Estimator that loads variables from another model

I made it :-)

Could you please have a look?

Thanks

bob/learn/tensorflow/estimators/Siamese.py

@@ -13,7 +13,7 @@ import time
 from tensorflow.python.estimator import estimator
 from bob.learn.tensorflow.utils import predict_using_tensors
 #from bob.learn.tensorflow.loss import mean_cross_entropy_center_loss
-from . import check_features
+from . import check_features, is_trainable_checkpoint
 
 import logging
 logger = logging.getLogger("bob.learn")
@@ -76,6 +76,8 @@ class Siamese(estimator.Estimator):
                  loss_op=None,
                  model_dir="",
                  validation_batch_size=None,
+                 params=None,
+                 extra_checkpoint=None                 
               ):
 
         self.architecture = architecture
@@ -83,6 +85,7 @@ class Siamese(estimator.Estimator):
         self.n_classes=n_classes
         self.loss_op=loss_op
         self.loss = None
+        self.extra_checkpoint = extra_checkpoint        
 
         if self.architecture is None:
             raise ValueError("Please specify a function to build the architecture !!")
@@ -97,22 +100,38 @@ class Siamese(estimator.Estimator):
             raise ValueError("Number of classes must be greated than 0")
 
         def _model_fn(features, labels, mode, params, config):
-            
+
             if mode == tf.estimator.ModeKeys.TRAIN:
+                # Building one graph, by default everything is trainable
+                if  self.extra_checkpoint is None:
+                    is_trainable = True
+                else:
+                    is_trainable = is_trainable_checkpoint(self.extra_checkpoint)
 
                 # The input function needs to have dictionary pair with the `left` and `right` keys
                 if not 'left' in features.keys() or not 'right' in features.keys():
                     raise ValueError("The input function needs to contain a dictionary with the keys `left` and `right` ")
-            
+
+
                 # Building one graph
-                prelogits_left = self.architecture(features['left'])[0]
-                prelogits_right = self.architecture(features['right'], reuse=True)[0]
+                prelogits_left = self.architecture(features['left'], is_trainable=is_trainable)[0]
+                prelogits_right = self.architecture(features['right'], reuse=True, is_trainable=is_trainable)[0]
+
+                if self.extra_checkpoint is not None:
+                    import ipdb; ipdb.set_trace();
+                    tf.contrib.framework.init_from_checkpoint(self.extra_checkpoint["checkpoint_path"],
+                                                              dict({"Dummy/": "Dummy/"}))
 
+                    tf.contrib.framework.init_from_checkpoint(self.extra_checkpoint["checkpoint_path"],
+                                                              dict({"Dummy/": "Dummy1/"}))
+
+    
                 # Compute Loss (for both TRAIN and EVAL modes)
                 self.loss = self.loss_op(prelogits_left, prelogits_right, labels)
                 # Configure the Training Op (for TRAIN mode)
                 global_step = tf.contrib.framework.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)
 
@@ -135,5 +154,6 @@ class Siamese(estimator.Estimator):
 
         super(Siamese, self).__init__(model_fn=_model_fn,
                                      model_dir=model_dir,
+                                     params=params,
                                      config=config)