[FRONTEND][TENSORFLOW] Helper function to add shapes into the graph. …

…Use tmp folder for model files and clean it.

nnvm/python/nnvm/testing/tf.py

@@ -8,6 +8,7 @@
 import os.path
 import collections
 import numpy as np
+from tvm.contrib import util
 
 # Tensorflow imports
 import tensorflow as tf
@@ -43,6 +44,31 @@ def ProcessGraphDefParam(graph_def):
             raise TypeError('graph_def must be a GraphDef proto.')
     return graph_def
 
+
+def AddShapesToGraphDef(out_node):
+    """ Add shapes attribute to nodes of the graph.
+        Input graph here is the default graph in context.
+
+    Parameters
+    ----------
+    out_node: String
+        Final output node of the graph.
+
+    Returns
+    -------
+    graph_def : Obj
+        tensorflow graph definition with shapes attribute added to nodes.
+
+    """
+
+    with tf.Session() as sess:
+        graph_def = tf.graph_util.convert_variables_to_constants(
+            sess,
+            sess.graph.as_graph_def(add_shapes=True),
+            [out_node],
+            )
+        return graph_def
+
 class NodeLookup(object):
     """Converts integer node ID's to human readable labels."""
 
@@ -128,13 +154,18 @@ def get_workload(model_path):
     model_url = os.path.join(repo_base, model_path)
 
     from mxnet.gluon.utils import download
-    download(model_url, model_name)
+
+    temp = util.tempdir()
+    path_model = temp.relpath(model_name)
+
+    download(model_url, path_model)
 
     # Creates graph from saved graph_def.pb.
-    with tf.gfile.FastGFile(os.path.join("./", model_name), 'rb') as f:
+    with tf.gfile.FastGFile(path_model, 'rb') as f:
         graph_def = tf.GraphDef()
         graph_def.ParseFromString(f.read())
         graph = tf.import_graph_def(graph_def, name='')
+        temp.remove()
         return graph_def
 
 #######################################################################

nnvm/tests/python/frontend/tensorflow/test_forward.py

@@ -99,11 +99,8 @@ def compare_tf_with_tvm(in_data, in_name, out_name, init_global_variables=False)
     with tf.Session() as sess:
         if init_global_variables:
             sess.run(variables.global_variables_initializer())
-        final_graph_def = tf.graph_util.convert_variables_to_constants(
-            sess,
-            sess.graph.as_graph_def(add_shapes=True),
-            [out_node],
-            )
+
+        final_graph_def = nnvm.testing.tf.AddShapesToGraphDef(out_node)
 
         tf_output = run_tf_graph(sess, in_data, in_name, out_name)
         tvm_output = run_tvm_graph(final_graph_def, in_data,

tutorials/nnvm/from_tensorflow.py

@@ -64,7 +64,6 @@
 download(map_proto_url, map_proto)
 download(lable_map_url, lable_map)
 
-
 ######################################################################
 # Import model
 # ------------
@@ -76,7 +75,8 @@
     graph = tf.import_graph_def(graph_def, name='')
     # Call the utility to import the graph definition into default graph.
     graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
-
+    # Add shapes to the graph.
+    graph_def = nnvm.testing.tf.AddShapesToGraphDef('softmax')
 
 ######################################################################
 # Decode image