wehub-resource-sync
67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test configs for constant ops."""
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import numpy as np
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import tensorflow as tf
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from tensorflow.lite.testing.zip_test_utils import create_tensor_data
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from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
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from tensorflow.lite.testing.zip_test_utils import MAP_TF_TO_NUMPY_TYPE
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from tensorflow.lite.testing.zip_test_utils import register_make_test_function
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# This function tests various TensorFLow functions that generates Const op,
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# including `tf.ones`, `tf.zeros` and random functions.
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@register_make_test_function()
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def make_constant_tests(options):
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"""Make a set of tests to do constant ops."""
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test_parameters = [{
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"dtype": [tf.float32, tf.int32],
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"input_shape": [[], [1], [2], [1, 1, 1, 1], [2, 2, 2, 2]],
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"constant_is_also_output": [True, False],
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# Models should not be rejected regardless whether it has unread inputs.
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"has_unread_input": [True, False],
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}]
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def build_graph(parameters):
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"""Build a constant graph given `parameters`."""
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dummy_input = tf.compat.v1.placeholder(
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dtype=parameters["dtype"],
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name="input1",
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shape=parameters["input_shape"])
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constant = tf.constant(
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create_tensor_data(parameters["dtype"], parameters["input_shape"]))
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outputs = [tf.maximum(dummy_input, constant)]
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if parameters["constant_is_also_output"]:
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outputs.append(constant)
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inputs = [dummy_input]
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if parameters["has_unread_input"]:
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unread_input = tf.compat.v1.placeholder(
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dtype=parameters["dtype"],
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name="unread_input",
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shape=parameters["input_shape"])
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inputs.append(unread_input)
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return inputs, outputs
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def build_inputs(parameters, sess, inputs, outputs):
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dummy_input = np.zeros(
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parameters["input_shape"],
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dtype=MAP_TF_TO_NUMPY_TYPE[parameters["dtype"]])
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return [dummy_input], sess.run(outputs, feed_dict={inputs[0]: dummy_input})
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
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