pixel_buffer += buffer_w - (high.x - low.x); \
}
-ccl_device_inline void filter_get_features(int2 pixel, ccl_global float ccl_restrict_ptr buffer, float *features, float ccl_restrict_ptr mean, int pass_stride)
+ccl_device_inline void filter_get_features(int2 pixel,
+ const ccl_global float *ccl_restrict buffer,
+ float *features,
+ const float *ccl_restrict mean,
+ int pass_stride)
{
features[0] = pixel.x;
features[1] = pixel.y;
}
}
-ccl_device_inline void filter_get_feature_scales(int2 pixel, ccl_global float ccl_restrict_ptr buffer, float *scales, float ccl_restrict_ptr mean, int pass_stride)
+ccl_device_inline void filter_get_feature_scales(int2 pixel,
+ const ccl_global float *ccl_restrict buffer,
+ float *scales,
+ const float *ccl_restrict mean,
+ int pass_stride)
{
scales[0] = fabsf(pixel.x - mean[0]);
scales[1] = fabsf(pixel.y - mean[1]);
scale[3] = scale[4] = scale[5] = 1.0f/max(sqrtf(scale[3]), 0.01f);
}
-ccl_device_inline float3 filter_get_pixel_color(ccl_global float ccl_restrict_ptr buffer, int pass_stride)
+ccl_device_inline float3 filter_get_pixel_color(const ccl_global float *ccl_restrict buffer,
+ int pass_stride)
{
return make_float3(ccl_get_feature(buffer, 0), ccl_get_feature(buffer, 1), ccl_get_feature(buffer, 2));
}
-ccl_device_inline float filter_get_pixel_variance(ccl_global float ccl_restrict_ptr buffer, int pass_stride)
+ccl_device_inline float filter_get_pixel_variance(const ccl_global float *ccl_restrict buffer,
+ int pass_stride)
{
return average(make_float3(ccl_get_feature(buffer, 0), ccl_get_feature(buffer, 1), ccl_get_feature(buffer, 2)));
}
ccl_device_inline void design_row_add(float *design_row,
int rank,
- ccl_global float ccl_restrict_ptr transform,
+ const ccl_global float *ccl_restrict transform,
int stride,
int row,
float feature)
/* Fill the design row. */
ccl_device_inline void filter_get_design_row_transform(int2 p_pixel,
- ccl_global float ccl_restrict_ptr p_buffer,
+ const ccl_global float *ccl_restrict p_buffer,
int2 q_pixel,
- ccl_global float ccl_restrict_ptr q_buffer,
+ const ccl_global float *ccl_restrict q_buffer,
int pass_stride,
int rank,
float *design_row,
- ccl_global float ccl_restrict_ptr transform,
+ const ccl_global float *ccl_restrict transform,
int stride)
{
design_row[0] = 1.0f;
pixel_buffer += buffer_w - (pixel.x - low.x); \
}
-ccl_device_inline void filter_get_features_sse(__m128 x, __m128 y, __m128 active_pixels, float ccl_restrict_ptr buffer, __m128 *features, __m128 ccl_restrict_ptr mean, int pass_stride)
+ccl_device_inline void filter_get_features_sse(__m128 x, __m128 y,
+ __m128 active_pixels,
+ const float *ccl_restrict buffer,
+ __m128 *features,
+ const __m128 ccl_restrict *mean,
+ int pass_stride)
{
features[0] = x;
features[1] = y;
features[i] = _mm_mask_ps(features[i], active_pixels);
}
-ccl_device_inline void filter_get_feature_scales_sse(__m128 x, __m128 y, __m128 active_pixels, float ccl_restrict_ptr buffer, __m128 *scales, __m128 ccl_restrict_ptr mean, int pass_stride)
+ccl_device_inline void filter_get_feature_scales_sse(__m128 x, __m128 y,
+ __m128 active_pixels,
+ const float *ccl_restrict buffer,
+ __m128 *scales,
+ const __m128 *ccl_restrict mean,
+ int pass_stride)
{
scales[0] = _mm_mask_ps(_mm_fabs_ps(_mm_sub_ps(x, mean[0])), active_pixels);
scales[1] = _mm_mask_ps(_mm_fabs_ps(_mm_sub_ps(y, mean[1])), active_pixels);
CCL_NAMESPACE_BEGIN
-ccl_device_inline void kernel_filter_nlm_calc_difference(int dx, int dy, float ccl_restrict_ptr weightImage, float ccl_restrict_ptr varianceImage, float *differenceImage, int4 rect, int w, int channel_offset, float a, float k_2)
