Migrate conv2d layer

src/backends/cuda/layers/conv2d.cu

@@ -0,0 +1,73 @@
+#include <vector>
+
+#include "activation.hpp"
+#include "conv2d.hpp"
+#include "convolution.cuh"
+#include "cuda_helper.cuh"
+#include "layer.hpp"
+#include "matmul.cuh"
+#include "vector.cuh"
+
+using namespace CUDANet::Layers;
+
+void Conv2d::initCUDA() {
+    d_output = nullptr;
+    CUDA_CHECK(cudaMalloc(
+        (void**)&d_output,
+        sizeof(float) * outputSize.first * outputSize.second * numFilters
+    ));
+
+    d_weights = nullptr;
+    CUDA_CHECK(cudaMalloc(
+        (void**)&d_weights, sizeof(float) * kernelSize.first *
+                                kernelSize.second * inputChannels * numFilters
+    ));
+
+    d_biases = nullptr;
+    CUDA_CHECK(cudaMalloc((void**)&d_biases, sizeof(float) * numFilters));
+}
+
+void Conv2d::delCUDA() {
+    cudaFree(d_output);
+    cudaFree(d_weights);
+    cudaFree(d_biases);
+}
+
+void Conv2d::toCuda() {
+    CUDA_CHECK(cudaMemcpy(
+        d_weights, weights.data(),
+        sizeof(float) * kernelSize.first * kernelSize.second * inputChannels *
+            numFilters,
+        cudaMemcpyHostToDevice
+    ));
+
+    CUDA_CHECK(cudaMemcpy(
+        d_biases, biases.data(), sizeof(float) * numFilters,
+        cudaMemcpyHostToDevice
+    ));
+}
+
+float* Conv2d::forwardCUDA(const float* d_input) {
+    // Convolve
+    dim3 block(8, 8, 8);
+    dim3 grid(
+        (outputSize.first + block.x - 1) / block.x,
+        (outputSize.second + block.y - 1) / block.y,
+        (numFilters + block.z - 1) / block.z
+    );
+
+    CUDANet::Utils::clear(d_output, outputSize.first * outputSize.second * numFilters);
+
+    Kernels::convolution<<<grid, block>>>(
+        d_input, d_weights, d_biases, d_output, inputSize, inputChannels,
+        paddingSize, kernelSize, stride, numFilters, outputSize
+    );
+    CUDA_CHECK(cudaGetLastError());
+
+    // Apply activation
+    activation->activate(d_output);
+
+    CUDA_CHECK(cudaDeviceSynchronize());
+
+    return d_output;
+}

src/layers/conv2d.cpp

@@ -0,0 +1,111 @@
+#include <stdexcept>
+#include <vector>
+
+#include "activation.hpp"
+#include "conv2d.hpp"
+#include "layer.hpp"
+
+using namespace CUDANet::Layers;
+
+Conv2d::Conv2d(
+    shape2d        inputSize,
+    int            inputChannels,
+    shape2d        kernelSize,
+    shape2d        stride,
+    int            numFilters,
+    shape2d        paddingSize,
+    ActivationType activationType
+)
+    : inputSize(inputSize),
+      inputChannels(inputChannels),
+      kernelSize(kernelSize),
+      stride(stride),
+      numFilters(numFilters),
+      paddingSize(paddingSize) {
+    outputSize = {
+        (inputSize.first - kernelSize.first + 2 * paddingSize.first) /
+                stride.first +
+            1,
+        (inputSize.second - kernelSize.second + 2 * paddingSize.second) /
+                stride.second +
+            1
+    };
+
+    activation = new Activation(
+        activationType, outputSize.first * outputSize.second * numFilters
+    );
+
+    weights.resize(
+        kernelSize.first * kernelSize.second * inputChannels * numFilters
+    );
+    initializeWeights();
+
+    biases.resize(numFilters);
+    initializeBiases();
+
+#ifdef USE_CUDA
+    initCUDA();
+    toCuda();
+#endif
+}
+
+Conv2d::~Conv2d() {
+#ifdef USE_CUDA
+    delCUDA();
+#endif
+    delete activation;
+}
+
+void Conv2d::initializeWeights() {
+    std::fill(weights.begin(), weights.end(), 0.0f);
+}
+
+void Conv2d::initializeBiases() {
+    std::fill(biases.begin(), biases.end(), 0.0f);
+}
+
+void Conv2d::setWeights(const float* weights_input) {
+    std::copy(weights_input, weights_input + weights.size(), weights.begin());
+#ifdef USE_CUDA
+    toCuda();
+#endif
+}
+
+std::vector<float> Conv2d::getWeights() {
+    return weights;
+}
+
+void Conv2d::setBiases(const float* biases_input) {
+    std::copy(biases_input, biases_input + biases.size(), biases.begin());
+#ifdef USE_CUDA
+    toCuda();
+#endif
+}
+
+std::vector<float> Conv2d::getBiases() {
+    return biases;
+}
+
+float* Conv2d::forwardCPU(const float* input) {
+    throw std::logic_error("Not implemented");
+}
+
+float* Conv2d::forward(const float* input) {
+#ifdef USE_CUDA
+    return forwardCUDA(input);
+#else
+    return forwardCPU(input);
+#endif
+}
+
+int Conv2d::getOutputSize() {
+    return outputSize.first * outputSize.second * numFilters;
+}
+
+int Conv2d::getInputSize() {
+    return inputSize.first * inputSize.second * inputChannels;
+}
+
+shape2d Conv2d::getOutputDims() {
+    return outputSize;
+}

