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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. 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 "gtest/gtest.h"
#include "support/tflite/kernels/register.h"
#include "tensorflow/contrib/lite/model.h"
#include "tensorflow/contrib/lite/builtin_op_data.h"
#include "env.h"
#include "memory.h"
#include "util/environment.h"
#include "support/tflite/Diff.h"
#include "support/tflite/Quantization.h"
#include "support/tflite/interp/FunctionBuilder.h"
#include <iostream>
#include <cassert>
#include <chrono>
#include <random>
using namespace tflite;
using namespace tflite::ops::builtin;
TEST(NNAPI_Quickcheck_add_9, simple_test)
{
int verbose = 1;
int tolerance = 1;
nnfw::util::env::IntAccessor("VERBOSE").access(verbose);
nnfw::util::env::IntAccessor("TOLERANCE").access(tolerance);
// Set random seed
int SEED = std::chrono::system_clock::now().time_since_epoch().count();
nnfw::util::env::IntAccessor("SEED").access(SEED);
#define INT_VALUE(NAME, VALUE) IntVar NAME##_Value(#NAME, VALUE);
#include "add_9.lst"
#undef INT_VALUE
const int32_t LEFT_N = LEFT_N_Value();
const int32_t LEFT_C = LEFT_C_Value();
const int32_t LEFT_H = LEFT_H_Value();
const int32_t LEFT_W = LEFT_W_Value();
const int32_t RIGHT_N = RIGHT_N_Value();
const int32_t RIGHT_C = RIGHT_C_Value();
const int32_t RIGHT_H = RIGHT_H_Value();
const int32_t RIGHT_W = RIGHT_W_Value();
const int32_t OFM_N = std::max(LEFT_N, RIGHT_N);
const int32_t OFM_C = std::max(LEFT_C, RIGHT_C);
const int32_t OFM_H = std::max(LEFT_H, RIGHT_H);
const int32_t OFM_W = std::max(LEFT_W, RIGHT_W);
// Initialize random number generator
std::minstd_rand random(SEED);
std::cout << "Configurations:" << std::endl;
#define PRINT_NEWLINE() \
{ \
std::cout << std::endl; \
}
#define PRINT_VALUE(value) \
{ \
std::cout << " " << #value << ": " << (value) << std::endl; \
}
PRINT_VALUE(SEED);
PRINT_NEWLINE();
PRINT_VALUE(LEFT_N);
PRINT_VALUE(LEFT_H);
PRINT_VALUE(LEFT_W);
PRINT_VALUE(LEFT_C);
PRINT_NEWLINE();
PRINT_VALUE(RIGHT_N);
PRINT_VALUE(RIGHT_H);
PRINT_VALUE(RIGHT_W);
PRINT_VALUE(RIGHT_C);
PRINT_NEWLINE();
PRINT_VALUE(OFM_N);
PRINT_VALUE(OFM_H);
PRINT_VALUE(OFM_W);
PRINT_VALUE(OFM_C);
#undef PRINT_VALUE
#undef PRINT_NEWLINE
// Configure left data
const uint32_t left_size = LEFT_N * LEFT_C * LEFT_H * LEFT_W;
const uint32_t right_size = RIGHT_N * RIGHT_C * RIGHT_H * RIGHT_W;
float left_data[left_size] = {
0.0f,
};
float right_data[right_size] = {
0.0f,
};
// Fill left data with random data
{
std::normal_distribution<float> left_dist(-1.0f, +1.0f);
float value = 10.0f;
for (uint32_t off = 0; off < left_size; ++off)
{
left_data[off] = value;
}
value = 1.0f;
for (uint32_t off = 0; off < right_size; ++off)
{
right_data[off] = value++;
}
}
auto setup = [&](Interpreter &interp) {
// Comment from 'context.h'
//
// Parameters for asymmetric quantization. Quantized values can be converted
// back to float using:
// real_value = scale * (quantized_value - zero_point);
//
// Q: Is this necessary?
TfLiteQuantizationParams quantization = make_default_quantization();
// On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N)
interp.AddTensors(3);
// Configure output
interp.SetTensorParametersReadWrite(0, kTfLiteFloat32 /* type */, "output" /* name */,
{OFM_N, OFM_H, OFM_W, OFM_C} /* dims */, quantization);
// Configure input(s)
interp.SetTensorParametersReadOnly(
1, kTfLiteFloat32 /* type */, "left" /* name */, {LEFT_W, LEFT_C} /* dims */, quantization,
reinterpret_cast<const char *>(left_data), left_size * sizeof(float));
// Configure input(s)
interp.SetTensorParametersReadOnly(2, kTfLiteFloat32 /* type */, "right" /* name */,
{RIGHT_N, RIGHT_H, RIGHT_W, RIGHT_C} /* dims */,
quantization, reinterpret_cast<const char *>(right_data),
right_size * sizeof(float));
// Add Convolution Node
//
// NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free
// So, param should be allocated with malloc
auto param = make_alloc<TfLiteAddParams>();
param->activation = kTfLiteActNone;
// Run Add and store the result into Tensor #0
// - Read LHS from Tensor #1
// - Read RHS from Tensor #2,
interp.AddNodeWithParameters({1, 2}, {0}, nullptr, 0, reinterpret_cast<void *>(param),
BuiltinOpResolver().FindOp(BuiltinOperator_ADD, 1));
interp.SetInputs({});
interp.SetOutputs({0});
};
const nnfw::support::tflite::interp::FunctionBuilder builder(setup);
RandomTestParam param;
param.verbose = verbose;
param.tolerance = tolerance;
int res = RandomTestRunner{SEED, param}.run(builder);
EXPECT_EQ(res, 0);
}
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