+ccl_device_inline void kernel_filter_nlm_calc_difference(int dx, int dy,
+ const float *ccl_restrict weightImage,
+ const float *ccl_restrict varianceImage,
+ float *differenceImage,
+ int4 rect,
+ int w,
+ int channel_offset,
+ float a,
+ float k_2)
{
for(int y = rect.y; y < rect.w; y++) {
for(int x = rect.x; x < rect.z; x++) {
}
}
-ccl_device_inline void kernel_filter_nlm_blur(float ccl_restrict_ptr differenceImage, float *outImage, int4 rect, int w, int f)
+ccl_device_inline void kernel_filter_nlm_blur(const float *ccl_restrict differenceImage,
+ float *outImage,
+ int4 rect,
+ int w,
+ int f)
{
#ifdef __KERNEL_SSE3__
int aligned_lowx = (rect.x & ~(3));
}
}
-ccl_device_inline void kernel_filter_nlm_calc_weight(float ccl_restrict_ptr differenceImage, float *outImage, int4 rect, int w, int f)
+ccl_device_inline void kernel_filter_nlm_calc_weight(const float *ccl_restrict differenceImage,
+ float *outImage,
+ int4 rect,
+ int w,
+ int f)
{
for(int y = rect.y; y < rect.w; y++) {
for(int x = rect.x; x < rect.z; x++) {
}
}
-ccl_device_inline void kernel_filter_nlm_update_output(int dx, int dy, float ccl_restrict_ptr differenceImage, float ccl_restrict_ptr image, float *outImage, float *accumImage, int4 rect, int w, int f)
+ccl_device_inline void kernel_filter_nlm_update_output(int dx, int dy,
+ const float *ccl_restrict differenceImage,
+ const float *ccl_restrict image,
+ float *outImage,
+ float *accumImage,
+ int4 rect,
+ int w,
+ int f)
{
for(int y = rect.y; y < rect.w; y++) {
for(int x = rect.x; x < rect.z; x++) {
}
ccl_device_inline void kernel_filter_nlm_construct_gramian(int dx, int dy,
- float ccl_restrict_ptr differenceImage,
- float ccl_restrict_ptr buffer,
+ const float *ccl_restrict differenceImage,
+ const float *ccl_restrict buffer,
float *color_pass,
float *variance_pass,
float *transform,
}
}
-ccl_device_inline void kernel_filter_nlm_normalize(float *outImage, float ccl_restrict_ptr accumImage, int4 rect, int w)
+ccl_device_inline void kernel_filter_nlm_normalize(float *outImage,
+ const float *ccl_restrict accumImage,
+ int4 rect,
+ int w)
{
for(int y = rect.y; y < rect.w; y++) {
for(int x = rect.x; x < rect.z; x++) {
ccl_device_inline void kernel_filter_nlm_calc_difference(int x, int y,
int dx, int dy,
- ccl_global float ccl_restrict_ptr weightImage,
- ccl_global float ccl_restrict_ptr varianceImage,
+ const ccl_global float *ccl_restrict weightImage,
+ const ccl_global float *ccl_restrict varianceImage,
ccl_global float *differenceImage,
int4 rect, int w,
int channel_offset,
}
ccl_device_inline void kernel_filter_nlm_blur(int x, int y,
- ccl_global float ccl_restrict_ptr differenceImage,
+ const ccl_global float *ccl_restrict differenceImage,
ccl_global float *outImage,
int4 rect, int w, int f)
{
}
ccl_device_inline void kernel_filter_nlm_calc_weight(int x, int y,
- ccl_global float ccl_restrict_ptr differenceImage,
+ const ccl_global float *ccl_restrict differenceImage,
ccl_global float *outImage,
int4 rect, int w, int f)
{
ccl_device_inline void kernel_filter_nlm_update_output(int x, int y,
int dx, int dy,
- ccl_global float ccl_restrict_ptr differenceImage,
- ccl_global float ccl_restrict_ptr image,
+ const ccl_global float *ccl_restrict differenceImage,
+ const ccl_global float *ccl_restrict image,
ccl_global float *outImage,
ccl_global float *accumImage,
int4 rect, int w, int f)
ccl_device_inline void kernel_filter_nlm_construct_gramian(int fx, int fy,
int dx, int dy,
- ccl_global float ccl_restrict_ptr differenceImage,
