Add BN support with run-time mean and variance calculation

python/tvm/relay/frontend/tensorflow.py

@@ -887,7 +887,14 @@ def _impl(inputs, attr, params):
         if 'U' in attr:
             need_cast = True
             inputs[0] = _op.cast(inputs[0], dtype=attr['U'].name)
-
+        # Check if mean and variance are empty
+        # If so, replace them with Mean and Variance Ops
+        # For run-time calculation
+        moving_mean_shape = [int(n) for n in inputs[3].type_annotation.shape]
+        moving_variance_shape = [int(n) for n in inputs[4].type_annotation.shape]
+        if (moving_mean_shape[0] == 0 and moving_variance_shape[0] == 0):
+            inputs[3] = _op.mean(inputs[0], axis=axis, keepdims=False, exclude=True)
+            inputs[4] = _op.variance(inputs[0], axis=axis, keepdims=False, exclude=True)
         out = AttrCvt(op_name='batch_norm',
                       transforms={'scale_after_normalization':'scale',
                                   'variance_epsilon':'epsilon'},

tests/python/frontend/tensorflow/test_bn_trainingmod.py

@@ -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/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 article 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_fusedbatchnorm():
+    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)
+    mod, params = relay.frontend.from_tensorflow(constant_graph, layout=layout, outputs=['sum'])
+    with relay.build_config(opt_level=3):
+        graph, lib, params = relay.build(mod,
+                                     target=target,
+                                     target_host = target,
+                                     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_fusedbatchnorm()