/*
- * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+ * Copyright (c) 2023 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.
* limitations under the License.
*/
-#include "kernels/Pack.h"
#include "kernels/TestUtils.h"
-#include "luci_interpreter/TestMemoryManager.h"
+#include "luci_interpreter/test_models/pack/PackKernel.h"
+
+#include "loader/ModuleLoader.h"
namespace luci_interpreter
{
-namespace kernels
-{
namespace
{
using namespace testing;
-template <typename T>
-void Check(std::vector<std::initializer_list<int32_t>> input_shapes,
- std::initializer_list<int32_t> output_shape, std::vector<std::vector<T>> input_datas,
- std::initializer_list<T> output_data, int32_t axis)
+class PackTest : public ::testing::Test
{
- std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
- constexpr DataType element_type = getElementType<T>();
- std::vector<const Tensor *> inputs(input_datas.size());
- std::vector<Tensor> tmp_inputs;
- for (int i = 0; i < input_datas.size(); i++)
- {
- if (std::is_same<T, float>::value || std::is_same<T, int32_t>::value ||
- std::is_same<T, int64_t>::value)
- {
- tmp_inputs.push_back(Tensor(element_type, input_shapes[i], {}, ""));
- memory_manager->allocate_memory(tmp_inputs[i]);
- tmp_inputs[i].writeData(input_datas[i].data(), input_datas[i].size() * sizeof(T));
- }
- else if (std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value)
- {
- tmp_inputs.push_back(Tensor(element_type, input_shapes[i], {{1.0f / 255}, {128}}, ""));
- memory_manager->allocate_memory(tmp_inputs[i]);
- tmp_inputs[i].writeData(input_datas[i].data(), input_datas[i].size() * sizeof(T));
- }
- else
- {
- assert((std::is_same<T, int16_t>::value) && "unexpected dtype is tested");
- tmp_inputs.push_back(Tensor(element_type, input_shapes[i], {{1.0f}, {0}}, ""));
- memory_manager->allocate_memory(tmp_inputs[i]);
- tmp_inputs[i].writeData(input_datas[i].data(), input_datas[i].size() * sizeof(T));
- }
- }
- for (int i = 0; i < input_datas.size(); i++)
- {
- inputs[i] = &tmp_inputs[i];
- }
+ // Do nothing
+};
+
+template <typename T> std::vector<T> checkPackKernel(test_kernel::TestDataBase<T> *test_data_base)
+{
+ MemoryManager memory_manager{};
+ RuntimeModule runtime_module{};
+ bool dealloc_input = true;
- Tensor output_tensor = makeOutputTensor(element_type);
- if (std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value)
+ // 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);
+
+ auto *main_runtime_graph = runtime_module.getMainGraph();
+ assert(main_runtime_graph->getNumOfInputTensors() == 2);
+
+ // Set input 1 data
{
- output_tensor = makeOutputTensor(element_type, 1.0f / 255, 128);
+ 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);
}
- else if (std::is_same<T, int16_t>::value)
+
+ // Set input 2 data
{
- output_tensor = makeOutputTensor(element_type, 1.0f, 0);
+ auto *input_tensor_data = reinterpret_cast<T *>(main_runtime_graph->configureGraphInput(1));
+ std::copy(test_data_base->get_input_data_by_index(1).begin(),
+ test_data_base->get_input_data_by_index(1).end(), input_tensor_data);
}
- PackParams params{};
- params.axis = axis;
- params.values_count = input_datas.size();
- Pack kernel(inputs, &output_tensor, params);
+ runtime_module.execute();
- kernel.configure();
- memory_manager->allocate_memory(output_tensor);
- kernel.execute();
