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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/L2Normalize.h"
#include "kernels/TestUtils.h"
#include "luci_interpreter/TestMemoryManager.h"
namespace luci_interpreter
{
namespace kernels
{
namespace
{
using namespace testing;
template <typename T>
void Check(std::initializer_list<int32_t> input_shape, std::initializer_list<int32_t> output_shape,
std::initializer_list<float> input_data, std::initializer_list<float> output_data)
{
std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
Tensor input_tensor =
makeInputTensor<DataType::FLOAT32>(input_shape, input_data, memory_manager.get());
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
L2NormParams params{};
params.activation = Activation::NONE;
L2Normalize kernel(&input_tensor, &output_tensor, params);
kernel.configure();
memory_manager->allocate_memory(output_tensor);
kernel.execute();
EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(output_data));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
}
template <>
void Check<uint8_t>(std::initializer_list<int32_t> input_shape,
std::initializer_list<int32_t> output_shape,
std::initializer_list<float> input_data,
std::initializer_list<float> output_data)
{
std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
std::pair<float, int32_t> quant_param =
quantizationParams<uint8_t>(std::min(input_data) < 0 ? std::min(input_data) : 0.f,
std::max(input_data) > 0 ? std::max(input_data) : 0.f);
Tensor input_tensor = makeInputTensor<DataType::U8>(
input_shape, quant_param.first, quant_param.second, input_data, memory_manager.get());
Tensor output_tensor = makeOutputTensor(DataType::U8, 1. / 128., 128);
L2NormParams params{};
params.activation = Activation::NONE;
L2Normalize kernel(&input_tensor, &output_tensor, params);
kernel.configure();
memory_manager->allocate_memory(output_tensor);
kernel.execute();
EXPECT_THAT(dequantizeTensorData(output_tensor),
FloatArrayNear(output_data, output_tensor.scale()));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
}
template <typename T> class L2NormalizeTest : public ::testing::Test
{
};
using DataTypes = ::testing::Types<float, uint8_t>;
TYPED_TEST_CASE(L2NormalizeTest, DataTypes);
TYPED_TEST(L2NormalizeTest, Simple)
{
Check<TypeParam>({1, 1, 1, 6}, {1, 1, 1, 6}, {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1},
{-0.55, 0.3, 0.35, 0.6, -0.35, 0.05});
}
TEST(L2NormalizeTest, ActivationType_NEG)
{
std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
std::vector<float> input_data = {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1};
Tensor input_tensor =
makeInputTensor<DataType::FLOAT32>({1, 1, 1, 6}, input_data, memory_manager.get());
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
L2NormParams params{};
params.activation = Activation::RELU6;
L2Normalize kernel(&input_tensor, &output_tensor, params);
EXPECT_ANY_THROW(kernel.configure());
}
TEST(L2NormalizeTest, InvalidOutputQuantParam_NEG)
{
std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
std::vector<float> input_data = {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1};
Tensor input_tensor =
makeInputTensor<DataType::U8>({1, 1, 1, 6}, 1. / 64., 127, input_data, memory_manager.get());
Tensor output_tensor = makeOutputTensor(DataType::U8, 1. / 64., 127);
L2NormParams params{};
params.activation = Activation::NONE;
L2Normalize kernel(&input_tensor, &output_tensor, params);
EXPECT_ANY_THROW(kernel.configure());
}
} // namespace
} // namespace kernels
} // namespace luci_interpreter
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