460 lines
18 KiB
C++
460 lines
18 KiB
C++
/* Copyright 2023 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/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 TestSplitTwoOutputsFloat(int* input_dims_data, const float* input_data,
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int* axis_dims_data, const int32_t* axis_data,
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int* output1_dims_data,
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const float* expected_output1_data,
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int* output2_dims_data,
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const float* expected_output2_data,
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float* output1_data, float* output2_data) {
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TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
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TfLiteIntArray* axis_dims = IntArrayFromInts(axis_dims_data);
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TfLiteIntArray* output1_dims = IntArrayFromInts(output1_dims_data);
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TfLiteIntArray* output2_dims = IntArrayFromInts(output2_dims_data);
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const int output1_dims_count = ElementCount(*output1_dims);
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const int output2_dims_count = ElementCount(*output2_dims);
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constexpr int input_size = 1;
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constexpr int output_size = 2;
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constexpr int axis_size = 1;
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constexpr int tensors_size = input_size + output_size + axis_size;
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TfLiteTensor tensors[tensors_size] = {
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CreateTensor(axis_data, axis_dims), CreateTensor(input_data, input_dims),
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CreateTensor(output1_data, output1_dims),
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CreateTensor(output2_data, output2_dims)};
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// Currently only support constant axis tensor.
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tensors[0].allocation_type = kTfLiteMmapRo;
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// Place a unique value in the uninitialized output buffer.
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for (int i = 0; i < output1_dims_count; ++i) {
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output1_data[i] = 23;
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}
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for (int i = 0; i < output2_dims_count; ++i) {
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output2_data[i] = 23;
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}
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int inputs_array_data[] = {2, 0, 1};
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TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
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int outputs_array_data[] = {2, 2, 3};
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TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
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const TFLMRegistration registration = Register_SPLIT();
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micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
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outputs_array, nullptr);
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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 < output1_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_NEAR(expected_output1_data[i], output1_data[i], 1e-5f);
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}
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for (int i = 0; i < output2_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_NEAR(expected_output2_data[i], output2_data[i], 1e-5f);
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}
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}
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void TestSplitFourOutputsFloat(
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int* input_dims_data, const float* input_data, int* axis_dims_data,
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const int32_t* axis_data, int* output1_dims_data,
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const float* expected_output1_data, int* output2_dims_data,
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const float* expected_output2_data, int* output3_dims_data,
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const float* expected_output3_data, int* output4_dims_data,
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const float* expected_output4_data, float* output1_data,
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float* output2_data, float* output3_data, float* output4_data) {
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TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
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TfLiteIntArray* axis_dims = IntArrayFromInts(axis_dims_data);
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TfLiteIntArray* output1_dims = IntArrayFromInts(output1_dims_data);
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TfLiteIntArray* output2_dims = IntArrayFromInts(output2_dims_data);
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TfLiteIntArray* output3_dims = IntArrayFromInts(output3_dims_data);
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TfLiteIntArray* output4_dims = IntArrayFromInts(output4_dims_data);
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const int output1_dims_count = ElementCount(*output1_dims);
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const int output2_dims_count = ElementCount(*output2_dims);
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const int output3_dims_count = ElementCount(*output3_dims);
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const int output4_dims_count = ElementCount(*output4_dims);
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constexpr int input_size = 1;
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constexpr int output_size = 4;
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constexpr int axis_size = 1;
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constexpr int tensors_size = input_size + output_size + axis_size;
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TfLiteTensor tensors[tensors_size] = {
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CreateTensor(axis_data, axis_dims),
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CreateTensor(input_data, input_dims),
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CreateTensor(output1_data, output1_dims),
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CreateTensor(output2_data, output2_dims),
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CreateTensor(output3_data, output1_dims),
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CreateTensor(output4_data, output1_dims)};
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// Currently only support constant axis tensor.
