mirror of
https://github.com/lordmathis/CUDANet.git
synced 2025-11-06 09:44:28 +00:00
355 lines
11 KiB
Plaintext
355 lines
11 KiB
Plaintext
#include <cuda_runtime_api.h>
|
|
#include <gtest/gtest.h>
|
|
|
|
#include <iostream>
|
|
|
|
#include "conv2d.cuh"
|
|
|
|
class Conv2dTest : public ::testing::Test {
|
|
protected:
|
|
dim2d inputSize;
|
|
int inputChannels;
|
|
dim2d kernelSize;
|
|
dim2d stride;
|
|
int numFilters;
|
|
dim2d paddingSize;
|
|
CUDANet::Layers::ActivationType activationType;
|
|
std::vector<float> input;
|
|
std::vector<float> kernels;
|
|
std::vector<float> expected;
|
|
|
|
float *d_input;
|
|
float *d_output;
|
|
CUDANet::Layers::Conv2d *conv2dLayer;
|
|
|
|
virtual void SetUp() override {
|
|
d_input = nullptr;
|
|
d_output = nullptr;
|
|
conv2dLayer = nullptr;
|
|
}
|
|
|
|
virtual void TearDown() override {
|
|
if (d_input) {
|
|
cudaFree(d_input);
|
|
}
|
|
delete conv2dLayer;
|
|
}
|
|
|
|
void runTest() {
|
|
cudaError_t cudaStatus;
|
|
|
|
conv2dLayer = new CUDANet::Layers::Conv2d(
|
|
inputSize, inputChannels, kernelSize, stride, numFilters,
|
|
paddingSize, activationType
|
|
);
|
|
|
|
conv2dLayer->setWeights(kernels.data());
|
|
|
|
cudaStatus =
|
|
cudaMalloc((void **)&d_input, sizeof(float) * input.size());
|
|
EXPECT_EQ(cudaStatus, cudaSuccess);
|
|
|
|
cudaStatus = cudaMemcpy(
|
|
d_input, input.data(), sizeof(float) * input.size(),
|
|
cudaMemcpyHostToDevice
|
|
);
|
|
EXPECT_EQ(cudaStatus, cudaSuccess);
|
|
|
|
d_output = conv2dLayer->forward(d_input);
|
|
|
|
int outputHeight =
|
|
(inputSize.first - kernelSize.first + 2 * paddingSize.first) /
|
|
stride.first +
|
|
1;
|
|
int outputWidth =
|
|
(inputSize.second - kernelSize.second + 2 * paddingSize.second) /
|
|
stride.second +
|
|
1;
|
|
int outputSize = outputHeight * outputWidth * numFilters;
|
|
EXPECT_EQ(outputSize, conv2dLayer->getOutputSize());
|
|
|
|
std::vector<float> output(outputSize);
|
|
cudaStatus = cudaMemcpy(
|
|
output.data(), d_output, sizeof(float) * output.size(),
|
|
cudaMemcpyDeviceToHost
|
|
);
|
|
EXPECT_EQ(cudaStatus, cudaSuccess);
|
|
|
|
for (int i = 0; i < output.size(); ++i) {
|
|
EXPECT_NEAR(expected[i], output[i], 1e-5f);
|
|
}
|
|
}
|
|
};
|
|
|
|
TEST_F(Conv2dTest, SimpleTest) {
|
|
inputSize = {4, 4};
|
|
inputChannels = 1;
|
|
kernelSize = {2, 2};
|
|
stride = {1, 1};
|
|
numFilters = 1;
|
|
paddingSize = {0, 0};
|
|
activationType = CUDANet::Layers::ActivationType::NONE;
|
|
|
|
input = {
|
|
// clang-format off
|
|
1.0f, 2.0f, 3.0f, 4.0f,
|
|
5.0f, 6.0f, 7.0f, 8.0f,
|
|
9.0f, 10.0f, 11.0f, 12.0f,
|
|
13.0f, 14.0f, 15.0f, 16.0f
|
|
