text: # pylint: disable=import-self, invalid-name, unused-argument
  """
  Tensorflow testcases
  ====================
  This article is a test script to test tensorflow operator with NNVM.
  """
  from __future__ import print_function
  import numpy as np
  import nnvm.compiler
  import tvm
  import tensorflow as tf
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import graph_util
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops import variables
  from tensorflow.python.ops import init_ops
  from tensorflow.core.framework import graph_pb2
  
  import nnvm.testing.tf
  
  #######################################################################
  # Generic run functions for TVM & tensorflow
  # ------------------------------------------
  def run_tvm_graph(graph_def, input_data, input_node, num_output=1, target='llvm'):
      """ Generic function to compile on nnvm and execute on tvm """
  
      layout = None
      if target == "cuda":
          layout = "NCHW"
  
      sym, params = nnvm.frontend.from_tensorflow(graph_def, layout=layout)
      target_host = 'llvm'
      if isinstance(input_data, list):
          shape_dict = {}
          dtype_dict = {}
          for i, e in enumerate(input_node):
              shape_dict[e] = input_data[i].shape
              dtype_dict[e] = input_data[i].dtype
      else:
          shape_dict = {input_node: input_data.shape}
          dtype_dict = {input_node: input_data.dtype}
  
      graph, lib, params = nnvm.compiler.build(sym, target=target, target_host=target_host, shape=shape_dict,
                                               dtype=dtype_dict, params=params)
  
      ctx = tvm.context(target, 0)
      from tvm.contrib import graph_runtime
      m = graph_runtime.create(graph, lib, ctx)
      # set inputs
      if isinstance(input_data, list):
          for i, e in enumerate(input_node):
              m.set_input(e, tvm.nd.array(input_data[i].astype(input_data[i].dtype)))
      else:
          m.set_input(input_node, tvm.nd.array(input_data.astype(input_data.dtype)))
  
      m.set_input(**params)
      # execute
      m.run()
      # get outputs
      if num_output > 1:
          tvm_output_list = []
          for i in range(0, num_output):
              tvm_output = m.get_output(i)
              tvm_output_list.append(tvm_output.asnumpy())
          return tvm_output_list
      else:
          tvm_output = m.get_output(0)
          return tvm_output.asnumpy()
  
  def run_tf_graph(sess, input_data, input_node, output_node):
      """ Generic function to execute tensorflow """
  
      tensor = sess.graph.get_tensor_by_name(output_node)
  
      if isinstance(input_data, list):
          input_dict = {}
          for i, e in enumerate(input_node):
              input_dict[e] = input_data[i]
      else:
          input_dict = {input_node: input_data}
  
      output_data = sess.run(tensor, input_dict)
      return output_data
  
  
  def compare_tf_with_tvm(in_data, in_name, out_name, init_global_variables=False, no_gpu=False):
      """Generic function to generate and compare tensorflow and TVM output"""
  
      out_node = out_name.split(':')[0] if ":" in out_name else out_name
  
      if isinstance(in_name, list):
          in_node = [0]*len(in_name)
          for i in range(len(in_name)):
              in_node[i] = in_name[i].split(':')[0] if ":" in in_name[i] else in_name[i]
      else:
          in_node = in_name.split(':')[0] if ":" in in_name else in_name
  
      with tf.Session() as sess:
          if init_global_variables:
              sess.run(variables.global_variables_initializer())
          final_graph_def = tf.graph_util.convert_variables_to_constants(
              sess,
              sess.graph.as_graph_def(add_shapes=True),
              [out_node],
              )
  
          tf_output = run_tf_graph(sess, in_data, in_name, out_name)
  
          for device in ["llvm", "cuda"]:
              ctx = tvm.context(device, 0)
              if not ctx.exist:
                  print("Skip because %s is not enabled" % device)
                  continue
              if no_gpu and device == 'cuda':
                  continue
  
              tvm_output = run_tvm_graph(final_graph_def, in_data, in_node, target=device)
              tvm.testing.assert_allclose(tf_output, tvm_output, atol=1e-5, rtol=1e-5)
  
          sess.close()
  
  def is_gpu_available():
      from tensorflow.python.client import device_lib
      local_device_protos = device_lib.list_local_devices()
      gpu_list = [x.name for x in local_device_protos if x.device_type == 'GPU']
      if len(gpu_list) < 0:
          print("Tensorflow GPU:", gpu_list)
          return True
      else:
          return False
  
  #######################################################################
  # Pooling
  # -------
  def _test_pooling_iteration(input_shape, **kwargs):
      """ One iteration of pool operation with given shapes and attributes """
  
      x = -np.arange(
          np.prod(input_shape), dtype=np.float32).reshape(input_shape) - 1
  
      with tf.Graph().as_default():
          in_data = array_ops.placeholder(shape=input_shape, dtype='float32')
          nn_ops.pool(in_data, **kwargs)
  
          if kwargs['pooling_type'] == 'MAX':
              out_name = 'max_pool:0'
          else:
              out_name = 'avg_pool:0'
  
          compare_tf_with_tvm(x, 'Placeholder:0', out_name)
  
  def _test_pooling(input_shape, **kwargs):
      _test_pooling_iteration(input_shape, **kwargs)
  
      if is_gpu_available():
          input_shape = [input_shape[ii] for ii in (0, 3, 1, 2)]
          kwargs['data_layout'] = 'NCHW'
          _test_pooling_iteration(input_shape, **kwargs)
  
  def test_forward_pooling():
      """ Pooling """
  
      for pool_type in ['AVG', 'MAX']:
              _test_pooling(input_shape=[2, 9, 10, 2],
                           window_shape=[1, 1],
                           padding='SAME',
                           pooling_type=pool_type,
                           dilation_rate=[1, 1],
                           strides=[1, 1])
  
