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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/MaxPool2D.h"
#include "kernels/TestUtils.h"
namespace luci_interpreter
{
namespace kernels
{
namespace
{
using namespace testing;
TEST(MaxPool2DTest, Float)
{
Shape input_shape{1, 3, 5, 1};
std::vector<float> input_data{
1, -1, 0, -2, 2, //
-7, -6, -5, -4, -3, //
5, 4, 3, 6, 7, //
};
Tensor input_tensor = makeInputTensor<DataType::FLOAT32>(input_shape, input_data);
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
Pool2DParams params{};
params.padding = Padding::VALID;
params.filter_height = 2;
params.filter_width = 3;
params.stride_height = 1;
params.stride_width = 2;
params.activation = Activation::RELU6;
MaxPool2D kernel(&input_tensor, &output_tensor, params);
kernel.configure();
kernel.execute();
std::vector<float> ref_output_data{
1, 2, //
5, 6, //
};
std::initializer_list<int32_t> ref_output_shape{1, 2, 2, 1};
EXPECT_THAT(extractTensorData<float>(output_tensor),
ElementsAreArray(ArrayFloatNear(ref_output_data)));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
}
TEST(MaxPool2DTest, Uint8)
{
std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-15.9375, 15.9375);
std::vector<float> input_data{
0, -6, 12, 4, //
-3, -2, 10, 7, //
};
Tensor input_tensor{DataType::U8, {1, 2, 4, 1}, {{quant_param.first}, {quant_param.second}}, ""};
Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second);
std::vector<uint8_t> quantize_input =
quantize<uint8_t>(input_data, quant_param.first, quant_param.second);
input_tensor.writeData(quantize_input.data(), quantize_input.size() * sizeof(uint8_t));
Pool2DParams params{};
params.padding = Padding::VALID;
params.filter_height = 2;
params.filter_width = 2;
params.stride_height = 2;
params.stride_width = 2;
params.activation = Activation::RELU6;
MaxPool2D kernel(&input_tensor, &output_tensor, params);
kernel.configure();
kernel.execute();
std::vector<float> ref_output_data{0.0, 6.0};
std::initializer_list<int32_t> ref_output_shape{1, 1, 2, 1};
EXPECT_THAT(dequantize<uint8_t>(extractTensorData<uint8_t>(output_tensor), output_tensor.scale(),
output_tensor.zero_point()),
ElementsAreArray(ArrayFloatNear(ref_output_data)));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
}
} // namespace
} // namespace kernels
} // namespace luci_interpreter
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