wehub-resource-sync
65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
# Copyright 2021 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 irfft2d."""
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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 ExtraConvertOptions
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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 register_make_test_function
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@register_make_test_function()
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def make_irfft2d_tests(options):
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"""Make a set of tests to do irfft2d."""
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test_parameters = [{
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"input_dtype": [tf.complex64],
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"input_shape": [[4, 3]],
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"fft_length": [[4, 4], [2, 2], [2, 4]]
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}, {
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"input_dtype": [tf.complex64],
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"input_shape": [[3, 8, 5]],
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"fft_length": [[2, 4], [2, 8], [8, 8]]
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}, {
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"input_dtype": [tf.complex64],
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"input_shape": [[3, 1, 9]],
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"fft_length": [[1, 8], [1, 16]]
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}]
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def build_graph(parameters):
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input_value = tf.compat.v1.placeholder(
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dtype=parameters["input_dtype"],
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name="input",
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shape=parameters["input_shape"])
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outs = tf.signal.irfft2d(input_value, fft_length=parameters["fft_length"])
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return [input_value], [outs]
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def build_inputs(parameters, sess, inputs, outputs):
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rfft_length = []
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rfft_length.append(parameters["input_shape"][-2])
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rfft_length.append((parameters["input_shape"][-1] - 1) * 2)
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rfft_input = create_tensor_data(np.float32, parameters["input_shape"])
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rfft_result = np.fft.rfft2(rfft_input, rfft_length)
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return [rfft_result], sess.run(
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outputs, feed_dict=dict(zip(inputs, [rfft_result])))
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extra_convert_options = ExtraConvertOptions()
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extra_convert_options.allow_custom_ops = True
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs,
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extra_convert_options)
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