official PyTorch and NVIDIA builds.
Alternatively, you can use pip to install PyTorch:
pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117
This command achieves the same result as the Conda command but uses pip, the Python package installer. The --extra-index-url flag is essential here as it tells pip to look for PyTorch binaries from the specified URL, which hosts PyTorch builds for different CUDA versions. The +cu117 suffix in the package names explicitly indicates the CUDA 11.7 build. Choosing the right PyTorch version based on your CUDA toolkit is vital for harnessing the power of your GPU and achieving optimal performance in deep learning tasks.
Preparing for MMSegmentation Installation: Best Practices
Before diving into the actual MMSegmentation installation, it's essential to follow certain best practices to ensure a smooth and successful setup. These preparatory steps involve installing MMCV (the core library for OpenMMLab projects) and other necessary dependencies. By taking these steps, you can avoid potential compatibility issues and streamline the installation process. The OpenMMLab ecosystem, including MMSegmentation, relies heavily on MMCV for shared functionalities like data loading, model building blocks, and training utilities. Therefore, installing MMCV correctly is a prerequisite for using MMSegmentation effectively.
Step 0: Installing MMCV using MIM
MIM, the OpenMMLab package manager, simplifies the installation of MMCV and other OpenMMLab projects. MIM automatically handles dependency resolution and ensures that the correct versions of packages are installed. Using MIM is the recommended way to install MMCV as it reduces the chances of encountering version conflicts or missing dependencies. The first step is to install MIM itself:
pip install -U openmim
The -U flag ensures that pip upgrades MIM to the latest version if it's already installed. After installing MIM, you can use it to install mmengine and MMCV:
mim install mmengine
mim install "mmcv>=2.0.0"
The mim install mmengine command installs the MMEngine, a foundational library for OpenMMLab projects, providing essential tools and components for building and training deep learning models. The mim install "mmcv>=2.0.0" command installs MMCV version 2.0.0 or later. Specifying a minimum version ensures that you have the necessary features and bug fixes required by MMSegmentation. MIM handles the installation process, including downloading the appropriate packages and resolving any dependencies. This method is preferred over manual installation as it guarantees compatibility within the OpenMMLab ecosystem. Proper installation of MMCV is a critical step that lays the groundwork for a seamless MMSegmentation installation experience.
Step 1: Installing MMSegmentation
With the environment set up and dependencies installed, you can now proceed with installing MMSegmentation. There are two primary methods for installing MMSegmentation, depending on your development needs:
Scenario A: Installing from Source for Development
If you intend to actively develop and modify MMSegmentation, installing it from source is the recommended approach. This method provides the flexibility to make changes to the codebase and immediately test them without needing to reinstall the package. Installing from source is particularly beneficial for researchers, developers, and contributors who need to debug, extend, or customize MMSegmentation's functionality. This approach allows for a deeper understanding of the framework and facilitates collaboration within the OpenMMLab community.
To install MMSegmentation from source, follow these steps:
git clone -b main https://github.com/open-mmlab/mmsegmentation.git
cd mmsegmentation
pip install -v -e .
The git clone -b main https://github.com/open-mmlab/mmsegmentation.git command clones the MMSegmentation repository from GitHub. The -b main flag specifies that you want to clone the main branch, which typically contains the latest stable code. Cloning the repository downloads the entire MMSegmentation codebase to your local machine, giving you full access to the source files. The cd mmsegmentation command navigates you into the cloned directory, where you can then proceed with the installation.
pip install -v -e . is the command that installs MMSegmentation in editable mode. The -e flag stands for "editable" and creates a symbolic link from your Python environment to the MMSegmentation source code. This means that any changes you make to the code in the cloned repository will be immediately reflected in your Python environment without requiring a reinstall. The -v flag enables verbose mode, providing more detailed output during the installation process, which can be helpful for debugging any potential issues. Installing in editable mode is highly recommended for developers as it significantly speeds up the development cycle by eliminating the need for repeated installations after each code change.
Scenario B: Installing as a Dependency via Pip
If you plan to use MMSegmentation as a library in your project without actively developing it, installing it via pip is the simpler option. This method installs MMSegmentation as a pre-built package, similar to how you would install any other Python library. Installing via pip is suitable for users who primarily want to leverage MMSegmentation's existing functionalities without the need for customization or code modifications. This approach streamlines the installation process and is ideal for users who prioritize ease of use and stability.
To install MMSegmentation via pip, use the following command:
pip install "mmsegmentation>=1.0.0"
This command installs the latest version of MMSegmentation that is greater than or equal to 1.0.0. Specifying a minimum version ensures that you have a compatible version with the features and functionalities you intend to use. Pip will automatically download and install MMSegmentation along with its dependencies, making the process straightforward and efficient. Installing MMSegmentation as a dependency is a convenient way to integrate it into your project and leverage its capabilities without the overhead of managing the source code directly.
Running Test Code
After successfully installing MMSegmentation, it's crucial to verify the installation by running test code. This step ensures that all components are correctly installed and that MMSegmentation is functioning as expected. Running test code helps identify any potential issues early on, such as missing dependencies or configuration errors. By confirming the installation, you can proceed with your segmentation tasks with confidence.
Step 1: Downloading Configuration and Checkpoint Files
To run the test code, you need to download the configuration file and the pre-trained checkpoint file. These files are essential for defining the model architecture and loading the pre-trained weights. The configuration file specifies the model's structure, training parameters, and data processing pipeline, while the checkpoint file contains the learned weights from a pre-trained model. Using pre-trained models is a common practice in deep learning as it can significantly reduce training time and improve performance, especially when working with limited datasets.
