* limitations under the License.
*/
-#include "kernels/Tanh.h"
#include "kernels/TestUtils.h"
-#include "luci_interpreter/TestMemoryManager.h"
+#include "luci_interpreter/test_models/tanh/FloatTanhKernel.h"
+#include "luci_interpreter/test_models/tanh/NegTanhKernel.h"
+
+#include "loader/ModuleLoader.h"
namespace luci_interpreter
{
-namespace kernels
-{
namespace
{
class TanhTest : public ::testing::Test
{
-protected:
- void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); }
-
- std::unique_ptr<IMemoryManager> _memory_manager;
+ // Do nothing
};
-TEST_F(TanhTest, Float)
+template <typename T> std::vector<T> checkTanhKernel(test_kernel::TestDataBase<T> *test_data_base)
{
- Shape input_shape{1, 2, 4, 1};
- std::vector<float> input_data{
- 0, -6, 2, 4, //
- 3, -2, 10, 1, //
- };
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Tanh kernel(&input_tensor, &output_tensor);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- std::vector<float> ref_output_data{
- 0, -0.9999877, 0.9640275, 0.999329, //
- 0.99505475, -0.9640275, 1, 0.7615941, //
- };
- EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data));
-}
+ MemoryManager memory_manager{};
+ RuntimeModule runtime_module{};
+ bool dealloc_input = true;
-TEST_F(TanhTest, Uint8)
-{
- float kMin = -1;
- float kMax = 127.f / 128.f;
- float kTanhTolerance = 2 * (1. / 256);
- std::pair<float, int32_t> input_quant_param = quantizationParams<uint8_t>(8 * kMin, 8 * kMax);
- std::pair<float, int32_t> output_quant_param = quantizationParams<uint8_t>(kMin, kMax);
- std::vector<float> input_data{
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- };
- Tensor input_tensor =
- makeInputTensor<DataType::U8>({2, 6, 4, 1}, input_quant_param.first, input_quant_param.second,
- input_data, _memory_manager.get());
- Tensor output_tensor =
- makeOutputTensor(DataType::U8, output_quant_param.first, output_quant_param.second);
-
- Tanh kernel(&input_tensor, &output_tensor);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- std::vector<float> ref_output_data{
- 0.0, -0.999987, 0.964027, 0.999329, //
- -0.999329, -0.96402, 0.99999, 0.76159, //
- 0.0, -0.999987, 0.964027, 0.999329, //
- -0.999329, -0.96402, 0.99999, 0.76159, //
- 0.0, -0.999987, 0.964027, 0.999329, //
- -0.999329, -0.96402, 0.99999, 0.76159, //
- 0.0, -0.999987, 0.964027, 0.999329, //
- -0.999329, -0.96402, 0.99999, 0.76159, //
- 0.0, -0.999987, 0.964027, 0.999329, //
- -0.999329, -0.96402, 0.99999, 0.76159, //
- 0.0, -0.999987, 0.964027, 0.999329, //
- -0.999329, -0.96402, 0.99999, 0.76159, //
- };
- std::vector<int32_t> ref_output_shape{2, 6, 4, 1};
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data, kTanhTolerance));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_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);
+
+ auto *main_runtime_graph = runtime_module.getMainGraph();
+ assert(main_runtime_graph->getNumOfInputTensors() == 1);
+
+ // 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);
+ }
+
+ runtime_module.execute();
+
+ assert(main_runtime_graph->getNumOfOutputTensors() == 1);
+
+ 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(TanhTest, InputTypeInvalid_NEG)
+TEST_F(TanhTest, Float_P)
{
- std::vector<int64_t> input_data{
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- };
- Tensor input_tensor =
- makeInputTensor<DataType::S64>({2, 6, 4, 1}, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Tanh kernel(&input_tensor, &output_tensor);
- _memory_manager->allocate_memory(output_tensor);
- EXPECT_ANY_THROW(kernel.execute());
+ test_kernel::TestDataFloatTanh test_data_kernel;
+ std::vector<float> output_data_vector = checkTanhKernel(&test_data_kernel);
+ EXPECT_THAT(output_data_vector, kernels::testing::FloatArrayNear(
+ test_data_kernel.get_output_data_by_index(0), 0.0001f));
}
-TEST_F(TanhTest, InputOutputMismatch_NEG)
+TEST_F(TanhTest, Input_output_type_mismatch_NEG)
{
- std::vector<float> input_data{
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- 0, -6, 2, 4, //
- -4, -2, 8, 1, //
- };
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>({2, 6, 4, 1}, input_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::U8);
-
- Tanh kernel(&input_tensor, &output_tensor);
- EXPECT_ANY_THROW(kernel.configure());
+ test_kernel::NegTestDataInputOutputTypeMismatchTanhKernel 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 S16 test
+
} // namespace
-} // namespace kernels
} // namespace luci_interpreter
projects / platform / core / ml / nnfw.git / blobdiff