// clang-format on
|
|
};
|
|
kernels = {
|
|
// clang-format off
|
|
1.0f,2.0f,
|
|
3.0f, 4.0f
|
|
// clang-format on
|
|
};
|
|
expected = {44.0f, 54.0f, 64.0f, 84.0f, 94.0f,
|
|
104.0f, 124.0f, 134.0f, 144.0f};
|
|
|
|
runTest();
|
|
}
|
|
|
|
TEST_F(Conv2dTest, PaddedTest) {
|
|
inputSize = {5, 5};
|
|
inputChannels = 3;
|
|
kernelSize = {3, 3};
|
|
stride = {1, 1};
|
|
numFilters = 2;
|
|
|
|
int paddingFirst =
|
|
CUDANET_SAME_PADDING(inputSize.first, kernelSize.first, stride.first);
|
|
int paddingSecond = CUDANET_SAME_PADDING(
|
|
inputSize.second, kernelSize.second, stride.second
|
|
);
|
|
paddingSize = {paddingFirst, paddingSecond};
|
|
|
|
activationType = CUDANet::Layers::ActivationType::NONE;
|
|
|
|
// clang-format off
|
|
input = {
|
|
// Channel 1
|
|
0.823f, 0.217f, 0.435f, 0.981f, 0.742f,
|
|
0.109f, 0.518f, 0.374f, 0.681f, 0.147f,
|
|
0.956f, 0.729f, 0.654f, 0.087f, 0.392f,
|
|
0.784f, 0.921f, 0.543f, 0.231f, 0.816f,
|
|
0.472f, 0.614f, 0.102f, 0.987f, 0.398f,
|
|
// Channel 2
|
|
0.051f, 0.756f, 0.841f, 0.293f, 0.128f,
|
|
0.417f, 0.632f, 0.095f, 0.184f, 0.529f,
|
|
0.871f, 0.958f, 0.213f, 0.347f, 0.725f,
|
|
0.461f, 0.012f, 0.278f, 0.195f, 0.649f,
|
|
0.853f, 0.707f, 0.988f, 0.988f, 0.322f,
|
|
// Channel 3
|
|
0.345f, 0.123f, 0.789f, 0.123f, 0.456f,
|
|
0.456f, 0.789f, 0.123f, 0.345f, 0.123f,
|
|
0.789f, 0.123f, 0.345f, 0.123f, 0.456f,
|
|
0.123f, 0.345f, 0.123f, 0.789f, 0.123f,
|
|
0.345f, 0.123f, 0.789f, 0.123f, 0.456f
|
|
};
|
|
|
|
kernels = {
|
|
// Filter 1, Channel 1
|
|
0.128f, 0.754f, 0.987f,
|
|
0.321f, 0.412f, 0.635f,
|
|
0.298f, 0.017f, 0.845f,
|
|
// Filter 1, Channel 2
|
|
0.514f, 0.729f, 0.952f,
|
|
0.684f, 0.378f, 0.159f,
|
|
0.823f, 0.547f, 0.216f,
|
|
// Filter 1, Channel 3
|
|
0.983f, 0.231f, 0.456f,
|
|
0.178f, 0.654f, 0.821f,
|
|
0.345f, 0.987f, 0.123f,
|
|
// Filter 2, Channel 1
|
|
0.789f, 0.543f, 0.210f,
|
|
0.012f, 0.371f, 0.638f,
|
|
0.456f, 0.198f, 0.907f,
|
|
// Filter 2, Channel 2
|
|
0.101f, 0.432f, 0.759f,
|
|
0.234f, 0.567f, 0.890f,
|
|
0.543f, 0.876f, 0.219f,
|
|
// Filter 2, Channel 3
|
|
0.345f, 0.678f, 0.011f,
|
|
0.678f, 0.011f, 0.345f,
|
|
0.011f, 0.345f, 0.678f
|
|
};
|
|
// clang-format on
|
|
|
|
// Generated by tools/generate_conv2d_test.py
|
|
expected = {
|
|
// Channel 1
|
|
2.29426f, 3.89173f, 4.17634f, 3.25501f, 2.07618f, 5.41483f, 7.09971f,
|
|
6.39811f, 5.71432f, 3.10928f, 5.12973f, 6.29638f, 5.26962f, 5.21997f,
|
|
3.05852f, 6.17517f, 7.19311f, 6.69771f, 6.2142f, 4.03242f, 3.3792f,
|
|
4.36444f, 4.396f, 4.69905f, 3.62061f,
|
|
// Channel 2
|
|
2.87914f, 3.71743f, 3.51854f, 2.98413f, 1.46579f, 4.94951f, 6.18983f,
