Authors: David Eigen, Christian Puhrsch and Rob Fergus
Email: deigen@cs.nyu.edu, cpuhrsch@nyu.edu, fergus@cs.nyu.edu
The demo loads the depth prediction network, compiles a theano function for inference, and infers depth for a single image. To run:
THEANO_FLAGS=device=gpu0 python demo_depth.py
This should create a file called "demo_nyud_depth_prediction.png" with the predicted depth for the input "demo_nyud_rgb.jpg". (Substitute the gpu you want to run on for gpu0).
This tree contains code for depth prediction network inference. While there is some code relating to training, much of the training code including most data processing is not provided here. We may release this in the future, however.
While developing this project, we made a few modifications in theano not currently part of the main codeline. While the above instructions should work for inference on a current unmodified theano build, it may take up more GPU memory than needed due to use of test values for shape information. The git patch file "theano_test_value_size.patch" is also included and might be used to enable this feature on your own tree.