InternImage 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.

• Installation
• Data Preparation
• Released Models
• Evaluation
• Training
• Manage Jobs with Slurm
• Image Demo
• Export

Installation

• Clone this repository:

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

• Create a conda virtual environment and activate it:

conda create -n internimage python=3.9
conda activate internimage

• 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:

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113  -f https://download.pytorch.org/whl/torch_stable.html

• 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, 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 other requirements:

pip install opencv-python termcolor yacs pyyaml scipy
# Please use a version of numpy lower than 2.0
pip install numpy==1.26.4
pip install pydantic==1.10.13

• Compile CUDA operators

Before compiling, please use the nvcc -V command to check whether your nvcc version matches the CUDA version of PyTorch.

cd ./ops_dcnv3
sh ./make.sh
# unit test (should see all checking is True)
python test.py

• You can also install the operator using precompiled .whl files DCNv3-1.0-whl

Data Preparation

Prepare datasets according to the guidelines in MMSegmentation.

Released Models

Evaluation

To evaluate our InternImage on ADE20K val, run:

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

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

python test.py configs/ade20k/upernet_internimage_t_512_160k_ade20k.py pretrained/upernet_internimage_t_512_160k_ade20k.pth --eval mIoU

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

sh dist_test.sh configs/ade20k/upernet_internimage_b_512_160k_ade20k.py pretrained/upernet_internimage_b_512_160k_ade20k.pth 8 --eval mIoU

Training

To train an InternImage on ADE20K, run:

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

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

sh dist_train.sh configs/ade20k/upernet_internimage_t_512_160k_ade20k.py 8

Manage Jobs with Slurm

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

GPUS=8 sh slurm_train.sh <partition> <job-name> configs/ade20k/upernet_internimage_xl_640_160k_ade20k.py

Image Demo

To inference a single/multiple image like this. If you specify image containing directory instead of a single image, it will process all the images in the directory.

CUDA_VISIBLE_DEVICES=0 python image_demo.py \
  data/ade/ADEChallengeData2016/images/validation/ADE_val_00000591.jpg \
  configs/ade20k/upernet_internimage_t_512_160k_ade20k.py  \
  checkpoint_dir/seg/upernet_internimage_t_512_160k_ade20k.pth  \
  --palette ade20k

Export

Install mmdeploy at first:

pip install mmdeploy==0.14.0

To export a segmentation model from PyTorch to TensorRT, run:

MODEL="model_name"
CKPT_PATH="/path/to/model/ckpt.pth"

python deploy.py \
    "./deploy/configs/mmseg/segmentation_tensorrt_static-512x512.py" \
    "./configs/ade20k/${MODEL}.py" \
    "${CKPT_PATH}" \
    "./deploy/demo.png" \
    --work-dir "./work_dirs/mmseg/${MODEL}" \
    --device cuda \
    --dump-info

For example, to export upernet_internimage_t_512_160k_ade20k from PyTorch to TensorRT, run:

MODEL="upernet_internimage_t_512_160k_ade20k"
CKPT_PATH="/path/to/model/ckpt/upernet_internimage_t_512_160k_ade20k.pth"

python deploy.py \
    "./deploy/configs/mmseg/segmentation_tensorrt_static-512x512.py" \
    "./configs/ade20k/${MODEL}.py" \
    "${CKPT_PATH}" \
    "./deploy/demo.png" \
    --work-dir "./work_dirs/mmseg/${MODEL}" \
    --device cuda \
    --dump-info