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/*
* Copyright (c) 2020 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 "kernels/Pad.h"
#include "kernels/TestUtils.h"
namespace luci_interpreter
{
namespace kernels
{
namespace
{
using namespace testing;
float GetTolerance(float min, float max) { return (max - min) / 255.0; }
TEST(Pad, Uint8)
{
float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-1.0f, 1.0f);
std::vector<float> input_data{-0.8, 0.2, 0.9, 0.7, 0.1, -0.3};
std::vector<int32_t> paddings_data{0, 0, 0, 2, 1, 3, 0, 0};
Tensor input_tensor =
makeInputTensor<DataType::U8>({1, 2, 3, 1}, quant_param.first, quant_param.second, input_data);
Tensor paddings_tensor = makeInputTensor<DataType::S32>({4, 2}, paddings_data);
Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second);
Pad kernel(&input_tensor, &paddings_tensor, &output_tensor);
kernel.configure();
kernel.execute();
std::vector<float> ref_output_data{0, -0.8, 0.2, 0.9, 0, 0, 0, 0, 0.7, 0.1, -0.3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
EXPECT_THAT(dequantizeTensorData(output_tensor),
FloatArrayNear(ref_output_data, kQuantizedTolerance));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 4, 7, 1}));
}
TEST(Pad, Float)
{
std::vector<float> input_data{1, 2, 3, 4, 5, 6};
std::vector<int32_t> paddings_data{1, 0, 0, 2, 0, 3, 0, 0};
Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({1, 2, 3, 1}, input_data);
Tensor paddings_tensor = makeInputTensor<DataType::S32>({4, 2}, paddings_data);
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
Pad kernel(&input_tensor, &paddings_tensor, &output_tensor);
kernel.configure();
kernel.execute();
std::vector<float> ref_output_data{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0, 0, 0, 4, 5,
6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
std::initializer_list<int32_t> ref_output_shape{2, 4, 6, 1};
EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
}
} // namespace
} // namespace kernels
} // namespace luci_interpreter
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