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tensors[0].allocation_type = kTfLiteMmapRo;
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// Place a unique value in the uninitialized output buffer.
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for (int i = 0; i < output1_dims_count; ++i) {
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output1_data[i] = 23;
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}
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for (int i = 0; i < output2_dims_count; ++i) {
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output2_data[i] = 23;
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}
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for (int i = 0; i < output3_dims_count; ++i) {
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output3_data[i] = 23;
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}
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for (int i = 0; i < output4_dims_count; ++i) {
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output4_data[i] = 23;
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}
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int inputs_array_data[] = {2, 0, 1};
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TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
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int outputs_array_data[] = {4, 2, 3, 4, 5};
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TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
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const TFLMRegistration registration = tflite::Register_SPLIT();
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micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
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outputs_array, nullptr);
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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 < output1_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_NEAR(expected_output1_data[i], output1_data[i], 1e-5f);
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}
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for (int i = 0; i < output2_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_NEAR(expected_output2_data[i], output2_data[i], 1e-5f);
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}
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for (int i = 0; i < output3_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_NEAR(expected_output3_data[i], output3_data[i], 1e-5f);
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}
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for (int i = 0; i < output4_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_NEAR(expected_output4_data[i], output4_data[i], 1e-5f);
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}
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}
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void TestSplitTwoOutputsQuantized(int* input_dims_data,
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const int8_t* input_data, int* axis_dims_data,
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const int32_t* axis_data,
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int* output1_dims_data,
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const int8_t* expected_output1_data,
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int* output2_dims_data,
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const int8_t* expected_output2_data,
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int8_t* output1_data, int8_t* output2_data) {
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TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
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TfLiteIntArray* axis_dims = IntArrayFromInts(axis_dims_data);
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TfLiteIntArray* output1_dims = IntArrayFromInts(output1_dims_data);
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TfLiteIntArray* output2_dims = IntArrayFromInts(output2_dims_data);
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const int output1_dims_count = ElementCount(*output1_dims);
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const int output2_dims_count = ElementCount(*output2_dims);
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constexpr int input_size = 1;
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constexpr int output_size = 2;
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constexpr int axis_size = 1;
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constexpr int tensors_size = input_size + output_size + axis_size;
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TfLiteTensor tensors[tensors_size] = {
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CreateTensor(axis_data, axis_dims),
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CreateQuantizedTensor(input_data, input_dims, 0, 10),
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CreateQuantizedTensor(output1_data, output1_dims, 0, 10),
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CreateQuantizedTensor(output2_data, output2_dims, 0, 10)};
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// Currently only support constant axis tensor.
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tensors[0].allocation_type = kTfLiteMmapRo;
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// Place a unique value in the uninitialized output buffer.
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for (int i = 0; i < output1_dims_count; ++i) {
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output1_data[i] = 23;
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}
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for (int i = 0; i < output2_dims_count; ++i) {
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output2_data[i] = 23;
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}
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int inputs_array_data[] = {2, 0, 1};
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TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
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int outputs_array_data[] = {2, 2, 3};
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TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
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const TFLMRegistration registration = tflite::Register_SPLIT();
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micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
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outputs_array, nullptr);
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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 < output1_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_EQ(expected_output1_data[i], output1_data[i]);
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}
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for (int i = 0; i < output2_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_EQ(expected_output2_data[i], output2_data[i]);
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}
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}
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void TestSplitTwoOutputsQuantized32(
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int* input_dims_data, const int32_t* input_data, int* axis_dims_data,
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const int32_t* axis_data, int* output1_dims_data,
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const int32_t* expected_output1_data, int* output2_dims_data,
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const int32_t* expected_output2_data, int32_t* output1_data,
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int32_t* output2_data) {
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TfLiteIntArray* input_dims = IntArrayFromInts(input_dims_data);
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TfLiteIntArray* axis_dims = IntArrayFromInts(axis_dims_data);
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TfLiteIntArray* output1_dims = IntArrayFromInts(output1_dims_data);
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TfLiteIntArray* output2_dims = IntArrayFromInts(output2_dims_data);
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const int output1_dims_count = ElementCount(*output1_dims);
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const int output2_dims_count = ElementCount(*output2_dims);
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constexpr int input_size = 1;
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constexpr int output_size = 2;
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constexpr int axis_size = 1;
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constexpr int tensors_size = input_size + output_size + axis_size;
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TfLiteTensor tensors[tensors_size] = {
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CreateTensor(axis_data, axis_dims), CreateTensor(input_data, input_dims),
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CreateTensor(output1_data, output1_dims),
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CreateTensor(output2_data, output2_dims)};
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// Currently only support constant axis tensor.
