message: Fix linter errors in test_tftrt.py
diff: @@ -18,33 +18,33 @@ from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  +import numpy as np
  import tensorflow as tf
  import tensorflow.contrib.tensorrt as trt
  -import numpy as np
  -def getSimpleGraphDef():
  +def get_simple_graph_def():
  """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")
  + 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")
  + 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")
  + tf.squeeze(v, name="output")
  return g.as_graph_def()
  -def runGraph(gdef, dumm_inp):
  +def run_graph(gdef, dumm_inp):
  gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.50)
  tf.reset_default_graph()
  g = tf.Graph()
  @@ -60,12 +60,12 @@ def runGraph(gdef, dumm_inp):
  if "__main__" in __name__:
  - inpDims = (100, 24, 24, 2)
  - dummy_input = np.random.random_sample(inpDims)
  - gdef = getSimpleGraphDef()
  - trt_graph = trt.create_inference_graph(gdef, ["output"],
  - inpDims[0]) # Get optimized graph
  - o1 = runGraph(gdef, dummy_input)
  - o2 = runGraph(trt_graph, dummy_input)
  - assert (np.array_equal(o1, o2))
  + inp_dims = (100, 24, 24, 2)
  + dummy_input = np.random.random_sample(inp_dims)
  + gdef = get_simple_graph_def()
  + # Get optimized graph
  + trt_graph = trt.create_inference_graph(gdef, ["output"], inp_dims[0])
  + o1 = run_graph(gdef, dummy_input)
  + o2 = run_graph(trt_graph, dummy_input)
  + assert np.array_equal(o1, o2)
  print("Pass")
  