/*
* 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.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-// TODO enable it
-#if 0
-#include "kernels/FullyConnected.h"
+
#include "kernels/TestUtils.h"
-#include "luci_interpreter/TestMemoryManager.h"
+#include "luci_interpreter/test_models/fully_connected/FloatFullyConnectedKernel.h"
+#include "luci_interpreter/test_models/fully_connected/U8FullyConnectedKernel.h"
+#include "luci_interpreter/test_models/fully_connected/NegFullyConnectedKernel.h"
+
+#include "loader/ModuleLoader.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> weights_shape,
- std::initializer_list<int32_t> bias_shape, std::initializer_list<int32_t> output_shape,
- std::initializer_list<float> input_data, std::initializer_list<float> weights_data,
- std::initializer_list<float> bias_data, std::initializer_list<float> output_data)
+class FullyConnectedTest : public ::testing::Test
{
- std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, memory_manager.get());
- Tensor weights_tensor =
- makeInputTensor<DataType::FLOAT32>(weights_shape, weights_data, memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- FullyConnectedParams params{};
- params.activation = Activation::RELU;
-
- FullyConnected kernel(&input_tensor, &weights_tensor, &bias_tensor, &output_tensor, params);
- kernel.configure();
- memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
- EXPECT_THAT(extractTensorData<T>(output_tensor), FloatArrayNear(output_data));
-}
+ // Do nothing
+};
-template <>
-void Check<int8_t>(std::initializer_list<int32_t> input_shape,
- std::initializer_list<int32_t> weights_shape,
- std::initializer_list<int32_t> bias_shape,
- std::initializer_list<int32_t> output_shape,
- std::initializer_list<float> input_data,
- std::initializer_list<float> weights_data,
- std::initializer_list<float> bias_data, std::initializer_list<float> output_data)
+template <typename T>
+std::vector<T> checkFullyConnectedKernel(test_kernel::TestDataBase<T> *test_data_base)
{
- std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
- const float quantized_tolerance = getTolerance(-127, 128, 255);
- std::pair<float, int32_t> input_quant_param = quantizationParams<int8_t>(-63.5, 64);
- std::pair<float, int32_t> output_quant_param = quantizationParams<int8_t>(-127, 128);
- Tensor input_tensor =
- makeInputTensor<DataType::S8>(input_shape, input_quant_param.first, input_quant_param.second,
- input_data, memory_manager.get());
- Tensor weights_tensor =
- makeInputTensor<DataType::S8>(weights_shape, input_quant_param.first, input_quant_param.second,
- weights_data, memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::S32>(bias_shape, input_quant_param.first * input_quant_param.first, 0,
- bias_data, memory_manager.get());
- Tensor output_tensor =
- makeOutputTensor(DataType::S8, output_quant_param.first, output_quant_param.second);
+ MemoryManager memory_manager{};
+ RuntimeModule runtime_module{};
+ bool dealloc_input = true;
- FullyConnectedParams params{};
- params.activation = Activation::RELU;
+ // 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);
- FullyConnected kernel(&input_tensor, &weights_tensor, &bias_tensor, &output_tensor, params);
- kernel.configure();
- memory_manager->allocate_memory(output_tensor);
- kernel.execute();
+ auto *main_runtime_graph = runtime_module.getMainGraph();
+ assert(main_runtime_graph->getNumOfInputTensors() == 1);
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
- EXPECT_THAT(dequantizeTensorData(output_tensor),
- FloatArrayNear(output_data, quantized_tolerance));
-}
+ // 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);
+ }
-template <>
-void Check<uint8_t>(
- std::initializer_list<int32_t> input_shape, std::initializer_list<int32_t> weights_shape,
- std::initializer_list<int32_t> bias_shape, std::initializer_list<int32_t> output_shape,
- std::initializer_list<float> input_data, std::initializer_list<float> weights_data,
- std::initializer_list<float> bias_data, std::initializer_list<float> output_data)
-{
- std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
- const float quantized_tolerance = getTolerance(-127, 128, 255);
- std::pair<float, int32_t> input_quant_param = quantizationParams<uint8_t>(-63.5, 64);
- std::pair<float, int32_t> output_quant_param = quantizationParams<uint8_t>(-127, 128);
- Tensor input_tensor =
- makeInputTensor<DataType::U8>(input_shape, input_quant_param.first, input_quant_param.second,
- input_data, memory_manager.get());
- Tensor weights_tensor =
- makeInputTensor<DataType::U8>(weights_shape, input_quant_param.first, input_quant_param.second,
- weights_data, memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::S32>(bias_shape, input_quant_param.first * input_quant_param.first, 0,
- bias_data, memory_manager.get());
- Tensor output_tensor =
- makeOutputTensor(DataType::U8, output_quant_param.first, output_quant_param.second);
+ runtime_module.execute();
- FullyConnectedParams params{};
- params.activation = Activation::RELU;
+ assert(main_runtime_graph->getNumOfOutputTensors() == 1);
