/*
* 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.
*/
-// TODO enable it
-#if 0
-#include "kernels/Conv2D.h"
#include "kernels/TestUtils.h"
-#include "luci_interpreter/TestMemoryManager.h"
+#include "luci_interpreter/test_models/conv2d/FloatConv2DKernel.h"
+#include "luci_interpreter/test_models/conv2d/U8Conv2DKernel.h"
+#include "luci_interpreter/test_models/conv2d/NegConv2DKernel.h"
+
+#include "loader/ModuleLoader.h"
namespace luci_interpreter
{
-namespace kernels
-{
namespace
{
class Conv2DTest : public ::testing::Test
{
-protected:
- void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); }
-
- std::unique_ptr<IMemoryManager> _memory_manager;
+ // Do nothing
};
-TEST_F(Conv2DTest, Float)
-{
- Shape input_shape{1, 4, 3, 2};
- Shape filter_shape{2, 2, 2, 2};
- Shape bias_shape{2};
- std::vector<float> input_data{
- 1, 2, 3, 4, 5, 6, // row = 0
- 7, 8, 9, 10, 11, 12, // row = 1
- 13, 14, 15, 16, 17, 18, // row = 2
- 19, 20, 21, 22, 23, 24, // row = 3
- };
- std::vector<float> filter_data{
- 1, 2, -3, -4, // out = 0, row = 0
- -5, 6, -7, 8, // out = 1, row = 0
- 4, -2, 3, -1, // out = 0, row = 1
- -8, -6, 7, 5, // out = 1, row = 1
- };
- std::vector<float> bias_data{1, 2};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::FLOAT32>(filter_shape, filter_data, _memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, _memory_manager.get());
- Tensor im2col(DataType::FLOAT32, Shape({}), {}, "");
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 1;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::RELU;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- kernel.configure();
- _memory_manager->allocate_memory(im2col);
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- std::vector<float> ref_output_data{
- 11, 16, 7, 20, // row = 0
- 0, 40, 0, 44, // row = 1
- };
- std::vector<int32_t> ref_output_shape{1, 2, 2, 2};
- EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
-}
-
-TEST_F(Conv2DTest, FloatPointwise)
-{
- Shape input_shape{1, 2, 2, 2};
- Shape filter_shape{2, 1, 1, 2};
- Shape bias_shape{2};
- std::vector<float> input_data{
- 1, 2, // row = 0, col = 0
- 3, 4, // row = 0, col = 1
- 5, 6, // row = 1, col = 0
- 7, 8, // row = 1, col = 1
- };
- std::vector<float> filter_data{
- -1, 2, // out = 0
- -3, 4, // out = 1
- };
- std::vector<float> bias_data{1, 2};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::FLOAT32>(filter_shape, filter_data, _memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, _memory_manager.get());
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
- Tensor im2col(DataType::FLOAT32, Shape({}), {}, "");
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 1;
- params.stride_width = 1;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::RELU;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- kernel.configure();
- _memory_manager->allocate_memory(im2col);
- _memory_manager->allocate_memory(output_tensor);
- kernel.execute();
-
- std::vector<float> ref_output_data{
- 4, 7, 6, 9, // row = 0
- 8, 11, 10, 13, // row = 1
- };
- std::vector<int32_t> ref_output_shape{1, 2, 2, 2};
- EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
-}
-
-TEST_F(Conv2DTest, FloatCheck)
-{
- Shape input_shape{2, 2, 4, 1};
- Shape filter_shape{3, 2, 2, 1};
- Shape bias_shape{3};
- std::vector<float> input_data{
- // First batch
- 1, 1, 1, 1, // row = 1
- 2, 2, 2, 2, // row = 2
- // Second batch
- 1, 2, 3, 4, // row = 1
- 1, 2, 3, 4, // row = 2
- };
- std::vector<float> filter_data{
- 1, 2, 3, 4, // first 2x2 filter
