tvm-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From GitBox <>
Subject [GitHub] [incubator-tvm] lfengad commented on a change in pull request #4990: [TF][Relay] BatchNorm support with run-time mean and variance calculation
Date Fri, 06 Mar 2020 13:49:54 GMT
lfengad commented on a change in pull request #4990: [TF][Relay] BatchNorm support with run-time
mean and variance calculation

 File path: tests/python/frontend/tensorflow/
 @@ -0,0 +1,61 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+BatchNorm without given mean and variance given testcases
+This is a test script to test fused_batch_norm operators
+in TensorFlow frontend when mean and variance are not given.
+import tvm
+import numpy as np
+import tensorflow as tf
+from tvm import relay
+from tensorflow.python.framework import graph_util
+def test_fused_batch_norm():
+    g = tf.Graph()
+    with g.as_default():
+        input_tensor = tf.placeholder(tf.float32, shape=(1, 12, 12, 32), name='input')
+        alpha = tf.constant(np.random.rand(32,), dtype=tf.float32, name='alpha')
+        beta = tf.constant(np.random.rand(32,), dtype=tf.float32, name='beta')
+        bn = tf.nn.fused_batch_norm(x=input_tensor, offset=beta, scale=alpha, name='bn')
+        out = tf.identity(bn[0], name='sum')
+    data = np.random.rand(1, 12, 12, 32)
+    with tf.Session(graph=out.graph) as sess:
+        tf_out =, feed_dict={input_tensor:data})
+        constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def,
+    layout = None
+    target = 'llvm'
+    ctx = tvm.cpu(0)
 Review comment:
   I have already modified the related code according to your suggestions, as in the newest
commit. Thank you so much for your help!

This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:

With regards,
Apache Git Services

View raw message