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Subject [GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #4990: [TF][Relay] BatchNorm support with run-time mean and variance calculation
Date Fri, 06 Mar 2020 15:05:16 GMT
FrozenGene commented on a change in pull request #4990: [TF][Relay] BatchNorm support with
run-time mean and variance calculation
URL: https://github.com/apache/incubator-tvm/pull/4990#discussion_r388952812
 
 

 ##########
 File path: tests/python/frontend/tensorflow/test_bn_dynamic.py
 ##########
 @@ -0,0 +1,63 @@
+# 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/LICENSE-2.0
+#
+# 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='output')
+    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,
['output'])
+
+    for device in ["llvm"]:
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
+            print("Skip because %s is not enabled" % device)
+            continue
+        mod, params = relay.frontend.from_tensorflow(constant_graph,
+                                                     outputs=['output'])
+        with relay.build_config(opt_level=3):
+            graph, lib, params = relay.build(mod,
+                                             target=device,
+                                             params=params)
+        from tvm.contrib import graph_runtime
+        m = graph_runtime.create(graph, lib, ctx)
+        m.set_input(**params)
+        m.set_input('input', data)
+        m.run()
+        tvm_out = m.get_output(0)
+        tvm.testing.assert_allclose(tvm_out.asnumpy(), tf_out.astype(tvm_out.dtype), rtol=1e-3)
+
+if __name__ == "__main__":
+    test_fused_batch_norm()
 
 Review comment:
   Sorry, I think I have another one comment. How about adding some more testing data?
   
   for example:
   
   ```python
   def verify_fused_batch_norm(shape):
       ...
   
   def test_fused_batch_norm():
       verify_fused_batch_norm(shape=(1, 12, 12, 32))
       verify_fused_batch_norm(shape=(1, 24, 24, 64))
       ...
   
   if __name__ == "__main__":
       test_fused_batch_norm()
   

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