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tensors[0].allocation_type = kTfLiteMmapRo;
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// Place a unique value in the uninitialized output buffer.
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for (int i = 0; i < output1_dims_count; ++i) {
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output1_data[i] = 23;
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}
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for (int i = 0; i < output2_dims_count; ++i) {
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output2_data[i] = 23;
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}
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int inputs_array_data[] = {2, 0, 1};
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TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data);
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int outputs_array_data[] = {2, 2, 3};
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TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data);
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const TFLMRegistration registration = tflite::Register_SPLIT();
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micro::KernelRunner runner(registration, tensors, tensors_size, inputs_array,
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outputs_array, nullptr);
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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 < output1_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_EQ(expected_output1_data[i], output1_data[i]);
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}
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for (int i = 0; i < output2_dims_count; ++i) {
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TF_LITE_MICRO_EXPECT_EQ(expected_output2_data[i], output2_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(TwoSplitFourDimensionalAxisZero) {
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int input_shape[] = {4, 2, 2, 2, 2};
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const float input_data[] = {1, 2, 3, 4, 5, 6, 7, 8,
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9, 10, 11, 12, 13, 14, 15, 16};
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int axis_shape[] = {1, 1};
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const int32_t axis_data[] = {0};
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int output1_shape[] = {4, 1, 2, 2, 2};
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const float golden1[] = {1, 2, 3, 4, 5, 6, 7, 8};
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int output2_shape[] = {4, 1, 2, 2, 2};
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const float golden2[] = {9, 10, 11, 12, 13, 14, 15, 16};
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constexpr int output1_dims_count = 8;
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constexpr int output2_dims_count = 8;
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float output1_data[output1_dims_count];
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float output2_data[output2_dims_count];
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tflite::testing::TestSplitTwoOutputsFloat(
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input_shape, input_data, axis_shape, axis_data, output1_shape, golden1,
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output2_shape, golden2, output1_data, output2_data);
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}
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TF_LITE_MICRO_TEST(TwoSplitFourDimensionalAxisOne) {
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int input_shape[] = {4, 2, 2, 2, 2};
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const float input_data[] = {1, 2, 3, 4, 5, 6, 7, 8,
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9, 10, 11, 12, 13, 14, 15, 16};
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int axis_shape[] = {1, 1};
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const int32_t axis_data[] = {1};
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int output1_shape[] = {4, 2, 1, 2, 2};
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const float golden1[] = {1, 2, 3, 4, 9, 10, 11, 12};
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int output2_shape[] = {4, 2, 1, 2, 2};
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const float golden2[] = {5, 6, 7, 8, 13, 14, 15, 16};
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constexpr int output1_dims_count = 8;
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constexpr int output2_dims_count = 8;
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float output1_data[output1_dims_count];
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float output2_data[output2_dims_count];
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tflite::testing::TestSplitTwoOutputsFloat(
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input_shape, input_data, axis_shape, axis_data, output1_shape, golden1,
