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1# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""Test utilities for tf.signal."""
16
17from tensorflow.core.protobuf import config_pb2
18from tensorflow.lite.python import interpreter
19from tensorflow.lite.python import lite
20from tensorflow.python.eager import def_function
21from tensorflow.python.grappler import tf_optimizer
22from tensorflow.python.training import saver
23
24
25def grappler_optimize(graph, fetches=None, config_proto=None):
26  """Tries to optimize the provided graph using grappler.
27
28  Args:
29    graph: A `tf.Graph` instance containing the graph to optimize.
30    fetches: An optional list of `Tensor`s to fetch (i.e. not optimize away).
31      Grappler uses the 'train_op' collection to look for fetches, so if not
32      provided this collection should be non-empty.
33    config_proto: An optional `tf.compat.v1.ConfigProto` to use when rewriting
34      the graph.
35
36  Returns:
37    A `tf.compat.v1.GraphDef` containing the rewritten graph.
38  """
39  if config_proto is None:
40    config_proto = config_pb2.ConfigProto()
41    config_proto.graph_options.rewrite_options.min_graph_nodes = -1
42  if fetches is not None:
43    for fetch in fetches:
44      graph.add_to_collection('train_op', fetch)
45  metagraph = saver.export_meta_graph(graph_def=graph.as_graph_def())
46  return tf_optimizer.OptimizeGraph(config_proto, metagraph)
47
48
49def tflite_convert(fn, input_templates):
50  """Converts the provided fn to tf.lite model.
51
52  Args:
53    fn: A callable that expects a list of inputs like input_templates that
54      returns a tensor or structure of tensors.
55    input_templates: A list of Tensors, ndarrays or TensorSpecs describing the
56      inputs that fn expects. The actual values of the Tensors or ndarrays are
57      unused.
58
59  Returns:
60    The serialized tf.lite model.
61  """
62  fn = def_function.function(fn)
63  concrete_func = fn.get_concrete_function(*input_templates)
64  converter = lite.TFLiteConverterV2([concrete_func])
65  return converter.convert()
66
67
68def evaluate_tflite_model(tflite_model, input_ndarrays):
69  """Evaluates the provided tf.lite model with the given input ndarrays.
70
71  Args:
72    tflite_model: bytes. The serialized tf.lite model.
73    input_ndarrays: A list of NumPy arrays to feed as input to the model.
74
75  Returns:
76    A list of ndarrays produced by the model.
77
78  Raises:
79    ValueError: If the number of input arrays does not match the number of
80      inputs the model expects.
81  """
82  the_interpreter = interpreter.Interpreter(model_content=tflite_model)
83  the_interpreter.allocate_tensors()
84
85  input_details = the_interpreter.get_input_details()
86  output_details = the_interpreter.get_output_details()
87
88  if len(input_details) != len(input_ndarrays):
89    raise ValueError('Wrong number of inputs: provided=%s, '
90                     'input_details=%s output_details=%s' % (
91                         input_ndarrays, input_details, output_details))
92  for input_tensor, data in zip(input_details, input_ndarrays):
93    the_interpreter.set_tensor(input_tensor['index'], data)
94  the_interpreter.invoke()
95  return [the_interpreter.get_tensor(details['index'])
96          for details in output_details]
97