- ccl_global float ccl_restrict_ptr buffer,
+ const ccl_global float *ccl_restrict differenceImage,
+ const ccl_global float *ccl_restrict buffer,
ccl_global float *color_pass,
ccl_global float *variance_pass,
- ccl_global float ccl_restrict_ptr transform,
+ const ccl_global float *ccl_restrict transform,
ccl_global int *rank,
ccl_global float *XtWX,
ccl_global float3 *XtWY,
ccl_device_inline void kernel_filter_nlm_normalize(int x, int y,
ccl_global float *outImage,
- ccl_global float ccl_restrict_ptr accumImage,
+ const ccl_global float *ccl_restrict accumImage,
int4 rect, int w)
{
outImage[y*w+x] /= accumImage[y*w+x];
int offset = tiles->offsets[tile];
int stride = tiles->strides[tile];
- ccl_global float ccl_restrict_ptr center_buffer = (ccl_global float*) tiles->buffers[tile];
+ const ccl_global float *ccl_restrict center_buffer = (ccl_global float*) tiles->buffers[tile];
center_buffer += (y*stride + x + offset)*buffer_pass_stride;
center_buffer += buffer_denoising_offset + 14;
int dx, int dy,
int w, int h,
int pass_stride,
- ccl_global float ccl_restrict_ptr buffer,
+ const ccl_global float *ccl_restrict buffer,
ccl_global float *color_pass,
ccl_global float *variance_pass,
- ccl_global float ccl_restrict_ptr transform,
+ const ccl_global float *ccl_restrict transform,
ccl_global int *rank,
float weight,
ccl_global float *XtWX,
CCL_NAMESPACE_BEGIN
-ccl_device void kernel_filter_construct_transform(float ccl_restrict_ptr buffer,
+ccl_device void kernel_filter_construct_transform(const float *ccl_restrict buffer,
int x, int y, int4 rect,
int pass_stride,
float *transform, int *rank,
/* Temporary storage, used in different steps of the algorithm. */
float tempmatrix[DENOISE_FEATURES*DENOISE_FEATURES];
float tempvector[2*DENOISE_FEATURES];
- float ccl_restrict_ptr pixel_buffer;
+ const float *ccl_restrict pixel_buffer;
int2 pixel;
CCL_NAMESPACE_BEGIN
-ccl_device void kernel_filter_construct_transform(ccl_global float ccl_restrict_ptr buffer,
+ccl_device void kernel_filter_construct_transform(const ccl_global float *ccl_restrict buffer,
int x, int y, int4 rect,
int pass_stride,
ccl_global float *transform,
max(rect.y, y - radius));
int2 high = make_int2(min(rect.z, x + radius + 1),
min(rect.w, y + radius + 1));
- ccl_global float ccl_restrict_ptr pixel_buffer;
+ const ccl_global float *ccl_restrict pixel_buffer;
int2 pixel;
CCL_NAMESPACE_BEGIN
-ccl_device void kernel_filter_construct_transform(float ccl_restrict_ptr buffer,
+ccl_device void kernel_filter_construct_transform(const float *ccl_restrict buffer,
int x, int y, int4 rect,
int pass_stride,
float *transform, int *rank,
int buffer_w = align_up(rect.z - rect.x, 4);
__m128 features[DENOISE_FEATURES];
- float ccl_restrict_ptr pixel_buffer;
+ const float *ccl_restrict pixel_buffer;
int2 pixel;
int2 low = make_int2(max(rect.x, x - radius),
#include "util/util_types.h"
#include "util/util_texture.h"
-#define ccl_restrict_ptr const * __restrict
-
#define ccl_addr_space
#define ccl_local_id(d) 0
#define ccl_restrict __restrict__
#define ccl_align(n) __align__(n)
-#define ccl_restrict_ptr const * __restrict__
#define CCL_MAX_LOCAL_SIZE (CUDA_THREADS_BLOCK_WIDTH*CUDA_THREADS_BLOCK_WIDTH)
# define ccl_addr_space
#endif
-#define ccl_restrict_ptr const * __restrict__
-
#define ccl_local_id(d) get_local_id(d)
#define ccl_global_id(d) get_global_id(d)
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_nlm_calc_difference(int dx, int dy,
- float ccl_restrict_ptr weightImage,
- float ccl_restrict_ptr varianceImage,
+ const float *ccl_restrict weightImage,
+ const float *ccl_restrict varianceImage,
