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Using Detectron

This document provides brief tutorials covering DensePose for inference and training on the DensePose-COCO dataset. This document is a modified version of the detectron/GETTING_STARTED.md.

  • For general information about DensePose, please see README.md.
  • For installation instructions, please see INSTALL.md.

Inference with Pretrained Models

1. Directory of Image Files

To run inference on a an image (or a directory of image files), you can use the infer_simple.py tool. In this example, we're using an end-to-end trained DensePose-RCNN model with a ResNet-101-FPN backbone from the model zoo:

python2 tools/infer_simple.py \
    --cfg configs/DensePose_ResNet101_FPN_s1x-e2e.yaml \
    --output-dir DensePoseData/infer_out/ \
    --image-ext jpg \
    --wts https://s3.amazonaws.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl \
    DensePoseData/demo_data/demo_im.jpg

DensePose should automatically download the model from the URL specified by the --wts argument. This tool will output visualizations of the detections in PDF format in the directory specified by --output-dir. Also, it will output two images *_IUV.png and *_INDS.png which consists of I,U, V channels and segmented instance indices respectively. Please see notebooks/DensePose-RCNN-Visualize-Results.ipynb for the visualizations of these outputs.

Testing with Pretrained Models

Make sure that you have downloaded the DensePose evaluation files as instructed in INSTALL.md. This example shows how to run an end-to-end trained DensePose-RCNN model from the model zoo using a single GPU for inference. As configured, this will run inference on all images in coco_2014_minival (which must be properly installed).

python2 tools/test_net.py \
    --cfg configs/DensePose_ResNet101_FPN_s1x-e2e.yaml \
    TEST.WEIGHTS https://s3.amazonaws.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl \
    NUM_GPUS 1

Training a Model

This example shows how to train a model using the DensePose-COCO dataset. The model will be an end-to-end trained DensePose-RCNN using a ResNet-50-FPN backbone.

python2 tools/train_net.py \
    --cfg configs/DensePose_ResNet50_FPN_single_GPU.yaml \
    OUTPUT_DIR /tmp/detectron-output

The models we have provided in the model zoo are trained using 8 gpus. As in any Detectron configs, we use linear scaling rule to adjust learning schedules. Please refer to the following paper: Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. We also provide learning rate and number of iterations for varying number of GPUs.