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CUDANet/test/layers/test_conv2d.cu
2024-03-09 21:08:16 +01:00

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#include <cuda_runtime_api.h>
#include <gtest/gtest.h>
#include <iostream>
#include "conv2d.cuh"
class Conv2dTest : public::testing::Test {
protected:
cudaError_t cudaStatus;
};
TEST_F(Conv2dTest, SimpleExample) {
int inputSize = 4;
int inputChannels = 1;
int kernelSize = 2;
int stride = 1;
std::string padding = "VALID";
int numFilters = 1;
Activation activation = LINEAR;
Layers::Conv2d conv2d(
inputSize,
inputChannels,
kernelSize,
stride,
padding,
numFilters,
activation
);
int outputSize = (inputSize - kernelSize) / stride + 1;
EXPECT_EQ(outputSize, conv2d.outputSize);
std::vector<float> input = {
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
};
std::vector<float> kernels = {
1.0f, 2.0f, 3.0f, 4.0f,
};
float* d_input;
float* d_output;
conv2d.setKernels(kernels);
// Allocate device memory
cudaStatus = cudaMalloc((void**)&d_input, sizeof(float) * inputSize * inputSize * inputChannels);
EXPECT_EQ(cudaStatus, cudaSuccess);
cudaStatus = cudaMalloc((void**)&d_output, sizeof(float) * outputSize * outputSize * numFilters);
EXPECT_EQ(cudaStatus, cudaSuccess);
// // Copy input to device
cudaStatus = cudaMemcpy(
d_input, input.data(), sizeof(float) * input.size(), cudaMemcpyHostToDevice
);
EXPECT_EQ(cudaStatus, cudaSuccess);
conv2d.forward(d_input, d_output);
std::vector<float> expected = {
44.0f, 54.0f, 64.0f,
84.0f, 94.0f, 104.0f,
124.0f, 134.0f, 144.0f
};
std::vector<float> output(outputSize * outputSize * numFilters);
cudaStatus = cudaMemcpy(
output.data(), d_output, sizeof(float) * output.size(),
cudaMemcpyDeviceToHost
);
EXPECT_EQ(cudaStatus, cudaSuccess);
for (int i = 0; i < output.size(); ++i) {
EXPECT_FLOAT_EQ(expected[i], output[i]);
}
}