wehub-resource-sync
96 lines
3.4 KiB
Python
96 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 reshape."""
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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_reshape_tests(options):
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"""Make a set of tests to do reshape."""
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# All shapes below are suitable for tensors with 420 elements.
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test_parameters = [
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{
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"dtype": [tf.float32, tf.int32],
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"input_shape": [[3, 4, 5, 7], [4, 105], [21, 5, 2, 2], [420]],
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"output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]],
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"constant_shape": [True, False],
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"fully_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape": [[1]],
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"output_shape": [[]],
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"constant_shape": [True, False],
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"fully_quantize": [False],
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},
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{
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"dtype": [tf.float32],
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"input_shape": [[3, 4, 5, 7], [4, 105], [21, 5, 2, 2], [420]],
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"output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]],
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"constant_shape": [True],
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"fully_quantize": [True],
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},
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{
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# Zero in input shape.
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"dtype": [tf.float32],
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"input_shape": [[1, 4, 0]],
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"output_shape": [[2, -1], [2, 0, -1]],
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"constant_shape": [True, False],
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"fully_quantize": [False],
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}
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]
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def build_graph(parameters):
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"""Build the graph for reshape tests."""
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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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# Get shape as either a placeholder or constants.
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if parameters["constant_shape"]:
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output_shape = parameters["output_shape"]
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input_tensors = [input_tensor]
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else:
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# The shape of the shape tensor.
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shape_tensor_shape = [len(parameters["output_shape"])]
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output_shape = tf.compat.v1.placeholder(
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dtype=tf.int32, name="output_shape", shape=shape_tensor_shape)
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input_tensors = [input_tensor, output_shape]
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out = tf.reshape(input_tensor, shape=output_shape)
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return input_tensors, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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"""Build inputs for reshape op."""
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values = [
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create_tensor_data(
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parameters["dtype"],
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parameters["input_shape"],
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min_value=-1,
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max_value=1)
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]
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if not parameters["constant_shape"]:
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values.append(np.array(parameters["output_shape"]))
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return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
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