97 lines
2.7 KiB
Markdown
97 lines
2.7 KiB
Markdown
# FlashInternImage for Semantic Segmentation
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This folder contains the implementation of the InternImage for semantic segmentation.
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Our segmentation code is developed on top of [MMSegmentation v0.27.0](https://github.com/open-mmlab/mmsegmentation/tree/v0.27.0).
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## Usage
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### Install
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- Clone this repo:
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```bash
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git clone https://github.com/OpenGVLab/DCNv4.git
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cd DCNv4
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```
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- Create a conda virtual environment and activate it:
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```bash
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conda create -n dcnv4 python=3.7 -y
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conda activate dcnv4
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```
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- Install `CUDA>=10.2` with `cudnn>=7` following
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the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
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- Install `PyTorch>=1.10.0` and `torchvision>=0.9.0` with `CUDA>=10.2`:
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For examples, to install torch==1.11 with CUDA==11.3 and nvcc:
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```bash
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conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch -y
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conda install -c conda-forge cudatoolkit-dev=11.3 -y # to install nvcc
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```
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- Install other requirements:
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note: conda opencv will break torchvision as not to support GPU, so we need to install opencv using pip.
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```bash
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conda install -c conda-forge termcolor yacs pyyaml scipy pip -y
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pip install opencv-python
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```
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- Install `timm` and `mmcv-full` and `mmsegmentation':
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```bash
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pip install -U openmim
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mim install mmcv-full==1.5.0
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mim install mmsegmentation==0.27.0
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pip install timm==0.6.11 mmdet==2.28.1
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```
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- Install DCNv4
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```bash
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pip install DCNv4
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```
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### Data Preparation
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Prepare datasets according to the [guidelines](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#prepare-datasets) in MMSegmentation.
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### Evaluation
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To evaluate our `FlashInternImage` on ADE20K val, run:
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```bash
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sh dist_test.sh <config-file> <checkpoint> <gpu-num> --eval mIoU
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```
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You can download checkpoint files from [here](https://huggingface.co/OpenGVLab/DCNv4). Then place it to segmentation/checkpoint_dir/seg.
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For example, to evaluate the `FlashInternImage-T` with a single GPU:
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```bash
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python test.py configs/ade20k/upernet_flash_internimage_t_512_160k_ade20k.py checkpoint_dir/seg/upernet_flash_internimage_t_512_160k_ade20k.pth --eval mIoU
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```
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For example, to evaluate the `FlashInternImage-B` with a single node with 8 GPUs:
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```bash
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sh dist_test.sh configs/ade20k/upernet_flash_internimage_b_512_160k_ade20k.py checkpoint_dir/seg/upernet_flash_internimage_b_512_160k_ade20k.pth 8 --eval mIoU
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```
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### Training
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To train an `FlashInternImage` on ADE20K, run:
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```bash
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sh dist_train.sh <config-file> <gpu-num>
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```
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For example, to train `FlashInternImage-T` with 8 GPU on 1 node (total batch size 16), run:
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```bash
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sh dist_train.sh configs/ade20k/upernet_flash_internimage_t_512_160k_ade20k.py 8
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```
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