|
|
4.98187f, 4.38372f, 3.35386f, 5.0364f, 5.3756f, 4.05993f, 4.89299f,
|
|
2.78625f, 5.33763f, 5.80899f, 5.89785f, 5.51095f, 3.74287f, 2.64053f,
|
|
4.05895f, 3.96482f, 4.30177f, 1.94269f
|
|
};
|
|
|
|
runTest();
|
|
}
|
|
|
|
TEST_F(Conv2dTest, StridedPaddedTest) {
|
|
inputSize = {5, 5};
|
|
inputChannels = 2;
|
|
kernelSize = {3, 3};
|
|
stride = {2, 2};
|
|
numFilters = 2;
|
|
|
|
int paddingFirst =
|
|
CUDANET_SAME_PADDING(inputSize.first, kernelSize.second, stride.first);
|
|
int paddingSecond = CUDANET_SAME_PADDING(
|
|
inputSize.second, kernelSize.second, stride.second
|
|
);
|
|
paddingSize = {paddingFirst, paddingSecond};
|
|
|
|
activationType = CUDANet::Layers::ActivationType::RELU;
|
|
|
|
// clang-format off
|
|
input = {
|
|
// Channel 1
|
|
0.946f, 0.879f, 0.382f, 0.542f, 0.453f,
|
|
0.128f, 0.860f, 0.778f, 0.049f, 0.974f,
|
|
0.400f, 0.874f, 0.161f, 0.271f, 0.580f,
|
|
0.373f, 0.078f, 0.366f, 0.396f, 0.181f,
|
|
0.246f, 0.112f, 0.179f, 0.979f, 0.026f,
|
|
// Channel 2
|
|
0.598f, 0.458f, 0.776f, 0.213f, 0.199f,
|
|
0.853f, 0.170f, 0.609f, 0.269f, 0.777f,
|
|
0.776f, 0.694f, 0.430f, 0.238f, 0.968f,
|
|
0.473f, 0.303f, 0.084f, 0.785f, 0.444f,
|
|
0.464f, 0.413f, 0.779f, 0.298f, 0.783f
|
|
};
|
|
kernels = {
|
|
// Filter 1, Channel 1
|
|
0.744f, 0.745f, 0.641f,
|
|
0.164f, 0.157f, 0.127f,
|
|
0.732f, 0.761f, 0.601f,
|
|
// Filter 1, Channel 2
|
|
0.475f, 0.335f, 0.499f,
|
|
0.833f, 0.793f, 0.176f,
|
|
0.822f, 0.163f, 0.175f,
|
|
// Filter 2, Channel 1
|
|
0.918f, 0.340f, 0.497f,
|
|
0.233f, 0.218f, 0.847f,
|
|
0.931f, 0.926f, 0.199f,
|
|
// Filter 2, Channel 2
|
|
0.510f, 0.432f, 0.567f,
|
|
0.236f, 0.397f, 0.739f,
|
|
0.939f, 0.891f, 0.006f
|
|
};
|
|
// clang-format on
|
|
|
|
expected = {// Channel 1
|
|
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 1.59803f, 2.84444f, 1.6201f,
|
|
0.0f, 0.0f, 2.38937f, 3.80762f, 3.39679f, 0.0f, 0.0f, 1.13102f,
|
|
2.33335f, 1.98488f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
|
|
// Channel 2
|
|
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.57732f, 3.55543f,
|
|
2.24675f, 0.0f, 0.0f, 3.36842f, 3.41373f, 3.14804f, 0.0f, 0.0f,
|
|
1.17963f, 2.55005f, 1.63218f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f
|
|
};
|
|
|
|
runTest();
|
|
}
|
|
|
|
TEST_F(Conv2dTest, NonSquareInputTest) {
|
|
inputSize = {4, 6}; // Non-square input
|
|
inputChannels = 1;
|
|
kernelSize = {2, 2};
|
|
stride = {1, 1};
|
|
numFilters = 1;
|
|
paddingSize = {0, 0};
|
|
activationType = CUDANet::Layers::ActivationType::NONE;
|
|
|
|
input = {
|
|
// clang-format off
|
|
0.946f, 0.879f, 0.382f, 0.542f, 0.453f, 0.128f,
