FlashInternImage for Semantic Segmentation

This folder contains the implementation of the InternImage for semantic segmentation.

Our segmentation code is developed on top of MMSegmentation v0.27.0.

Usage

Install

Clone this repo:

git clone https://github.com/OpenGVLab/DCNv4.git
cd DCNv4

Create a conda virtual environment and activate it:

conda create -n dcnv4 python=3.7 -y
conda activate dcnv4

Install CUDA>=10.2 with cudnn>=7 following the official installation instructions
Install PyTorch>=1.10.0 and torchvision>=0.9.0 with CUDA>=10.2:

For examples, to install torch==1.11 with CUDA==11.3 and nvcc:

conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch -y
conda install -c conda-forge cudatoolkit-dev=11.3 -y # to install nvcc

Install other requirements:

note: conda opencv will break torchvision as not to support GPU, so we need to install opencv using pip.

conda install -c conda-forge termcolor yacs pyyaml scipy pip -y
pip install opencv-python

Install timm and mmcv-full and `mmsegmentation':

pip install -U openmim
mim install mmcv-full==1.5.0
mim install mmsegmentation==0.27.0
pip install timm==0.6.11 mmdet==2.28.1

Install DCNv4

pip install DCNv4

Data Preparation

Prepare datasets according to the guidelines in MMSegmentation.

Evaluation

To evaluate our FlashInternImage on ADE20K val, run:

sh dist_test.sh <config-file> <checkpoint> <gpu-num> --eval mIoU

You can download checkpoint files from here. Then place it to segmentation/checkpoint_dir/seg.

For example, to evaluate the FlashInternImage-T with a single GPU:

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

For example, to evaluate the FlashInternImage-B with a single node with 8 GPUs:

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

Training

To train an FlashInternImage on ADE20K, run:

sh dist_train.sh <config-file> <gpu-num>

For example, to train FlashInternImage-T with 8 GPU on 1 node (total batch size 16), run:

sh dist_train.sh configs/ade20k/upernet_flash_internimage_t_512_160k_ade20k.py 8

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