Docker for mmdetection

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Docker for mmdetection

1. Git Clone

git clone https://github.com/open-mmlab/mmdetection.git

2. Install Docker

CUDA Version For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
Geforce 3070 requires CUDA 11 or higher and cuDNN 8.0 or higher because it has a compute capability of 8.6.

cd mmdetection/docker
gedit Dockerfile

2.1. Dockerfile

ARG PYTORCH="1.9.0"   
ARG CUDA="11.1"       
ARG CUDNN="8"         

FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel

ENV TORCH_CUDA_ARCH_LIST="8.6"    
ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../"

# To fix GPG key error when running apt-get update
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/7fa2af80.pub

RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*

# Install MMCV
RUN pip install --no-cache-dir --upgrade pip wheel setuptools
RUN pip install --no-cache-dir mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html

# Install MMDetection
RUN conda clean --all
WORKDIR /mmdetection
ENV FORCE_CUDA="1"

2.2. Build

@ /mmdetection/docker

docker build -t mmdetection .

2.3. Run

docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection/data mmdetection

{DATA_DRI} <– directories that collect data in your environment.

docker run --gpus all --shm-size=8g -it -v home:/mmdetection mmdetection

2.4. Using Docker containers in VS Code

@ vscode

a. ctrl + shift + p
b. remote-Containers:Attach to Running Container
c. choose container

2.4. Git clone in Container

@ New Terminal in VScode

git clone https://github.com/open-mmlab/mmdetection.git

mmdetection folder OPEN

@ New Terminal in VScode image

pip install --no-cache-dir -r requirements/build.txt
pip install --no-cache-dir -e .

output
image image

3. Dataset Download

github @ terminal(cd mmdetection)

python tools/misc/download_dataset.py --dataset-name coco2017

output
image

unzip install
unzip
image

apt-get update
apt-get install -y unzip

unzip
@ cd rm /mmdetection/data/coco

unzip annotations_trainval2017.zip
unzip test2017.zip
unzip train2017.zip
unzip val2017.zip

rm annotations_trainval2017.zip
rm test2017.zip
rm train2017.zip
rm val2017.zip

image

4. Tutorial

4.1. directory

Create a checkpoints directory and get the next model file. faster_rcnn_r50_fpn_1x_coco checkpoint file

4.2. tutorial.py

Create a tutorial_1.py file and paste the following code.

from mmdet.apis import init_detector, inference_detector
import mmcv

# Specify the path to model config and checkpoint file
config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'

# build the model from a config file and a checkpoint file
model = init_detector(config_file, checkpoint_file, device='cuda:0')

# test a single image and show the results
img = 'demo/demo.jpg'  # or img = mmcv.imread(img), which will only load it once
result = inference_detector(model, img)
# visualize the results in a new window
model.show_result(img, result)
# or save the visualization results to image files
model.show_result(img, result, out_file='demo/demo_result.jpg')

# test a video and show the results
video = mmcv.VideoReader('demo/demo.mp4')
for frame in video:
    result = inference_detector(model, frame)
    model.show_result(frame, result, wait_time=1)

4.3. Result

/demo/demo_result.jpg image

5. Restart

docker start [CONTAINER ID]

@ vscode

a. ctrl + shift + p
b. remote-Containers:Attach to Running Container
c. choose container