Add/fix copyright and fix format for the test script and other files.

tensorflow/contrib/tensorrt/test/test_tftrt.py

@@ -1,53 +1,70 @@
-#  Script to test TF-TensorRT integration
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 #
+# Licensed 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.
+# ==============================================================================
+"""Script to test TF-TensorRT integration."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+
 import tensorflow as tf
 import tensorflow.contrib.tensorrt as trt
 import numpy as np
 
-def getSimpleGraphDef():
-    ''' 
-    Create a simple graph and return its graph_def
-    '''
-    g=tf.Graph()
-    with g.as_default():
-        A=tf.placeholder(dtype=tf.float32,shape=(None,24,24,2),name="input")
-        e=tf.constant([ [[[ 1., 0.5, 4., 6., 0.5, 1. ],
-                        [ 1., 0.5, 1., 1., 0.5, 1. ]]] ],
-                      name="weights",dtype=tf.float32)
-        conv=tf.nn.conv2d(input=A,filter=e,strides=[1,2,2,1],padding="SAME",name="conv")
-        b=tf.constant([  4., 1.5, 2., 3., 5., 7. ],
-                      name="bias",dtype=tf.float32)
-        t=tf.nn.bias_add(conv,b,name="biasAdd")
-        relu=tf.nn.relu(t,"relu")
-        idty=tf.identity(relu,"ID")
-        v=tf.nn.max_pool(idty,[1,2,2,1],[1,2,2,1],"VALID",name="max_pool")
-        out = tf.squeeze(v,name="output")
-    return g.as_graph_def()
 
-def runGraph(gdef,dumm_inp):
-    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.50)
-    tf.reset_default_graph()
-    g=tf.Graph()
-    with g.as_default():
-        inp,out=tf.import_graph_def(graph_def=gdef,
-                            return_elements=["input","output"])
-        inp=inp.outputs[0]
-        out=out.outputs[0]
-    with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options),
-                    graph=g) as sess:
-        val=sess.run(out,{inp:dumm_inp})
-    return val
-if "__main__" in  __name__:
-    inpDims=(100,24,24,2)
-    dummy_input=np.random.random_sample(inpDims)
-    gdef=getSimpleGraphDef() #get graphdef
-    trt_graph=trt.CreateInferenceGraph(gdef,["output"],inpDims[0]) # get optimized graph
-    o1=runGraph(gdef,dummy_input)
-    o2=runGraph(trt_graph,dummy_input)
-    assert(np.array_equal(o1,o2))
-    
+def getSimpleGraphDef():
+  """Create a simple graph and return its graph_def"""
+  g = tf.Graph()
+  with g.as_default():
+    A = tf.placeholder(dtype=tf.float32, shape=(None, 24, 24, 2), name="input")
+    e = tf.constant(
+        [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]],
+        name="weights",
+        dtype=tf.float32)
+    conv = tf.nn.conv2d(
+        input=A, filter=e, strides=[1, 2, 2, 1], padding="SAME", name="conv")
+    b = tf.constant([4., 1.5, 2., 3., 5., 7.], name="bias", dtype=tf.float32)
+    t = tf.nn.bias_add(conv, b, name="biasAdd")
+    relu = tf.nn.relu(t, "relu")
+    idty = tf.identity(relu, "ID")
+    v = tf.nn.max_pool(
+        idty, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool")
+    out = tf.squeeze(v, name="output")
+  return g.as_graph_def()
+
+
+def runGraph(gdef, dumm_inp):
+  gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.50)
+  tf.reset_default_graph()
+  g = tf.Graph()
+  with g.as_default():
+    inp, out = tf.import_graph_def(
+        graph_def=gdef, return_elements=["input", "output"])
+    inp = inp.outputs[0]
+    out = out.outputs[0]
+  with tf.Session(
+      config=tf.ConfigProto(gpu_options=gpu_options), graph=g) as sess:
+    val = sess.run(out, {inp: dumm_inp})
+  return val
+
+
+if "__main__" in __name__:
+  inpDims = (100, 24, 24, 2)
+  dummy_input = np.random.random_sample(inpDims)
+  gdef = getSimpleGraphDef()
+  trt_graph = trt.CreateInferenceGraph(gdef, ["output"],
+                                       inpDims[0])  # Get optimized graph
+  o1 = runGraph(gdef, dummy_input)
+  o2 = runGraph(trt_graph, dummy_input)
+  assert (np.array_equal(o1, o2))

tensorflow/contrib/tensorrt/trt_conversion.i

@@ -1,4 +1,4 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

third_party/tensorrt/LICENSE

@@ -188,7 +188,7 @@ Copyright 2018 The TensorFlow Authors.  All rights reserved.
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright 2015, The TensorFlow Authors.
+   Copyright 2018, The TensorFlow Authors.
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.