float *differenceImage,
int4 rect, int w,
int channel_offset,
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
-kernel_cuda_filter_nlm_blur(float ccl_restrict_ptr differenceImage, float *outImage, int4 rect, int w, int f) {
+kernel_cuda_filter_nlm_blur(const float *ccl_restrict differenceImage, float *outImage, int4 rect, int w, int f) {
int x = blockDim.x*blockIdx.x + threadIdx.x + rect.x;
int y = blockDim.y*blockIdx.y + threadIdx.y + rect.y;
if(x < rect.z && y < rect.w) {
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
-kernel_cuda_filter_nlm_calc_weight(float ccl_restrict_ptr differenceImage, float *outImage, int4 rect, int w, int f) {
+kernel_cuda_filter_nlm_calc_weight(const float *ccl_restrict differenceImage, float *outImage, int4 rect, int w, int f) {
int x = blockDim.x*blockIdx.x + threadIdx.x + rect.x;
int y = blockDim.y*blockIdx.y + threadIdx.y + rect.y;
if(x < rect.z && y < rect.w) {
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_nlm_update_output(int dx, int dy,
- float ccl_restrict_ptr differenceImage,
- float ccl_restrict_ptr image,
+ const float *ccl_restrict differenceImage,
+ const float *ccl_restrict image,
float *outImage, float *accumImage,
int4 rect, int w,
int f) {
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
-kernel_cuda_filter_nlm_normalize(float *outImage, float ccl_restrict_ptr accumImage, int4 rect, int w) {
+kernel_cuda_filter_nlm_normalize(float *outImage, const float *ccl_restrict accumImage, int4 rect, int w) {
int x = blockDim.x*blockIdx.x + threadIdx.x + rect.x;
int y = blockDim.y*blockIdx.y + threadIdx.y + rect.y;
if(x < rect.z && y < rect.w) {
extern "C" __global__ void
CUDA_LAUNCH_BOUNDS(CUDA_THREADS_BLOCK_WIDTH, CUDA_KERNEL_MAX_REGISTERS)
kernel_cuda_filter_nlm_construct_gramian(int dx, int dy,
- float ccl_restrict_ptr differenceImage,
- float ccl_restrict_ptr buffer,
+ const float *ccl_restrict differenceImage,
+ const float *ccl_restrict buffer,
float *color_pass,
float *variance_pass,
float const* __restrict__ transform,
}
}
-__kernel void kernel_ocl_filter_construct_transform(ccl_global float ccl_restrict_ptr buffer,
+__kernel void kernel_ocl_filter_construct_transform(const ccl_global float *ccl_restrict buffer,
ccl_global float *transform,
ccl_global int *rank,
int4 filter_area,
__kernel void kernel_ocl_filter_nlm_calc_difference(int dx,
int dy,
- ccl_global float ccl_restrict_ptr weightImage,
- ccl_global float ccl_restrict_ptr varianceImage,
+ const ccl_global float *ccl_restrict weightImage,
+ const ccl_global float *ccl_restrict varianceImage,
ccl_global float *differenceImage,
int4 rect,
int w,
}
}
-__kernel void kernel_ocl_filter_nlm_blur(ccl_global float ccl_restrict_ptr differenceImage,
+__kernel void kernel_ocl_filter_nlm_blur(const ccl_global float *ccl_restrict differenceImage,
ccl_global float *outImage,
int4 rect,
int w,
}
}
-__kernel void kernel_ocl_filter_nlm_calc_weight(ccl_global float ccl_restrict_ptr differenceImage,
+__kernel void kernel_ocl_filter_nlm_calc_weight(const ccl_global float *ccl_restrict differenceImage,
ccl_global float *outImage,
int4 rect,
int w,
__kernel void kernel_ocl_filter_nlm_update_output(int dx,
int dy,
- ccl_global float ccl_restrict_ptr differenceImage,
- ccl_global float ccl_restrict_ptr image,
+ const ccl_global float *ccl_restrict differenceImage,
+ const ccl_global float *ccl_restrict image,
ccl_global float *outImage,
ccl_global float *accumImage,
int4 rect,
}
__kernel void kernel_ocl_filter_nlm_normalize(ccl_global float *outImage,
- ccl_global float ccl_restrict_ptr accumImage,
