Fix bias in conv layer

include/kernels/convolution.cuh

@@ -10,6 +10,7 @@ namespace CUDANet::Kernels {
  *
  * @param d_input Device pointer to the input matrix
  * @param d_kernel Device pointer to the convolution kernel
+ * @param d_bias Device pointer to the bias
  * @param d_output Device pointer to the output matrix
  * @param inputSize Width and height of the input matrix
  * @param nChannels Number of channels in the input matrix
@@ -21,6 +22,7 @@ namespace CUDANet::Kernels {
 __global__ void convolution(
     const float* __restrict__ d_input,
     const float* __restrict__ d_kernel,
+    const float* __restrict__ d_bias,
     float* __restrict__ d_output,
     const int inputSize,
     const int nChannels,

src/kernels/convolution.cu

@@ -7,6 +7,7 @@ using namespace CUDANet;
 __global__ void Kernels::convolution(
     const float* __restrict__ d_input,
     const float* __restrict__ d_kernel,
+    const float* __restrict__ d_bias,
     float* __restrict__ d_output,
     const int inputSize,
     const int nChannels,
@@ -51,5 +52,5 @@ __global__ void Kernels::convolution(
         }
     }
 
-    d_output[f * outputSize * outputSize + i * outputSize + j] = sum;
+    d_output[f * outputSize * outputSize + i * outputSize + j] = sum + d_bias[f];
 }

src/layers/conv2d.cu

@@ -42,12 +42,12 @@ Conv2d::Conv2d(
         sizeof(float) * kernelSize * kernelSize * inputChannels * numFilters
     ));
 
-    biases.resize(outputSize * outputSize * numFilters);
+    biases.resize(numFilters);
     initializeBiases();
 
     d_biases = nullptr;
     CUDA_CHECK(cudaMalloc(
-        (void**)&d_biases, sizeof(float) * outputSize * outputSize * numFilters
+        (void**)&d_biases, sizeof(float) * numFilters
     ));
 
     toCuda();
@@ -94,7 +94,7 @@ void Conv2d::toCuda() {
 
     CUDA_CHECK(cudaMemcpy(
         d_biases, biases.data(),
-        sizeof(float) * outputSize * outputSize * numFilters,
+        sizeof(float) * numFilters,
         cudaMemcpyHostToDevice
     ));
 }
@@ -109,15 +109,10 @@ float* Conv2d::forward(const float* d_input) {
     );
 
     Kernels::convolution<<<grid, block>>>(
-        d_input, d_weights, d_output, inputSize, inputChannels, paddingSize,
+        d_input, d_weights, d_biases, d_output, inputSize, inputChannels, paddingSize,
         kernelSize, stride, numFilters, outputSize
     );
 
-    // Add bias
-    Kernels::vec_vec_add<<<1, biases.size()>>>(
-        d_biases, d_output, d_output, biases.size()
-    );
-
     // Apply activation
     activation.activate(d_output);