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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.

Usage

Install

  • Clone this repo:
git clone https://github.com/OpenGVLab/InternImage.git
cd InternImage
  • Create a conda virtual environment and activate it:
conda create -n internimage python=3.7 -y
conda activate internimage

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
  • Compile CUDA operators
cd ./ops_dcnv3
sh ./make.sh
# unit test (should see all checking is True)
python test.py
  • You can also install the operator using .whl files DCNv3-1.0-whl

Data Preparation

Prepare datasets according to the guidelines in MMSegmentation.

Evaluation

To evaluate our InternImage 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 InternImage-T with a single GPU:

python test.py configs/ade20k/upernet_internimage_t_512_160k_ade20k.py checkpoint_dir/seg/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 checkpoint_dir/seg/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

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