4ce6a9b7a4
The C types in lite/c/c_api_internal.h are not actually internal, rather, they are common types used throughout the C++ and C APIs. Rename the header accordingly. PiperOrigin-RevId: 282494601 Change-Id: Ia784f35724d774db256ffcbbcdc5bb00e6574417
93 lines
3.5 KiB
C++
93 lines
3.5 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/experimental/micro/kernels/all_ops_resolver.h"
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#include "tensorflow/lite/experimental/micro/testing/micro_test.h"
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#include "tensorflow/lite/experimental/micro/testing/test_utils.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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CreateFloatTensor(input_data, input_dims, "input_tensor"),
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CreateFloatTensor(output_data, output_dims, "output_tensor"),
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};
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TfLiteContext context;
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PopulateContext(tensors, tensors_size, &context);
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::tflite::ops::micro::AllOpsResolver resolver;
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const TfLiteRegistration* registration =
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resolver.FindOp(tflite::BuiltinOperator_CEIL, 1);
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TF_LITE_MICRO_EXPECT_NE(nullptr, registration);
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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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int temporaries_array_data[] = {0};
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TfLiteIntArray* temporaries_array = IntArrayFromInts(temporaries_array_data);
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TfLiteNode node;
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node.inputs = inputs_array;
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node.outputs = outputs_array;
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node.temporaries = temporaries_array;
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node.user_data = nullptr;
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node.builtin_data = nullptr;
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node.custom_initial_data = nullptr;
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node.custom_initial_data_size = 0;
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node.delegate = nullptr;
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TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke);
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node));
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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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