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output2_shape, golden2, output1_data, output2_data);
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}
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TF_LITE_MICRO_TEST(TwoSplitFourDimensionalAxisTwo) {
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int input_shape[] = {4, 2, 2, 2, 2};
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const float input_data[] = {1, 2, 3, 4, 5, 6, 7, 8,
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9, 10, 11, 12, 13, 14, 15, 16};
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int axis_shape[] = {1, 1};
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const int32_t axis_data[] = {2};
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int output1_shape[] = {4, 2, 2, 1, 2};
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const float golden1[] = {1, 2, 5, 6, 9, 10, 13, 14};
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int output2_shape[] = {4, 2, 2, 1, 2};
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const float golden2[] = {3, 4, 7, 8, 11, 12, 15, 16};
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constexpr int output1_dims_count = 8;
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constexpr int output2_dims_count = 8;
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float output1_data[output1_dims_count];
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float output2_data[output2_dims_count];
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tflite::testing::TestSplitTwoOutputsFloat(
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input_shape, input_data, axis_shape, axis_data, output1_shape, golden1,
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output2_shape, golden2, output1_data, output2_data);
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}
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TF_LITE_MICRO_TEST(TwoSplitFourDimensionalAxisThree) {
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int input_shape[] = {4, 2, 2, 2, 2};
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const float input_data[] = {1, 2, 3, 4, 5, 6, 7, 8,
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9, 10, 11, 12, 13, 14, 15, 16};
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int axis_shape[] = {1, 1};
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const int32_t axis_data[] = {3};
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int output1_shape[] = {4, 2, 2, 2, 1};
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const float golden1[] = {1, 3, 5, 7, 9, 11, 13, 15};
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int output2_shape[] = {4, 2, 2, 2, 1};
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const float golden2[] = {2, 4, 6, 8, 10, 12, 14, 16};
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constexpr int output1_dims_count = 8;
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constexpr int output2_dims_count = 8;
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float output1_data[output1_dims_count];
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float output2_data[output2_dims_count];
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tflite::testing::TestSplitTwoOutputsFloat(
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input_shape, input_data, axis_shape, axis_data, output1_shape, golden1,
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output2_shape, golden2, output1_data, output2_data);
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}
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TF_LITE_MICRO_TEST(TwoSplitFourDimensionalNegativeAxis) {
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int input_shape[] = {4, 2, 2, 2, 2};
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const float input_data[] = {1, 2, 3, 4, 5, 6, 7, 8,
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9, 10, 11, 12, 13, 14, 15, 16};
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int axis_shape[] = {1, 1};
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const int32_t axis_data[] = {-4};
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int output1_shape[] = {4, 1, 2, 2, 2};
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const float golden1[] = {1, 2, 3, 4, 5, 6, 7, 8};
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int output2_shape[] = {4, 1, 2, 2, 2};
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const float golden2[] = {9, 10, 11, 12, 13, 14, 15, 16};
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constexpr int output1_dims_count = 8;
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constexpr int output2_dims_count = 8;
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float output1_data[output1_dims_count];
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float output2_data[output2_dims_count];
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tflite::testing::TestSplitTwoOutputsFloat(
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input_shape, input_data, axis_shape, axis_data, output1_shape, golden1,
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output2_shape, golden2, output1_data, output2_data);
