DCN_custom_op/detection

FlashInternImage for Object Detection

This folder contains the implementation of the FlashInternImage for object detection.

Our detection code is developed on top of MMDetection v2.28.1.

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:

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

• Install timm==0.6.11 and mmcv-full==1.5.0:

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

• Install other requirements:

pip install opencv-python termcolor yacs pyyaml scipy

• Install DCNv4

pip install DCNv4

Data Preparation

Prepare COCO according to the guidelines in MMDetection v2.28.1.

Evaluation

To evaluate our FlashInternImage on COCO val, run:

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

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

python test.py configs/coco/mask_rcnn_flash_intern_image_t_fpn_1x_coco.py checkpoint_dir/det/mask_rcnn_flash_internimage_t_fpn_1x_coco.pth --eval bbox segm

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

sh dist_test.sh configs/coco/mask_rcnn_flash_intern_image_b_fpn_1x_coco.py checkpoint_dir/det/mask_rcnn_flash_internimage_b_fpn_1x_coco.py 8 --eval bbox segm

Training on COCO

To train an FlashInternImage on COCO, run:

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

For example, to train FlashInternImage-T with 8 GPU on 1 node, run:

sh dist_train.sh configs/coco/mask_rcnn_flash_intern_image_t_fpn_1x_coco.py 8