Use the following command to download the necessary files:
mim download mmsegmentation --config pspnet_r50-d8_4xb2-40k_cityscapes-512x1024 --dest .
This command uses MIM to download the configuration file pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py and the corresponding checkpoint file to the current directory (.). The download process may take a few seconds or longer, depending on your network environment. Once the download is complete, you will have two files in your current folder: pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py and pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth. These files are necessary for running the inference demo and validating your MMSegmentation installation. Downloading these files ensures that you have the required resources to perform semantic segmentation tasks and test the functionality of MMSegmentation.
Step 2: Verifying Inference Demo
With the configuration and checkpoint files downloaded, you can now verify the inference demo. This demo runs the pre-trained model on a sample image and generates a segmentation mask, which visually represents the identified objects in the image. Running the inference demo is a crucial step in confirming that MMSegmentation is correctly installed and that the model can be loaded and executed successfully. It provides a tangible result that validates the entire installation process.
If you installed MMSegmentation from source, run the following command:
python demo/image_demo.py demo/demo.png configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth --device cuda:0 --out-file result.jpg
This command executes the image_demo.py script located in the demo directory. The script takes several arguments:
demo/demo.png: The path to the input image.configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py: The path to the configuration file.pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth: The path to the checkpoint file.--device cuda:0: Specifies that the inference should be run on the first GPU (cuda:0). If you don't have a GPU or want to use the CPU, you can change this to--device cpu.--out-file result.jpg: Specifies the path to save the output image with the segmentation mask.
After running the command, you should see a new image named result.jpg in the current folder. This image will have the original image with segmentation masks overlaid on top, visually highlighting the different objects identified by the model. The successful generation of this image confirms that MMSegmentation is correctly installed and functioning as expected. If the command executes without errors and the result.jpg image is generated, you can be confident that your MMSegmentation installation is successful.
Troubleshooting Common Errors
During the installation process, you might encounter certain errors. This section addresses some common issues and provides solutions.
Error 1: MMCV Version Incompatibility
One common error is related to MMCV version incompatibility. This error typically occurs when the installed version of MMCV does not meet the requirements of MMSegmentation.
Error Message:
Traceback (most recent call last):
File "demo/image_demo.py", line 6, in <module>
from mmseg.apis import inference_model, init_model, show_result_pyplot
File "/home/ubuntu/mmsegmentation/mmseg/__init__.py", line 61, in <module>
assert (mmcv_min_version <= mmcv_version < mmcv_max_version), \
AssertionError: MMCV==2.2.0 is used but incompatible. Please install mmcv>=2.0.0rc4.
This error message indicates that the installed MMCV version (2.2.0 in this example) is incompatible with MMSegmentation, which requires a version greater than or equal to 2.0.0rc4.
Error 2: ModuleNotFoundError: No module named 'mmcv._ext'
Another common error is the ModuleNotFoundError, which occurs when the mmcv._ext module cannot be found. This error often arises due to issues with the MMCV installation or compilation.
Error Message:
ModuleNotFoundError: No module named 'mmcv._ext'
Solutions
There are two primary ways to resolve these errors:
Solution 1:
pip install mmcv-lite==2.0.0rc4
This command installs a lightweight version of MMCV (mmcv-lite) with a specific version (2.0.0rc4). mmcv-lite is a smaller version of MMCV that includes only the essential functionalities, which can be sufficient for many MMSegmentation tasks. Installing mmcv-lite can sometimes resolve compatibility issues and reduce the installation footprint.
Solution 2:
mim install mmcv==2.1.0
If the first solution does not work, you can try installing a specific version of MMCV (2.1.0 in this case) using MIM. MIM ensures that the correct dependencies are installed along with MMCV, reducing the chances of encountering further issues. Using MIM to install MMCV is generally recommended as it handles the installation process more reliably than pip in the context of OpenMMLab projects.
After applying either of these solutions, try running the test code again to verify that the errors have been resolved.
Customizing Prediction Output: Removing Class Labels
By default, MMSegmentation's prediction output includes class labels, which can be useful for understanding the segmentation results. However, in some cases, you might want to remove these labels for cleaner visualizations or specific application requirements. MMSegmentation provides a simple way to customize the prediction output and remove the class labels.
To remove class labels from the prediction output, modify the show_result_pyplot function call in your code. Specifically, add the with_labels=False argument to the function.
Original Code:
show_result_pyplot(model, img_path, result, show=False, out_file=output_path, opacity=1)
Modified Code:
show_result_pyplot(model, img_path, result, show=False, out_file=output_path, opacity=1, with_labels=False)
By adding with_labels=False, you instruct the show_result_pyplot function to exclude class labels from the output visualization. This modification results in a cleaner segmentation mask without any text annotations. This customization option allows you to tailor the prediction output to your specific needs and preferences. Removing class labels can be particularly useful when you want to focus solely on the segmentation boundaries and regions without the distraction of text labels. It can also be beneficial when preparing visualizations for publications or presentations where a cleaner aesthetic is desired.
Conclusion
This guide provides a detailed walkthrough of installing MMSegmentation on Ubuntu 22.04, covering everything from setting up the environment to troubleshooting common errors. By following these steps, you should have a fully functional MMSegmentation installation ready for your semantic segmentation projects. Remember to consult the official MMSegmentation documentation for more advanced features and usage examples. Happy segmenting!
