| /* Copyright 2022 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.h" |
| |
| namespace tflite { |
| namespace testing { |
| namespace { |
| |
| void TestNegFloat(int* input_dims_data, const float* input_data, |
| const float* expected_output_data, int* output_dims_data, |
| float* output_data) { |
| TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data); |
| TfLiteIntArray* output_dims = IntArrayFromInts(output_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_NEG(); |
| micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array, |
| outputs_array, |
| /*builtin_data=*/nullptr); |
| |
| TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.InitAndPrepare()); |
| TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, runner.Invoke()); |
| |
| TF_LITE_MICRO_EXPECT_EQ(expected_output_data[0], output_data[0]); |
| for (int i = 0; i < output_dims_count; ++i) { |
| TF_LITE_MICRO_EXPECT_EQ(expected_output_data[i], output_data[i]); |
| } |
| } |
| |
| } // namespace |
| } // namespace testing |
| } // namespace tflite |
| |
| TF_LITE_MICRO_TESTS_BEGIN |
| |
| TF_LITE_MICRO_TEST(NegOpSingleFloat) { |
| int dims[] = {1, 2}; |
| const float input_data[] = {8.5, 0.0}; |
| const float golden[] = {-8.5, 0.0}; |
| float output_data[2]; |
| |
| tflite::testing::TestNegFloat(dims, input_data, golden, dims, output_data); |
| } |
| |
| TF_LITE_MICRO_TEST(NegOpFloat) { |
| int dims[] = {2, 2, 3}; |
| const float input_data[] = {-2.0f, -1.0f, 0.f, 1.0f, 2.0f, 3.0f}; |
| const float golden[] = {2.0f, 1.0f, -0.f, -1.0f, -2.0f, -3.0f}; |
| float output_data[6]; |
| |
| tflite::testing::TestNegFloat(dims, input_data, golden, dims, output_data); |
| } |
| |
| TF_LITE_MICRO_TESTS_END |