- -1, 1, -1, 1, // second 2x2 filter
- -1, -1, 1, 1, // third 2x2 filter
- };
- std::vector<float> bias_data{1, 2, 3};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::FLOAT32>(filter_shape, filter_data, _memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, _memory_manager.get());
- Tensor im2col(DataType::FLOAT32, Shape({}), {}, "");
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 2;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::NONE;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(im2col);
- kernel.execute();
-
- std::vector<float> ref_output_data{
- 18, 2, 5, // first batch, left
- 18, 2, 5, // first batch, right
- 17, 4, 3, // second batch, left
- 37, 4, 3, // second batch, right
- };
- std::vector<int32_t> ref_output_shape{2, 1, 2, 3};
- EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
-}
-
-TEST_F(Conv2DTest, Uint8)
-{
- std::vector<float> input_data{
- // First batch
- 1, 1, 1, 1, // row = 1
- 2, 2, 2, 2, // row = 2
- // Second batch
- 1, 2, 3, 4, // row = 1
- 1, 2, 3, 4, // row = 2
- };
- std::vector<float> filter_data{
- 1, 2, 3, 4, // first 2x2 filter
- -1, 1, -1, 1, // second 2x2 filter
- -1, -1, 1, 1, // third 2x2 filter
- };
- std::vector<float> bias_data{1, 2, 3};
-
- 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>({2, 2, 4, 1}, input_quant_param.first, input_quant_param.second,
- input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::U8>({3, 2, 2, 1}, input_quant_param.first, input_quant_param.second,
- filter_data, _memory_manager.get());
- Tensor bias_tensor = makeInputTensor<DataType::S32>(
- {3}, input_quant_param.first * input_quant_param.first, 0, bias_data, _memory_manager.get());
- Tensor im2col(DataType::U8, Shape({}), {}, "");
- Tensor output_tensor =
- makeOutputTensor(DataType::U8, output_quant_param.first, output_quant_param.second);
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 2;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::NONE;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(im2col);
- kernel.execute();
-
- std::vector<float> ref_output_data{
- 18, 2, 5, // first batch, left
- 18, 2, 5, // first batch, right
- 17, 4, 3, // second batch, left
- 37, 4, 3, // second batch, right
- };
- std::vector<int32_t> ref_output_shape{2, 1, 2, 3};
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
-}
-
-TEST_F(Conv2DTest, Uint8_CWQ)
-{
- const int output_channels = 3;
- std::vector<float> input_data{
- // First batch
- 1, 1, 1, 1, // row = 1
- 2, 2, 2, 2, // row = 2
- // Second batch
- 1, 2, 3, 4, // row = 1
- 1, 2, 3, 4, // row = 2
- };
- std::vector<float> filter_data{
- 1, 2, 3, 4, // first 2x2 filter
- -1, 1, -1, 1, // second 2x2 filter
- -1, -1, 1, 1, // third 2x2 filter
- };
- std::vector<float> bias_data{1, 2, 3};
- Shape filter_shape{output_channels, 2, 2, 1};
-
- std::pair<float, int32_t> input_quant_param = quantizationParams<uint8_t>(0, 4);
- std::pair<float, int32_t> output_quant_param = quantizationParams<uint8_t>(-127, 128);
-
- std::vector<std::pair<float, int32_t>> filter_quant_params;
- filter_quant_params.push_back(quantizationParams<uint8_t>(0, 4));
- filter_quant_params.push_back(quantizationParams<uint8_t>(-1, 1));
- filter_quant_params.push_back(quantizationParams<uint8_t>(-1, 1));
-
- std::vector<float> filter_scales;
- std::vector<int32_t> filter_zerops;
- for (auto iter : filter_quant_params)
- {
- filter_scales.push_back(iter.first);
- filter_zerops.push_back(iter.second);
- }
-
- std::vector<float> bias_scales;
- for (int i = 0; i < output_channels; ++i)
