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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 "memory.h"
#include "misc/environment.h"
#include "misc/feature/Shape.h"
#include "tflite/Diff.h"
#include "tflite/Quantization.h"
#include "tflite/interp/FunctionBuilder.h"
#include <chrono>
#include <random>
#include <iostream>
#include <cassert>
using namespace tflite;
using namespace nnfw::tflite;
TEST(NNAPI_Quickcheck_softmax_1, simple_test)
{
int verbose = 0;
int tolerance = 1;
nnfw::misc::env::IntAccessor("VERBOSE").access(verbose);
nnfw::misc::env::IntAccessor("TOLERANCE").access(tolerance);
#define INT_VALUE(NAME, VALUE) IntVar NAME##_Value(#NAME, VALUE);
#include "softmax_1.lst"
#undef INT_VALUE
const int32_t IFM_C = 1;
const int32_t IFM_H = IFM_H_Value();
const int32_t IFM_W = IFM_W_Value();
// Set random seed
int SEED = std::chrono::system_clock::now().time_since_epoch().count();
nnfw::misc::env::IntAccessor("SEED").access(SEED);
// Initialize random number generator
std::minstd_rand random(SEED);
const nnfw::misc::feature::Shape ifm_shape{IFM_C, IFM_H, IFM_W};
const int32_t OFM_C = IFM_C;
const int32_t OFM_H = IFM_H;
const int32_t OFM_W = IFM_W;
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(2);
// Configure Output Tensor
interp.SetTensorParametersReadWrite(0, kTfLiteFloat32 /* type */, "output" /* name */,
{1, IFM_H * IFM_W} /* dims */, quantization);
// Configure Input Tensor
interp.SetTensorParametersReadWrite(1, kTfLiteFloat32 /* type */, "input" /* name */,
{1, IFM_H * IFM_W} /* batch_size, input_size */,
quantization);
// Add Softmax Node
//
// NOTE AddNodeWithParameters take the ownership of param, and deallocate it with free
// So, param should be allocated with malloc
auto param = make_alloc<TfLiteSoftmaxParams>();
param->beta = 1.0;
// Run Softmax and store its result into Tensor #0
// - Read IFM from Tensor #1
interp.AddNodeWithParameters({1}, {0}, nullptr, 0, reinterpret_cast<void *>(param),
BuiltinOpResolver().FindOp(BuiltinOperator_SOFTMAX, 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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