/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright 2019 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.
* limitations under the License.
*/
-#include "kernels/AveragePool2D.h"
#include "kernels/TestUtils.h"
-#include "luci_interpreter/TestMemoryManager.h"
+#include "luci_interpreter/test_models/average_pool_2d/FloatAveragePool2DKernel.h"
+#include "luci_interpreter/test_models/average_pool_2d/NegAveragePool2DKernel.h"
+
+#include "loader/ModuleLoader.h"
namespace luci_interpreter
{
-namespace kernels
-{
namespace
{
class AveragePool2DTest : public ::testing::Test
{
-protected:
- void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); }
-
- std::unique_ptr<IMemoryManager> _memory_manager;
+ // Do nothing
};
-TEST_F(AveragePool2DTest, Float)
-{
- Shape input_shape{1, 3, 5, 1};
- std::vector<float> input_data{
- -4, -3, -2, -1, 0, //
- 1, 2, 3, 4, 5, //
- 6, 7, 8, 9, 10, //
- };
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
- Tensor scratchpad(DataType::FLOAT32, Shape({}), {}, "");
-
- 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;
-
- AveragePool2D kernel(&input_tensor, &output_tensor, &scratchpad, params);
- kernel.configure();
- _memory_manager->allocate_memory(scratchpad);
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- std::vector<float> ref_output_data{
- 0, 1.5, //
- 4.5, 6, //
- };
- EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 2, 2, 1}));
-}
-
-TEST_F(AveragePool2DTest, Uint8_0)
-{
- std::vector<float> input_data{
- 0, -6, 12, 4, //
- -3, -2, 10, 7, //
- };
- std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-15.9375f, 15.9375f);
- Tensor input_tensor = makeInputTensor<DataType::U8>(
- {1, 2, 4, 1}, quant_param.first, quant_param.second, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second);
- Tensor scratchpad(DataType::U8, Shape({}), {}, "");
-
- 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;
-
- AveragePool2D kernel(&input_tensor, &output_tensor, &scratchpad, params);
- kernel.configure();
- _memory_manager->allocate_memory(scratchpad);
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear({0.0, 6.0}));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 1, 2, 1}));
-}
-
-TEST_F(AveragePool2DTest, Uint8_1)
-{
- std::vector<float> input_data{
- 0, 6, 12, 4, //
- 3, 2, 10, 7, //
- };
-
- std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-15.9375f, 15.9375f);
- Tensor input_tensor = makeInputTensor<DataType::U8>(
- {1, 2, 4, 1}, quant_param.first, quant_param.second, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second);
- Tensor scratchpad(DataType::U8, Shape({}), {}, "");
-
- 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;
-
- AveragePool2D kernel(&input_tensor, &output_tensor, &scratchpad, params);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(scratchpad);
- kernel.execute();
-
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear({2.75, 6.0}));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 1, 2, 1}));
-}
-
-TEST_F(AveragePool2DTest, SInt16)
-{
- Shape input_shape{1, 3, 5, 1};
- std::vector<int32_t> ref_output_shape{1, 2, 2, 1};
- std::vector<float> input_data{
- -4, -3, -2, -1, 0, //
- 1, 2, 3, 4, 5, //
- 6, 7, 8, 9, 10, //
- };
- std::vector<float> ref_output_data{
- 0, 1.5, //
- 4.5, 6, //
- };
- Tensor input_tensor =
- makeInputTensor<DataType::S16>(input_shape, 0.5, 0, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::S16, 0.5, 0);
- Tensor scratchpad(DataType::S16, Shape({}), {}, "");
-
- 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;
-
- AveragePool2D kernel(&input_tensor, &output_tensor, &scratchpad, params);
- kernel.configure();
- _memory_manager->allocate_memory(scratchpad);
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data));
-}
-
-TEST_F(AveragePool2DTest, SInt8)
+template <typename T>
+std::vector<T> checkAveragePool2DKernel(test_kernel::TestDataBase<T> *test_data_base)
{
- Shape input_shape{1, 4, 5, 1};
- std::vector<int32_t> ref_output_shape{1, 2, 2, 1};
- std::vector<float> input_data{-7, -3, 0, 2, -5, 12, -15, 3, 10, 5,
- 7, -6, -1, 9, -2, 0, -5, 11, -1, -7};
- std::vector<float> ref_output_data{
- 0, 2.5, //
- 1, 1.5, //
- };
+ MemoryManager memory_manager{};
+ RuntimeModule runtime_module{};
+ bool dealloc_input = true;
- std::pair<float, int32_t> quant_param = quantizationParams<int8_t>(-15.9375f, 15.9375f);
