CPM architecture adapted in order to use the Human3.6M dataset's set of joints with the implementation of the manifold layer.

The architecture includes the manifold layer in all stages. Additionally, a fusion layer has been introduced which merge the heat-maps relative to the same joint (output of the stage and output of the manifold-layer).

0. /data/Human3.6M/Data/ has to contain all the subject directories for testing and training the model
1. models/cpm_architecture/data contains the learned model for the manifold layer
2. python/manifold/ contains all the dependencies for the manifold layer
3. models/cpm_architecture/jsonDatasets contains the files for training and testing
   • H36M_annotations.json (optional for testing)
   • H36M_annotations_testSet.json
   • H36M_masks.mat
4. models/cpm_architecture/lmdb contains the train lmdb database used for training the model (optional for testing)

sudo pip install protobuf
sudo pip install scikit-image
sudo pip install matplotlib
sudo apt-get install python-tk
sudo pip install mpldatacursor
sudo apt-get install python-yaml

###Libraries In order to have the proper visualisation the following changes are necessary.

Edit file

site-packages\mpl_toolkits\mplot3d\axes3d.py

self.pbaspect = [1.0, 1.0, 1.0]

and changing the code

xmin, xmax = self.get_xlim3d() / self.pbaspect[0]
ymin, ymax = self.get_ylim3d() / self.pbaspect[1]
zmin, zmax = self.get_zlim3d() / self.pbaspect[2]

  GLOG_logtostderr=1 build/tools/caffe train \
  -solver models/my_dir/caltech_finetune_solver_original.prototxt \
  -weights models/my_dir/bvlc_reference_caffenet.caffemodel \
  -gpu 0 2>&1 | tee results/log.txt

Append after all the commands

  --gpu=0,1

for using two GPUs.

NOTE: each GPU runs the batchsize specified in your train_val.prototxt. So if you go from 1 GPU to 2 GPU, your effective batchsize will double. e.g. if your train_val.prototxt specified a batchsize of 256, if you run 2 GPUs your effective batch size is now 512. So you need to adjust the batchsize when running multiple GPUs and/or adjust your solver params, specifically learning rate.

The current implementation uses a tree reduction strategy. e.g. if there are 4 GPUs in the system, 0:1, 2:3 will exchange gradients, then 0:2 (top of the tree) will exchange gradients, 0 will calculate updated model, 0->2, and then 0->1, 2->3.

For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced.

Current implementation has a "soft" assumption that the devices being used are homogeneous. In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used. e.g. if you combine a TitanX and a GTX980, performance will be limited by the 980. Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.

"nvidia-smi topo -m" will show you the connectivity matrix. You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard.

  caffe time -model /path/to/file/structure.prototxt -iterations 10

By default this is executed in CPU-mode. If instead a GPU-mode profiling is required, this is the command:

  caffe time -model /path/to/file/structure.prototxt -gpu 0 -iterations 10