rename fixed_batch_size to drop_remainder

bob/learn/tensorflow/dataset/tfrecords.py

@@ -339,7 +339,7 @@ def shuffle_data_and_labels_image_augmentation(
     per_image_normalization=True,
     random_gamma=False,
     random_crop=False,
-    fixed_batch_size=False,
+    drop_remainder=False,
 ):
     """
   Dump random batches from a list of tf-record files and applies some image augmentation
@@ -388,7 +388,7 @@ def shuffle_data_and_labels_image_augmentation(
      per_image_normalization:
          Linearly scales image to have zero mean and unit norm.
 
-     fixed_batch_size:
+     drop_remainder:
          If True, the last remaining batch that has smaller size than `batch_size' will be dropped.
 
   """
@@ -410,10 +410,7 @@ def shuffle_data_and_labels_image_augmentation(
     )
 
     dataset = dataset.shuffle(buffer_size)
-    if fixed_batch_size:
-        dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
-    else:
-        dataset = dataset.batch(batch_size)
+    dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
     dataset = dataset.repeat(epochs)
 
     dataset = dataset.map(lambda d, l, k: ({"data": d, "key": k}, l))
@@ -516,7 +513,7 @@ def batch_data_and_labels_image_augmentation(
     per_image_normalization=True,
     random_gamma=False,
     random_crop=False,
-    fixed_batch_size=False,
+    drop_remainder=False,
 ):
     """
   Dump in order batches from a list of tf-record files
@@ -538,7 +535,7 @@ def batch_data_and_labels_image_augmentation(
      epochs:
          Number of epochs to be batched
 
-     fixed_batch_size:
+     drop_remainder:
          If True, the last remaining batch that has smaller size than `batch_size' will be dropped.
 
   """
@@ -559,10 +556,7 @@ def batch_data_and_labels_image_augmentation(
         random_crop=random_crop,
     )
 
-    if fixed_batch_size:
-        dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
-    else:
-        dataset = dataset.batch(batch_size)
+    dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
     dataset = dataset.repeat(epochs)
 
     data, labels, key = dataset.make_one_shot_iterator().get_next()