Introduction
This repo is for training the source model on the Continuous Test-time Adaptation setting on SHIFT dataset.
It is based on MMSeg 0.3. Follow the below two steps to train:
- Installation of MMSeg.
- Prepare the training dataset
finish steps above and excute commands
sh tools/dist_train.sh config/deeplabv3_r50_shift_500x800.py
Installation of MMSeg
This section is quoted from MMSeg official guide.
Note that MMSeg used here is 0.3, while the one used in test-time adaptation evaluation is version of 0.11.
Get started: Install and Run MMSeg
## Prerequisites
In this section, we demonstrate how to prepare an environment with PyTorch.
MMSegmentation works on Linux, Windows and macOS. It requires Python 3.6+, CUDA 9.2+ and PyTorch 1.5+.
**Note:**
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.
**Step 0.** Download and install Miniconda from the (official website)[https://docs.conda.io/en/latest/miniconda.html].
**Step 1.** Create a conda environment and activate it.
```shell
conda create --name openmmlab python=3.8 -y
conda activate openmmlab
```
**Step 2.** Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.
On GPU platforms:
```shell
conda install pytorch torchvision -c pytorch
```
On CPU platforms:
```shell
conda install pytorch torchvision cpuonly -c pytorch
```
## Installation
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.
### Best Practices
**Step 0.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
```shell
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0"
```
**Step 1.** Install MMSegmentation.
Case a: If you develop and run mmseg directly, install it from source:
```shell
git clone -b main https://github.com/open-mmlab/mmsegmentation.git
cd mmsegmentation
pip install -v -e .
# '-v' means verbose, or more output
# '-e' means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.
```
### Customize Installation
#### CUDA versions
When installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:
- For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
- For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.
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.
**Note:**
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.
#### Install MMCV without MIM
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.
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.
For example, the following command install mmcv==2.0.0 built for PyTorch 1.10.x and CUDA 11.3.
```shell
pip install mmcv==2.0.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
```
#### Install on CPU-only platforms
MMSegmentation can be built for CPU only environment. In CPU mode you can train (requires MMCV version >= 2.0.0), test or inference a model.
#### Install on Google Colab
[Google Colab](https://research.google.com/) usually has PyTorch installed,
thus we only need to install MMCV and MMSegmentation with the following commands.
**Step 1.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
```shell
!pip3 install openmim
!mim install mmengine
!mim install "mmcv>=2.0.0"
```
**Step 2.** Install MMSegmentation from the source.
```shell
!git clone https://github.com/open-mmlab/mmsegmentation.git
%cd mmsegmentation
!git checkout main
!pip install -e .
```
**Step 3.** Verification.
```python
import mmseg
print(mmseg.__version__)
# Example output: 1.0.0
```
**Note:**
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.
### Using MMSegmentation with Docker
We provide a [Dockerfile](https://github.com/open-mmlab/mmsegmentation/blob/main/docker/Dockerfile) to build an image. Ensure that your [docker version](https://docs.docker.com/engine/install/) >=19.03.
```shell
# build an image with PyTorch 1.11, CUDA 11.3
# If you prefer other versions, just modified the Dockerfile
docker build -t mmsegmentation docker/
```
Run it with
```shell
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmsegmentation/data mmsegmentation
```
## Trouble shooting
If you have some issues during the installation, please first view the [FAQ](notes/faq.md) page.
You may [open an issue](https://github.com/open-mmlab/mmsegmentation/issues/new/choose) on GitHub if no solution is found.
Prepare the training dataset
The data split of SHIFT uesd here is SHIFT/discret/training/images/front.
wget https://dl.cv.ethz.ch/shift/discrete/images/train/front/img.zip
wget https://dl.cv.ethz.ch/shift/discrete/images/train/front/semseg.zip
wget https://dl.cv.ethz.ch/shift/discrete/images/train/front/seq.csv
- img.zip: input images
- smseg.zip: semantic segmentation groud truth
- seq.csv: containing sequence information need to select only the clear-daytime sequence for source model training.
More details refer to SHIFT offical website.
How to adapt to other mmsegmentation versions
Two modification on original MMSeg lies in two aspects:
- Add the Customed dataset class
- Add the training config
The silimar modifications will work for other verisons
Add the Custommed dataset class
mmseg/datasets/shift.py is to load SHIFT dataset.
Two functions is implemented:
- only the clear-daytime sequence is load while training
- 14 categories out of 22 are used, so label remapping is conducted
Add the training config
configs/deeplabv3_r50_shift_500x800.py is training setting.
The competition does not restrict the methods of semantic segmentation, models except deeplabv3_r50 is allowed to serve as source model.