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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/InstanceNorm.h"
#include "kernels/TestUtils.h"
#include "luci_interpreter/TestMemoryManager.h"
namespace luci_interpreter
{
namespace kernels
{
namespace
{
using namespace testing;
class InstanceNormTest : public ::testing::Test
{
protected:
void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); }
std::unique_ptr<IMemoryManager> _memory_manager;
};
TEST_F(InstanceNormTest, Simple)
{
Tensor input_tensor =
makeInputTensor<DataType::FLOAT32>({1, 2, 2, 1}, {1, 1, 1, 1}, _memory_manager.get());
Tensor gamma_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1}, _memory_manager.get());
Tensor beta_tensor = makeInputTensor<DataType::FLOAT32>({1}, {2}, _memory_manager.get());
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
InstanceNormParams params{};
params.epsilon = 0.1f;
params.activation = Activation::NONE;
InstanceNorm kernel(&input_tensor, &gamma_tensor, &beta_tensor, &output_tensor, params);
kernel.configure();
_memory_manager->allocate_memory(output_tensor);
kernel.execute();
EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear({2, 2, 2, 2}));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 2, 2, 1}));
}
TEST_F(InstanceNormTest, Single_gamma_beta)
{
Tensor input_tensor =
makeInputTensor<DataType::FLOAT32>({1, 2, 1, 2}, {1, 1, 1, 1}, _memory_manager.get());
Tensor gamma_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1}, _memory_manager.get());
Tensor beta_tensor = makeInputTensor<DataType::FLOAT32>({1}, {2}, _memory_manager.get());
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
InstanceNormParams params{};
params.epsilon = 0.1f;
params.activation = Activation::NONE;
InstanceNorm kernel(&input_tensor, &gamma_tensor, &beta_tensor, &output_tensor, params);
kernel.configure();
_memory_manager->allocate_memory(output_tensor);
kernel.execute();
EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear({2, 2, 2, 2}));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 2, 1, 2}));
}
TEST_F(InstanceNormTest, Wrong_gamma_beta_dim_NEG)
{
Tensor input_tensor =
makeInputTensor<DataType::FLOAT32>({1, 2, 1, 2}, {1, 1, 1, 1}, _memory_manager.get());
Tensor gamma_tensor = makeInputTensor<DataType::FLOAT32>({3}, {1, 1, 1}, _memory_manager.get());
Tensor beta_tensor = makeInputTensor<DataType::FLOAT32>({3}, {2, 2, 2}, _memory_manager.get());
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
InstanceNormParams params{};
params.epsilon = 0.1f;
params.activation = Activation::NONE;
InstanceNorm kernel(&input_tensor, &gamma_tensor, &beta_tensor, &output_tensor, params);
EXPECT_ANY_THROW(kernel.configure());
}
} // namespace
} // namespace kernels
} // namespace luci_interpreter
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