Python tensorflow.python.pywrap_tensorflow.TryFindKernelClass() Examples

The following are 5 code examples of tensorflow.python.pywrap_tensorflow.TryFindKernelClass(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.pywrap_tensorflow , or try the search function .
Example #1
Source File: selective_registration_header_lib.py    From lambda-packs with MIT License 5 votes vote down vote up 
def get_ops_and_kernels(proto_fileformat, proto_files, default_ops_str):
  """Gets the ops and kernels needed from the model files."""
  ops = set()

  for proto_file in proto_files:
    tf_logging.info('Loading proto file %s', proto_file)
    # Load GraphDef.
    file_data = gfile.GFile(proto_file, 'rb').read()
    if proto_fileformat == 'rawproto':
      graph_def = graph_pb2.GraphDef.FromString(file_data)
    else:
      assert proto_fileformat == 'textproto'
      graph_def = text_format.Parse(file_data, graph_pb2.GraphDef())

    # Find all ops and kernels used by the graph.
    for node_def in graph_def.node:
      if not node_def.device:
        node_def.device = '/cpu:0'
      kernel_class = pywrap_tensorflow.TryFindKernelClass(
          node_def.SerializeToString())
      if kernel_class:
        op_and_kernel = (str(node_def.op), kernel_class.decode('utf-8'))
        if op_and_kernel not in ops:
          ops.add(op_and_kernel)
      else:
        print(
            'Warning: no kernel found for op %s' % node_def.op, file=sys.stderr)

  # Add default ops.
  if default_ops_str and default_ops_str != 'all':
    for s in default_ops_str.split(','):
      op, kernel = s.split(':')
      op_and_kernel = (op, kernel)
      if op_and_kernel not in ops:
        ops.add(op_and_kernel)

  return list(sorted(ops))
Example #2
Source File: print_selective_registration_header.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def get_ops_and_kernels(proto_fileformat, proto_files, default_ops_str):
  """Gets the ops and kernels needed from the model files."""
  ops = set()

  for proto_file in proto_files:
    tf_logging.info('Loading proto file %s', proto_file)
    # Load GraphDef.
    file_data = gfile.GFile(proto_file).read()
    if proto_fileformat == 'rawproto':
      graph_def = graph_pb2.GraphDef.FromString(file_data)
    else:
      assert proto_fileformat == 'textproto'
      graph_def = text_format.Parse(file_data, graph_pb2.GraphDef())

    # Find all ops and kernels used by the graph.
    for node_def in graph_def.node:
      if not node_def.device:
        node_def.device = '/cpu:0'
      kernel_class = pywrap_tensorflow.TryFindKernelClass(
          node_def.SerializeToString())
      if kernel_class:
        op_and_kernel = (str(node_def.op), kernel_class.decode('utf-8'))
        if op_and_kernel not in ops:
          ops.add(op_and_kernel)
      else:
        print(
            'Warning: no kernel found for op %s' % node_def.op, file=sys.stderr)

  # Add default ops.
  if default_ops_str != 'all':
    for s in default_ops_str.split(','):
      op, kernel = s.split(':')
      op_and_kernel = (op, kernel)
      if op_and_kernel not in ops:
        ops.add(op_and_kernel)

  return list(sorted(ops))
Example #3
Source File: print_selective_registration_header.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def get_ops_and_kernels(proto_fileformat, proto_files, default_ops_str):
  """Gets the ops and kernels needed from the model files."""
  ops = set()

  for proto_file in proto_files:
    tf.logging.info('Loading proto file %s', proto_file)
    # Load GraphDef.
    file_data = tf.gfile.GFile(proto_file).read()
    if proto_fileformat == 'rawproto':
      graph_def = graph_pb2.GraphDef.FromString(file_data)
    else:
      assert proto_fileformat == 'textproto'
      graph_def = text_format.Parse(file_data, graph_pb2.GraphDef())

    # Find all ops and kernels used by the graph.
    for node_def in graph_def.node:
      if not node_def.device:
        node_def.device = '/cpu:0'
      kernel_class = pywrap_tensorflow.TryFindKernelClass(
          node_def.SerializeToString())
      if kernel_class:
        op_and_kernel = (str(node_def.op), kernel_class.decode('utf-8'))
        if op_and_kernel not in ops:
          ops.add(op_and_kernel)
      else:
        print(
            'Warning: no kernel found for op %s' % node_def.op, file=sys.stderr)

  # Add default ops.
  if default_ops_str != 'all':
    for s in default_ops_str.split(','):
      op, kernel = s.split(':')
      op_and_kernel = (op, kernel)
      if op_and_kernel not in ops:
        ops.add(op_and_kernel)

  return list(sorted(ops))
Example #4
Source File: selective_registration_header_lib.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def get_ops_and_kernels(proto_fileformat, proto_files, default_ops_str):
  """Gets the ops and kernels needed from the model files."""
  ops = set()

  for proto_file in proto_files:
    tf_logging.info('Loading proto file %s', proto_file)
    # Load GraphDef.
    file_data = gfile.GFile(proto_file, 'rb').read()
    if proto_fileformat == 'rawproto':
      graph_def = graph_pb2.GraphDef.FromString(file_data)
    else:
      assert proto_fileformat == 'textproto'
      graph_def = text_format.Parse(file_data, graph_pb2.GraphDef())

    # Find all ops and kernels used by the graph.
    for node_def in graph_def.node:
      if not node_def.device:
        node_def.device = '/cpu:0'
      kernel_class = pywrap_tensorflow.TryFindKernelClass(
          node_def.SerializeToString())
      if kernel_class:
        op_and_kernel = (str(node_def.op), kernel_class.decode('utf-8'))
        if op_and_kernel not in ops:
          ops.add(op_and_kernel)
      else:
        print(
            'Warning: no kernel found for op %s' % node_def.op, file=sys.stderr)

  # Add default ops.
  if default_ops_str and default_ops_str != 'all':
    for s in default_ops_str.split(','):
      op, kernel = s.split(':')
      op_and_kernel = (op, kernel)
      if op_and_kernel not in ops:
        ops.add(op_and_kernel)

  return list(sorted(ops))
Example #5
Source File: print_selective_registration_header.py    From keras-lambda with MIT License 5 votes vote down vote up 
def get_ops_and_kernels(proto_fileformat, proto_files, default_ops_str):
  """Gets the ops and kernels needed from the model files."""
  ops = set()

  for proto_file in proto_files:
    tf_logging.info('Loading proto file %s', proto_file)
    # Load GraphDef.
    file_data = gfile.GFile(proto_file).read()
    if proto_fileformat == 'rawproto':
      graph_def = graph_pb2.GraphDef.FromString(file_data)
    else:
      assert proto_fileformat == 'textproto'
      graph_def = text_format.Parse(file_data, graph_pb2.GraphDef())

    # Find all ops and kernels used by the graph.
    for node_def in graph_def.node:
      if not node_def.device:
        node_def.device = '/cpu:0'
      kernel_class = pywrap_tensorflow.TryFindKernelClass(
          node_def.SerializeToString())
      if kernel_class:
        op_and_kernel = (str(node_def.op), kernel_class.decode('utf-8'))
        if op_and_kernel not in ops:
          ops.add(op_and_kernel)
      else:
        print(
            'Warning: no kernel found for op %s' % node_def.op, file=sys.stderr)

  # Add default ops.
  if default_ops_str != 'all':
    for s in default_ops_str.split(','):
      op, kernel = s.split(':')
      op_and_kernel = (op, kernel)
      if op_and_kernel not in ops:
        ops.add(op_and_kernel)

  return list(sorted(ops))