diff options
Diffstat (limited to 'intern/cycles/kernel/filter/filter_transform_sse.h')
| -rw-r--r-- | intern/cycles/kernel/filter/filter_transform_sse.h | 192 |
1 files changed, 102 insertions, 90 deletions
diff --git a/intern/cycles/kernel/filter/filter_transform_sse.h b/intern/cycles/kernel/filter/filter_transform_sse.h index 10bd3e477e9..22397b292db 100644 --- a/intern/cycles/kernel/filter/filter_transform_sse.h +++ b/intern/cycles/kernel/filter/filter_transform_sse.h @@ -18,98 +18,110 @@ CCL_NAMESPACE_BEGIN ccl_device void kernel_filter_construct_transform(const float *ccl_restrict buffer, CCL_FILTER_TILE_INFO, - int x, int y, int4 rect, - int pass_stride, int frame_stride, + int x, + int y, + int4 rect, + int pass_stride, + int frame_stride, bool use_time, - float *transform, int *rank, - int radius, float pca_threshold) + float *transform, + int *rank, + int radius, + float pca_threshold) { - int buffer_w = align_up(rect.z - rect.x, 4); - - float4 features[DENOISE_FEATURES]; - const float *ccl_restrict pixel_buffer; - int3 pixel; - - int num_features = use_time? 11 : 10; - - /* === Calculate denoising window. === */ - int2 low = make_int2(max(rect.x, x - radius), - max(rect.y, y - radius)); - int2 high = make_int2(min(rect.z, x + radius + 1), - min(rect.w, y + radius + 1)); - int num_pixels = (high.y - low.y) * (high.x - low.x) * tile_info->num_frames; - - /* === Shift feature passes to have mean 0. === */ - float4 feature_means[DENOISE_FEATURES]; - math_vector_zero_sse(feature_means, num_features); - FOR_PIXEL_WINDOW_SSE { - filter_get_features_sse(x4, y4, t4, active_pixels, pixel_buffer, features, use_time, NULL, pass_stride); - math_vector_add_sse(feature_means, num_features, features); - } END_FOR_PIXEL_WINDOW_SSE - - float4 pixel_scale = make_float4(1.0f / num_pixels); - for(int i = 0; i < num_features; i++) { - feature_means[i] = reduce_add(feature_means[i]) * pixel_scale; - } - - /* === Scale the shifted feature passes to a range of [-1; 1], will be baked into the transform later. === */ - float4 feature_scale[DENOISE_FEATURES]; - math_vector_zero_sse(feature_scale, num_features); - FOR_PIXEL_WINDOW_SSE { - filter_get_feature_scales_sse(x4, y4, t4, active_pixels, pixel_buffer, features, use_time, feature_means, pass_stride); - math_vector_max_sse(feature_scale, features, num_features); - } END_FOR_PIXEL_WINDOW_SSE - - filter_calculate_scale_sse(feature_scale, use_time); - - /* === Generate the feature transformation. === - * This transformation maps the num_features-dimentional feature space to a reduced feature (r-feature) space - * which generally has fewer dimensions. This mainly helps to prevent overfitting. */ - float4 feature_matrix_sse[DENOISE_FEATURES*DENOISE_FEATURES]; - math_matrix_zero_sse(feature_matrix_sse, num_features); - FOR_PIXEL_WINDOW_SSE { - filter_get_features_sse(x4, y4, t4, active_pixels, pixel_buffer, features, use_time, feature_means, pass_stride); - math_vector_mul_sse(features, num_features, feature_scale); - math_matrix_add_gramian_sse(feature_matrix_sse, num_features, features, make_float4(1.0f)); - } END_FOR_PIXEL_WINDOW_SSE - - float feature_matrix[DENOISE_FEATURES*DENOISE_FEATURES]; - math_matrix_hsum(feature_matrix, num_features, feature_matrix_sse); - - math_matrix_jacobi_eigendecomposition(feature_matrix, transform, num_features, 1); - - *rank = 0; - /* Prevent overfitting when a small window is used. */ - int max_rank = min(num_features, num_pixels/3); - if(pca_threshold < 0.0f) { - float threshold_energy = 0.0f; - for(int i = 0; i < num_features; i++) { - threshold_energy += feature_matrix[i*num_features+i]; - } - threshold_energy *= 1.0f - (-pca_threshold); - - float reduced_energy = 0.0f; - for(int i = 0; i < max_rank; i++, (*rank)++) { - if(i >= 2 && reduced_energy >= threshold_energy) - break; - float s = feature_matrix[i*num_features+i]; - reduced_energy += s; - } - } - else { - for(int i = 0; i < max_rank; i++, (*rank)++) { - float s = feature_matrix[i*num_features+i]; - if(i >= 2 && sqrtf(s) < pca_threshold) - break; - } - } - - math_matrix_transpose(transform, num_features, 1); - - /* Bake the feature scaling into the transformation matrix. */ - for(int i = 0; i < num_features; i++) { - math_vector_scale(transform + i*num_features, feature_scale[i][0], *rank); - } + int buffer_w = align_up(rect.z - rect.x, 4); + + float4 features[DENOISE_FEATURES]; + const float *ccl_restrict pixel_buffer; + int3 pixel; + + int num_features = use_time ? 11 : 10; + + /* === Calculate denoising window. === */ + int2 low = make_int2(max(rect.x, x - radius), max(rect.y, y - radius)); + int2 high = make_int2(min(rect.z, x + radius + 1), min(rect.w, y + radius + 1)); + int num_pixels = (high.y - low.y) * (high.x - low.x) * tile_info->num_frames; + + /* === Shift feature passes to have mean 0. === */ + float4 feature_means[DENOISE_FEATURES]; + math_vector_zero_sse(feature_means, num_features); + FOR_PIXEL_WINDOW_SSE + { + filter_get_features_sse( + x4, y4, t4, active_pixels, pixel_buffer, features, use_time, NULL, pass_stride); + math_vector_add_sse(feature_means, num_features, features); + } + END_FOR_PIXEL_WINDOW_SSE + + float4 pixel_scale = make_float4(1.0f / num_pixels); + for (int i = 0; i < num_features; i++) { + feature_means[i] = reduce_add(feature_means[i]) * pixel_scale; + } + + /* === Scale the shifted feature passes to a range of [-1; 1], will be baked into the transform later. === */ + float4 feature_scale[DENOISE_FEATURES]; + math_vector_zero_sse(feature_scale, num_features); + FOR_PIXEL_WINDOW_SSE + { + filter_get_feature_scales_sse( + x4, y4, t4, active_pixels, pixel_buffer, features, use_time, feature_means, pass_stride); + math_vector_max_sse(feature_scale, features, num_features); + } + END_FOR_PIXEL_WINDOW_SSE + + filter_calculate_scale_sse(feature_scale, use_time); + + /* === Generate the feature transformation. === + * This transformation maps the num_features-dimentional feature space to a reduced feature (r-feature) space + * which generally has fewer dimensions. This mainly helps to prevent overfitting. */ + float4 feature_matrix_sse[DENOISE_FEATURES * DENOISE_FEATURES]; + math_matrix_zero_sse(feature_matrix_sse, num_features); + FOR_PIXEL_WINDOW_SSE + { + filter_get_features_sse( + x4, y4, t4, active_pixels, pixel_buffer, features, use_time, feature_means, pass_stride); + math_vector_mul_sse(features, num_features, feature_scale); + math_matrix_add_gramian_sse(feature_matrix_sse, num_features, features, make_float4(1.0f)); + } + END_FOR_PIXEL_WINDOW_SSE + + float feature_matrix[DENOISE_FEATURES * DENOISE_FEATURES]; + math_matrix_hsum(feature_matrix, num_features, feature_matrix_sse); + + math_matrix_jacobi_eigendecomposition(feature_matrix, transform, num_features, 1); + + *rank = 0; + /* Prevent overfitting when a small window is used. */ + int max_rank = min(num_features, num_pixels / 3); + if (pca_threshold < 0.0f) { + float threshold_energy = 0.0f; + for (int i = 0; i < num_features; i++) { + threshold_energy += feature_matrix[i * num_features + i]; + } + threshold_energy *= 1.0f - (-pca_threshold); + + float reduced_energy = 0.0f; + for (int i = 0; i < max_rank; i++, (*rank)++) { + if (i >= 2 && reduced_energy >= threshold_energy) + break; + float s = feature_matrix[i * num_features + i]; + reduced_energy += s; + } + } + else { + for (int i = 0; i < max_rank; i++, (*rank)++) { + float s = feature_matrix[i * num_features + i]; + if (i >= 2 && sqrtf(s) < pca_threshold) + break; + } + } + + math_matrix_transpose(transform, num_features, 1); + + /* Bake the feature scaling into the transformation matrix. */ + for (int i = 0; i < num_features; i++) { + math_vector_scale(transform + i * num_features, feature_scale[i][0], *rank); + } } CCL_NAMESPACE_END |