              _test_pooling(input_shape=[2, 10, 9, 2],
                           window_shape=[1, 1],
                           padding='SAME',
                           pooling_type=pool_type,
                           dilation_rate=[1, 1],
                           strides=[1, 1])
  
              _test_pooling(input_shape=[2, 9, 10, 2],
                           window_shape=[2, 1],
                           padding='SAME',
                           pooling_type=pool_type,
                           dilation_rate=[1, 1],
                           strides=[1, 1])
  
              _test_pooling(input_shape=[2, 10, 9, 2],
                           window_shape=[2, 3],
                           padding='SAME',
                           pooling_type=pool_type,
                           dilation_rate=[1, 1],
                           strides=[2, 1])
  
  #######################################################################
  # Convolution
  # -----------
  
  def _test_convolution(tensor_in_sizes, filter_in_sizes,
                        dilations, strides, padding, data_format):
      """ One iteration of convolution with given shapes and attributes """
  
      total_size_1 = 1
      total_size_2 = 1
      for s in tensor_in_sizes:
          total_size_1 *= s
      for s in filter_in_sizes:
          total_size_2 *= s
      # Initializes the input tensor with array containing incrementing
      # numbers from 1.
      data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
      filter_array = [f * 1.0 for f in range(1, total_size_2 + 1)]
  
      with tf.Graph().as_default():
          in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
          in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
          strides = [1] + strides + [1]
          dilations = [1] + dilations + [1]
  
          nn_ops.conv2d(in_data,
                        in_filter,
                        strides=strides,
                        padding=padding,
                        data_format=data_format)
  
          compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
                              'Placeholder:0', 'Conv2D:0')
  
  def test_forward_convolution():
      if is_gpu_available():
          _test_convolution([4, 176, 8, 8], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NCHW')
          _test_convolution([4, 19, 17, 17], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NCHW')
          _test_convolution([4, 124, 17, 17], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NCHW')
          _test_convolution([4, 12, 17, 17], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NCHW')
  
      _test_convolution([4, 8, 8, 176], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NHWC')
      _test_convolution([4, 17, 17, 19], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NHWC')
      _test_convolution([4, 17, 17, 124], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NHWC')
      _test_convolution([4, 17, 17, 12], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NHWC')
  
  #######################################################################
  # Reshape
  # -------
  
  def _test_reshape(data, out_shape):
      """ One iteration of reshape operation with given data and out shape """
  
      with tf.Graph().as_default():
          in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
          array_ops.reshape(in_data, out_shape)
  
          compare_tf_with_tvm(data, 'Placeholder:0', 'Reshape:0')
  
  def test_forward_reshape():
      _test_reshape(np.arange(6.0), [2, 3])
      _test_reshape(np.arange(6), [-1, 2])
      _test_reshape(np.arange(6), [3, -1])
      _test_reshape(np.arange(6), [-1])
  
  #######################################################################
  # Squeeze
  # -------
  
  def _test_squeeze(data, squeeze_dims=None):
      """ One iteration of squeeze """
  
      if squeeze_dims is None:
          squeeze_dims = []
  
      with tf.Graph().as_default():
          in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
  
          if squeeze_dims:
              array_ops.squeeze(in_data, squeeze_dims)
          else:
              array_ops.squeeze(in_data)
  
          compare_tf_with_tvm(data, 'Placeholder:0', 'Squeeze:0')
  
  def test_forward_squeeze():
      """ Squeeze """
  
      # Nothing to squeeze.
      _test_squeeze(np.arange(2).reshape((2)))
      _test_squeeze(np.arange(6).reshape((2, 3)))
  
      # Squeeze the middle element away.
      _test_squeeze(np.arange(4).reshape((2, 1, 2)))
  
      # Squeeze on both ends.
      _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)))
  
      # Positive squeeze dim index.
      _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [0])
      _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [2, 4])
      _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [0, 4, 2])
  
      # Negative squeeze dim index.
      _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [-1])
      _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [-3, -5])
      _test_squeeze(np.arange(6).reshape((1, 2, 1, 3, 1)), [-3, -5, -1])
  
  #######################################################################
  # ConcatV2
  # --------
  
  def _test_concat_v2(data, dim):
      """ One iteration of ConcatV2 """
  
      with tf.Graph().as_default():
          gen_array_ops._concat_v2(data, dim)
  
          compare_tf_with_tvm(data, ['ConcatV2/values_0:0', 'ConcatV2/values_1:0'],
                              'ConcatV2:0')
  
  def _test_forward_concat_v2():
      t1 = np.array([])
      t2 = np.array([])
      test_concat_v2([t1, t2], 0)
  
      t1 = np.array([[1, 2, 3], [4, 5, 6]])
      t2 = np.array([[7, 8, 9], [10, 11, 12]])
  