- bias_scales.push_back(filter_quant_params[i].first * input_quant_param.first);
- std::vector<int32_t> zerop(output_channels, 0);
-
- Tensor input_tensor =
- makeInputTensor<DataType::U8>({2, 2, 4, 1}, input_quant_param.first, input_quant_param.second,
- input_data, _memory_manager.get());
- Tensor filter_tensor = makeInputTensor<DataType::U8>(filter_shape, filter_scales, filter_zerops,
- 0, filter_data, _memory_manager.get());
- Tensor bias_tensor = makeInputTensor<DataType::S32>({output_channels}, bias_scales, zerop, 0,
- bias_data, _memory_manager.get());
- Tensor im2col(DataType::U8, Shape({}), {}, "");
- Tensor output_tensor =
- makeOutputTensor(DataType::U8, output_quant_param.first, output_quant_param.second);
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 2;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::NONE;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(im2col);
- kernel.execute();
-
- std::vector<float> ref_output_data{
- 18, 2, 5, // first batch, left
- 18, 2, 5, // first batch, right
- 17, 4, 3, // second batch, left
- 37, 4, 3, // second batch, right
- };
- std::vector<int32_t> ref_output_shape{2, 1, 2, 3};
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
-}
-
-TEST_F(Conv2DTest, SInt8_CWQ)
+template <typename T> std::vector<T> checkConv2DKernel(test_kernel::TestDataBase<T> *test_data_base)
{
- const int output_channels = 3;
- std::vector<float> input_data{
- // First batch
- 1, 1, 1, 1, // row = 1
- 2, 2, 2, 2, // row = 2
- // Second batch
- 1, 2, 3, 4, // row = 1
- 1, 2, 3, 4, // row = 2
- };
- std::vector<float> filter_data{
- 1, 2, 3, 4, // first 2x2 filter
- -1, 1, -1, 1, // second 2x2 filter
- -1, -1, 1, 1, // third 2x2 filter
- };
- std::vector<float> bias_data{1, 2, 3};
- Shape filter_shape{output_channels, 2, 2, 1};
+ MemoryManager memory_manager{};
+ RuntimeModule runtime_module{};
+ bool dealloc_input = true;
- std::pair<float, int32_t> input_quant_param = quantizationParams<int8_t>(0, 4);
- std::pair<float, int32_t> output_quant_param = quantizationParams<int8_t>(-127, 128);
+ // 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);
- std::vector<std::pair<float, int32_t>> filter_quant_params;
- filter_quant_params.push_back(std::pair<float, int32_t>(0.5, 0));
- filter_quant_params.push_back(std::pair<float, int32_t>(0.25, 0));
- filter_quant_params.push_back(std::pair<float, int32_t>(0.125, 0));
+ auto *main_runtime_graph = runtime_module.getMainGraph();
+ assert(main_runtime_graph->getNumOfInputTensors() == 1);
- std::vector<float> filter_scales;
- std::vector<int32_t> filter_zerops;
- for (auto iter : filter_quant_params)
+ // set input data
{
- filter_scales.push_back(iter.first);
- filter_zerops.push_back(iter.second);
+ 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> bias_scales;
- for (int i = 0; i < output_channels; ++i)
- bias_scales.push_back(filter_quant_params[i].first * input_quant_param.first);
- std::vector<int32_t> zerop(output_channels, 0);
-
- Tensor input_tensor =
- makeInputTensor<DataType::S8>({2, 2, 4, 1}, input_quant_param.first, input_quant_param.second,
- input_data, _memory_manager.get());
- Tensor filter_tensor = makeInputTensor<DataType::S8>(filter_shape, filter_scales, filter_zerops,
- 0, filter_data, _memory_manager.get());
- Tensor bias_tensor = makeInputTensor<DataType::S32>({output_channels}, bias_scales, zerop, 0,
- bias_data, _memory_manager.get());
- Tensor im2col(DataType::S8, Shape({}), {}, "");
- Tensor output_tensor =
- makeOutputTensor(DataType::S8, output_quant_param.first, output_quant_param.second);
