lfengad commented on a change in pull request #4990: [TF][Relay] BatchNorm support with runtime
mean and variance calculation
URL: https://github.com/apache/incubatortvm/pull/4990#discussion_r388912080
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File path: tests/python/frontend/tensorflow/test_bn_dynamic.py
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@@ 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
+#
+# http://www.apache.org/licenses/LICENSE2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# 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:
+ sess.run([tf.global_variables_initializer()])
+ tf_out = sess.run(out, feed_dict={input_tensor:data})
+ constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def,
['sum'])
+
+ 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!

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