      _test_concat_v2([t1, t2], 1)
  
  #######################################################################
  # Sigmoid
  # -------
  
  def _test_sigmoid(data):
      """ One iteration of sigmoid """
  
      with tf.Graph().as_default():
          in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
          sigmoid_out = math_ops.sigmoid(in_data)
  
          compare_tf_with_tvm(data, 'Placeholder:0', 'Sigmoid:0')
  
  def test_forward_sigmoid():
      """ Sigmoid """
  
      _test_sigmoid(np.random.uniform(size=(3, 4, 4, 3)).astype('float32'))
  
  #######################################################################
  # Argmin/Argmax
  # -------------
  
  def _test_argx(func, data, **kwargs):
  
      with tf.Graph().as_default():
          inp = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="c0")
          func(inp, name="argx0", **kwargs, output_type=tf.int32)
  
          compare_tf_with_tvm(data, 'c0:0', 'argx0:0')
  
  def test_forward_argminmax():
      for axis in [None,0,1,2]:
          data = np.random.uniform(size=(8,4,9)).astype('float32')
          _test_argx(tf.argmax, data=data, axis=axis)
          _test_argx(tf.argmin, data=data, axis=axis)
  
  #######################################################################
  # Reduce
  # ------
  
  def _test_reduce(func, data, **kwargs):
      """ One iteration of a reduce operation"""
  
      with tf.Graph().as_default():
          inp = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="c0")
          func(inp, name="reducex0", **kwargs)
  
          compare_tf_with_tvm(data, 'c0:0', 'reducex0:0')
  
  def test_forward_reduce():
      data = np.random.uniform(size=(8,4,9)).astype('float32')
      _test_reduce(tf.reduce_sum, data=data)
      _test_reduce(tf.reduce_sum, data=data, axis=0)
      _test_reduce(tf.reduce_sum, data=data, axis=(0,1))
  
  
  #######################################################################
  # Variable
  # --------
  
  def _test_variable(data):
      """ One iteration of a variable """
  
      tf.reset_default_graph()
      input_op = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
      input_tensor = array_ops.reshape(input_op, data.shape)
  
      size = input_tensor.shape.dims[1]
      with variable_scope.variable_scope("linear", reuse=None):
          w = variable_scope.get_variable(
              "w", shape=[size, size], dtype=input_tensor.dtype)
      math_ops.matmul(input_tensor, w)
  
      compare_tf_with_tvm(data, 'Placeholder:0', 'MatMul:0', init_global_variables=True)
  
  def test_forward_variable():
      """Variable type op test"""
      _test_variable(np.random.uniform(size=(32, 100)).astype('float32'))
  
  
  #######################################################################
  # StridedSlice
  # ------------
  
  def _test_stridedslice(ip_shape, begin, end, stride, dtype,
                               begin_mask=0, end_mask=0, new_axis_mask=0,
                               shrink_axis_mask=0, ellipsis_mask=0):
      """ One iteration of a Stridedslice """
  
      tf.reset_default_graph()
      in_data = tf.placeholder(dtype, ip_shape, name="in_data")
      tf.strided_slice(in_data, begin, end, stride, begin_mask=begin_mask,
                           end_mask=end_mask, new_axis_mask=new_axis_mask,
                           shrink_axis_mask=shrink_axis_mask,
                           ellipsis_mask=ellipsis_mask, name="strided_slice")
      np_data = np.random.uniform(size=ip_shape).astype(dtype)
  
      compare_tf_with_tvm(np_data, 'in_data:0', 'strided_slice:0')
  
