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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 "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 "tflite/Diff.h"
#include "tflite/TensorShapeUtils.h"
#include "tflite/interp/FunctionBuilder.h"
#include <iostream>
#include <cassert>
#include <chrono>
#include <random>
using namespace tflite;
using namespace tflite::ops::builtin;
TEST(NNAPI_Quickcheck_add_3, simple_test)
{
// Set random seed
int SEED = std::chrono::system_clock::now().time_since_epoch().count();
nnfw::util::env::IntAccessor("SEED").access(SEED);
// Initialize random number generator
std::minstd_rand random(SEED);
#define STR_VALUE(NAME, VALUE) StrVar NAME##_Value(#NAME, VALUE);
#include "add_3.lst"
#undef STR_VALUE
const auto LHS_SHAPE = nnfw::util::tensor::Shape::from(LHS_SHAPE_Value());
const auto RHS_SHAPE = nnfw::util::tensor::Shape::from(RHS_SHAPE_Value());
const auto OUT_SHAPE = nnfw::support::tflite::broadcast(LHS_SHAPE, RHS_SHAPE);
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(LHS_SHAPE);
PRINT_VALUE(RHS_SHAPE);
PRINT_VALUE(OUT_SHAPE);
#undef PRINT_VALUE
#undef PRINT_NEWLINE
auto setup = [&](Interpreter &interp) {
using nnfw::support::tflite::as_dims;
// 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;
quantization.scale = 1;
quantization.zero_point = 0;
// 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 */,
as_dims(OUT_SHAPE), quantization);
// Configure input(s)
interp.SetTensorParametersReadWrite(1, kTfLiteFloat32 /* type */, "left" /* name */,
as_dims(LHS_SHAPE), quantization);
interp.SetTensorParametersReadWrite(2, kTfLiteFloat32 /* type */, "right" /* name */,
as_dims(RHS_SHAPE), quantization);
// 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 Left from Tensor #1
// - Read Left from Tensor #2,
interp.AddNodeWithParameters({1, 2}, {0}, nullptr, 0, reinterpret_cast<void *>(param),
BuiltinOpResolver().FindOp(BuiltinOperator_ADD, 1));
interp.SetInputs({1, 2});
interp.SetOutputs({0});
};
const nnfw::support::tflite::interp::FunctionBuilder builder(setup);
RandomTestParam param;
param.verbose = 0;
param.tolerance = 1;
nnfw::util::env::IntAccessor("VERBOSE").access(param.verbose);
nnfw::util::env::IntAccessor("TOLERANCE").access(param.tolerance);
int res = RandomTestRunner{SEED, param}.run(builder);
EXPECT_EQ(res, 0);
}
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