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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2017 The TensorFlow Authors. 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/LogSoftmax.h"
#include "kernels/TestUtils.h"
namespace luci_interpreter
{
namespace kernels
{
namespace
{
using namespace testing;
TEST(LogSoftmaxTest, Float)
{
Shape input_shape{2, 4};
std::vector<float> input_data{
0, -6, 2, 4, //
3, -2, 10, 1, //
};
Tensor input_tensor = makeInputTensor<DataType::FLOAT32>(input_shape, input_data);
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
LogSoftmax kernel(&input_tensor, &output_tensor);
kernel.configure();
kernel.execute();
std::vector<float> ref_output_data{
-4.14297, -10.14297, -2.14297, -.142971, //
-7.00104, -12.00104, -.00104087, -9.00104, //
};
EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data));
}
TEST(LogSoftmaxTest, Uint8)
{
float kMin = -10;
float kMax = 10;
float kLogSoftmaxQuantizedTolerance = 16. / 256;
std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(kMin, kMax);
std::vector<float> input_data{
0, -6, 2, 4, //
3, -2, 10, 1, //
};
Tensor input_tensor =
makeInputTensor<DataType::U8>({2, 4}, quant_param.first, quant_param.second, input_data);
Tensor output_tensor = makeOutputTensor(DataType::U8, 16. / 256, 255);
LogSoftmax kernel(&input_tensor, &output_tensor);
kernel.configure();
kernel.execute();
std::vector<float> ref_output_data{
-4.14297, -10.14297, -2.14297, -.142971, //
-7.00104, -12.00104, -.00104087, -9.00104, //
};
std::vector<int32_t> ref_output_shape{2, 4};
EXPECT_THAT(dequantizeTensorData(output_tensor),
FloatArrayNear(ref_output_data, kLogSoftmaxQuantizedTolerance));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
EXPECT_THAT(extractTensorData<uint8_t>(output_tensor),
::testing::ElementsAreArray({189, 93, 221, 253, 142, 63, 255, 111}));
}
TEST(LogSoftmaxTest, InvalidInputOutputType_NEG)
{
std::vector<float> input_data{
0, -6, 2, 4, //
3, -2, 10, 1, //
};
Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({2, 4}, input_data);
Tensor output_tensor = makeOutputTensor(DataType::U8, 16. / 256, 255);
LogSoftmax kernel(&input_tensor, &output_tensor);
EXPECT_ANY_THROW(kernel.configure());
}
TEST(LogSoftmaxTest, InvalidOutputQuantParam_NEG)
{
std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-10, 10);
std::vector<float> input_data{
0, -6, 2, 4, //
3, -2, 10, 1, //
};
Tensor input_tensor =
makeInputTensor<DataType::U8>({2, 4}, quant_param.first, quant_param.second, input_data);
Tensor output_tensor = makeOutputTensor(DataType::U8, 20. / 256, 255);
LogSoftmax kernel(&input_tensor, &output_tensor);
EXPECT_ANY_THROW(kernel.configure());
}
} // namespace
} // namespace kernels
} // namespace luci_interpreter
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