Caffe2 Utility Module

This module contains various methods that are generally used for organizing and training and inference.

Requirements

1. Python 2.7
2. Ubuntu 16.04
3. Pre-installed caffe2.

Usage

This module has to be downloaded before using the utility functions. Note: Still working on developing this package, and most of the code is taken from tutorials of caffe2.

Below are the utility functions supported currently:

write_db(db_type, db_name, features, labels)
convertRawDataToNCHWFormat(raw, num_channels, width, height)
AddInputLayer(model, batch_size, db, db_type)
AddAccuracy(model, softmax, label)
AddTrainingParameters(model, softmax, label)
AddBookKeepingOperators(model)
SaveNet(test_model, prefix, tensor_shape_chw)

Example:

import caffe2_utils as u

# Dataset conversions.
u.convertRawDataToNCHWFormat(<numpy_raw_features>, num_channels, width, height)
u.write_db(<db_format>, <db_name>, <numpy_features_nchw>, <numpy_labels>)

...

TODO: Need to add various features into this utility package.