|
|
0.128f, 0.860f, 0.778f, 0.049f, 0.974f, 0.400f,
|
|
0.400f, 0.874f, 0.161f, 0.271f, 0.580f, 0.373f,
|
|
0.078f, 0.366f, 0.396f, 0.181f, 0.246f, 0.112f
|
|
// clang-format on
|
|
};
|
|
kernels = {0.744f, 0.745f, 0.164f, 0.157f};
|
|
expected = {1.51469f, 1.20175f, 0.82328f, 0.90169f, 0.65493f,
|
|
0.93875f, 1.38806f, 0.68429f, 0.89759f, 1.17634f,
|
|
1.01898f, 0.8924f, 0.41504f, 0.70203f, 0.76733f};
|
|
|
|
runTest();
|
|
}
|
|
|
|
TEST_F(Conv2dTest, NonSquareKernelTest) {
|
|
inputSize = {4, 4};
|
|
inputChannels = 1;
|
|
kernelSize = {1, 3}; // Non-square kernel
|
|
stride = {1, 1};
|
|
numFilters = 1;
|
|
paddingSize = {0, 0};
|
|
activationType = CUDANet::Layers::ActivationType::NONE;
|
|
|
|
input = {
|
|
// clang-format off
|
|
0.946f, 0.879f, 0.382f, 0.542f,
|
|
0.128f, 0.860f, 0.778f, 0.049f,
|
|
0.400f, 0.874f, 0.161f, 0.271f,
|
|
0.078f, 0.366f, 0.396f, 0.181f
|
|
// clang-format on
|
|
};
|
|
kernels = {0.744f, 0.745f, 0.164f};
|
|
expected = {1.42133f, 1.02745f, 0.86352f, 1.22749f,
|
|
0.97513f, 0.81465f, 0.39565f, 0.59701f};
|
|
|
|
runTest();
|
|
}
|
|
|
|
TEST_F(Conv2dTest, NonSquareStrideTest) {
|
|
inputSize = {4, 4};
|
|
inputChannels = 1;
|
|
kernelSize = {2, 2};
|
|
stride = {1, 2}; // Non-square stride
|
|
numFilters = 1;
|
|
paddingSize = {0, 0};
|
|
activationType = CUDANet::Layers::ActivationType::NONE;
|
|
|
|
input = {
|
|
// clang-format off
|
|
0.946f, 0.879f, 0.382f, 0.542f,
|
|
0.128f, 0.860f, 0.778f, 0.049f,
|
|
0.400f, 0.874f, 0.161f, 0.271f,
|
|
0.078f, 0.366f, 0.396f, 0.181f
|
|
// clang-format on
|
|
};
|
|
kernels = {0.144f, 0.745f, 0.964f, 0.164f};
|
|
expected = {1.05551f, 1.21683f, 1.18807f, 0.34818f, 0.84395f, 0.63651f};
|
|
|
|
runTest();
|
|
}
|
|
|
|
TEST_F(Conv2dTest, NonSquarePaddingTest) {
|
|
inputSize = {4, 4};
|
|
inputChannels = 1;
|
|
kernelSize = {2, 2};
|
|
stride = {1, 1};
|
|
numFilters = 1;
|
|
paddingSize = {1, 2}; // Non-square padding
|
|
activationType = CUDANet::Layers::ActivationType::NONE;
|
|
|
|
input = {
|
|
// clang-format off
|
|
0.946f, 0.879f, 0.382f, 0.542f,
|
|
0.128f, 0.860f, 0.778f, 0.049f,
|
|
0.400f, 0.874f, 0.161f, 0.271f,
|
|
0.078f, 0.366f, 0.396f, 0.181f
|
|
// clang-format on
|
|
};
|
|
kernels = {0.144f, 0.745f, 0.964f, 0.164f};
|
|
expected = {0.0f, 0.15514f, 1.0561f, 0.91f, 0.45714f, 0.52249f, 0.0f,
|
|
0.0f, 0.72576f, 1.05551f, 1.3678f, 1.21683f, 0.12528f, 0.0f,
|
|
0.0f, 0.16096f, 1.18807f, 1.57239f, 0.34818f, 0.2683f, 0.0f,
|
|
0.0f, 0.31079f, 0.84395f, 0.66357f, 0.63651f, 0.21351f, 0.0f,
|
|
0.0f, 0.05811f, 0.2839f, 0.34772f, 0.19187f, 0.02606f, 0.0f};
|
|
} |