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}
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TF_LITE_MICRO_TEST(FourSplit) {
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int input_shape[] = {1, 4};
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const float input_data[] = {1, 2, 3, 4};
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int axis_shape[] = {1, 1};
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const int32_t axis_data[] = {0};
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int output1_shape[] = {1, 1};
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const float golden1[] = {1};
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int output2_shape[] = {1, 1};
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const float golden2[] = {2};
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int output3_shape[] = {1, 1};
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const float golden3[] = {3};
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int output4_shape[] = {1, 1};
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const float golden4[] = {4};
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|
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constexpr int output1_dims_count = 1;
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constexpr int output2_dims_count = 1;
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constexpr int output3_dims_count = 1;
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constexpr int output4_dims_count = 1;
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float output1_data[output1_dims_count];
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float output2_data[output2_dims_count];
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float output3_data[output3_dims_count];
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|
float output4_data[output4_dims_count];
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|
tflite::testing::TestSplitFourOutputsFloat(
|
|
input_shape, input_data, axis_shape, axis_data, output1_shape, golden1,
|
|
output2_shape, golden2, output3_shape, golden3, output4_shape, golden4,
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|
output1_data, output2_data, output3_data, output4_data);
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|
}
|
|
|
|
TF_LITE_MICRO_TEST(TwoSplitOneDimensional) {
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int input_shape[] = {1, 2};
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const float input_data[] = {1, 2};
|
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int axis_shape[] = {1, 1};
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|
const int32_t axis_data[] = {0};
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int output1_shape[] = {1, 1};
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const float golden1[] = {1};
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|
int output2_shape[] = {1, 1};
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const float golden2[] = {2};
|
|
|
|
constexpr int output1_dims_count = 8;
|
|
constexpr int output2_dims_count = 8;
|
|
float output1_data[output1_dims_count];
|
|
float output2_data[output2_dims_count];
|
|
tflite::testing::TestSplitTwoOutputsFloat(
|
|
input_shape, input_data, axis_shape, axis_data, output1_shape, golden1,
|
|
output2_shape, golden2, output1_data, output2_data);
|
|
}
|
|
|
|
TF_LITE_MICRO_TEST(TwoSplitFourDimensionalQuantized) {
|
|
int input_shape[] = {4, 2, 2, 2, 2};
|
|
const int8_t input_data[] = {1, 2, 3, 4, 5, 6, 7, 8,
|
|
9, 10, 11, 12, 13, 14, 15, 16};
|
|
int axis_shape[] = {1, 1};
|
|
const int32_t axis_data[] = {1};
|
|
int output1_shape[] = {4, 2, 1, 2, 2};
|
|
const int8_t golden1[] = {1, 2, 3, 4, 9, 10, 11, 12};
|
|
int output2_shape[] = {4, 2, 1, 2, 2};
|
|
const int8_t golden2[] = {5, 6, 7, 8, 13, 14, 15, 16};
|
|
|
|
constexpr int output1_dims_count = 8;
|
|
constexpr int output2_dims_count = 8;
|
|
int8_t output1_data[output1_dims_count];
|
|
int8_t output2_data[output2_dims_count];
|
|
tflite::testing::TestSplitTwoOutputsQuantized(
|
|
input_shape, input_data, axis_shape, axis_data, output1_shape, golden1,
|
|
output2_shape, golden2, output1_data, output2_data);
|
|
}
|
|
|
|
TF_LITE_MICRO_TEST(TwoSplitFourDimensionalQuantized32) {
|
|
int input_shape[] = {4, 2, 2, 2, 2};
|
|
const int32_t input_data[] = {1, 2, 3, 4, 5, 6, 7, 8,
|
|
9, 10, 11, 12, 13, 14, 15, 16};
|
|
int axis_shape[] = {1, 1};
|
|
const int32_t axis_data[] = {1};
|
|
int output1_shape[] = {4, 2, 1, 2, 2};
|
|
const int32_t golden1[] = {1, 2, 3, 4, 9, 10, 11, 12};
|
|
int output2_shape[] = {4, 2, 1, 2, 2};
|
|
const int32_t golden2[] = {5, 6, 7, 8, 13, 14, 15, 16};
|
|
|
|
constexpr int output1_dims_count = 8;
|
|
constexpr int output2_dims_count = 8;
|
|
int32_t output1_data[output1_dims_count];
|
|
int32_t output2_data[output2_dims_count];
|
|
tflite::testing::TestSplitTwoOutputsQuantized32(
|
|
input_shape, input_data, axis_shape, axis_data, output1_shape, golden1,
|
|
output2_shape, golden2, output1_data, output2_data);
|
|
}
|
|
|
|
TF_LITE_MICRO_TESTS_END
|