training

Training

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Contents

1. Prerequisites
2. Training
   1. Phase 1 - Generating LMDB Files
   2. Phase 2 - Training
3. Training Hardware
4. Training Examples
   1. OpenPose BODY_25B
   2. Single-Network Whole-Body Pose Estimation

Prerequisites

All the following scripts are meant to be used with our modified version of the Matlab COCO API, cloned into dataset/COCO/ as dataset/COCO/cocoapi/.

Training

This directory contains multiple scripts to generate the scripts for training and to actually train the models. It is split into 2 sections:

1. Body Training: Used to train the COCO body model.
2. Whole-Body Training: Used to train the whole-body model.
3. By mixing the scripts from points 1 and 2, any kind of training is possible (e.g., body and hands, face only, etc.). However, the only examples available are for: body (COCO), body-foot, and whole-body. Thus, only questions about these 3 types will be answered.

Depending on the kind of model you are trying to learn, use the following training steps:

1. Either download or generate the LMDB files for training:
   • Option a) Download the LMDB files ready to be used by running cd training && bash a_downloadAndUpzipLmdbs.sh. It will download them into dataset/ with names following the format dataset/lmdb_X, where X will be similar to the dataset name.
   • Option b) Generate the LMDB files by yourself:
     • COCO:
       • Option a) Download the required LMDB by running cd training; bash a_lmdbGetBody.sh.
       • Option b)
         1. Run cd training; bash a0_getData.sh to obtain the COCO images in dataset/COCO/cocoapi/images/, keypoints annotations in dataset/COCO/annotations/ and our custom COCO official toolbox in dataset/COCO/cocoapi/.
         2. Run a1_coco_jsonToNegativesJson.m in Matlab to generate the LMDB with the images with no people on them.
         3. Run a2_coco_jsonToMat.m in Matlab to convert the annotation format from json to mat in dataset/COCO/mat/.
         4. Run a3_coco_matToMasks.m in Matlab to obatin the mask images for unlabeled person. You can use 'parfor' in Matlab to speed up the code.
         5. Run a4_coco_matToRefinedJson.m to generate a json file in dataset/COCO/json/ directory. The json files contain raw informations needed for training.
         6. Run python c_generateLmdbs.py to generate the COCO and background-COCO LMDBs.
     • Foot / Face / Hand / Dome:
       • Option a) Download the required LMDBs by running cd training; bash a_lmdbGetFace.sh; bash a_lmdbGetFoot.sh; bash a_lmdbGetHands.sh; bash a_lmdbGetDome.sh.
       • Option b)
         1. Download the datasets.
         2. Run a2_coco_jsonToMat.m analogously to COCO, but with the foot/face/hand option.
         3. Run a4_coco_matToRefinedJson.m analogously to COCO, but with the foot/face/hand option.
         4. Run python c_generateLmdbs.py again to generate the (COCO+foot)/face/hand LMDB.
     • MPII:
       • Option a) Download the required LMDB by running cd training; bash a_lmdbGetMpii.sh.
       • Option b)
         1. Download Images (12.9 GB) and Annotations (12.5 MB) from the MPII dataset into dataset/MPII/.
         2. Run a0_convertMatToInitialJson.m
         3. Run python a1_generateFinalJsonAndMasks.py with sMode = 1 to generate the masks.
         4. Run python a1_generateFinalJsonAndMasks.py with sMode = 2 to generate the final JSON file.
         5. Run python c_generateLmdbs.py again to generate the MPII LMDB.
2. Train model:
   • a) Download and compile our modified Caffe:
     • OpenPose Caffe Training: github.com/CMU-Perceptual-Computing-Lab/openpose_caffe_train.
     • Compile it by running: make all -j{num_cores} && make pycaffe -j{num_cores}.
   • b) Generate the Caffe ProtoTxt and shell file for training by running python d_setLayers.py.
     • Set sCaffeFolder to the path of OpenPose Caffe Train.
     • Set sAddFoot to 1 or 0 to enable/disable combined body-foot.
     • Set sAddMpii, sAddFace and sAddHands to 1 or 0 to enable/disable boyd mpii/face/hands (if 1, then all the above must be also 1).
     • Set sAddDome to 1 or 0 to enable/disable the Dome whole-body dataset (if 1, then all the above must be also 1).
     • Flag sProbabilityOnlyBackground fixes the percentage of images that will come from the non-people dataset (called negative dataset).
     • Sett sSuperModel to 1 train the whole-body dataset, or to train a heavier but also more accurate body-foot dataset. Set it to 0 for the original OpenPose body-foot dataset.
     • Flags carVersion and sAddDistance are deprecated.
   • c) Download the pretrained VGG-19 model and unzip it into dataset/vgg/ as dataset/vgg/VGG_ILSVRC_19_layers.caffemodel and dataset/vgg/vgg_deploy.prototxt. The first 10 layers are used as backbone.
   • d) Train:
     • Go to the auto-generated training_results/pose/ directory.
     • Run bash train_pose.sh 0,1,2,3 (generated by d_setLayers.py) to start the training with the 4 GPUs (0-3).

Training Hardware

Our best resuls are obtained with 4-GPU machines and a batch size of 10, training for about 800k iterations (and picking the model with maximum accuracy among those).

Training Examples

OpenPose BODY_25B

To train an improved version of the BODY_25B OpenPose model available in OpenPose, set sAddFoot = sAddMpii = 1, and sAddDome = sSuperModel = 0. This should match the current example in training/d_setLayers.py.example.

Single-Network Whole-Body Pose Estimation

To train the model used for Single-Network Whole-Body Pose Estimation paper, set sAddFoot = sAddMpii = sAddDome = sSuperModel = 1.