- Tensor input_tensor = makeInputTensor<DataType::S8>(
- input_shape, quant_param.first, quant_param.second, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::S8, quant_param.first, quant_param.second);
- Tensor scratchpad(DataType::S8, Shape({}), {}, "");
+ // Load model with single op
+ auto *model_data_raw = reinterpret_cast<const char *>(test_data_base->get_model_ptr());
+ ModuleLoader::load(&runtime_module, &memory_manager, model_data_raw, dealloc_input);
- Pool2DParams params{};
- params.padding = Padding::VALID;
- params.filter_height = 2;
- params.filter_width = 3;
- params.stride_height = 2;
- params.stride_width = 2;
- params.activation = Activation::RELU6;
+ auto *main_runtime_graph = runtime_module.getMainGraph();
+ assert(main_runtime_graph->getNumOfInputTensors() == 1);
- AveragePool2D kernel(&input_tensor, &output_tensor, &scratchpad, params);
- kernel.configure();
- _memory_manager->allocate_memory(scratchpad);
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
+ // Set input data
+ {
+ auto *input_tensor_data = reinterpret_cast<T *>(main_runtime_graph->configureGraphInput(0));
+ std::copy(test_data_base->get_input_data_by_index(0).begin(),
+ test_data_base->get_input_data_by_index(0).end(), input_tensor_data);
+ }
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data));
-}
+ runtime_module.execute();
-TEST_F(AveragePool2DTest, Invalid_Input_Shape_NEG)
-{
- Shape input_shape{1, 3, 5};
- std::vector<float> input_data{
- -4, -3, -2, -1, 0, //
- 1, 2, 3, 4, 5, //
- 6, 7, 8, 9, 10, //
- };
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
- Tensor scratchpad(DataType::FLOAT32, Shape({}), {}, "");
+ assert(main_runtime_graph->getNumOfOutputTensors() == 1);
- 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;
-
- AveragePool2D kernel(&input_tensor, &output_tensor, &scratchpad, params);
- EXPECT_ANY_THROW(kernel.configure());
+ T *output_data = reinterpret_cast<T *>(main_runtime_graph->getOutputDataByIndex(0));
+ const size_t num_elements = (main_runtime_graph->getOutputDataSizeByIndex(0) / sizeof(T));
+ std::vector<T> output_data_vector(output_data, output_data + num_elements);
+ return output_data_vector;
}
-TEST_F(AveragePool2DTest, In_Out_Type_NEG)
+TEST_F(AveragePool2DTest, Float_P)
{
- Shape input_shape{1, 3, 5, 1};
- std::vector<float> input_data{
- -4, -3, -2, -1, 0, //
- 1, 2, 3, 4, 5, //
- 6, 7, 8, 9, 10, //
- };
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::U8);
- Tensor scratchpad(DataType::FLOAT32, Shape({}), {}, "");
-
- 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;
-
- AveragePool2D kernel(&input_tensor, &output_tensor, &scratchpad, params);
- EXPECT_ANY_THROW(kernel.configure());
+ test_kernel::TestDataFloatAveragePool2D test_data_kernel;
+ std::vector<float> output_data_vector = checkAveragePool2DKernel(&test_data_kernel);
+ EXPECT_THAT(output_data_vector, kernels::testing::FloatArrayNear(
+ test_data_kernel.get_output_data_by_index(0), 0.001f));
}
-TEST_F(AveragePool2DTest, Quant_Param_NEG)
+TEST_F(AveragePool2DTest, InputOutputTypeMismatch_NEG)
{
- std::vector<float> input_data{
- 0, -6, 12, 4, //
- -3, -2, 10, 7, //
- };
-
- std::pair<float, int32_t> quant_param1 = quantizationParams<uint8_t>(-15.9375f, 15.9375f);
- std::pair<float, int32_t> quant_param2 = quantizationParams<uint8_t>(-7.875f, 7.875f);
- Tensor input_tensor = makeInputTensor<DataType::U8>(
- {1, 2, 4, 1}, quant_param1.first, quant_param1.second, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param2.first, quant_param2.second);
- Tensor scratchpad(DataType::U8, Shape({}), {}, "");
-
- 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;
-
- AveragePool2D kernel(&input_tensor, &output_tensor, &scratchpad, params);
- EXPECT_ANY_THROW(kernel.configure());
+ test_kernel::NegTestDataInputOutputTypeMismatchAveragePool2DKernel test_data_kernel;
+
+ MemoryManager memory_manager{};
+ RuntimeModule runtime_module{};
+ bool dealloc_input = true;
+ // Load model with single op
+ auto *model_data_raw = reinterpret_cast<const char *>(test_data_kernel.get_model_ptr());
+ EXPECT_DEATH(ModuleLoader::load(&runtime_module, &memory_manager, model_data_raw, dealloc_input),
+ "");
}
} // namespace
-} // namespace kernels
} // namespace luci_interpreter
projects / platform / core / ml / nnfw.git / blobdiff