wehub-resource-sync
120 lines
3.9 KiB
C++
120 lines
3.9 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "tensorflow/lite/tools/utils.h"
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#include <sys/types.h>
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#include <cstdint>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include "absl/types/span.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/types/half.h"
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namespace tflite::tools {
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namespace {
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using ::testing::ElementsAre;
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using ::testing::FloatEq;
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using ::testing::SizeIs;
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// Helper function to test TfLiteTensorToFloat32Array.
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template <typename T>
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void TestTfLiteTensorToFloat32Array(TfLiteType type) {
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T data[] = {1, 2, 3, 4};
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TfLiteTensor tensor;
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tensor.data.data = data;
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tensor.type = type;
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// Create an int array with 1 dimension and the array size is 4.
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tensor.dims = TfLiteIntArrayCreate(1);
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tensor.dims->data[0] = 4;
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std::vector<float> result(4, 0.0);
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const auto status =
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utils::TfLiteTensorToFloat32Array(tensor, absl::MakeSpan(result));
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TfLiteIntArrayFree(tensor.dims);
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ASSERT_EQ(status, kTfLiteOk);
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ASSERT_EQ(result.size(), 4);
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for (int i = 0; i < 4; ++i) {
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EXPECT_THAT(result[i], FloatEq(static_cast<float>(data[i])));
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}
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}
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// Helper function to test TfLiteTensorToFloat32Array.
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template <typename T>
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void TestTfLiteTensorToInt64Array(TfLiteType type) {
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T data[] = {1, 2, 3, 4};
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TfLiteTensor tensor;
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tensor.data.data = data;
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tensor.type = type;
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// Create an int array with 1 dimension and the array size is 4.
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tensor.dims = TfLiteIntArrayCreate(1);
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tensor.dims->data[0] = 4;
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std::vector<int64_t> result(4, 0);
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const auto status =
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utils::TfLiteTensorToInt64Array(tensor, absl::MakeSpan(result));
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TfLiteIntArrayFree(tensor.dims);
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ASSERT_EQ(status, kTfLiteOk);
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ASSERT_EQ(result.size(), 4);
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for (int i = 0; i < 4; ++i) {
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EXPECT_EQ(result[i], static_cast<int64_t>(data[i]));
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}
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}
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// Tests TfLiteTensorToFloat32Array for supported TfLiteTypes.
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TEST(Utils, TfLiteTensorToFloat32Array) {
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TestTfLiteTensorToFloat32Array<float>(kTfLiteFloat32);
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TestTfLiteTensorToFloat32Array<double>(kTfLiteFloat64);
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}
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// Tests TfLiteTensorToFloat32Array for kTfLiteFloat16.
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TEST(Utils, TfLiteTensorToFloat32ArrayFp16) {
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half data[] = {
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half(1.0f),
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half(2.0f),
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half(3.0f),
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half(4.0f),
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};
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TfLiteTensor tensor;
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tensor.data.data = data;
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tensor.type = kTfLiteFloat16;
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// Create an int array with 1 dimension and the array size is 4.
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tensor.dims = TfLiteIntArrayCreate(1);
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tensor.dims->data[0] = 4;
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std::vector<float> result(4, 0.0);
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const TfLiteStatus status =
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utils::TfLiteTensorToFloat32Array(tensor, absl::MakeSpan(result));
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TfLiteIntArrayFree(tensor.dims);
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ASSERT_EQ(status, kTfLiteOk);
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ASSERT_THAT(result, SizeIs(4));
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EXPECT_THAT(result, ElementsAre(FloatEq(1.0f), FloatEq(2.0f), FloatEq(3.0f),
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FloatEq(4.0f)));
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}
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TEST(Utils, TfLiteTensorToInt64Array) {
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TestTfLiteTensorToInt64Array<int8_t>(kTfLiteInt8);
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TestTfLiteTensorToInt64Array<uint8_t>(kTfLiteUInt8);
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TestTfLiteTensorToInt64Array<int16_t>(kTfLiteInt16);
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TestTfLiteTensorToInt64Array<uint16_t>(kTfLiteUInt16);
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TestTfLiteTensorToInt64Array<int>(kTfLiteInt32);
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TestTfLiteTensorToInt64Array<uint32_t>(kTfLiteUInt32);
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TestTfLiteTensorToInt64Array<int64_t>(kTfLiteInt64);
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TestTfLiteTensorToInt64Array<uint64_t>(kTfLiteUInt64);
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}
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} // namespace
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} // namespace tflite::tools
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