+ assert(main_runtime_graph->getNumOfOutputTensors() == 1);
- EXPECT_THAT(extractTensorData<T>(output_tensor), ::testing::ElementsAreArray(output_data));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
+ 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;
}
-template <typename T> class PackTest : public ::testing::Test
+TEST_F(PackTest, Float_P)
{
-};
-
-using DataTypes = ::testing::Types<uint8_t, int8_t, int16_t, int32_t, int64_t, float>;
-TYPED_TEST_SUITE(PackTest, DataTypes);
-
-TYPED_TEST(PackTest, ThreeInputs)
-{
- Check<TypeParam>(/*input_shapes=*/{{2}, {2}, {2}},
- /*output_shape=*/{3, 2},
- /*input_datas=*/
- {{1, 4}, {2, 5}, {3, 6}},
- /*output_data=*/
- {1, 4, 2, 5, 3, 6}, /*axis=*/0);
-
- SUCCEED();
+ test_kernel::TestDataFloatPack test_data_pack_kernel;
+ std::vector<float> output_data_vector = checkPackKernel(&test_data_pack_kernel);
+ EXPECT_THAT(output_data_vector, test_data_pack_kernel.get_output_data_by_index(0));
}
-TYPED_TEST(PackTest, NegAxis)
+TEST_F(PackTest, Int_P)
{
- Check<TypeParam>(/*input_shapes=*/{{2}, {2}, {2}},
- /*output_shape=*/{2, 3},
- /*input_datas=*/
- {{1, 4}, {2, 5}, {3, 6}},
- /*output_data=*/
- {1, 2, 3, 4, 5, 6}, /*axis=*/-1);
-
- SUCCEED();
+ test_kernel::TestDataIntPack test_data_pack_kernel;
+ std::vector<int32_t> output_data_vector = checkPackKernel(&test_data_pack_kernel);
+ EXPECT_THAT(output_data_vector, test_data_pack_kernel.get_output_data_by_index(0));
}
-TEST(Pack, MismatchingInputValuesCount_NEG)
+TEST_F(PackTest, QuantU8_P)
{
- std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
- std::vector<float> input1_data{1, 4};
- std::vector<float> input2_data{2, 5};
- std::vector<float> input3_data{3, 6};
- Tensor input1_tensor = makeInputTensor<DataType::FLOAT32>({2}, input1_data, memory_manager.get());
- Tensor input2_tensor = makeInputTensor<DataType::FLOAT32>({2}, input2_data, memory_manager.get());
- Tensor input3_tensor = makeInputTensor<DataType::FLOAT32>({2}, input3_data, memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
- PackParams params{};
- {
- params.axis = 0;
- params.values_count = 2;
-
- Pack kernel({&input1_tensor, &input2_tensor, &input3_tensor}, &output_tensor, params);
- EXPECT_ANY_THROW(kernel.configure());
- }
+ test_kernel::TestDataQuantU8Pack test_data_pack_kernel;
+ std::vector<uint8_t> output_data_vector = checkPackKernel(&test_data_pack_kernel);
+ EXPECT_THAT(output_data_vector, test_data_pack_kernel.get_output_data_by_index(0));
}
-TEST(Pack, InvalidInputAxis_NEG)
-{
- std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
- std::vector<float> input1_data{1, 4};
- std::vector<float> input2_data{2, 5};
- std::vector<float> input3_data{3, 6};
- Tensor input1_tensor = makeInputTensor<DataType::FLOAT32>({2}, input1_data, memory_manager.get());
- Tensor input2_tensor = makeInputTensor<DataType::FLOAT32>({2}, input2_data, memory_manager.get());
- Tensor input3_tensor = makeInputTensor<DataType::FLOAT32>({2}, input3_data, memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
- PackParams params{};
- {
- params.axis = 2;
- params.values_count = 3;
-
- Pack kernel({&input1_tensor, &input2_tensor, &input3_tensor}, &output_tensor, params);
- EXPECT_ANY_THROW(kernel.configure());
- }
-}
+// TODO: add negative tests?
} // namespace
-} // namespace kernels
} // namespace luci_interpreter
projects / platform / core / ml / nnfw.git / blobdiff