src/layers/conv2d.cu

@@ -1,144 +0,0 @@
-#include <iostream>
-#include <vector>
-
-#include "activation.hpp"
-#include "conv2d.cuh"
-#include "convolution.cuh"
-#include "cuda_helper.cuh"
-#include "layer.hpp"
-#include "matmul.cuh"
-#include "vector.cuh"
-
-using namespace CUDANet::Layers;
-
-Conv2d::Conv2d(
-    shape2d          inputSize,
-    int            inputChannels,
-    shape2d          kernelSize,
-    shape2d          stride,
-    int            numFilters,
-    shape2d          paddingSize,
-    ActivationType activationType
-)
-    : inputSize(inputSize),
-      inputChannels(inputChannels),
-      kernelSize(kernelSize),
-      stride(stride),
-      numFilters(numFilters),
-      paddingSize(paddingSize) {
-
-    outputSize = {
-        (inputSize.first - kernelSize.first + 2 * paddingSize.first) /
-                stride.first + 1,
-        (inputSize.second - kernelSize.second + 2 * paddingSize.second) /
-                stride.second + 1
-    };
-
-    activation =
-        new Activation(activationType, outputSize.first * outputSize.second * numFilters);
-
-    d_output = nullptr;
-    CUDA_CHECK(cudaMalloc(
-        (void**)&d_output, sizeof(float) * outputSize.first * outputSize.second * numFilters
-    ));
-
-    weights.resize(kernelSize.first * kernelSize.second * inputChannels * numFilters);
-    initializeWeights();
-
-    d_weights = nullptr;
-    CUDA_CHECK(cudaMalloc(
-        (void**)&d_weights,
-        sizeof(float) * kernelSize.first * kernelSize.second * inputChannels * numFilters
-    ));
-
-    biases.resize(numFilters);
-    initializeBiases();
-
-    d_biases = nullptr;
-    CUDA_CHECK(cudaMalloc((void**)&d_biases, sizeof(float) * numFilters));
-
-    toCuda();
-}
-
-Conv2d::~Conv2d() {
-    cudaFree(d_output);
-    cudaFree(d_weights);
-    cudaFree(d_biases);
-    delete activation;
-}
-
-void Conv2d::initializeWeights() {
-    std::fill(weights.begin(), weights.end(), 0.0f);
-}
-
-void Conv2d::initializeBiases() {
-    std::fill(biases.begin(), biases.end(), 0.0f);
-}
-
-void Conv2d::setWeights(const float* weights_input) {
-    std::copy(weights_input, weights_input + weights.size(), weights.begin());
-    toCuda();
-}
-
-std::vector<float> Conv2d::getWeights() {
-    return weights;
-}
-
-void Conv2d::setBiases(const float* biases_input) {
-    std::copy(biases_input, biases_input + biases.size(), biases.begin());
-    toCuda();
-}
-
-std::vector<float> Conv2d::getBiases() {
-    return biases;
-}
-
-void Conv2d::toCuda() {
-    CUDA_CHECK(cudaMemcpy(
-        d_weights, weights.data(),
-        sizeof(float) * kernelSize.first * kernelSize.second * inputChannels * numFilters,
-        cudaMemcpyHostToDevice
-    ));
-
-    CUDA_CHECK(cudaMemcpy(
-        d_biases, biases.data(), sizeof(float) * numFilters,
-        cudaMemcpyHostToDevice
-    ));
-}
-
-float* Conv2d::forward(const float* d_input) {
-    // Convolve
-    dim3 block(8, 8, 8);
-    dim3 grid(
-        (outputSize.first + block.x - 1) / block.x,
-        (outputSize.second + block.y - 1) / block.y,
-        (numFilters + block.z - 1) / block.z
-    );
-
-    CUDANet::Utils::clear(d_output, outputSize.first * outputSize.second * numFilters);
-
-    Kernels::convolution<<<grid, block>>>(
-        d_input, d_weights, d_biases, d_output, inputSize, inputChannels,
-        paddingSize, kernelSize, stride, numFilters, outputSize
-    );
-    CUDA_CHECK(cudaGetLastError());
-
-    // Apply activation
-    activation->activate(d_output);
-
-    CUDA_CHECK(cudaDeviceSynchronize());
-
-    return d_output;
-}
-
-int Conv2d::getOutputSize() {
-    return outputSize.first * outputSize.second * numFilters;
-}
-
-int Conv2d::getInputSize() {
-    return inputSize.first * inputSize.second * inputChannels;
-}
-
-shape2d Conv2d::getOutputDims() {
-    return outputSize;
-}