Updates

• Patched the train.py script to accept tensors as input
• Crafted a script that generates LFW pairs for validation
• Implemented a validation mechanism using embeddings

bob/learn/tensorflow/script/lfw_db_to_tfrecords.py

+#!/usr/bin/env python
+
+"""Script that converts bob.db.lfw database to TF records
+
+Usage:
+  %(prog)s <data-path> <output-file> [--extension=<arg> --protocol=<arg> --verbose]
+  %(prog)s --help
+  %(prog)s --version
+
+Options:
+  -h --help  show this help message and exit
+  <data-path>          Path that contains the features
+  --extension=<arg>    Default feature extension   [default: .hdf5]
+  --protocol=<arg>     One of the LFW protocols    [default: view1]
+
+
+The possible protocol options are the following:
+  'view1', 'fold1', 'fold2', 'fold3', 'fold4', 'fold5', 'fold6', 'fold7', 'fold8', 'fold9', 'fold10'
+
+More details about our interface to LFW database can be found in
+https://www.idiap.ch/software/bob/docs/bob/bob.db.lfw/master/index.html.
+
+
+"""
+
+import tensorflow as tf
+from bob.io.base import create_directories_safe
+from bob.bio.base.utils import load, read_config_file
+from bob.core.log import setup, set_verbosity_level
+import bob.db.lfw
+import os
+import bob.io.image
+import bob.io.base
+
+logger = setup(__name__)
+
+
+def _bytes_feature(value):
+  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+
+def _int64_feature(value):
+  return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
+
+def file_to_label(client_ids, f):
+    return client_ids[str(f.client_id)]
+
+def get_pairs(all_pairs, match=True):
+
+    pairs = []
+    for p in all_pairs:
+        if p.is_match == match:
+            pairs.append(p.enroll_file)
+            pairs.append(p.probe_file)
+
+    return pairs
+
+def main(argv=None):
+    from docopt import docopt
+    args = docopt(__doc__, version='')
+
+    data_path   = args['<data-path>']
+    output_file = args['<output-file>']
+    extension   = args['--extension']
+    protocol    = args['--protocol']
+    
+    #Setting the reader
+    reader = bob.io.base.load
+
+    # Sets-up logging
+    if args['--verbose']:
+        verbosity = 2
+        set_verbosity_level(logger, verbosity)
+
+    # Loading LFW models
+    database = bob.db.lfw.Database()
+    all_pairs = get_pairs(database.pairs(protocol=protocol), match=True)
+    client_ids = list(set([f.client_id for f in all_pairs]))
+    client_ids = dict(zip(client_ids, range(len(client_ids))))
+    
+    create_directories_safe(os.path.dirname(output_file))
+    
+    n_files = len(all_pairs)
+    with tf.python_io.TFRecordWriter(output_file) as writer:
+      for i, f in enumerate(all_pairs):
+        logger.info('Processing file %d out of %d', i + 1, n_files)
+
+        path = f.make_path(data_path, extension)
+        data = reader(path).astype('float32').tostring()
+
+        feature = {'train/data': _bytes_feature(data),
+                   'train/label': _int64_feature(file_to_label(client_ids, f))}
+
+        example = tf.train.Example(features=tf.train.Features(feature=feature))
+        writer.write(example.SerializeToString())
+
+
+if __name__ == '__main__':
+  main()