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/*
* Copyright (c) 2021 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/SpaceToBatchND.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> block_shape_shape,
std::initializer_list<int32_t> paddings_shape,
std::initializer_list<int32_t> output_shape, std::initializer_list<float> input_data,
std::initializer_list<int32_t> block_shape_data,
std::initializer_list<int32_t> paddings_data, std::initializer_list<float> output_data)
{
std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
constexpr DataType element_type = getElementType<T>();
Tensor input_tensor =
makeInputTensor<element_type>(input_shape, input_data, memory_manager.get());
Tensor block_shape_tensor =
makeInputTensor<DataType::S32>(block_shape_shape, block_shape_data, memory_manager.get());
Tensor paddings_tensor =
makeInputTensor<DataType::S32>(paddings_shape, paddings_data, memory_manager.get());
Tensor output_tensor = makeOutputTensor(element_type);
SpaceToBatchND kernel(&input_tensor, &block_shape_tensor, &paddings_tensor, &output_tensor);
kernel.configure();
memory_manager->allocate_memory(output_tensor);
kernel.execute();
EXPECT_THAT(extractTensorData<T>(output_tensor), ::testing::ElementsAreArray(output_data));
EXPECT_THAT(extractTensorShape(output_tensor), output_shape);
}
template <>
void Check<uint8_t>(
std::initializer_list<int32_t> input_shape, std::initializer_list<int32_t> block_shape_shape,
std::initializer_list<int32_t> paddings_shape, std::initializer_list<int32_t> output_shape,
std::initializer_list<float> input_data, std::initializer_list<int32_t> block_shape_data,
std::initializer_list<int32_t> paddings_data, std::initializer_list<float> output_data)
{
std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
std::pair<float, int32_t> input_quant_param =
quantizationParams<uint8_t>(std::min(input_data), std::max(input_data));
Tensor input_tensor =
makeInputTensor<DataType::U8>(input_shape, input_quant_param.first, input_quant_param.second,
input_data, memory_manager.get());
Tensor block_shape_tensor =
makeInputTensor<DataType::S32>(block_shape_shape, block_shape_data, memory_manager.get());
Tensor paddings_tensor =
makeInputTensor<DataType::S32>(paddings_shape, paddings_data, memory_manager.get());
Tensor output_tensor =
makeOutputTensor(DataType::U8, input_quant_param.first, input_quant_param.second);
SpaceToBatchND kernel(&input_tensor, &block_shape_tensor, &paddings_tensor, &output_tensor);
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), output_shape);
}
template <typename T> class SpaceToBatchNDTest : public ::testing::Test
{
};
using DataTypes = ::testing::Types<float, uint8_t>;
TYPED_TEST_SUITE(SpaceToBatchNDTest, DataTypes);
TYPED_TEST(SpaceToBatchNDTest, Simple)
{
Check<TypeParam>(/*input_shape=*/{1, 5, 2, 1}, /*block_shape_shape=*/{2},
/*paddings_shape=*/{2, 2},
/*output_shape=*/{6, 2, 2, 1},
/*input_data=*/{-1.0, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8, -0.9, 1.0},
/*block_shape_data=*/{3, 2}, /*paddings_data=*/{1, 0, 2, 0},
/*output_data=*/{0, 0, 0, -0.5, 0, 0, 0, 0.6, 0, -1.0, 0, -0.7,
0, 0.2, 0, 0.8, 0, -0.3, 0, -0.9, 0, 0.4, 0, 1.0});
}
TEST(SpaceToBatchNDTest, Invalid_Shape_NEG)
{
std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
Tensor input_tensor = makeInputTensor<DataType::FLOAT32>(
{1, 3, 3, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9}, memory_manager.get());
Tensor block_shape_tensor = makeInputTensor<DataType::S32>({2}, {2, 2}, memory_manager.get());
Tensor paddings_tensor =
makeInputTensor<DataType::S32>({2, 2}, {0, 0, 0, 0}, memory_manager.get());
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
SpaceToBatchND kernel(&input_tensor, &block_shape_tensor, &paddings_tensor, &output_tensor);
EXPECT_ANY_THROW(kernel.configure());
}
} // namespace
} // namespace kernels
} // namespace luci_interpreter
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