The DensePose-RCNN system is implemented within the
detectron framework. This document is based on the Detectron installation instructions, for troubleshooting please refer to the
detectron installation document.
- NVIDIA GPU, Linux, Python2
- Caffe2, various standard Python packages, and the COCO API; Instructions for installing these dependencies are found below
- Detectron operators currently do not have CPU implementation; a GPU system is required.
- Detectron has been tested extensively with CUDA 8.0 and cuDNN 6.0.21.
To install Caffe2 with CUDA support, follow the installation instructions from the Caffe2 website. If you already have Caffe2 installed, make sure to update your Caffe2 to a version that includes the Detectron module.
Please ensure that your Caffe2 installation was successful before proceeding by running the following commands and checking their output as directed in the comments.
# To check if Caffe2 build was successful python2 -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure" # To check if Caffe2 GPU build was successful # This must print a number > 0 in order to use Detectron python2 -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())'
caffe2 Python package is not found, you likely need to adjust your
PYTHONPATH environment variable to include its location (
build is the Caffe2 CMake build directory).
Install the COCO API:
# COCOAPI=/path/to/clone/cocoapi git clone https://github.com/cocodataset/cocoapi.git $COCOAPI cd $COCOAPI/PythonAPI # Install into global site-packages make install # Alternatively, if you do not have permissions or prefer # not to install the COCO API into global site-packages python2 setup.py install --user
Note that instructions like
# COCOAPI=/path/to/install/cocoapi indicate that you should pick a path where you'd like to have the software cloned and then set an environment variable (
COCOAPI in this case) accordingly.
Clone the Densepose repository:
# DENSEPOSE=/path/to/clone/densepose git clone https://github.com/facebookresearch/densepose $DENSEPOSE
Install Python dependencies:
pip install -r $DENSEPOSE/requirements.txt
Set up Python modules:
cd $DENSEPOSE && make
Check that Detectron tests pass (e.g. for
Build the custom operators library:
cd $DENSEPOSE && make ops
Check that the custom operator tests pass:
Fetch DensePose data.
Get necessary files to run, train and evaluate DensePose.
cd $DENSEPOSE/DensePoseData bash get_densepose_uv.sh
For training, download the DensePose-COCO dataset:
For evaluation, get the necessary files:
Setting-up the COCO dataset.
Create a symlink for the COCO dataset in your
ln -s /path/to/coco $DENSEPOSE/detectron/datasets/data/coco
Create symlinks for the DensePose-COCO annotations
ln -s $DENSEPOSE/DensePoseData/DensePose_COCO/densepose_coco_2014_minival.json $DENSEPOSE/detectron/datasets/data/coco/annotations/ ln -s $DENSEPOSE/DensePoseData/DensePose_COCO/densepose_coco_2014_train.json $DENSEPOSE/detectron/datasets/data/coco/annotations/ ln -s $DENSEPOSE/DensePoseData/DensePose_COCO/densepose_coco_2014_valminusminival.json $DENSEPOSE/detectron/datasets/data/coco/annotations/
Your local COCO dataset copy at
/path/to/coco should have the following directory structure:
coco |_ coco_train2014 | |_ <im-1-name>.jpg | |_ ... | |_ <im-N-name>.jpg |_ coco_val2014 |_ ... |_ annotations |_ instances_train2014.json |_ ...
We provide a
Dockerfile that you can use to build a Densepose image on top of a Caffe2 image that satisfies the requirements outlined at the top. If you would like to use a Caffe2 image different from the one we use by default, please make sure that it includes the Detectron module.
Build the image:
cd $DENSEPOSE/docker docker build -t densepose:c2-cuda9-cudnn7 .
Run the image (e.g. for
nvidia-docker run --rm -it densepose:c2-cuda9-cudnn7 python2 detectron/tests/test_batch_permutation_op.py
To run inference in a docker container based on the prepared docker image,
one would need to make all the required data accessible within the container.
For that one should first follow the steps described in Fetch DensePose Data
section. One could also prefetch all the necessary weights files used for training / inference.
Then one should start a container with the host
DensePoseData and COCO directories mounted:
nvidia-docker run -v $DENSEPOSE/DensePoseData:/denseposedata -v /path/to/coco:/coco -it densepose:c2-cuda9-cudnn7 bash
Within the container one needs to replace the local
DensePoseData directory with the host one:
mv /densepose/DensePoseData /densepose/DensePoseDataLocal ln -s /denseposedata DensePoseData
and perform steps described in COCO dataset setup:
ln -s /coco /densepose/detectron/datasets/data/coco ln -s /densepose/DensePoseData/DensePose_COCO/densepose_coco_2014_minival.json /densepose/detectron/datasets/data/coco/annotations/ ln -s /densepose/DensePoseData/DensePose_COCO/densepose_coco_2014_train.json /densepose/detectron/datasets/data/coco/annotations/ ln -s /densepose/DensePoseData/DensePose_COCO/densepose_coco_2014_valminusminival.json /densepose/detectron/datasets/data/coco/annotations/
exit the container and commit the change
docker commit $(docker ps --last 1 -q) densepose:c2-cuda9-cudnn7-wdata
The new image can be used to run inference / training. However, one needs to
remember to mount
DensePoseData and COCO directories:
nvidia-docker run --rm -v $DENSEPOSE/DensePoseData:/denseposedata -v /path/to/coco:/coco -it densepose:c2-cuda9-cudnn7-wdata <inference_or_training_command>