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Diffstat (limited to 'intern/cycles/kernel/filter/filter_transform_gpu.h')
| -rw-r--r-- | intern/cycles/kernel/filter/filter_transform_gpu.h | 119 |
1 files changed, 119 insertions, 0 deletions
diff --git a/intern/cycles/kernel/filter/filter_transform_gpu.h b/intern/cycles/kernel/filter/filter_transform_gpu.h new file mode 100644 index 00000000000..83a1222bbdb --- /dev/null +++ b/intern/cycles/kernel/filter/filter_transform_gpu.h @@ -0,0 +1,119 @@ +/* + * Copyright 2011-2017 Blender Foundation + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +CCL_NAMESPACE_BEGIN + +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, + ccl_global int *rank, + int radius, float pca_threshold, + int transform_stride, int localIdx) +{ + int buffer_w = align_up(rect.z - rect.x, 4); + +#ifdef __KERNEL_CUDA__ + ccl_local float shared_features[DENOISE_FEATURES*CCL_MAX_LOCAL_SIZE]; + ccl_local_param float *features = shared_features + localIdx*DENOISE_FEATURES; +#else + float features[DENOISE_FEATURES]; +#endif + + /* === 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); + const ccl_global float *ccl_restrict pixel_buffer; + int2 pixel; + + + + + /* === Shift feature passes to have mean 0. === */ + float feature_means[DENOISE_FEATURES]; + math_vector_zero(feature_means, DENOISE_FEATURES); + FOR_PIXEL_WINDOW { + filter_get_features(pixel, pixel_buffer, features, NULL, pass_stride); + math_vector_add(feature_means, features, DENOISE_FEATURES); + } END_FOR_PIXEL_WINDOW + + math_vector_scale(feature_means, 1.0f / num_pixels, DENOISE_FEATURES); + + /* === Scale the shifted feature passes to a range of [-1; 1], will be baked into the transform later. === */ + float feature_scale[DENOISE_FEATURES]; + math_vector_zero(feature_scale, DENOISE_FEATURES); + + FOR_PIXEL_WINDOW { + filter_get_feature_scales(pixel, pixel_buffer, features, feature_means, pass_stride); + math_vector_max(feature_scale, features, DENOISE_FEATURES); + } END_FOR_PIXEL_WINDOW + + filter_calculate_scale(feature_scale); + + + + /* === Generate the feature transformation. === + * This transformation maps the DENOISE_FEATURES-dimentional feature space to a reduced feature (r-feature) space + * which generally has fewer dimensions. This mainly helps to prevent overfitting. */ + float feature_matrix[DENOISE_FEATURES*DENOISE_FEATURES]; + math_matrix_zero(feature_matrix, DENOISE_FEATURES); + FOR_PIXEL_WINDOW { + filter_get_features(pixel, pixel_buffer, features, feature_means, pass_stride); + math_vector_mul(features, feature_scale, DENOISE_FEATURES); + math_matrix_add_gramian(feature_matrix, DENOISE_FEATURES, features, 1.0f); + } END_FOR_PIXEL_WINDOW + + math_matrix_jacobi_eigendecomposition(feature_matrix, transform, DENOISE_FEATURES, transform_stride); + *rank = 0; + /* Prevent overfitting when a small window is used. */ + int max_rank = min(DENOISE_FEATURES, num_pixels/3); + if(pca_threshold < 0.0f) { + float threshold_energy = 0.0f; + for(int i = 0; i < DENOISE_FEATURES; i++) { + threshold_energy += feature_matrix[i*DENOISE_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*DENOISE_FEATURES+i]; + reduced_energy += s; + } + } + else { + for(int i = 0; i < max_rank; i++, (*rank)++) { + float s = feature_matrix[i*DENOISE_FEATURES+i]; + if(i >= 2 && sqrtf(s) < pca_threshold) + break; + } + } + + math_matrix_transpose(transform, DENOISE_FEATURES, transform_stride); + + /* Bake the feature scaling into the transformation matrix. */ + for(int i = 0; i < DENOISE_FEATURES; i++) { + for(int j = 0; j < (*rank); j++) { + transform[(i*DENOISE_FEATURES + j)*transform_stride] *= feature_scale[i]; + } + } +} + +CCL_NAMESPACE_END |