Updates

• config file from train.py accepts tensors as input
• TFRecord, add keyword argument to flag the shuffling mechanism
• Added InceptionResnetV1 in our basket of networks
• Added an option to sum the regularization losses in the CrossEntropy loss
• Implemented validation with embeddings (officially appending to the tensorboard)
• Created method Trainer.load_variables_from_external_model that load variables from another model in the current graph
• Implemented MeanSoftMaxLossCenterLoss (http://ydwen.github.io/papers/WenECCV16.pdf)
• Patched InceptionV2 (Added the flag trainable for each variable in the model)
• Implemented TFRecordImage (read, data augmentation, scaling and casting to float32)
• Added data type as input in the TFRecord datashufflers

bob/learn/tensorflow/datashuffler/TFRecord.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
 # @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# @date: Wed 11 May 2016 09:39:36 CEST 
 
 import numpy
 import tensorflow as tf
@@ -11,24 +10,26 @@ from bob.learn.tensorflow.datashuffler.Normalizer import Linear
 
 
 class TFRecord(object):
-
     """
-    Datashuffler that wraps tfrecords    
+    Generic datashuffler that wraps the batching using tfrecords.
+    
+    **Parameters**
+
+      filename_queue: Tensorflow producer
+      input_shape: Shape of the input in the tfrecord
+      input_dtype: Type of the raw data saved in the tf record
+      batch_size: Size of the batch
+      seed: Seed
+      prefetch_capacity: Capacity of the bucket for prefetching 
+      prefetch_threads: Number of threads in the prefetching
     """
-
     def __init__(self,filename_queue,
                          input_shape=[None, 28, 28, 1],
-                         output_shape=[None, 28, 28, 1],
                          input_dtype=tf.float32,
                          batch_size=32,
                          seed=10,
                          prefetch_capacity=50,
-                         prefetch_threads=5, 
-                         shuffle=True,
-                         normalization=False,
-                         random_flip=True,
-                         random_crop=True
-                         ):
+                         prefetch_threads=5):
 
         # Setting the seed for the pseudo random number generator
         self.seed = seed
@@ -42,7 +43,6 @@ class TFRecord(object):
         # Preparing the inputs
         self.filename_queue = filename_queue
         self.input_shape = tuple(input_shape)
-        self.output_shape = output_shape
 
         # Prefetch variables
         self.prefetch = True
@@ -51,10 +51,6 @@ class TFRecord(object):
         
         self.data_ph = None
         self.label_ph = None
-        
-        self.shuffle = shuffle
-        self.normalization = normalization
-        self.random_crop = random_crop
 
     def __call__(self, element, from_queue=False):
         """
@@ -91,33 +87,17 @@ class TFRecord(object):
         
         # Convert the image data from string back to the numbers
         image = tf.decode_raw(features['train/data'], self.input_dtype)
-        #image = tf.decode_raw(features['train/data'], tf.uint8)
         
         # Cast label data into int32
         label = tf.cast(features['train/label'], tf.int64)
         
         # Reshape image data into the original shape
         image = tf.reshape(image, self.input_shape[1:])
-
-        # Casting to float32
-        image = tf.cast(image, tf.float32)
         
-        if self.random_crop:
-            image = tf.image.resize_image_with_crop_or_pad(image, self.output_shape[1], self.output_shape[2])
-
-        # normalizing data
-        if self.normalization:
-            image = tf.image.per_image_standardization(image)
-
-        image.set_shape(tuple(self.output_shape[1:]))
-
-        if self.shuffle:
-            data_ph, label_ph = tf.train.shuffle_batch([image, label], batch_size=self.batch_size,
-                             capacity=self.prefetch_capacity, num_threads=self.prefetch_threads,
-                             min_after_dequeue=1, name="shuffle_batch")
-        else:
-            data_ph, label_ph = tf.train.batch([image, label], batch_size=self.batch_size,
-                             capacity=self.prefetch_capacity, num_threads=self.prefetch_threads, name="batch")
+        
+        data_ph, label_ph = tf.train.shuffle_batch([image, label], batch_size=self.batch_size,
+                         capacity=self.prefetch_capacity, num_threads=self.prefetch_threads,
+                         min_after_dequeue=1, name="shuffle_batch")
         
         
         self.data_ph = data_ph
@@ -125,17 +105,5 @@ class TFRecord(object):
 
 
     def get_batch(self):
-        """
-        Shuffle the Memory dataset and get a random batch.
-
-        ** Returns **
-
-        data:
-          Selected samples
-
-        labels:
-          Correspondent labels
-        """
-
         pass