84 lines
3.0 KiB
C++
84 lines
3.0 KiB
C++
/* Copyright 2018 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/builtin_op_data.h"
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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/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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namespace {
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void TestCeil(const int* input_dims_data, const float* input_data,
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const float* expected_output_data, float* output_data) {
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TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
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TfLiteIntArray* output_dims = IntArrayFromInts(input_dims_data);
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const int output_dims_count = ElementCount(*output_dims);
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constexpr int inputs_size = 1;
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constexpr int outputs_size = 1;
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constexpr int tensors_size = inputs_size + outputs_size;
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TfLiteTensor tensors[tensors_size] = {
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CreateTensor(input_data, input_dims),
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CreateTensor(output_data, output_dims),
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};
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int inputs_array_data[] = {1, 0};
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TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
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int outputs_array_data[] = {1, 1};
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TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
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const TfLiteRegistration registration = ops::micro::Register_CEIL();
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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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} // namespace
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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(SingleDim) {
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float output_data[2];
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const int input_dims[] = {1, 2};
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const float input_values[] = {8.5, 0.0};
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const float golden[] = {9, 0};
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tflite::testing::TestCeil(input_dims, input_values, golden, output_data);
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}
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TF_LITE_MICRO_TEST(MultiDims) {
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float output_data[10];
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const int input_dims[] = {4, 2, 1, 1, 5};
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const float input_values[] = {
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0.0001, 8.0001, 0.9999, 9.9999, 0.5,
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-0.0001, -8.0001, -0.9999, -9.9999, -0.5,
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};
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const float golden[] = {1, 9, 1, 10, 1, 0, -8, 0, -9, 0};
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tflite::testing::TestCeil(input_dims, input_values, golden, output_data);
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}
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TF_LITE_MICRO_TESTS_END
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