diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
index 5a17d5f7b8f0..cd4e36e28e2a 100644
--- a/python/tvm/relay/frontend/tensorflow.py
+++ b/python/tvm/relay/frontend/tensorflow.py
@@ -306,8 +306,10 @@ def _impl(inputs, attr, params):
         use_bias = len(inputs) == 3
         channel_axis = 1 if attr['data_format'] == "NCHW" else 3
 
+        # Ignore the new attributes from TF2.0, for now.
         out = AttrCvt(
             op_name=_dimension_picker('conv'),
+            ignores=['explicit_paddings'],
             transforms={
                 'kernel_shape': 'kernel_size',
                 'data_format': 'data_layout',
@@ -404,8 +406,9 @@ def _impl(inputs, attr, params):
         # NHWC
         attr['layout'] = 'NHWC'
 
+        # Ignore the new attributes from TF2.0, for now.
         return AttrCvt(op_name="resize",
-                       ignores=['Tdim'],
+                       ignores=['Tdim', 'half_pixel_centers'],
                        extras={'method': "bilinear"})(inputs, attr)
     return _impl
 
diff --git a/python/tvm/relay/testing/tf.py b/python/tvm/relay/testing/tf.py
index a56e6fe1782d..79d0d8257953 100644
--- a/python/tvm/relay/testing/tf.py
+++ b/python/tvm/relay/testing/tf.py
@@ -80,7 +80,7 @@ def AddShapesToGraphDef(session, out_node):
 
     """
 
-    graph_def = tf.graph_util.convert_variables_to_constants(
+    graph_def = tf.compat.v1.graph_util.convert_variables_to_constants(
         session,
         session.graph.as_graph_def(add_shapes=True),
         [out_node],
@@ -112,13 +112,13 @@ def load(self, label_lookup_path, uid_lookup_path):
             dict from integer node ID to human-readable string.
 
         """
-        if not tf.gfile.Exists(uid_lookup_path):
+        if not tf.compat.v1.io.gfile.exists(uid_lookup_path):
             tf.logging.fatal('File does not exist %s', uid_lookup_path)
-        if not tf.gfile.Exists(label_lookup_path):
+        if not tf.compat.v1.io.gfile.exists(label_lookup_path):
             tf.logging.fatal('File does not exist %s', label_lookup_path)
 
         # Loads mapping from string UID to human-readable string
-        proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
+        proto_as_ascii_lines = tf.compat.v1.gfile.GFile(uid_lookup_path).readlines()
         uid_to_human = {}
         p = re.compile(r'[n\d]*[ \S,]*')
         for line in proto_as_ascii_lines:
@@ -129,7 +129,7 @@ def load(self, label_lookup_path, uid_lookup_path):
 
         # Loads mapping from string UID to integer node ID.
         node_id_to_uid = {}
-        proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
+        proto_as_ascii = tf.compat.v1.gfile.GFile(label_lookup_path).readlines()
         for line in proto_as_ascii:
             if line.startswith('  target_class:'):
                 target_class = int(line.split(': ')[1])
@@ -209,7 +209,7 @@ def get_workload(model_path, model_sub_path=None):
         path_model = download_testdata(model_url, model_path, module='tf')
 
     # Creates graph from saved graph_def.pb.
-    with tf.gfile.FastGFile(path_model, 'rb') as f:
+    with tf.compat.v1.gfile.FastGFile(path_model, 'rb') as f:
         graph_def = tf.GraphDef()
         graph_def.ParseFromString(f.read())
         graph = tf.import_graph_def(graph_def, name='')
@@ -299,7 +299,7 @@ def _create_ptb_vocabulary(data_dir):
     file_name = 'ptb.train.txt'
     def _read_words(filename):
         """Read the data for creating vocabulary"""
-        with tf.gfile.GFile(filename, "r") as f:
+        with tf.compat.v1.gfile.GFile(filename, "r") as f:
             return f.read().encode("utf-8").decode("utf-8").replace("\n", "<eos>").split()
 
     def _build_vocab(filename):
diff --git a/tutorials/frontend/from_tensorflow.py b/tutorials/frontend/from_tensorflow.py
index 34865f021230..2c109cbaf907 100644
--- a/tutorials/frontend/from_tensorflow.py
+++ b/tutorials/frontend/from_tensorflow.py
@@ -89,14 +89,14 @@
 # ------------
 # Creates tensorflow graph definition from protobuf file.
 
-with tf.gfile.FastGFile(model_path, 'rb') as f:
-    graph_def = tf.GraphDef()
+with tf.compat.v1.gfile.GFile(model_path, 'rb') as f:
+    graph_def = tf.compat.v1.GraphDef()
     graph_def.ParseFromString(f.read())
     graph = tf.import_graph_def(graph_def, name='')
     # Call the utility to import the graph definition into default graph.
     graph_def = tf_testing.ProcessGraphDefParam(graph_def)
     # Add shapes to the graph.
-    with tf.Session() as sess:
+    with tf.compat.v1.Session() as sess:
         graph_def = tf_testing.AddShapesToGraphDef(sess, 'softmax')
 
 ######################################################################
@@ -187,8 +187,8 @@
 def create_graph():
     """Creates a graph from saved GraphDef file and returns a saver."""
     # Creates graph from saved graph_def.pb.
-    with tf.gfile.FastGFile(model_path, 'rb') as f:
-        graph_def = tf.GraphDef()
+    with tf.compat.v1.gfile.GFile(model_path, 'rb') as f:
+        graph_def = tf.compat.v1.GraphDef()
         graph_def.ParseFromString(f.read())
         graph = tf.import_graph_def(graph_def, name='')
         # Call the utility to import the graph definition into default graph.
@@ -206,14 +206,14 @@ def run_inference_on_image(image):
     -------
         Nothing
     """
-    if not tf.gfile.Exists(image):
+    if not tf.compat.v1.io.gfile.exists(image):
         tf.logging.fatal('File does not exist %s', image)
-    image_data = tf.gfile.FastGFile(image, 'rb').read()
+    image_data = tf.compat.v1.gfile.GFile(image, 'rb').read()
 
     # Creates graph from saved GraphDef.
     create_graph()
 
-    with tf.Session() as sess:
+    with tf.compat.v1.Session() as sess:
         softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
         predictions = sess.run(softmax_tensor,
                                {'DecodeJpeg/contents:0': image_data})