* limitations under the License.
*/
-#include "kernels/Relu6.h"
#include "kernels/TestUtils.h"
-#include "luci_interpreter/TestMemoryManager.h"
+#include "luci_interpreter/test_models/relu6/FloatReLU6Kernel.h"
+#include "luci_interpreter/test_models/relu6/NegReLU6Kernel.h"
+
+#include "loader/ModuleLoader.h"
namespace luci_interpreter
{
-namespace kernels
-{
namespace
{
using namespace testing;
-class Relu6Test : public ::testing::Test
+class ReLU6Test : public ::testing::Test
{
-protected:
- void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); }
-
- std::unique_ptr<IMemoryManager> _memory_manager;
+ // Do nothing
};
-TEST_F(Relu6Test, FloatSimple)
-{
- std::vector<float> input_data{
- 0.0f, 1.0f, 3.0f, // Row 1
- 7.0f, -1.0f, -2.0f, // Row 2
- };
-
- std::vector<float> ref_output_data{
- 0.0f, 1.0f, 3.0f, // Row 1
- 6.0f, 0.0f, 0.0f, // Row 2
- };
-
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>({2, 3}, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Relu6 kernel(&input_tensor, &output_tensor);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- EXPECT_THAT(extractTensorData<float>(output_tensor),
- ::testing::ElementsAreArray(ref_output_data));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({2, 3}));
-}
-
-TEST_F(Relu6Test, Uint8Quantized)
+template <typename T> std::vector<T> checkReLU6Kernel(test_kernel::TestDataBase<T> *test_data_base)
{
- // Choose min / max in such a way that there are exactly 256 units to avoid rounding errors.
- const float f_min = (-128.0 / 128.0) * 10;
- const float f_max = (127.0 / 128.0) * 10;
- const float tolerance = (f_max - f_min) / 255.0;
-
- std::vector<float> input_data{
- 0, -6, 2, 8, //
- -2, 3, 7, 1, //
- };
+ MemoryManager memory_manager{};
+ RuntimeModule runtime_module{};
+ bool dealloc_input = true;
- std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(f_min, f_max);
- 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);
+ // 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);
- Relu6 kernel(&input_tensor, &output_tensor);
- 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({1, 2, 4, 1}));
- EXPECT_THAT(extractTensorData<uint8_t>(output_tensor),
- ::testing::ElementsAreArray({128, 128, 154, 205, 128, 166, 205, 141}));
- EXPECT_THAT(dequantizeTensorData(output_tensor),
- FloatArrayNear({0, 0, 2, 6, 0, 3, 6, 1}, tolerance));
-}
-
-TEST_F(Relu6Test, Uint8Requantized)
-{
- // Choose min / max in such a way that there are exactly 256 units to avoid rounding errors.
- const float in_min = (-128.0 / 128.0) * 10;
- const float in_max = (127.0 / 128.0) * 10;
- const float out_min = (0.0 / 256.0) * 0;
- const float out_max = (255.0 / 256.0) * 6;
- const float tolerance = (in_max - in_min) / 255.0;
+ // 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);
+ }
- std::vector<float> input_data{
- 0, -6, 2, 8, //
- -2, 3, 7, 1, //
- };
+ runtime_module.execute();
- std::pair<float, int32_t> quant_input = quantizationParams<uint8_t>(in_min, in_max);
- Tensor input_tensor = makeInputTensor<DataType::U8>(
- {1, 2, 4, 1}, quant_input.first, quant_input.second, input_data, _memory_manager.get());
+ assert(main_runtime_graph->getNumOfOutputTensors() == 1);
- std::pair<float, int32_t> quant_output = quantizationParams<uint8_t>(out_min, out_max);
- Tensor output_tensor = makeOutputTensor(DataType::U8, quant_output.first, quant_output.second);
-
- Relu6 kernel(&input_tensor, &output_tensor);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 2, 4, 1}));
- EXPECT_THAT(extractTensorData<uint8_t>(output_tensor),
- ::testing::ElementsAreArray({0, 0, 87, 255, 0, 127, 255, 43}));
- EXPECT_THAT(dequantizeTensorData(output_tensor),
- FloatArrayNear({0, 0, 2, 6, 0, 3, 6, 1}, 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;
}
-TEST_F(Relu6Test, Input_Output_Type_NEG)
+TEST_F(ReLU6Test, Float_P)
{
- Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1.f}, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::U8);
-
- Relu6 kernel(&input_tensor, &output_tensor);
- EXPECT_ANY_THROW(kernel.configure());
+ test_kernel::TestDataFloatReLU6 test_data_kernel;
+ std::vector<float> output_data_vector = checkReLU6Kernel(&test_data_kernel);
+ EXPECT_THAT(output_data_vector, kernels::testing::FloatArrayNear(
+ test_data_kernel.get_output_data_by_index(0), 0.0001f));
}
-TEST_F(Relu6Test, Invalid_Input_Type_NEG)
+TEST_F(ReLU6Test, Input_output_type_mismatch_NEG)
{
- Tensor input_tensor = makeInputTensor<DataType::S64>({1}, {1}, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::S64);
-
- Relu6 kernel(&input_tensor, &output_tensor);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- EXPECT_ANY_THROW(kernel.execute());
+ test_kernel::NegTestDataInputOutputTypeMismatchReLU6Kernel 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