- FullyConnected kernel(&input_tensor, &weights_tensor, &bias_tensor, &output_tensor, params);
- kernel.configure();
- memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
- EXPECT_THAT(dequantizeTensorData(output_tensor),
- FloatArrayNear(output_data, quantized_tolerance));
+ 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 FullyConnectedTest : public ::testing::Test
+TEST_F(FullyConnectedTest, Float_P)
{
-};
-
-using DataTypes = ::testing::Types<float, uint8_t, int8_t>;
-TYPED_TEST_SUITE(FullyConnectedTest, DataTypes);
+ test_kernel::TestDataFloatFullyConnected test_data_kernel;
+ std::vector<float> output_data_vector = checkFullyConnectedKernel(&test_data_kernel);
+ EXPECT_THAT(output_data_vector, kernels::testing::FloatArrayNear(
+ test_data_kernel.get_output_data_by_index(0), 0.0001f));
+}
-TYPED_TEST(FullyConnectedTest, Simple)
+TEST_F(FullyConnectedTest, U8_P)
{
- Check<TypeParam>({3, 2, 2, 1}, {3, 6}, {3}, {2, 3},
- {
- -3, -5, 5, 4, 9, -2, // batch = 0
- -3, -2, -4, 9, -8, 1, // batch = 1
- },
- {
- -3, -7, 4, -4, -6, 4, // unit = 0
- 3, 5, 2, 3, -3, -8, // unit = 1
- -3, 7, 4, 9, 0, -5, // unit = 2
- },
- {-1, -5, -8},
- {
- 0, 0, 32, // batch = 0
- 22, 11, 47, // batch = 1
- });
+ test_kernel::TestDataU8FullyConnected test_data_kernel;
+ std::vector<uint8_t> output_data_vector = checkFullyConnectedKernel(&test_data_kernel);
+ EXPECT_THAT(output_data_vector, test_data_kernel.get_output_data_by_index(0));
}
-TEST(FullyConnectedTest, InvalidBiasType_NEG)
+TEST_F(FullyConnectedTest, Wrong_weight_type_NEG)
{
- Shape input_shape{3, 2, 2, 1};
- std::vector<float> input_data{
- -3, -5, 5, 4, 9, -2, // batch = 0
- -3, -2, -4, 9, -8, 1, // batch = 1
- };
- Shape weights_shape{3, 6};
- std::vector<float> weights_data{
- -3, -7, 4, -4, -6, 4, // unit = 0
- 3, 5, 2, 3, -3, -8, // unit = 1
- -3, 7, 4, 9, 0, -5, // unit = 2
- };
- Shape bias_shape{3};
- std::vector<int32_t> bias_data{-1, -5, -8};
-
- std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
-
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, memory_manager.get());
- Tensor weights_tensor =
- makeInputTensor<DataType::FLOAT32>(weights_shape, weights_data, memory_manager.get());
- Tensor bias_tensor = makeInputTensor<DataType::S32>(bias_shape, bias_data, memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- FullyConnectedParams params{};
- params.activation = Activation::RELU;
-
- FullyConnected kernel(&input_tensor, &weights_tensor, &bias_tensor, &output_tensor, params);
- EXPECT_ANY_THROW(kernel.configure());
+ test_kernel::NegTestDataWrongWeightTypeFullyConnectedKernel 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),
+ "");
}
-TEST(FullyConnectedTest, InvalidWeightShapeDim_NEG)
+TEST_F(FullyConnectedTest, Wrong_weight_shape_NEG)
{
- Shape input_shape{3, 2, 2, 1};
- std::vector<float> input_data{
- -3, -5, 5, 4, 9, -2, // batch = 0
- -3, -2, -4, 9, -8, 1, // batch = 1
- };
- Shape weights_shape{1, 3, 6};
- std::vector<float> weights_data{
- -3, -7, 4, -4, -6, 4, // unit = 0
- 3, 5, 2, 3, -3, -8, // unit = 1
- -3, 7, 4, 9, 0, -5, // unit = 2
- };
- Shape bias_shape{3};
- std::vector<float> bias_data{-1, -5, -8};
-
- std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
-
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, memory_manager.get());
- Tensor weights_tensor =
- makeInputTensor<DataType::FLOAT32>(weights_shape, weights_data, memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- FullyConnectedParams params{};
- params.activation = Activation::RELU;
-
- FullyConnected kernel(&input_tensor, &weights_tensor, &bias_tensor, &output_tensor, params);
- EXPECT_ANY_THROW(kernel.configure());
+ test_kernel::NegTestDataWrongWeightShapeFullyConnectedKernel 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),
+ "");
}
-TEST(FullyConnectedTest, BiasElementNumWeightDimMismatch_NEG)
+TEST_F(FullyConnectedTest, Wrong_bias_shape_NEG)
{
- Shape input_shape{3, 2, 2, 1};
- std::vector<float> input_data{
- -3, -5, 5, 4, 9, -2, // batch = 0
- -3, -2, -4, 9, -8, 1, // batch = 1
- };
- Shape weights_shape{6, 3};
- std::vector<float> weights_data{
- -3, -7, 4, // unit = 0
- -4, -6, 4, // unit = 1
- 3, 5, 2, // unit = 2
- 3, -3, -8, // unit = 3
- -3, 7, 4, // unit = 4
- 9, 0, -5, // unit = 5
- };
- Shape bias_shape{3};
- std::vector<float> bias_data{-1, -5, -8};
-
- std::unique_ptr<IMemoryManager> memory_manager = std::make_unique<TestMemoryManager>();
-
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, memory_manager.get());
- Tensor weights_tensor =
- makeInputTensor<DataType::FLOAT32>(weights_shape, weights_data, memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- FullyConnectedParams params{};
- params.activation = Activation::RELU;
-
- FullyConnected kernel(&input_tensor, &weights_tensor, &bias_tensor, &output_tensor, params);
- EXPECT_ANY_THROW(kernel.configure());
+ test_kernel::NegTestDataWrongBiasShapeFullyConnectedKernel 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),
+ "");
}
+// TODO: add tests for S8
+
} // namespace
-} // namespace kernels
} // namespace luci_interpreter
-#endif
projects / platform / core / ml / nnfw.git / blobdiff