  def test_forward_stridedslice():
      '''test StridedSlice'''
      return
      _test_stridedslice((3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1], 'float32')
      _test_stridedslice((3, 4, 3), [1, 0], [4, 3], [2, 1], 'float32', ellipsis_mask=8)
      _test_stridedslice((3, 4, 3), [1, 1, 0], [4, 4, 2], [2, 1, 1], 'float32', new_axis_mask=5)
      _test_stridedslice((3, 4, 3), [1, 1, 1], [4, 4, 1], [2, 1, 1], 'float32', ellipsis_mask=2, new_axis_mask=4)
      _test_stridedslice((3, 4, 3), [1, 1, 2], [4, 4, 3], [2, 1, 1], 'float32', ellipsis_mask=4, new_axis_mask=2)
      _test_stridedslice((3, 4, 3), [1, 1, 2], [4, 4, 3], [2, 1, 1], 'float32', ellipsis_mask=2, new_axis_mask=3)
      _test_stridedslice((3, 4, 3), [1, 1, 0], [4, 4, 1], [2, 1, 1], 'float32', ellipsis_mask=2, new_axis_mask=3)
      _test_stridedslice((3, 4, 3), [1, 1, 2], [4, 4, 3], [2, 1, 1], 'float32', ellipsis_mask=2, new_axis_mask=2)
      _test_stridedslice((3,4), [1, 0], [4, 4], [1, 1], 'float32', shrink_axis_mask=2)
      _test_stridedslice((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1], 'float32', shrink_axis_mask=2, new_axis_mask=2)
      _test_stridedslice((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1], 'float32', shrink_axis_mask=1, new_axis_mask=2)
      _test_stridedslice((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1], 'float32', shrink_axis_mask=2, new_axis_mask=1)
      _test_stridedslice((3, 4, 5, 4, 5, 6), [0, 0], [2, 3], [1, 1], 'float32', shrink_axis_mask=5, new_axis_mask=1)
      _test_stridedslice((3, 4, 5, 4, 5, 6), [0, 0, 1, 2, 1], [2, 3, 4, 5, 3], [1, 1, 2, 2, 1],
                         'float32', shrink_axis_mask=5, new_axis_mask=1, ellipsis_mask=2, begin_mask=8, end_mask=8)
      _test_stridedslice((3, 4, 5, 4, 5, 6), [0, 0, 1, 2, 1], [2, 3, 4, 5, 3], [1, 1, 2, 2, 1],
                         'float32', shrink_axis_mask=8, new_axis_mask=1, ellipsis_mask=2, begin_mask=5, end_mask=5)
      _test_stridedslice((3, 4, 5, 4, 5, 6), [0, 0, 1, 2, 1], [2, 3, 4, 5, 3], [1, 1, 2, 2, 1],
                         'float32', shrink_axis_mask=16, new_axis_mask=1, ellipsis_mask=2, begin_mask=5, end_mask=5)
      _test_stridedslice((3, 4, 5, 4, 5, 6), [1, 2, 0, -3], [4, 5, 3, 3], [2, 2, 1, 1],
                         'float32', shrink_axis_mask=8, new_axis_mask=1, ellipsis_mask=2, begin_mask=5,
                         end_mask=8)
  
  
  #######################################################################
  # Gather
  # ------
  
  def _test_gather(ip_shape, indice_shape, indice_value, axis, dtype):
      """ One iteration of a Gather """
  
      tf.reset_default_graph()
      in_data = tf.placeholder(dtype, ip_shape, name="in_data")
      indices = tf.placeholder("int32", indice_shape, name="indices")
      tf.gather(in_data, indices, axis=axis)
      np_data = np.random.uniform(size=ip_shape).astype(dtype)
  
      def _fill_indices(indice_value):
          indices = np.array(ip_shape, dtype=dtype)
          if isinstance(indice_value, int):
              indices = np.array([indice_value], dtype='int32')
          else:
              indices = np.asarray(indice_value, dtype='int32')
          return indices
      np_indices = _fill_indices(indice_value)
  
      compare_tf_with_tvm([np_data, np_indices], ['in_data:0', 'indices:0'], 'GatherV2:0')
  
  def test_forward_gather():
      '''test gather layer'''
      _test_gather((4,), (1,), 1, 0, 'int32')
      _test_gather((4,), (1,), 1, 0, 'float32')
      _test_gather((1,4), (1,), [0], 0, 'int32')
      _test_gather((4,), (1,2,2), [[[1,0],[0,1]]], 0, 'float32')
      _test_gather((2,2), (1,2,2), [[[1,0],[0,1]]], 0, 'int32')
      _test_gather((2,2), (1,2,2), [[[1,0],[0,1]]], 1, 'int32')
      _test_gather((2,2), (1,2,2), [[[1,0],[0,1]]], 0, 'float32')
      _test_gather((3,3,3), (1,1,2), [[[1,0]]], 0, 'int32')
      _test_gather((3,3,3), (1,1,2), [[[1,0]]], 2, 'int32')
      _test_gather((4,3,5,6), (1,4), [[2,1,0,0]], 0, 'float32')
  
  
  #######################################################################
  # Multi Input to graph
  # --------------------
  
  def test_forward_multi_input():
      with tf.Graph().as_default():
          in1 = tf.placeholder(tf.int32, shape=[3, 3], name='in1')
          in2 = tf.placeholder(tf.int32, shape=[3, 3], name='in2')
          in3 = tf.placeholder(tf.int32, shape=[3, 3], name='in3')
          in4 = tf.placeholder(tf.int32, shape=[3, 3], name='in4')
  
          out1 = tf.add(in1, in2, name='out1')
          out2 = tf.subtract(in3, in4, name='out2')
          out = tf.multiply(out1, out2, name='out')
          in_data = np.arange(9, dtype='int32').reshape([3, 3])
  
          compare_tf_with_tvm([in_data, in_data, in_data, in_data],
                              ['in1:0', 'in2:0', 'in3:0', 'in4:0'], 'out:0')
  
  #######################################################################
  # Resize Bilinear
  # ---------------
  
  def _test_resize_bilinear(in_shape, to_shape, align_corners):
      """ One iteration of resize bilinear """
  
      data = np.random.uniform(size=in_shape).astype('float32')
      shape_data = np.array(to_shape).astype('int32')
  
      with tf.Graph().as_default():
          in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
          shape_data = constant_op.constant(shape_data, shape=shape_data.shape, dtype=shape_data.dtype)
          tf.image.resize_bilinear(in_data, shape_data, align_corners=align_corners)
  
          compare_tf_with_tvm(data, 'Placeholder:0', 'ResizeBilinear:0')
  
  def test_forward_resize_bilinear():
      """ Resize Bilinear """
  
