Exportable DensePose inference using TorchScript

This is unofficial inference implementation of DensePose from detectron2

The project is focused on creating simple and TorchScript compilable inference interface for the original pretrained models to free them from the heavy dependency on the detectron2 framework.

Only inference is supported, no training. Also no confidence estimation or bootstapping pipelines were implemented.

Quickstart

To run already exported model (which you might find in the Releases section) you only need PyTorch and OpenCV (for image reading):

pip install torch torchvision opencv-python

Then you can run the model using the small example script:

python run.py <model.pt> <input.[jpg|png|mp4|avi]>

This will run the model and save the result in the same directory as the input.

Exporting a model by yourself

To export a model you need to have a model checkpoint and a config file. You can find them in the table below

python export.py <config> <model> [--fp16]

If --fp16 is specified, the model will be exported in fp16 mode. This will reduce the model size at the cost of precision.

Example of exporting an R_50_FPN_s1x_legacy model into fp16 format model:

python export.py configs/densepose_rcnn_R_50_FPN_s1x_legacy.yaml https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x_legacy/164832157/model_final_d366fa.pkl --fp16

License

All models available for download are licensed under the Creative Commons Attribution-ShareAlike 3.0 license

Legacy Models

Baselines trained using schedules from Güler et al, 2018

Name	lr sched	train time (s/iter)	inference time (s/im)	train mem (GB)	box AP	segm AP	dp. AP GPS	dp. AP GPSm	model id	download
R_50_FPN_s1x_legacy	s1x	0.307	0.051	3.2	58.1	58.2	52.1	54.9	164832157	model | metrics
R_101_FPN_s1x_legacy	s1x	0.390	0.063	4.3	59.5	59.3	53.2	56.0	164832182	model | metrics

python export.py configs/densepose_rcnn_R_50_FPN_s1x_legacy.yaml https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x_legacy/164832157/model_final_d366fa.pkl

python export.py configs/densepose_rcnn_R_101_FPN_s1x_legacy.yaml https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x_legacy/164832182/model_final_10af0e.pkl

Improved Baselines, Original Fully Convolutional Head

These models use an improved training schedule and Panoptic FPN head from Kirillov et al, 2019.

Name	lr sched	train time (s/iter)	inference time (s/im)	train mem (GB)	box AP	segm AP	dp. AP GPS	dp. AP GPSm	model id	download
R_50_FPN_s1x	s1x	0.359	0.066	4.5	61.2	67.2	63.7	65.3	165712039	model | metrics
R_101_FPN_s1x	s1x	0.428	0.079	5.8	62.3	67.8	64.5	66.2	165712084	model | metrics

python export.py configs/densepose_rcnn_R_50_FPN_s1x.yaml https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl

python export.py configs/densepose_rcnn_R_101_FPN_s1x.yaml https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_s1x/165712084/model_final_c6ab63.pkl

Improved Baselines, DeepLabV3 Head

These models use an improved training schedule, Panoptic FPN head from Kirillov et al, 2019 and DeepLabV3 head from Chen et al, 2017.

Name	lr sched	train time (s/iter)	inference time (s/im)	train mem (GB)	box AP	segm AP	dp. AP GPS	dp. AP GPSm	model id	download
R_50_FPN_DL_s1x	s1x	0.392	0.070	6.7	61.1	68.3	65.6	66.7	165712097	model | metrics
R_101_FPN_DL_s1x	s1x	0.478	0.083	7.0	62.3	68.7	66.3	67.6	165712116	model | metrics

python export.py configs/densepose_rcnn_R_50_FPN_DL_s1x.yaml https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_DL_s1x/165712097/model_final_0ed407.pkl

python export.py configs/densepose_rcnn_R_101_FPN_DL_s1x.yaml https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_s1x/165712116/model_final_844d15.pkl

@InProceedings{Guler2018DensePose,
  title={DensePose: Dense Human Pose Estimation In The Wild},
  author={R\{i}za Alp G\"uler, Natalia Neverova, Iasonas Kokkinos},
  journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018}
}