wehub-resource-sync
104 lines
3.4 KiB
Python
104 lines
3.4 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 transpose."""
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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 register_make_test_function
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@register_make_test_function()
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def make_transpose_tests(options):
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"""Make a set of tests to do transpose."""
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# TODO(nupurgarg): Add test for uint8.
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test_parameters = [{
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"dtype": [tf.int32, tf.int64, tf.float32],
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"input_shape": [[2, 2, 3]],
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"perm": [[0, 1, 2], [0, 2, 1]],
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"constant_perm": [True, False],
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"fully_quantize": [False],
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}, {
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"dtype": [tf.float32],
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"input_shape": [[1, 2, 3, 4]],
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"perm": [[0, 1, 2, 3], [3, 0, 1, 2]],
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"constant_perm": [True, False],
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"fully_quantize": [False],
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}, {
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"dtype": [tf.float32],
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"input_shape": [[1, 2, 3, 4, 5]],
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"perm": [[4, 3, 2, 1, 0]],
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"constant_perm": [True, False],
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"fully_quantize": [False],
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}, {
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"dtype": [tf.float32],
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"input_shape": [[2, 2, 3]],
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"perm": [[0, 1, 2], [0, 2, 1]],
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"constant_perm": [True],
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"fully_quantize": [True],
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}, {
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"dtype": [tf.float32],
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"input_shape": [[1, 2, 3, 4]],
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"perm": [[0, 1, 2, 3], [3, 0, 1, 2]],
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"constant_perm": [True],
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"fully_quantize": [True],
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}, {
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"dtype": [tf.float32],
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"input_shape": [[1, 2, 3, 4, 5]],
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"perm": [[0, 1, 2, 3, 4], [3, 4, 0, 1, 2]],
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"constant_perm": [True],
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"fully_quantize": [True, False],
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}, {
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"dtype": [tf.float32],
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"input_shape": [[2, 2]],
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"perm": [[-2, -1]],
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"constant_perm": [True, False],
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"fully_quantize": [False],
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}]
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def build_graph(parameters):
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"""Build a transpose graph given `parameters`."""
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input_tensor = tf.compat.v1.placeholder(
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dtype=parameters["dtype"],
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name="input",
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shape=parameters["input_shape"])
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if parameters["constant_perm"]:
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perm = parameters["perm"]
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input_tensors = [input_tensor]
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else:
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shape = len(parameters["perm"])
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perm = tf.compat.v1.placeholder(dtype=tf.int32, name="perm", shape=shape)
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input_tensors = [input_tensor, perm]
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out = tf.transpose(a=input_tensor, perm=perm)
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return input_tensors, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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values = [
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create_tensor_data(parameters["dtype"], parameters["input_shape"],
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min_value=-1, max_value=1)
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]
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if not parameters["constant_perm"]:
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values.append(np.array(parameters["perm"]))
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return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
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make_zip_of_tests(
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options,
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test_parameters,
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build_graph,
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build_inputs)
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