Use tf.train.get_or_create_global_step (introduced in 1.2) instead of the contrib one

https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/train/get_or_create_global_step

bob/learn/tensorflow/estimators/Logits.py

@@ -117,7 +117,7 @@ class Logits(estimator.Estimator):
                 else:
                     is_trainable = is_trainable_checkpoint(self.extra_checkpoint)
 
-                # Building the training graph                
+                # Building the training graph
                 prelogits = self.architecture(data, mode=mode, trainable_variables=is_trainable)[0]
                 logits = append_logits(prelogits, n_classes)
 
@@ -128,11 +128,11 @@ class Logits(estimator.Estimator):
                     tf.contrib.framework.init_from_checkpoint(self.extra_checkpoint["checkpoint_path"],
                                                               self.extra_checkpoint["scopes"])
 
-                global_step = tf.contrib.framework.get_or_create_global_step()
+                global_step = tf.train.get_or_create_global_step()
                 train_op = self.optimizer.minimize(self.loss, global_step=global_step)
                 return tf.estimator.EstimatorSpec(mode=mode, loss=self.loss,
                                                   train_op=train_op)
-            
+
 
             # Building the training graph for PREDICTION OR VALIDATION
             prelogits = self.architecture(data, mode=mode, trainable_variables=False)[0]
@@ -162,7 +162,7 @@ class Logits(estimator.Estimator):
 
             # IF Validation
             self.loss = self.loss_op(logits, labels)
-            
+
             if self.embedding_validation:
                 predictions_op = predict_using_tensors(
                     predictions["embeddings"], labels,
@@ -286,7 +286,7 @@ class LogitsCenterLoss(estimator.Estimator):
                 else:
                     is_trainable = is_trainable_checkpoint(self.extra_checkpoint)
 
-                # Building the training graph                
+                # Building the training graph
                 prelogits = self.architecture(data, mode=mode, trainable_variables=is_trainable)[0]
                 logits = append_logits(prelogits, n_classes)
 
@@ -334,15 +334,15 @@ class LogitsCenterLoss(estimator.Estimator):
             # IF Validation
             loss_dict = mean_cross_entropy_center_loss(logits, prelogits, labels, self.n_classes,
                                                        alpha=self.alpha, factor=self.factor)
-            self.loss = loss_dict['loss']                                                       
-            
+            self.loss = loss_dict['loss']
+
             if self.embedding_validation:
                 predictions_op = predict_using_tensors(
                     predictions["embeddings"], labels, num=validation_batch_size)
                 eval_metric_ops = {"accuracy": tf.metrics.accuracy(
                     labels=labels, predictions=predictions_op)}
                 return tf.estimator.EstimatorSpec(mode=mode, loss=self.loss, eval_metric_ops=eval_metric_ops)
-            
+
             else:
                 # Add evaluation metrics (for EVAL mode)
                 eval_metric_ops = {