193 lines
7.5 KiB
C++
193 lines
7.5 KiB
C++
/* Copyright 2019 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/c/common.h"
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#include "tensorflow/lite/micro/all_ops_resolver.h"
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#include "tensorflow/lite/micro/debug_log.h"
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#include "tensorflow/lite/micro/kernels/kernel_runner.h"
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#include "tensorflow/lite/micro/test_helpers.h"
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#include "tensorflow/lite/micro/testing/micro_test.h"
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namespace tflite {
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namespace testing {
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void TestElementwiseFloat(const TfLiteRegistration& registration,
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const int* input_dims_data, const float* input_data,
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const int* output_dims_data,
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const float* expected_output_data,
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float* output_data) {
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TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
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TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data);
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const int output_dims_count = ElementCount(*output_dims);
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constexpr int input_size = 1;
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constexpr int output_size = 1;
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constexpr int tensors_size = input_size + output_size;
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TfLiteTensor tensors[tensors_size] = {CreateTensor(input_data, input_dims),
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CreateTensor(output_data, output_dims)};
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// Place a unique value in the uninitialized output buffer.
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for (int i = 0; i < output_dims_count; ++i) {
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output_data[i] = 23;
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}
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static int inputs_array_data[] = {1, 0};
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TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
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static int outputs_array_data[] = {1, 1};
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TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
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micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
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outputs_array,
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/*builtin_data=*/nullptr, micro_test::reporter);
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare());
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke());
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for (int i = 0; i < output_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_NEAR(expected_output_data[i], output_data[i], 1e-5f);
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}
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}
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void TestElementwiseBool(const TfLiteRegistration& registration,
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const int* input_dims_data, const bool* input_data,
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const int* output_dims_data,
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const bool* expected_output_data, bool* output_data) {
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TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
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TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data);
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const int output_dims_count = ElementCount(*output_dims);
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constexpr int input_size = 1;
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constexpr int output_size = 1;
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constexpr int tensors_size = input_size + output_size;
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TfLiteTensor tensors[tensors_size] = {CreateTensor(input_data, input_dims),
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CreateTensor(output_data, output_dims)};
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// Place false in the uninitialized output buffer.
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for (int i = 0; i < output_dims_count; ++i) {
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output_data[i] = false;
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}
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const int inputs_array_data[] = {1, 0};
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TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
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const int outputs_array_data[] = {1, 1};
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TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
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micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
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outputs_array,
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/*builtin_data=*/nullptr, micro_test::reporter);
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare());
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke());
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for (int i = 0; i < output_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_EQ(expected_output_data[i], output_data[i]);
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}
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}
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} // namespace testing
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} // namespace tflite
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TF_LITE_MICRO_TESTS_BEGIN
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TF_LITE_MICRO_TEST(Abs) {
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constexpr int output_dims_count = 4;
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const int shape[] = {2, 2, 2};
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const float input[] = {0.01, -0.01, 10, -10};
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const float golden[] = {0.01, 0.01, 10, 10};
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float output_data[output_dims_count];
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tflite::testing::TestElementwiseFloat(tflite::ops::micro::Register_ABS(),
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shape, input, shape, golden,
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output_data);
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}
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TF_LITE_MICRO_TEST(Sin) {
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constexpr int output_dims_count = 4;
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const int shape[] = {2, 2, 2};
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const float input[] = {0, 3.1415926, -3.1415926, 1};
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const float golden[] = {0, 0, 0, 0.84147};
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float output_data[output_dims_count];
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tflite::testing::TestElementwiseFloat(tflite::ops::micro::Register_SIN(),
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shape, input, shape, golden,
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output_data);
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}
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TF_LITE_MICRO_TEST(Cos) {
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constexpr int output_dims_count = 4;
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const int shape[] = {2, 2, 2};
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const float input[] = {0, 3.1415926, -3.1415926, 1};
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const float golden[] = {1, -1, -1, 0.54030};
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float output_data[output_dims_count];
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tflite::testing::TestElementwiseFloat(tflite::ops::micro::Register_COS(),
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shape, input, shape, golden,
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output_data);
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}
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TF_LITE_MICRO_TEST(Log) {
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constexpr int output_dims_count = 4;
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const int shape[] = {2, 2, 2};
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const float input[] = {1, 2.7182818, 0.5, 2};
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const float golden[] = {0, 1, -0.6931472, 0.6931472};
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float output_data[output_dims_count];
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tflite::testing::TestElementwiseFloat(tflite::ops::micro::Register_LOG(),
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shape, input, shape, golden,
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output_data);
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}
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TF_LITE_MICRO_TEST(Sqrt) {
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constexpr int output_dims_count = 4;
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const int shape[] = {2, 2, 2};
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const float input[] = {0, 1, 2, 4};
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const float golden[] = {0, 1, 1.41421, 2};
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float output_data[output_dims_count];
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tflite::testing::TestElementwiseFloat(tflite::ops::micro::Register_SQRT(),
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shape, input, shape, golden,
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output_data);
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}
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TF_LITE_MICRO_TEST(Rsqrt) {
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constexpr int output_dims_count = 4;
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const int shape[] = {2, 2, 2};
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const float input[] = {1, 2, 4, 9};
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const float golden[] = {1, 0.7071, 0.5, 0.33333};
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float output_data[output_dims_count];
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tflite::testing::TestElementwiseFloat(tflite::ops::micro::Register_RSQRT(),
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shape, input, shape, golden,
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output_data);
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}
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TF_LITE_MICRO_TEST(Square) {
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constexpr int output_dims_count = 4;
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const int shape[] = {2, 2, 2};
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const float input[] = {1, 2, 0.5, -3.0};
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const float golden[] = {1, 4.0, 0.25, 9.0};
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float output_data[output_dims_count];
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tflite::testing::TestElementwiseFloat(tflite::ops::micro::Register_SQUARE(),
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shape, input, shape, golden,
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output_data);
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}
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TF_LITE_MICRO_TEST(LogicalNot) {
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constexpr int output_dims_count = 4;
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const int shape[] = {2, 2, 2};
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const bool input[] = {true, false, false, true};
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const bool golden[] = {false, true, true, false};
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bool output_data[output_dims_count];
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tflite::testing::TestElementwiseBool(
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tflite::ops::micro::Register_LOGICAL_NOT(), shape, input, shape, golden,
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output_data);
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}
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TF_LITE_MICRO_TESTS_END
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