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 2;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::NONE;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(im2col);
- kernel.execute();
-
- std::vector<float> ref_output_data{
- 18, 2, 5, // first batch, left
- 18, 2, 5, // first batch, right
- 17, 4, 3, // second batch, left
- 37, 4, 3, // second batch, right
- };
- std::vector<int32_t> ref_output_shape{2, 1, 2, 3};
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data));
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
-}
-
-TEST_F(Conv2DTest, SInt16)
-{
- Shape input_shape{1, 4, 3, 2};
- Shape filter_shape{2, 2, 2, 2};
- Shape bias_shape{2};
- std::vector<int32_t> ref_output_shape{1, 2, 2, 2};
-
- std::vector<float> input_data{
- 1, 2, 3, 4, 5, 6, // row = 0
- 7, 8, 9, 10, 11, 12, // row = 1
- 13, 14, 15, 16, 17, 18, // row = 2
- 19, 20, 21, 22, 23, 24, // row = 3
- };
- std::vector<float> filter_data{
- 1, 2, -3, -4, // out = 0, row = 0
- -5, 6, -7, 8, // out = 1, row = 0
- 4, -2, 3, -1, // out = 0, row = 1
- -8, -6, 7, 5, // out = 1, row = 1
- };
- std::vector<float> bias_data{1, 2};
- std::vector<float> ref_output_data{
- 11, 16, 7, 20, // row = 0
- 0, 40, 0, 44, // row = 1
- };
-
- Tensor input_tensor =
- makeInputTensor<DataType::S16>(input_shape, 0.25, 0, input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::S16>(filter_shape, 0.2, 0, filter_data, _memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::S64>(bias_shape, 0.25 * 0.2, 0, bias_data, _memory_manager.get());
- Tensor im2col(DataType::S16, Shape({}), {}, "");
- Tensor output_tensor = makeOutputTensor(DataType::S16, 0.5, 0);
+ runtime_module.execute();
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 1;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::RELU;
+ assert(main_runtime_graph->getNumOfOutputTensors() == 1);
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(im2col);
- kernel.execute();
-
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data));
+ 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(Conv2DTest, SInt16_CWQ_weights)
+TEST_F(Conv2DTest, Float_P)
{
- Shape input_shape{1, 2, 2, 2}; // Batch x H x W x C
- Shape filter_shape{3, 1, 1, 2}; // Out channels x H x W x In Channels
- Shape bias_shape{3};
- std::vector<int32_t> ref_output_shape{1, 2, 2, 3};
-
- std::vector<float> input_data{
- 1, 2, // row = 0, col 0
- 3, 4, // row = 0, col 1
- 5, 6, // row = 1, col 0
- 7, 8, // row = 1, col 1
- };
- std::vector<float> filter_data{
- 4, -3, // out = 0
- 1, -3, // out = 1
- 5, -3, // out = 2
- };
- std::vector<float> bias_data{1, 10, 5};
- std::vector<float> ref_output_data{
- 0, 5, 4, // row 0, col 0
- 1, 1, 8, // row 0, col 1
- 3, 0, 12, // row 1, col 0
- 5, 0, 16, // row 1, col 1
- };
-
- float input_scale = 0.25f;
- float output_scale = 0.05f;
- std::vector<float> filter_scales = {0.25f, 0.2f, 0.1f};
- std::vector<float> bias_scales;
- for (int i = 0; i < filter_scales.size(); ++i)
- bias_scales.push_back(filter_scales[i] * input_scale);
- std::vector<int32_t> zerop = {0, 0, 0};
-
- Tensor input_tensor =
- makeInputTensor<DataType::S16>(input_shape, input_scale, 0, input_data, _memory_manager.get());
- Tensor filter_tensor = makeInputTensor<DataType::S16>(filter_shape, filter_scales, zerop, 0,
- filter_data, _memory_manager.get());
- Tensor bias_tensor = makeInputTensor<DataType::S64>(bias_shape, bias_scales, zerop, 0, bias_data,
- _memory_manager.get());
- Tensor im2col(DataType::S16, Shape({}), {}, "");
