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/*
* 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
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