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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/ext/kernels/register.h"
#include "tensorflow/lite/model.h"
#include "tensorflow/lite/builtin_op_data.h"
#include "env.h"
#include "misc/environment.h"
#include "tflite/Diff.h"
#include "tflite/Quantization.h"
#include "tflite/interp/FunctionBuilder.h"
#include <iostream>
#include <cassert>
#include <chrono>
#include <random>
using namespace tflite;
using namespace nnfw::tflite;
template <typename T> T *make_malloc(void) { return reinterpret_cast<T *>(malloc(sizeof(T))); }
TEST(NNAPI_Quickcheck_fully_connected_1, simple_test)
{
int verbose = 0;
int tolerance = 1;
nnfw::misc::env::IntAccessor("VERBOSE").access(verbose);
nnfw::misc::env::IntAccessor("TOLERANCE").access(tolerance);
// Set random seed
int SEED = std::chrono::system_clock::now().time_since_epoch().count();
nnfw::misc::env::IntAccessor("SEED").access(SEED);
#define INT_VALUE(NAME, VALUE) IntVar NAME##_Value(#NAME, VALUE);
#include "fully_connected_quan_1.lst"
#undef INT_VALUE
const int32_t IFM_C = IFM_C_Value();
const int32_t IFM_H = IFM_H_Value();
const int32_t IFM_W = IFM_W_Value();
const int32_t KER_H = KER_H_Value();
const int32_t KER_W = IFM_C_Value() * IFM_H_Value() * IFM_W_Value();
const int32_t OUT_LEN = KER_H;
// 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(IFM_C);
PRINT_VALUE(IFM_H);
PRINT_VALUE(IFM_W);
PRINT_NEWLINE();
PRINT_VALUE(KER_H);
PRINT_VALUE(KER_W);
PRINT_NEWLINE();
PRINT_VALUE(OUT_LEN);
#undef PRINT_VALUE
#undef PRINT_NEWLINE
// Configure Kernel Data
const uint32_t kernel_size = KER_H * KER_W;
float kernel_data[kernel_size] = {
0.0f,
};
// Fill kernel data with random data
{
std::normal_distribution<float> kernel_dist(-1.0f, +1.0f);
for (uint32_t off = 0; off < kernel_size; ++off)
{
kernel_data[off++] = kernel_dist(random);
}
}
// Configure Bias Data
const auto bias_size = KER_H;
int32_t bias_data[bias_size] = {
0,
};
// Fill bias data with random data
{
std::normal_distribution<float> bias_dist(-1.0f, +1.0f);
for (uint32_t off = 0; off < bias_size; ++off)
{
bias_data[off] = static_cast<int32_t>(bias_dist(random));
}
}
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();
quantization.scale = FLOAT_NEAREST_TO_1;
quantization.zero_point = 0;
// On AddTensors(N) call, T/F Lite interpreter creates N tensors whose index is [0 ~ N)
interp.AddTensors(4);
// Configure OFM
interp.SetTensorParametersReadWrite(0, kTfLiteUInt8 /* type */, "output" /* name */,
{1 /*N*/, KER_H} /* dims */, quantization);
// Configure IFM
interp.SetTensorParametersReadWrite(1, kTfLiteUInt8 /* type */, "input" /* name */,
{1 /*N*/, IFM_H, IFM_W, IFM_C} /* dims */, quantization);
// NOTE kernel_data & bias_data should live longer than interpreter!
interp.SetTensorParametersReadOnly(
2, kTfLiteUInt8 /* type */, "filter" /* name */, {KER_H, KER_W} /* dims */, quantization,
reinterpret_cast<const char *>(kernel_data), kernel_size * sizeof(uint8_t));
interp.SetTensorParametersReadOnly(
3, kTfLiteInt32 /* type */, "bias" /* name */, {bias_size} /* dims */, quantization,
reinterpret_cast<const char *>(bias_data), bias_size * sizeof(int32_t));
// Add Fully Connected Node
//
// NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free
// So, param should be allocated with malloc
auto param = make_malloc<TfLiteFullyConnectedParams>();
param->activation = kTfLiteActRelu;
// Run Convolution and store its result into Tensor #0
// - Read IFM from Tensor #1
// - Read Filter from Tensor #2,
// - Read Bias from Tensor #3
interp.AddNodeWithParameters({1, 2, 3}, {0}, nullptr, 0, reinterpret_cast<void *>(param),
BuiltinOpResolver().FindOp(BuiltinOperator_FULLY_CONNECTED, 1));
// Set Tensor #1 as Input #0, and Tensor #0 as Output #0
interp.SetInputs({1});
interp.SetOutputs({0});
};
const nnfw::tflite::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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