- Tensor output_tensor = makeOutputTensor(DataType::S16, output_scale, 0);
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 1;
- params.stride_width = 1;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::RELU;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- kernel.configure();
- _memory_manager->allocate_memory(output_tensor);
- _memory_manager->allocate_memory(im2col);
- kernel.execute();
-
- EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape));
- EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data));
+ test_kernel::TestDataFloatConv2D test_data_kernel;
+ std::vector<float> output_data_vector = checkConv2DKernel(&test_data_kernel);
+ EXPECT_THAT(output_data_vector, kernels::testing::FloatArrayNear(
+ test_data_kernel.get_output_data_by_index(0), 0.0001f));
}
-TEST_F(Conv2DTest, Unsupported_Type_Configure_NEG)
+TEST_F(Conv2DTest, U8_P)
{
- Shape input_shape{1, 4, 3, 2};
- Shape filter_shape{2, 2, 2, 2};
- Shape bias_shape{2};
- std::vector<int32_t> input_data{
- 1, 2, 3, 4, 5, 6, // row = 0
- 7, 8, 9, 10, 11, 12, // row = 1
- 13, 14, 15, 16, 17, 18, // row = 2
- 19, 20, 21, 22, 23, 24, // row = 3
- };
- std::vector<float> filter_data{
- 1, 2, -3, -4, // out = 0, row = 0
- -5, 6, -7, 8, // out = 1, row = 0
- 4, -2, 3, -1, // out = 0, row = 1
- -8, -6, 7, 5, // out = 1, row = 1
- };
- std::vector<float> bias_data{1, 2};
- Tensor input_tensor =
- makeInputTensor<DataType::S32>(input_shape, input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::FLOAT32>(filter_shape, filter_data, _memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, _memory_manager.get());
- Tensor im2col(DataType::FLOAT32, Shape({}), {}, "");
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 1;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::RELU;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- EXPECT_ANY_THROW(kernel.configure());
+ test_kernel::TestDataU8Conv2D test_data_kernel;
+ std::vector<uint8_t> output_data_vector = checkConv2DKernel(&test_data_kernel);
+ EXPECT_THAT(output_data_vector, test_data_kernel.get_output_data_by_index(0));
}
-TEST_F(Conv2DTest, Invalid_Bias_Type_NEG)
+TEST_F(Conv2DTest, Input_type_mismatch_NEG)
{
- Shape input_shape{1, 4, 3, 2};
- Shape filter_shape{2, 2, 2, 2};
- Shape bias_shape{2};
- std::vector<float> input_data{
- 1, 2, 3, 4, 5, 6, // row = 0
- 7, 8, 9, 10, 11, 12, // row = 1
- 13, 14, 15, 16, 17, 18, // row = 2
- 19, 20, 21, 22, 23, 24, // row = 3
- };
- std::vector<float> filter_data{
- 1, 2, -3, -4, // out = 0, row = 0
- -5, 6, -7, 8, // out = 1, row = 0
- 4, -2, 3, -1, // out = 0, row = 1
- -8, -6, 7, 5, // out = 1, row = 1
- };
- std::vector<uint8_t> bias_data{1, 2};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::FLOAT32>(filter_shape, filter_data, _memory_manager.get());
- Tensor bias_tensor = makeInputTensor<DataType::U8>(bias_shape, bias_data, _memory_manager.get());
- Tensor im2col(DataType::FLOAT32, Shape({}), {}, "");
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
+ test_kernel::NegTestDataInputMismatchConv2DKernel test_data_kernel;
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 1;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::RELU;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- EXPECT_ANY_THROW(kernel.configure());
-}
-
-TEST_F(Conv2DTest, Invalid_Bias_Data_NEG)
-{
- Shape input_shape{1, 4, 3, 2};
- Shape filter_shape{2, 2, 2, 2};
- Shape bias_shape{3};
- std::vector<float> input_data{
- 1, 2, 3, 4, 5, 6, // row = 0
- 7, 8, 9, 10, 11, 12, // row = 1