+ const ccl_global float *ccl_restrict accumImage,
int4 rect,
int w) {
int x = get_global_id(0) + rect.x;
__kernel void kernel_ocl_filter_nlm_construct_gramian(int dx,
int dy,
- ccl_global float ccl_restrict_ptr differenceImage,
- ccl_global float ccl_restrict_ptr buffer,
+ const ccl_global float *ccl_restrict differenceImage,
+ const ccl_global float *ccl_restrict buffer,
ccl_global float *color_pass,
ccl_global float *variance_pass,
- ccl_global float ccl_restrict_ptr transform,
+ const ccl_global float *ccl_restrict transform,
ccl_global int *rank,
ccl_global float *XtWX,
ccl_global float3 *XtWY,
/* Elementary vector operations. */
-ccl_device_inline void math_vector_add(float *a, float ccl_restrict_ptr b, int n)
+ccl_device_inline void math_vector_add(float *a, const float *ccl_restrict b, int n)
{
for(int i = 0; i < n; i++)
a[i] += b[i];
}
-ccl_device_inline void math_vector_mul(float *a, float ccl_restrict_ptr b, int n)
+ccl_device_inline void math_vector_mul(float *a, const float *ccl_restrict b, int n)
{
for(int i = 0; i < n; i++)
a[i] *= b[i];
}
-ccl_device_inline void math_vector_mul_strided(ccl_global float *a, float ccl_restrict_ptr b, int astride, int n)
+ccl_device_inline void math_vector_mul_strided(ccl_global float *a, const float *ccl_restrict b, int astride, int n)
{
for(int i = 0; i < n; i++)
a[i*astride] *= b[i];
a[i] *= b;
}
-ccl_device_inline void math_vector_max(float *a, float ccl_restrict_ptr b, int n)
+ccl_device_inline void math_vector_max(float *a, const float *ccl_restrict b, int n)
{
for(int i = 0; i < n; i++)
a[i] = max(a[i], b[i]);
* The Gramian matrix of v is vt*v, so element (i,j) is v[i]*v[j]. */
ccl_device_inline void math_matrix_add_gramian(float *A,
int n,
- float ccl_restrict_ptr v,
+ const float *ccl_restrict v,
float weight)
{
for(int row = 0; row < n; row++)
* The Gramian matrix of v is vt*v, so element (i,j) is v[i]*v[j]. */
ccl_device_inline void math_trimatrix_add_gramian_strided(ccl_global float *A,
int n,
- float ccl_restrict_ptr v,
+ const float *ccl_restrict v,
float weight,
int stride)
{
/* Add Gramian matrix of v to A.
* The Gramian matrix of v is v^T*v, so element (i,j) is v[i]*v[j]. */
-ccl_device_inline void math_matrix_add_gramian_sse(__m128 *A, int n, __m128 ccl_restrict_ptr v, __m128 weight)
+ccl_device_inline void math_matrix_add_gramian_sse(__m128 *A, int n, const __m128 *ccl_restrict v, __m128 weight)
{
for(int row = 0; row < n; row++)
for(int col = 0; col <= row; col++)
MAT(A, n, row, col) = _mm_add_ps(MAT(A, n, row, col), _mm_mul_ps(_mm_mul_ps(v[row], v[col]), weight));
}
-ccl_device_inline void math_vector_add_sse(__m128 *V, int n, __m128 ccl_restrict_ptr a)
+ccl_device_inline void math_vector_add_sse(__m128 *V, int n, const __m128 *ccl_restrict a)
{
for(int i = 0; i < n; i++)
V[i] = _mm_add_ps(V[i], a[i]);
}
-ccl_device_inline void math_vector_mul_sse(__m128 *V, int n, __m128 ccl_restrict_ptr a)
+ccl_device_inline void math_vector_mul_sse(__m128 *V, int n, const __m128 *ccl_restrict a)
{
for(int i = 0; i < n; i++)
V[i] = _mm_mul_ps(V[i], a[i]);
}
-ccl_device_inline void math_vector_max_sse(__m128 *a, __m128 ccl_restrict_ptr b, int n)
+ccl_device_inline void math_vector_max_sse(__m128 *a, const __m128 *ccl_restrict b, int n)
{
for(int i = 0; i < n; i++)
a[i] = _mm_max_ps(a[i], b[i]);
}
-ccl_device_inline void math_matrix_hsum(float *A, int n, __m128 ccl_restrict_ptr B)
+ccl_device_inline void math_matrix_hsum(float *A, int n, const __m128 *ccl_restrict B)
{
for(int row = 0; row < n; row++)
for(int col = 0; col <= row; col++)