im2col + convolve non-square filters, padding, and stride

1 files changed, 52 insertions, 39 deletions

diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 79faa6cb..b565d2d3 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -12,23 +12,25 @@ namespace caffe {
 
 template <typename Dtype>
 __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, const int height_col, const int width_col,
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int height_col, const int width_col,
     Dtype* data_col) {
   CUDA_KERNEL_LOOP(index, n) {
     int w_out = index % width_col;
     int h_index = index / width_col;
     int h_out = h_index % height_col;
     int channel_in = h_index / height_col;
-    int channel_out = channel_in * ksize * ksize;
-    int h_in = h_out * stride - pad;
-    int w_in = w_out * stride - pad;
+    int channel_out = channel_in * kernel_h * kernel_w;
+    int h_in = h_out * stride_h - pad_h;
+    int w_in = w_out * stride_w - pad_w;
     Dtype* data_col_ptr = data_col;
     data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
     const Dtype* data_im_ptr = data_im;
     data_im_ptr += (channel_in * height + h_in) * width + w_in;
-    for (int i = 0; i < ksize; ++i) {
-      for (int j = 0; j < ksize; ++j) {
+    for (int i = 0; i < kernel_h; ++i) {
+      for (int j = 0; j < kernel_w; ++j) {
         int h = h_in + i;
         int w = w_in + j;
         *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
@@ -41,17 +43,20 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
 
 template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_col) {
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    Dtype* data_col) {
   // We are going to launch channels * height_col * width_col kernels, each
   // kernel responsible for copying a single-channel grid.
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
+  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
   int num_kernels = channels * height_col * width_col;
   // NOLINT_NEXT_LINE(whitespace/operators)
   im2col_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
-      num_kernels, data_im, height, width, ksize, pad, stride, height_col,
+      num_kernels, data_im, height, width, kernel_h, kernel_w, pad_h,
+      pad_w, stride_h, stride_w, height_col,
       width_col, data_col);
   CUDA_POST_KERNEL_CHECK;
 }
@@ -59,40 +64,46 @@ void im2col_gpu(const Dtype* data_im, const int channels,
 
 // Explicit instantiation
 template void im2col_gpu<float>(const float* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, float* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    float* data_col);
 template void im2col_gpu<double>(const double* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, double* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    double* data_col);
 
 template <typename Dtype>
 __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
-    const int height, const int width, const int channels, const int ksize,
-    const int pad, const int stride, const int height_col, const int width_col,
+    const int height, const int width, const int channels,
+    const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int height_col, const int width_col,
     Dtype* data_im) {
   CUDA_KERNEL_LOOP(index, n) {
     Dtype val = 0;
-    int w = index % width + pad;
-    int h = (index / width) % height + pad;
+    int w = index % width + pad_w;
+    int h = (index / width) % height + pad_h;
     int c = index / (width * height);
     // compute the start and end of the output
-    int w_col_start = (w < ksize) ? 0 : (w - ksize) / stride + 1;
-    int w_col_end = min(w / stride + 1, width_col);
-    int h_col_start = (h < ksize) ? 0 : (h - ksize) / stride + 1;
-    int h_col_end = min(h / stride + 1, height_col);
+    int w_col_start = (w < patch_w) ? 0 : (w - patch_w) / stride_w + 1;
+    int w_col_end = min(w / stride_w + 1, width_col);
+    int h_col_start = (h < patch_h) ? 0 : (h - patch_h) / stride_h + 1;
+    int h_col_end = min(h / stride_h + 1, height_col);
     /*
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
       for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
         // the col location: [c * width * height + h_out, w_out]
-        int c_col = c * ksize * ksize + (h - h_col * stride) * ksize + (w - w_col * stride);
+        int c_col = c * patch_h * patch_w + (h - h_col * stride_h) * ksize
+            + (w - w_col * stride_w);
         val += data_col[(c_col * height_col + h_col) * width_col + w_col];
       }
     }
     */
     // equivalent implementation
-    int offset = (c * ksize * ksize + h * ksize + w) * height_col * width_col;
-    int coeff_h_col = (1 - stride * ksize * height_col) * width_col;
-    int coeff_w_col = (1 - stride * height_col * width_col);
+    int offset = (c * patch_h * patch_w + h * patch_h + w) * height_col * width_col;
+    int coeff_h_col = (1 - stride_h * patch_w * height_col) * width_col;
+    int coeff_w_col = (1 - stride_w * height_col * width_col);
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
       for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
         val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
@@ -104,29 +115,31 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_im) {
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_im) {
+  int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
   int num_kernels = channels * height * width;
   // To avoid involving atomic operations, we will launch one kernel per
   // bottom dimension, and then in the kernel add up the top dimensions.
   // NOLINT_NEXT_LINE(whitespace/operators)
   col2im_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
-      num_kernels, data_col, height, width, channels, ksize, pad, stride,
+      num_kernels, data_col, height, width, channels, patch_h, patch_w,
+      pad_h, pad_w, stride_h, stride_w,
       height_col, width_col, data_im);
   CUDA_POST_KERNEL_CHECK;
 }
 
-
 // Explicit instantiation
 template void col2im_gpu<float>(const float* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, float* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, float* data_im);
 template void col2im_gpu<double>(const double* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, double* data_im);
-
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, double* data_im);
 
 }  // namespace caffe