      _test_resize_bilinear((4, 16, 32, 32), [50, 50], False)
      _test_resize_bilinear((6, 32, 64, 64), [20, 20], True)
  
  
  #######################################################################
  # LSTM
  # ----
  
  def _test_lstm_cell(batch_size, num_hidden, num_layers, forget_bias, dtype):
      """ One iteration of a LSTM cell """
  
      tf.reset_default_graph()
      input_size = num_hidden
      input_data = np.full((batch_size, input_size), 1., dtype=dtype)
      in_state_c = np.full((num_layers, batch_size, num_hidden), 0.1, dtype=dtype)
      in_state_h = np.full((num_layers, batch_size, num_hidden), 0.1, dtype=dtype)
  
      def _get_tensorflow_output():
          with tf.Session() as sess:
              with variable_scope.variable_scope(
                  "root", initializer=init_ops.constant_initializer(0.5)):
                  m0 = array_ops.zeros([batch_size, num_hidden])
                  m1 = array_ops.zeros([batch_size, num_hidden])
                  x=tf.placeholder(shape=(batch_size, input_size), dtype=dtype)
                  g, ((out_m0, out_m1)) = \
                       tf.contrib.rnn.LSTMBlockCell(num_hidden,
                                                    forget_bias=forget_bias)(x, ((m0, m1)))
                  sess.run([variables.global_variables_initializer()])
                  res = sess.run([g, out_m0, out_m1], {
                      x.name: np.array([[1., 1.]]),
                      m0.name: 0.1 * np.ones([batch_size, num_hidden]),
                      m1.name: 0.1 * np.ones([batch_size, num_hidden]),
                  })
              graph_def = sess.graph.as_graph_def(add_shapes=True)
              final_graph_def = graph_util.convert_variables_to_constants(
                  sess,
                  graph_def,
                  ['root/lstm_cell/LSTMBlockCell'])
              return final_graph_def, res
  
      graph_def, tf_out = _get_tensorflow_output()
      tvm_output = run_tvm_graph(graph_def, [input_data, in_state_c, in_state_h],
                                 ['root/Placeholder', 'root/lstm_cell/LSTMBlockCell_c',
                                  'root/lstm_cell/LSTMBlockCell_h'], num_output=2)
      assert isinstance(tvm_output, list)
  
      out = tvm_output[0]
      out_state = tvm_output[1]
      out_state_tup = np.split(out_state, indices_or_sections=2, axis=1)
      out_state_c = np.reshape(out_state_tup[0], (batch_size, num_hidden))
      out_state_h = np.reshape(out_state_tup[1], (batch_size, num_hidden))
      tvm_out = [out, out_state_c, out_state_h]
      tvm.testing.assert_allclose(tf_out, tvm_out, rtol=1e-3, atol=1e-3)
  
  def test_forward_lstm():
      '''test LSTM block cell'''
      _test_lstm_cell(1, 2, 1, 0.0, 'float32')
  
  
  
  #######################################################################
  # Pack
  # ---
  def _test_pack(axis, shape, **kwargs):
  
      a = np.arange(np.prod(shape), dtype=np.float32).reshape(shape)
      b = np.arange(np.prod(shape), dtype=np.float32).reshape(shape)
  
      with tf.Graph().as_default():
          tf_a = array_ops.placeholder(shape=shape, dtype='float32', name='pl_a')
          tf_b = array_ops.placeholder(shape=shape, dtype='float32', name='pl_b')
          tf_c = tf.stack([tf_a,tf_b], axis=axis, **kwargs)
          assert tf_c.op.op_def.name == 'Pack', "tf.stack() is expected to produce 'Pack' operation"
  
          compare_tf_with_tvm([a,b], ['pl_a:0','pl_b:0'], 'stack:0')
  
  def test_forward_pack():
      for axis in range(-3,3):
          _test_pack(axis, [3,2,1])
      for axis in range(-1,1):
          _test_pack(axis, [3])
      _test_pack(0, [])
  
  #######################################################################
  # Pad
  # ---
  def _test_pad(input_shape, paddings, mode, **kwargs):
      """ One iteration of pad operation with given shape"""
  
      x = np.arange(np.prod(input_shape), dtype=np.float32).reshape(input_shape)
  
      with tf.Graph().as_default():
          in_data = array_ops.placeholder(shape=input_shape, dtype='float32')
          pad_values = constant_op.constant(paddings)
          pad = tf.pad(in_data, paddings=pad_values, mode=mode, **kwargs)
  
          if mode == 'CONSTANT':
              if 'constant_values' in kwargs:
                  out_name = 'PadV2:0'
              else:
                  out_name = 'Pad:0'
  
          compare_tf_with_tvm(x, 'Placeholder:0', out_name)
  
  def test_forward_pad():
      """ Pad """
      _test_pad((2, 3), [[1,1], [2,2]], mode="CONSTANT")
      _test_pad((2, 3), [[1,1], [2,2]], mode="CONSTANT", constant_values=1.0)
  
