Removed code duplication between TFRecord and TFRecordImage.

This merge request removes all the redundant code from TFRecordImage.

In it's current state, that class doesn't make use of inheritance at all.

bob/learn/tensorflow/datashuffler/TFRecord.py

@@ -11,7 +11,7 @@ import numpy
 class TFRecord(object):
     """
     Generic datashuffler that wraps the batching using tfrecords.
-    
+
     **Parameters**
 
       filename_queue: Tensorflow producer
@@ -19,7 +19,7 @@ class TFRecord(object):
       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_capacity: Capacity of the bucket for prefetching
       prefetch_threads: Number of threads in the prefetching
     """
     def __init__(self,filename_queue,
@@ -36,7 +36,7 @@ class TFRecord(object):
 
         self.input_dtype = input_dtype
 
-        # TODO: Check if the bacth size is higher than the input data
+        # TODO: Check if the batch size is higher than the input data
         self.batch_size = batch_size
 
         # Preparing the inputs
@@ -47,14 +47,14 @@ class TFRecord(object):
         self.prefetch = True
         self.prefetch_capacity = prefetch_capacity
         self.prefetch_threads = prefetch_threads
-        
+
         self.data_ph = None
         self.label_ph = None
 
+
     def __call__(self, element, from_queue=False):
         """
         Return the necessary placeholder
-        
         """
 
         if not element in ["data", "label"]:
@@ -70,35 +70,47 @@ class TFRecord(object):
             return self.label_ph
 
 
-    def create_placeholders(self):
+    def __load_features(self):
+        """
+        Load features from queue
+        """
 
         feature = {'train/data': tf.FixedLenFeature([], tf.string),
                    'train/label': tf.FixedLenFeature([], tf.int64)}
 
         # Define a reader and read the next record
         reader = tf.TFRecordReader()
-        
+
         _, serialized_example = reader.read(self.filename_queue)
-        
-        
+
+
         # Decode the record read by the reader
         features = tf.parse_single_example(serialized_example, features=feature)
-        
+
         # Convert the image data from string back to the numbers
         image = tf.decode_raw(features['train/data'], self.input_dtype)
-        
+
         # 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:])
-        
-        
+
+        return image, label
+
+
+    def create_placeholders(self):
+        """
+        Create placeholder data from features.
+        """
+        image, label = self.__load_features()
+
+
         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
         self.label_ph = label_ph