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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 <OperationsUtils.h>
#include <kernel/acl/nnfw_kernel_acl.h>
#include <kernel/acl/Conv2D.h>
// TODO: fix include path in CMakeFiles
#include "../util.h"
using namespace nnfw::kernel::acl;
TEST(KernelACL_TC, convFloat32_3x3to1x1)
{
float inputData[9];
const nnfw::rt::Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 };
float filterData[9];
const nnfw::rt::Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 };
float biasData[1] = { 1.0 };
const nnfw::rt::Shape biasShape = { OperandType::FLOAT32, {1}, 1.0, 0 };
int32_t padding_left = 0;
int32_t padding_right = 0;
int32_t padding_top = 0;
int32_t padding_bottom = 0;
int32_t stride_width = 1;
int32_t stride_height = 1;
int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU);
float outputData[1];
const nnfw::rt::Shape outputShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 };
bool bret;
util::initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0);
util::initData(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0);
util::initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0);
bret = convFloat32(inputData, inputShape,
filterData, filterShape,
biasData, biasShape,
padding_left, padding_right,
padding_top, padding_bottom,
stride_width, stride_height,
activation,
outputData, outputShape);
EXPECT_EQ(bret, true);
float expectData[] = { 10.0f };
bret = util::compareData(outputData, expectData, outputShape);
EXPECT_EQ(bret, true);
}
TEST(KernelACL_TC, convFloat32_3x3to3x3)
{
float inputData[9];
const nnfw::rt::Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 };
float filterData[9];
const nnfw::rt::Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 };
float biasData[1] = { 1.0 };
const nnfw::rt::Shape biasShape = { OperandType::FLOAT32, {1}, 1.0, 0 };
int32_t padding_left = 1;
int32_t padding_right = 1;
int32_t padding_top = 1;
int32_t padding_bottom = 1;
int32_t stride_width = 1;
int32_t stride_height = 1;
int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU);
float outputData[9];
const nnfw::rt::Shape outputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 };
bool bret;
util::initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0);
util::initData(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0);
util::initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0);
bret = convFloat32(inputData, inputShape,
filterData, filterShape,
biasData, biasShape,
padding_left, padding_right,
padding_top, padding_bottom,
stride_width, stride_height,
activation,
outputData, outputShape);
EXPECT_EQ(bret, true);
float expectData[] = {
5.0f, 7.0f, 5.0f,
7.0f, 10.0f, 7.0f,
5.0f, 7.0f, 5.0f
};
bret = util::compareData(outputData, expectData, outputShape);
EXPECT_EQ(bret, true);
}
TEST(KernelACL_TC, convFloat32_3x3to3x3_RELU)
{
float inputData[9];
const nnfw::rt::Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 };
float filterData[9];
const nnfw::rt::Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 };
float biasData[1] = { -5.0f };
const nnfw::rt::Shape biasShape = { OperandType::FLOAT32, {1}, 1.0, 0 };
int32_t padding_left = 1;
int32_t padding_right = 1;
int32_t padding_top = 1;
int32_t padding_bottom = 1;
int32_t stride_width = 1;
int32_t stride_height = 1;
int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU);
float outputData[9];
const nnfw::rt::Shape outputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 };
bool bret;
util::initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0);
util::initData(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0);
util::initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0);
bret = convFloat32(inputData, inputShape,
filterData, filterShape,
biasData, biasShape,
padding_left, padding_right,
padding_top, padding_bottom,
stride_width, stride_height,
activation,
outputData, outputShape);
EXPECT_EQ(bret, true);
float expectData[] =
{
0.0f, 1.0f, 0.0f,
1.0f, 4.0f, 1.0f,
0.0f, 1.0f, 0.0f
};
bret = util::compareData(outputData, expectData, outputShape);
EXPECT_EQ(bret, true);
}
TEST(KernelACL_TC, convFloat32_3x5to3x3)
{
float inputData[15] = {
1,2,3,4,5,
6,7,8,9,10,
11,12,13,14,15
};
const nnfw::rt::Shape inputShape = { OperandType::FLOAT32, {1,3,5,1}, 1.0, 0 };
float filterData[18] = {
1,1,1, 1,1,1, 1,1,1,
2,2,2, 2,2,2, 2,2,2
};
const nnfw::rt::Shape filterShape = { OperandType::FLOAT32, {2,3,3,1}, 1.0, 0 };
float biasData[2] = { 1.0, 1.0 };
const nnfw::rt::Shape biasShape = { OperandType::FLOAT32, {2}, 1.0, 0 };
int32_t padding_left = 1;
int32_t padding_right = 1;
int32_t padding_top = 1;
int32_t padding_bottom = 1;
int32_t stride_width = 1;
int32_t stride_height = 1;
int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU);
float outputData[30];
const nnfw::rt::Shape outputShape = { OperandType::FLOAT32, {1,3,5,2}, 1.0, 0 };
bool bret;
util::initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0);
bret = convFloat32(inputData, inputShape,
filterData, filterShape,
biasData, biasShape,
padding_left, padding_right,
padding_top, padding_bottom,
stride_width, stride_height,
activation,
outputData, outputShape);
EXPECT_EQ(bret, true);
float expectNCHW[] = {
17.0f, 28.0f, 34.0f, 40.0f, 29.0f,
40.0f, 64.0f, 73.0f, 82.0f, 58.0f,
37.0f, 58.0f, 64.0f, 70.0f, 49.0f,
33.0f, 55.0f, 67.0f, 79.0f, 57.0f,
79.0f, 127.0f, 145.0f, 163.0f, 115.0f,
73.0f, 115.0f, 127.0f, 139.0f, 97.0f
};
float expectData[30];
util::NCHW2NHWC(expectNCHW, expectData, outputShape);
bret = util::compareData(outputData, expectData, outputShape);
EXPECT_EQ(bret, true);
}
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