detectron/projects/DensePose/doc/GETTING_STARTED.md

• Getting Started with DensePose
• • Inference with Pre-trained Models
  • Training
  • Evaluation
  • Tools

Getting Started with DensePose

Inference with Pre-trained Models

1. Pick a model and its config file from Model Zoo, for example densepose_rcnn_R_50_FPN_s1x.yaml
2. Run the Apply Net tool to visualize the results or save the to disk. For example, to use contour visualization for DensePose, one can run:

python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml densepose_rcnn_R_50_FPN_s1x.pkl image.jpg dp_contour,bbox --output image_densepose_contour.png

Please see Apply Net for more details on the tool.

Training

To train a model one can use the train_net.py script.
This script was used to train all DensePose models in Model Zoo.
For example, to launch end-to-end DensePose-RCNN training with ResNet-50 FPN backbone
on 8 GPUs following the s1x schedule, one can run

python train_net.py --config-file configs/densepose_rcnn_R_50_FPN_s1x.yaml --num-gpus 8

The configs are made for 8-GPU training. To train on 1 GPU, one can apply the
linear learning rate scaling rule:

python train_net.py --config-file configs/densepose_rcnn_R_50_FPN_s1x.yaml \
    SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025

Evaluation

Model testing can be done in the same way as training, except for an additional flag --eval-only and
model location specification through MODEL.WEIGHTS model.pth in the command line

python train_net.py --config-file configs/densepose_rcnn_R_50_FPN_s1x.yaml \
    --eval-only MODEL.WEIGHTS model.pth

Tools

We provide tools which allow one to:

• easily view DensePose annotated data in a dataset;
• perform DensePose inference on a set of images;
• visualize DensePose model results;

query_db is a tool to print or visualize DensePose data in a dataset.
Please refer to Query DB for more details on this tool

apply_net is a tool to print or visualize DensePose results.
Please refer to Apply Net for more details on this tool