237 lines
8.5 KiB
C++
237 lines
8.5 KiB
C++
/* Copyright 2017 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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// used to set the quantization parameters for the int8_t and uint8_t tests
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constexpr float kInputMin = -2.0;
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constexpr float kInputMax = 2.0;
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constexpr float kOutputMin = -1.0;
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constexpr float kOutputMax = 127.0 / 128.0;
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TfLiteTensor CreateL2NormTensor(const float* data, TfLiteIntArray* dims,
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bool is_input) {
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return CreateFloatTensor(data, dims);
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}
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template <typename T>
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TfLiteTensor CreateL2NormTensor(const T* data, TfLiteIntArray* dims,
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bool is_input) {
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float kInputScale = ScaleFromMinMax<T>(kInputMin, kInputMax);
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int kInputZeroPoint = ZeroPointFromMinMax<T>(kInputMin, kInputMax);
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float kOutputScale = ScaleFromMinMax<T>(kOutputMin, kOutputMax);
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int kOutputZeroPoint = ZeroPointFromMinMax<T>(kOutputMin, kOutputMax);
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TfLiteTensor tensor;
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if (is_input) {
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tensor = CreateQuantizedTensor(data, dims, kInputScale, kInputZeroPoint);
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} else {
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tensor = CreateQuantizedTensor(data, dims, kOutputScale, kOutputZeroPoint);
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}
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tensor.quantization.type = kTfLiteAffineQuantization;
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return tensor;
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}
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template <typename T>
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void TestL2Normalization(const int* input_dims_data, const T* input_data,
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const T* expected_output_data, T* output_data) {
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TfLiteIntArray* dims = IntArrayFromInts(input_dims_data);
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const int output_dims_count = ElementCount(*dims);
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constexpr int tensors_size = 2;
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TfLiteTensor tensors[tensors_size] = {
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CreateL2NormTensor(input_data, dims, true),
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CreateL2NormTensor(output_data, dims, false),
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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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TfLiteL2NormParams builtin_data = {
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.activation = kTfLiteActNone,
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};
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const TfLiteRegistration registration =
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ops::micro::Register_L2_NORMALIZATION();
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micro::KernelRunner runner(
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registration, tensors, tensors_size, inputs_array, outputs_array,
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reinterpret_cast<void*>(&builtin_data), 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_EQ(expected_output_data[i], output_data[i]);
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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(SimpleFloatTest) {
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const int input_dims[] = {4, 1, 1, 1, 6};
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constexpr int data_length = 6;
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const float input_data[data_length] = {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1};
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const float expected_output_data[data_length] = {-0.55, 0.3, 0.35,
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0.6, -0.35, 0.05};
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float output_data[data_length];
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tflite::testing::TestL2Normalization<float>(
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input_dims, input_data, expected_output_data, output_data);
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}
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TF_LITE_MICRO_TEST(ZerosVectorFloatTest) {
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const int input_dims[] = {4, 1, 1, 1, 6};
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constexpr int data_length = 6;
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const float input_data[data_length] = {0, 0, 0, 0, 0, 0};
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const float expected_output_data[data_length] = {0, 0, 0, 0, 0, 0};
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float output_data[data_length];
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tflite::testing::TestL2Normalization<float>(
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input_dims, input_data, expected_output_data, output_data);
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}
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TF_LITE_MICRO_TEST(SimpleFloatWithRankLessThanFourTest) {
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const int input_dims[] = {4, 1, 1, 1, 6};
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constexpr int data_length = 6;
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const float input_data[data_length] = {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1};
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const float expected_output_data[data_length] = {-0.55, 0.3, 0.35,
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0.6, -0.35, 0.05};
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float output_data[data_length];
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tflite::testing::TestL2Normalization<float>(
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input_dims, input_data, expected_output_data, output_data);
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}
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TF_LITE_MICRO_TEST(MultipleBatchFloatTest) {
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const int input_dims[] = {4, 3, 1, 1, 6};
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constexpr int data_length = 18;
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const float input_data[data_length] = {
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-1.1, 0.6, 0.7, 1.2, -0.7, 0.1, // batch 1
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-1.1, 0.6, 0.7, 1.2, -0.7, 0.1, // batch 2