- 13, 14, 15, 16, 17, 18, // row = 2
- 19, 20, 21, 22, 23, 24, // row = 3
- };
- std::vector<float> filter_data{
- 1, 2, -3, -4, // out = 0, row = 0
- -5, 6, -7, 8, // out = 1, row = 0
- 4, -2, 3, -1, // out = 0, row = 1
- -8, -6, 7, 5, // out = 1, row = 1
- };
- std::vector<float> bias_data{1, 2, 3};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::FLOAT32>(filter_shape, filter_data, _memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, _memory_manager.get());
- Tensor im2col(DataType::FLOAT32, Shape({}), {}, "");
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 1;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::RELU;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- EXPECT_ANY_THROW(kernel.configure());
+ 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_F(Conv2DTest, Invalid_Input_Shape_NEG)
+TEST_F(Conv2DTest, Wrong_bias_type_NEG)
{
- Shape input_shape{1, 4, 6, 1};
- Shape filter_shape{2, 2, 2, 2};
- Shape bias_shape{2};
- std::vector<float> input_data{
- 1, 2, 3, 4, 5, 6, // row = 0
- 7, 8, 9, 10, 11, 12, // row = 1
- 13, 14, 15, 16, 17, 18, // row = 2
- 19, 20, 21, 22, 23, 24, // row = 3
- };
- std::vector<float> filter_data{
- 1, 2, -3, -4, // out = 0, row = 0
- -5, 6, -7, 8, // out = 1, row = 0
- 4, -2, 3, -1, // out = 0, row = 1
- -8, -6, 7, 5, // out = 1, row = 1
- };
- std::vector<float> bias_data{1, 2};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::FLOAT32>(filter_shape, filter_data, _memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, _memory_manager.get());
- Tensor im2col(DataType::FLOAT32, Shape({}), {}, "");
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
+ test_kernel::NegTestDataWrongBiasTypeConv2DKernel test_data_kernel;
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 1;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::RELU;
-
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- EXPECT_ANY_THROW(kernel.configure());
+ 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_F(Conv2DTest, Invalid_fused_act_tanh_NEG)
+TEST_F(Conv2DTest, Invalid_input_type_NEG)
{
- Shape input_shape{1, 4, 3, 2};
- Shape filter_shape{2, 2, 2, 2};
- Shape bias_shape{2};
- std::vector<float> input_data{
- 1, 2, 3, 4, 5, 6, // row = 0
- 7, 8, 9, 10, 11, 12, // row = 1
- 13, 14, 15, 16, 17, 18, // row = 2
- 19, 20, 21, 22, 23, 24, // row = 3
- };
- std::vector<float> filter_data{
- 1, 2, -3, -4, // out = 0, row = 0
- -5, 6, -7, 8, // out = 1, row = 0
- 4, -2, 3, -1, // out = 0, row = 1
- -8, -6, 7, 5, // out = 1, row = 1
- };
- std::vector<float> bias_data{1, 2};
- Tensor input_tensor =
- makeInputTensor<DataType::FLOAT32>(input_shape, input_data, _memory_manager.get());
- Tensor filter_tensor =
- makeInputTensor<DataType::FLOAT32>(filter_shape, filter_data, _memory_manager.get());
- Tensor bias_tensor =
- makeInputTensor<DataType::FLOAT32>(bias_shape, bias_data, _memory_manager.get());
- Tensor im2col(DataType::FLOAT32, Shape({}), {}, "");
- Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
-
- Conv2DParams params{};
- params.padding = Padding::VALID;
- params.stride_height = 2;
- params.stride_width = 1;
- params.dilation_height_factor = 1;
- params.dilation_width_factor = 1;
- params.activation = Activation::TANH;
+ test_kernel::NegTestDataInvalidInputTypeConv2DKernel test_data_kernel;
- Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, &im2col, params);
- EXPECT_ANY_THROW(kernel.configure());
+ 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
-#endif
projects / platform / core / ml / nnfw.git / blobdiff