Fix #447: Changed format for exporting models to MetaGraphDef for Tensorflow

example/tensorflow/code_template/tensorflow_sample.py

@@ -11,9 +11,9 @@
 # Read the protobuf text and build a tf.GraphDef
 with open(model_file_name, 'r') as model_file:
     model_protobuf = text_format.Parse(model_file.read(),
-                                       tf.GraphDef())
+                                       tf.MetaGraphDef())
 
 # Import the GraphDef built above into the default graph
-tf.import_graph_def(model_protobuf)
+tf.train.import_meta_graph(model_protobuf)
 
 # You can now add operations on top of the imported graph

ide/tasks.py

@@ -311,19 +311,22 @@ def isProcessPossible(layerId):
         json_str = json_str.strip("'<>() ").replace('\'', '\"')
         lrnLayer = imp.load_source('LRN', BASE_DIR + '/keras_app/custom_layers/lrn.py')
 
+        # clear clutter from previous graph built by keras to avoid duplicates
+        K.clear_session()
+
         model = model_from_json(json_str, {'LRN': lrnLayer.LRN})
 
-        sess = K.get_session()
-        tf.train.write_graph(sess.graph.as_graph_def(add_shapes=True), output_fld,
-                             output_file + '.pbtxt', as_text=True)
+        tf.train.export_meta_graph(
+            os.path.join(output_fld, output_file + '.meta'),
+            as_text=True)
 
         Channel(reply_channel).send({
             'text': json.dumps({
                 'result': 'success',
                 'action': 'ExportNet',
                 'id': 'randomId',
-                'name': randomId + '.pbtxt',
-                'url': '/media/' + randomId + '.pbtxt',
+                'name': randomId + '.meta',
+                'url': '/media/' + randomId + '.meta',
                 'customLayers': custom_layers_response
             })
         })

tensorflow_app/views/import_graphdef.py

@@ -1,7 +1,6 @@
 import numpy as np
 import tensorflow as tf
 from google.protobuf import text_format
-from tensorflow.core.framework import graph_pb2
 from django.views.decorators.csrf import csrf_exempt
 from django.http import JsonResponse
 import math
@@ -125,6 +124,46 @@ def get_padding(node, layer, session, input_layer_name, input_layer_dim):
         return int(pad_h), int(pad_w)
 
 
+def get_graph_def_from(model_protobuf):
+    """
+    Parses and returns a GraphDef from input protobuf.
+
+    Args:
+        model_protobuf: a binary or text protobuf message.
+
+    Returns:
+        a tf.GraphDef object with the GraphDef from model_protobuf
+
+    Raises:
+        ValueError: if a GraphDef cannot be parsed from model_protobuf
+    """
+    try:
+        meta_graph_def = text_format.Merge(model_protobuf, tf.MetaGraphDef())
+        graph_def = meta_graph_def.graph_def
+        return graph_def
+    except (text_format.ParseError, UnicodeDecodeError):
+        # not a valid text metagraphdef
+        pass
+    try:
+        graph_def = text_format.Merge(model_protobuf, tf.GraphDef())
+        return graph_def
+    except (text_format.ParseError, UnicodeDecodeError):
+        pass
+    try:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(model_protobuf)
+        return graph_def
+    except Exception:
+        pass
+    try:
+        meta_graph_def = tf.MetaGraphDef()
+        meta_graph_def.ParseFromString(model_protobuf)
+        return meta_graph_def.graph_def
+    except Exception:
+        pass
+    raise ValueError('Invalid model protobuf')
+
+
 @csrf_exempt
 def import_graph_def(request):
     if request.method == 'POST':
@@ -151,15 +190,14 @@ def import_graph_def(request):
             return JsonResponse({'result': 'error', 'error': 'No GraphDef model found'})
 
         tf.reset_default_graph()
-        graph_def = graph_pb2.GraphDef()
         d = {}
         order = []
         input_layer_name = ''
         input_layer_dim = []
 
         try:
-            text_format.Merge(config, graph_def)
-        except Exception:
+            graph_def = get_graph_def_from(config)
+        except ValueError:
             return JsonResponse({'result': 'error', 'error': 'Invalid GraphDef'})
 
         tf.import_graph_def(graph_def, name='')

tutorials/tensorflow_prototxt_usage.md

@@ -6,34 +6,34 @@ In order to export a Tensorflow model from Fabrik:
 <img src="https://raw.githubusercontent.com/Cloud-CV/Fabrik/master/tutorials/exportbutton.png">
 
 2. A drop-down list should appear. Select Tensorflow.
-    * This should download a pbtxt file to your computer.  
+    * This should download a ```.meta``` file to your computer.  
     <img src="https://raw.githubusercontent.com/Cloud-CV/Fabrik/master/tutorials/export_tensorflow.png">
 
-3. Rename the file to ```model.pbtxt```.
+3. Rename the file to ```model.meta```.
 
-4. Load the model from the ProtoBuf file using the following code:
+4. Load the model from ```model.meta``` using the following code:
 
     ```
     import tensorflow as tf
     from google.protobuf import text_format
 
     # read the graphdef from the model file
-    with open('model.pbtxt', 'r') as model_file:
+    with open('model.meta', 'r') as model_file:
 	    model_protobuf = text_format(model_file.read(),
-		    	                 tf.Graphdef())
+		    	                 tf.MetaGraphDef())
 
     # import the graphdef into the default graph
-    tf.import_graph_def(model_protobuf)
+    tf.train.import_meta_graph(model_protobuf)
     ```
 
 ### Code template
 
-[The code template](../example/tensorflow/code_template/tensorflow_sample.py) loads the model from a pbtxt file into the default graph. Additional operations like layers and optimizers can be then built onto the graph as required.
+[The code template](../example/tensorflow/code_template/tensorflow_sample.py) loads the model from a ```.meta``` file into the default graph. Additional operations like layers and optimizers can be then built onto the graph as required.
 
 To run the code, run:
 
 ```
-python tensorflow_sample.py model.pbtxt
+python tensorflow_sample.py model.meta
 ```
 
-Replace ```model.pbtxt``` with the model file that you want to use.
+Replace ```model.meta``` with the model file that you want to use.