  
  #######################################################################
  # Inception V3
  # ------------
  def test_forward_inception_v3():
      '''test inception V3 model'''
      with tf.Graph().as_default():
          graph_def = nnvm.testing.tf.get_workload('InceptionV3/inception_v3_2016_08_28_frozen-with_shapes.pb')
          # Call the utility to import the graph definition into default graph.
          graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
  
          data = np.random.uniform(size=(1, 299, 299, 3)).astype('float32')
  
          with tf.Session() as sess:
              tf_output = run_tf_graph(sess, data, 'input:0', 'InceptionV3/Predictions/Reshape_1:0')
              tvm_output = run_tvm_graph(graph_def, data, 'input')
              tvm.testing.assert_allclose(tf_output, tvm_output, rtol=1e-5, atol=1e-5)
  
  #######################################################################
  # Inception V1
  # ------------
  def test_forward_inception_v1():
      '''test inception V1 model'''
      with tf.Graph().as_default():
          graph_def = nnvm.testing.tf.get_workload("InceptionV1/classify_image_graph_def-with_shapes.pb")
          # Call the utility to import the graph definition into default graph.
          graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
  
          # Build an image from random data.
          from PIL import Image
          from tvm.contrib import util
  
          img_array = np.random.uniform(size=(1, 600, 600, 3)).astype("uint8")
          img = Image.frombuffer('RGB', (600, 600), img_array.tostring(), 'raw', 'RGB', 0, 1)
          temp = util.tempdir()
          img_path = temp.relpath("tf-test.jpg")
          img.save(img_path);
  
          import os.path
          if not tf.gfile.Exists(os.path.join(img_path)):
              tf.logging.fatal('File does not exist %s', image)
          data = tf.gfile.FastGFile(os.path.join(img_path), 'rb').read()
  
          temp.remove()
  
          # Extract tensorflow decoded image frame for tvm input
          with tf.Session() as sess:
              tvm_data = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'DecodeJpeg:0')
  
          with tf.Session() as sess:
              tf_output = run_tf_graph(sess, data, 'DecodeJpeg/contents:0', 'softmax:0')
              tvm_output = run_tvm_graph(graph_def, tvm_data, 'DecodeJpeg/contents')
              tvm.testing.assert_allclose(tf_output, tvm_output, rtol=1e-5, atol=1e-5)
  
  #######################################################################
  # Mobilenet
  # ---------
  def test_forward_mobilenet():
      '''test mobilenet model'''
      with tf.Graph().as_default():
          graph_def = nnvm.testing.tf.get_workload("MobilenetV1/mobilenet_v1_1.0_224_frozen-with-shapes.pb")
          # Call the utility to import the graph definition into default graph.
          graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
  
          data = np.random.uniform(size=(1, 224, 224, 3)).astype('float32')
          out_node = 'MobilenetV1/Predictions/Reshape_1'
  
          with tf.Session() as sess:
              tf_output = run_tf_graph(sess, data, 'input:0', out_node + ':0')
              tvm_output = run_tvm_graph(graph_def, data, 'input')
              tvm.testing.assert_allclose(np.squeeze(tvm_output), np.squeeze(tf_output), rtol=1e-5, atol=1e-5)
  
  #######################################################################
  # ResnetV2
  # ---------
  def test_forward_resnetv2():
      '''test resnet model'''
      if is_gpu_available():
          with tf.Graph().as_default():
              graph_def = nnvm.testing.tf.get_workload("ResnetV2/resnet-20180601_resnet_v2_imagenet-shapes.pb")
              # Call the utility to import the graph definition into default graph.
              graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
  
              data = np.random.uniform(size=(128, 224, 224, 3)).astype('float32')
              out_node = 'ArgMax'
  
              with tf.Session() as sess:
                  tf_output = run_tf_graph(sess, data, 'input_tensor:0', out_node + ':0')
                  tvm_output = run_tvm_graph(graph_def, data, 'input_tensor', tf_output.shape, 'float32')
                  tvm.testing.assert_allclose(np.squeeze(tvm_output), np.squeeze(tf_output), rtol=1e-5, atol=1e-5)
  
  #######################################################################
  # PTB
  # ---
  dir(tf.contrib)
  def test_forward_ptb():
      '''test ptb model'''
      config = nnvm.testing.tf.get_config()
      num_steps = config.num_steps
      num_hidden = config.hidden_size
      num_layers = config.num_layers
      batch_size = config.batch_size
      vocab_size = config.vocab_size
      out_sample_shape = (batch_size, vocab_size)
      out_state_shape = (num_layers, 2, batch_size, num_hidden)
      #Sample input
      inpt = "we have no useful information on"
      cnt_sample = 20
  
      def _pretty_print(items, is_char_model, id2word):
          if not is_char_model:
              return ' '.join([id2word[x] for x in items])
          else:
              return ''.join([id2word[x] for x in items]).replace('_', ' ')
  
      def _get_tvm_graph_module(graph_def):
          sym, params = nnvm.frontend.from_tensorflow(graph_def)
  
