tflite-micro/tensorflow/lite/micro/kernels/cast_test.cc

/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/micro/kernels/kernel_runner.h"
#include "tensorflow/lite/micro/test_helpers.h"
#include "tensorflow/lite/micro/testing/micro_test_v2.h"

namespace tflite {
namespace testing {
namespace {

template <typename inputT, typename outputT>
void TestCast(int* input_dims_data, const inputT* input_data,
              const outputT* expected_output_data, outputT* output_data) {
  TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
  TfLiteIntArray* output_dims = IntArrayFromInts(input_dims_data);
  const int output_dims_count = ElementCount(*output_dims);
  constexpr int inputs_size = 1;
  constexpr int outputs_size = 1;
  constexpr int tensors_size = inputs_size + outputs_size;
  TfLiteTensor tensors[tensors_size] = {
      CreateTensor(input_data, input_dims),
      CreateTensor(output_data, output_dims),
  };

  int inputs_array_data[] = {1, 0};
  TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
  int outputs_array_data[] = {1, 1};
  TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);

  const TFLMRegistration registration = Register_CAST();
  micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
                             outputs_array,
                             /*builtin_data=*/nullptr);

  EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare());
  EXPECT_EQ(kTfLiteOk, runner.Invoke());

  for (int i = 0; i < output_dims_count; ++i) {
    EXPECT_EQ(expected_output_data[i], output_data[i]);
  }
}

}  // namespace
}  // namespace testing
}  // namespace tflite

TEST(CastTest, CastFloatToInt8) {
  int8_t output_data[6];
  int input_dims[] = {2, 3, 2};

  // TODO(b/178391195): Test negative and out-of-range numbers.
  const float input_values[] = {100.f, 1.0f, 0.f, 0.4f, 1.999f, 1.1f};
  const int8_t golden[] = {100, 1, 0, 0, 1, 1};
  tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}

TEST(CastTest, CastFloatToInt16) {
  int16_t output_data[6];
  int input_dims[] = {2, 3, 2};

  // TODO(b/178391195): Test negative and out-of-range numbers.
  const float input_values[] = {100.f, 1.0f, 0.f, 0.4f, 1.999f, 1.1f};
  const int16_t golden[] = {100, 1, 0, 0, 1, 1};
  tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}

TEST(CastTest, CastInt8ToFloat) {
  float output_data[6];
  int input_dims[] = {2, 3, 2};
  const int8_t input_values[] = {123, 0, 1, 2, 3, 4};
  const float golden[] = {123.f, 0.f, 1.f, 2.f, 3.f, 4.f};
  tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}

TEST(CastTest, CastInt16ToFloat) {
  float output_data[6];
  int input_dims[] = {2, 3, 2};
  const int16_t input_values[] = {123, 0, 1, 2, 3, 4};
  const float golden[] = {123.f, 0.f, 1.f, 2.f, 3.f, 4.f};
  tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}

TEST(CastTest, CastInt16ToInt32) {
  int32_t output_data[6];
  int input_dims[] = {2, 3, 2};
  const int16_t input_values[] = {123, 0, 1, 2, 3, 4};
  const int32_t golden[] = {123, 0, 1, 2, 3, 4};
  tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}

TEST(CastTest, CastInt32ToInt16) {
  int16_t output_data[6];
  int input_dims[] = {2, 3, 2};
  const int32_t input_values[] = {123, 0, 1, 2, 3, 4};
  const int16_t golden[] = {123, 0, 1, 2, 3, 4};
  tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}

TEST(CastTest, CastUInt32ToInt32) {
  int32_t output_data[6];
  int input_dims[] = {2, 2, 3};
  const uint32_t input_values[] = {100, 200, 300, 400, 500, 600};
  const int32_t golden[] = {100, 200, 300, 400, 500, 600};
  tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}

TEST(CastTest, CastInt32ToUInt32) {
  uint32_t output_data[6];
  int input_dims[] = {2, 2, 3};
  const int32_t input_values[] = {100, 200, 300, 400, 500, 600};
  const uint32_t golden[] = {100, 200, 300, 400, 500, 600};
  tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}

TEST(CastTest, CastBoolToFloat) {
  float output_data[6];
  int input_dims[] = {2, 2, 3};
  const bool input_values[] = {true, true, false, true, false, true};
  const float golden[] = {1.f, 1.0f, 0.f, 1.0f, 0.0f, 1.0f};
  tflite::testing::TestCast(input_dims, input_values, golden, output_data);
}

TF_LITE_MICRO_TESTS_MAIN