Spaces:
Running
Running
|  | |
|  | |
|  | |
| # Perspective Transformer Nets | |
| ## Introduction | |
| This is the TensorFlow implementation for the NIPS 2016 work ["Perspective Transformer Nets: Learning Single-View 3D Object Reconstrution without 3D Supervision"](https://papers.nips.cc/paper/6206-perspective-transformer-nets-learning-single-view-3d-object-reconstruction-without-3d-supervision.pdf) | |
| Re-implemented by Xinchen Yan, Arkanath Pathak, Jasmine Hsu, Honglak Lee | |
| Reference: [Orginal implementation in Torch](https://github.com/xcyan/nips16_PTN) | |
| ## How to run this code | |
| This implementation is ready to be run locally or ["distributed across multiple machines/tasks"](https://www.tensorflow.org/deploy/distributed). | |
| You will need to set the task number flag for each task when running in a distributed fashion. | |
| Please refer to the original paper for parameter explanations and training details. | |
| ### Installation | |
| * TensorFlow | |
| * This code requires the latest open-source TensorFlow that you will need to build manually. | |
| The [documentation](https://www.tensorflow.org/install/install_sources) provides the steps required for that. | |
| * Bazel | |
| * Follow the instructions [here](http://bazel.build/docs/install.html). | |
| * Alternately, Download bazel from | |
| [https://github.com/bazelbuild/bazel/releases](https://github.com/bazelbuild/bazel/releases) | |
| for your system configuration. | |
| * Check for the bazel version using this command: bazel version | |
| * matplotlib | |
| * Follow the instructions [here](https://matplotlib.org/users/installing.html). | |
| * You can use a package repository like pip. | |
| * scikit-image | |
| * Follow the instructions [here](http://scikit-image.org/docs/dev/install.html). | |
| * You can use a package repository like pip. | |
| * PIL | |
| * Install from [here](https://pypi.python.org/pypi/Pillow/2.2.1). | |
| ### Dataset | |
| This code requires the dataset to be in *tfrecords* format with the following features: | |
| * image | |
| * Flattened list of image (float representations) for each view point. | |
| * mask | |
| * Flattened list of image masks (float representations) for each view point. | |
| * vox | |
| * Flattened list of voxels (float representations) for the object. | |
| * This is needed for using vox loss and for prediction comparison. | |
| You can download the ShapeNet Dataset in tfrecords format from [here](https://drive.google.com/file/d/0B12XukcbU7T7OHQ4MGh6d25qQlk)<sup>*</sup>. | |
| <sup>*</sup> Disclaimer: This data is hosted personally by Arkanath Pathak for non-commercial research purposes. Please cite the [ShapeNet paper](https://arxiv.org/pdf/1512.03012.pdf) in your works when using ShapeNet for non-commercial research purposes. | |
| ### Pretraining: pretrain_rotator.py for each RNN step | |
| $ bazel run -c opt :pretrain_rotator -- --step_size={} --init_model={} | |
| Pass the init_model as the checkpoint path for the last step trained model. | |
| You'll also need to set the inp_dir flag to where your data resides. | |
| ### Training: train_ptn.py with last pretrained model. | |
| $ bazel run -c opt :train_ptn -- --init_model={} | |
| ### Example TensorBoard Visualizations | |
| To compare the visualizations make sure to set the model_name flag different for each parametric setting: | |
| This code adds summaries for each loss. For instance, these are the losses we encountered in the distributed pretraining for ShapeNet Chair Dataset with 10 workers and 16 parameter servers: | |
|  | |
| You can expect such images after fine tuning the training as "grid_vis" under **Image** summaries in TensorBoard: | |
|  | |
| Here the third and fifth columns are the predicted masks and voxels respectively, alongside their ground truth values. | |
| A similar image for when trained on all ShapeNet Categories (Voxel visualizations might be skewed): | |
|  | |