          #Cell inputs 'c and 'h' consist of all layers values
          shape_dict = {'Model/Placeholder': (batch_size, num_steps),
                        'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_c':(num_layers, batch_size, num_hidden),
                        'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_h':(num_layers, batch_size, num_hidden)}
          dtype_dict = {'Model/Placeholder': 'int32',
                        'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_c':'float32',
                        'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_h':'float32'}
          target = 'llvm'
          graph, lib, params = nnvm.compiler.build(sym, target, shape_dict,
                                                   dtype=dtype_dict, params=params)
          from tvm.contrib import graph_runtime
          ctx = tvm.cpu(0)
          return params, graph_runtime.create(graph, lib, ctx)
  
      def _do_tvm_sample(model, data, in_states, params, num_samples):
          """Sampled from the model"""
          samples = []
          state = in_states
          sample = None
          def _get_sample(data, state):
              input_data = np.full((batch_size, num_steps), data, dtype="int32")
              in_state_tup = np.split(state, indices_or_sections=2, axis=1)
              in_state_c = np.reshape(in_state_tup[0], (num_layers, batch_size, num_hidden))
              in_state_h = np.reshape(in_state_tup[1], (num_layers, batch_size, num_hidden))
  
              model.set_input('Model/Placeholder', tvm.nd.array(input_data.astype("int32")))
              model.set_input('Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_c',
                          tvm.nd.array(in_state_c.astype("float32")))
              model.set_input('Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_h',
                          tvm.nd.array(in_state_h.astype("float32")))
              model.set_input(**params)
              model.run()
              tvm_output = model.get_output(0, tvm.nd.empty(out_sample_shape,
                                                        "float32")).asnumpy()
              state_output = model.get_output(1, tvm.nd.empty(out_state_shape,
                                                          "float32")).asnumpy()
              sample = nnvm.testing.tf.pick_from_weight(tvm_output[0])
              return sample, state_output
  
          for x in data:
              sample, state = _get_sample(x, state)
  
          if sample is not None:
              samples.append(sample)
          else:
              samples.append(0)
  
          k = 1
          while k < num_samples:
              sample, state = _get_sample(samples[-1], state)
              samples.append(sample)
              k += 1
          return samples, state
  
      with tf.Graph().as_default():
          word_to_id, id_to_word, graph_def = nnvm.testing.tf.get_workload_ptb()
          vocab_size = len(word_to_id)
          # Call the utility to import the graph definition into default graph.
          graph_def = nnvm.testing.tf.ProcessGraphDefParam(graph_def)
          sess = tf.Session()
  
      #TVM graph module creation
      params, m = _get_tvm_graph_module(graph_def)
  
      # Create 10 predicted statments of 20 words
      cnt_stm = 0
      while cnt_stm < 10:
          cnt_stm += 1
          in_state = np.full((num_layers, 2, batch_size, num_hidden), 0, dtype="float32")
          seed_for_sample = inpt.split()
          tvm_samples, tvm_state = _do_tvm_sample(m, [word_to_id[word] \
                                                      for word in seed_for_sample],
                                                  in_state, params, cnt_sample)
          tvm_sample_str = _pretty_print(tvm_samples, False, id_to_word)
          tf_samples, tf_state = nnvm.testing.tf.do_tf_sample(sess,
                                  [word_to_id[word] for word in seed_for_sample],
                                  in_state, cnt_sample)
          tf_sample_str = _pretty_print(tf_samples, False, id_to_word)
          inpt = tvm_sample_str
          tvm.testing.assert_allclose(tf_samples, tvm_samples, rtol=1e-5, atol=1e-5)
          assert(tvm_sample_str == tf_sample_str)
  
  #######################################################################
  # LRN (Local Response Normalization)
  # ----------------------------------
  
  def _test_lrn(ishape, size, axis, bias, alpha, beta):
      """ testing local response normalization """
      lrn_depth_radius = size / 2
  
      inp_array = np.random.uniform(size=ishape).astype(np.float32)
  
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype, name="lrn0_data")
          nn_ops.local_response_normalization(in1,
                                              name="lrn",
                                              depth_radius=lrn_depth_radius,
                                              bias=bias,
                                              alpha=alpha,
                                              beta=beta)
  
          compare_tf_with_tvm(inp_array, 'lrn0_data:0', 'lrn:0')
  
  def test_forward_lrn():
      _test_lrn((1, 3, 20, 20), 3, 1, 1.0, 1.0, 0.5)
  
  #######################################################################
  # l2_normalize
  # ------------
  
  def _test_l2_normalize(ishape, eps, axis):
      """ testing l2 normalize (uses max, sum, square, sqrt frontend operators)"""
  
      inp_array = np.random.uniform(size=ishape).astype(np.float32)
  
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
          nn.l2_normalize(in1,
                          axis=axis,
                          epsilon=eps,
                          name=None,
                          dim=None)
  
          compare_tf_with_tvm(inp_array, 'Placeholder:0', 'l2_normalize:0')
  
  def test_forward_l2_normalize():
      _test_l2_normalize((1, 3, 20, 20), 0.001, (0,))
  
  #######################################################################
  # transpose
  # ---------
  def _test_forward_transpose(ishape, axes=None):
      input = np.random.uniform(size=ishape).astype(np.float32)
  