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-1.1, 0.6, 0.7, 1.2, -0.7, 0.1, // batch 3
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};
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const float expected_output_data[data_length] = {
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-0.55, 0.3, 0.35, 0.6, -0.35, 0.05, // batch 1
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-0.55, 0.3, 0.35, 0.6, -0.35, 0.05, // batch 2
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-0.55, 0.3, 0.35, 0.6, -0.35, 0.05, // batch 3
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};
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float output_data[data_length];
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tflite::testing::TestL2Normalization<float>(
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input_dims, input_data, expected_output_data, output_data);
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}
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TF_LITE_MICRO_TEST(ZerosVectorUint8Test) {
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const int input_dims[] = {4, 1, 1, 1, 6};
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constexpr int data_length = 6;
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const uint8_t input_data[data_length] = {127, 127, 127, 127, 127, 127};
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const uint8_t expected_output[data_length] = {128, 128, 128, 128, 128, 128};
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uint8_t output_data[data_length];
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tflite::testing::TestL2Normalization<uint8_t>(input_dims, input_data,
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expected_output, output_data);
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}
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TF_LITE_MICRO_TEST(SimpleUint8Test) {
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const int input_dims[] = {4, 1, 1, 1, 6};
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constexpr int data_length = 6;
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const uint8_t input_data[data_length] = {57, 165, 172, 204, 82, 133};
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const uint8_t expected_output[data_length] = {
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58, 166, 173, 205, 83, 134,
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};
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uint8_t output_data[data_length];
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tflite::testing::TestL2Normalization<uint8_t>(input_dims, input_data,
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expected_output, output_data);
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}
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TF_LITE_MICRO_TEST(SimpleInt8Test) {
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const int input_dims[] = {4, 1, 1, 1, 6};
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constexpr int data_length = 6;
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const int8_t input_data[data_length] = {-71, 37, 44, 76, -46, 5};
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const int8_t expected_output[data_length] = {-70, 38, 45, 77, -45, 6};
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int8_t output_data[data_length];
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tflite::testing::TestL2Normalization<int8_t>(input_dims, input_data,
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expected_output, output_data);
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}
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TF_LITE_MICRO_TEST(ZerosVectorInt8Test) {
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const int input_dims[] = {4, 1, 1, 1, 6};
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constexpr int data_length = 6;
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const int8_t input_data[data_length] = {-1, -1, -1, -1, -1, -1};
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const int8_t expected_output[data_length] = {0, 0, 0, 0, 0, 0};
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int8_t output_data[data_length];
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tflite::testing::TestL2Normalization<int8_t>(input_dims, input_data,
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expected_output, output_data);
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}
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TF_LITE_MICRO_TEST(MultipleBatchUint8Test) {
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const int input_dims[] = {2, 3, 6};
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constexpr int data_length = 18;
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const uint8_t input_data[data_length] = {
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57, 165, 172, 204, 82, 133, // batch 1
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57, 165, 172, 204, 82, 133, // batch 2
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57, 165, 172, 204, 82, 133, // batch 3
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};
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const uint8_t expected_output[data_length] = {
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58, 166, 173, 205, 83, 134, // batch 1
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58, 166, 173, 205, 83, 134, // batch 2
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58, 166, 173, 205, 83, 134, // batch 3
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};
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uint8_t output_data[data_length];
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tflite::testing::TestL2Normalization<uint8_t>(input_dims, input_data,
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expected_output, output_data);
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}
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TF_LITE_MICRO_TEST(MultipleBatchInt8Test) {
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const int input_dims[] = {2, 3, 6};
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constexpr int data_length = 18;
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const int8_t input_data[data_length] = {
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-71, 37, 44, 76, -46, 5, // batch 1
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-71, 37, 44, 76, -46, 5, // batch 2
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-71, 37, 44, 76, -46, 5, // batch 3
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};
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const int8_t expected_output[data_length] = {
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-70, 38, 45, 77, -45, 6, // batch 1
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-70, 38, 45, 77, -45, 6, // batch 2
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-70, 38, 45, 77, -45, 6, // batch 3
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};
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int8_t output_data[data_length];
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tflite::testing::TestL2Normalization<int8_t>(input_dims, input_data,
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expected_output, output_data);
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}
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TF_LITE_MICRO_TESTS_END
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