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=input.shape, dtype=input.dtype, name="transpose_data")
  
          if axes is None:
              tf.transpose(in1)
          else:
              tf.transpose(in1, perm=axes)
  
          compare_tf_with_tvm(input, 'transpose_data:0', 'transpose:0')
  
  def test_forward_transpose():
      _test_forward_transpose((2, 3, 4))
      _test_forward_transpose((7, 8, 8, 10))
      _test_forward_transpose((2, 3, 4), (1, 2, 0))
      _test_forward_transpose((2, 3, 4), (0, 1, 2))
      _test_forward_transpose((2, 3, 4, 5), (3, 0, 1, 2))
  
  
  def test_forward_ceil():
      ishape = (1, 3, 10, 10)
      inp_array = np.random.uniform(size=ishape).astype(np.float32)
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
          tf.ceil(in1)
          compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Ceil:0')
  
  def test_forward_floor():
      ishape = (1, 3, 10, 10)
      inp_array = np.random.uniform(size=ishape).astype(np.float32)
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
          tf.floor(in1)
          compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Floor:0')
  
  def test_forward_relu():
      ishape = (1, 3, 10, 10)
      inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
          tf.nn.relu(in1)
          compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Relu:0')
  
  def test_forward_leaky_relu():
      ishape = (1, 3, 10, 10)
      inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
          tf.nn.leaky_relu(in1, alpha=0.4)
          compare_tf_with_tvm(inp_array, 'Placeholder:0', 'LeakyRelu:0')
  
  def test_forward_elu():
      ishape = (1, 3, 10, 10)
      inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
          tf.nn.elu(in1)
          compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Elu:0')
  
  def test_forward_selu():
      ishape = (1, 3, 10, 10)
      inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
          tf.nn.selu(in1)
          compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Selu:0')
  
  def test_forward_tanh():
      ishape = (1, 3, 10, 10)
      inp_array = np.random.uniform(-5, 5, size=ishape).astype(np.float32)
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
          tf.nn.tanh(in1)
          compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Tanh:0')
  
  #######################################################################
  # Mean
  # ----
  def test_forward_mean():
      def check_mean(ishape, **kwargs):
          inp_array = np.random.uniform(size=ishape).astype(np.float32)
          with tf.Graph().as_default():
              in1 = tf.placeholder(shape=inp_array.shape, dtype=inp_array.dtype)
              tf.keras.backend.mean(in1, **kwargs)
              compare_tf_with_tvm(inp_array, 'Placeholder:0', 'Mean:0', no_gpu=True)
  
      check_mean((10, 8, 16, 32))
      check_mean((10, 8, 16, 32), axis=(2,3))
      check_mean((10, 8, 16, 32), axis=(1,2), keepdims=True)
  
  #######################################################################
  # Relational operators
  # --------------------
  def _test_forward_rel_op(data, func):
      with tf.Graph().as_default():
          in1 = tf.placeholder(shape=data[0].shape, dtype=data[0].dtype, name='in1')
          in2 = tf.placeholder(shape=data[1].shape, dtype=data[1].dtype, name='in2')
          op = func(in1, in2, name='op')
          out = tf.cast(op, tf.int32, name='out1')
          compare_tf_with_tvm([data[0], data[1]], ['in1:0', 'in2:0'], 'out1:0')
  
  def test_forward_rel_ops():
      t1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
      t2 = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])
      _test_forward_rel_op([t1, t2], math_ops.less)
      _test_forward_rel_op([t1, t2], math_ops.greater)
      _test_forward_rel_op([t1, t2], math_ops.less_equal)
      _test_forward_rel_op([t1, t2], math_ops.greater_equal)
      _test_forward_rel_op([t1, t2], math_ops.equal)
      _test_forward_rel_op([t1, t2], math_ops.not_equal)
  
  
  #######################################################################
  # Main
  # ----
  if __name__ == '__main__':
      # Transforms
      test_forward_transpose()
      test_forward_reshape()
      test_forward_squeeze()
      test_forward_pack()
      test_forward_resize_bilinear()
      test_forward_pad()
      test_forward_gather()
      #test_forward_stridedslice()
  
      # Activations
      test_forward_sigmoid()
      test_forward_relu()
      test_forward_leaky_relu()
      test_forward_elu()
      test_forward_selu()
      test_forward_tanh()
  
      # Reductions
      test_forward_argminmax()
      test_forward_reduce()
      test_forward_mean()
  
      # NN
      test_forward_convolution()
      test_forward_pooling()
      if tf.__version__ == '1.4.1':
          _test_forward_concat_v2()
      test_forward_lrn()
      test_forward_l2_normalize()
  
      # General
      test_forward_multi_input()
      test_forward_variable()
  
      # End to End
      test_forward_inception_v3()
      test_forward_inception_v1()
      test_forward_mobilenet()
      test_forward_resnetv2()
      test_forward_ptb()
  
      # RNN
      test_forward_lstm()
  
      # Elementwise
      test_forward_ceil()
      test_forward_floor()
  
      # Relational ops
      test_forward_rel_ops()
  