249 lines
9.7 KiB
C++
249 lines
9.7 KiB
C++
/*
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* Copyright 2011-2017 Blender Foundation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "device/device_denoising.h"
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#include "kernel/filter/filter_defines.h"
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CCL_NAMESPACE_BEGIN
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DenoisingTask::DenoisingTask(Device *device, const DeviceTask &task)
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: tile_info_mem(device, "denoising tile info mem", MEM_READ_WRITE),
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storage(device),
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buffer(device),
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device(device)
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{
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radius = task.denoising_radius;
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nlm_k_2 = powf(2.0f, lerp(-5.0f, 3.0f, task.denoising_strength));
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if(task.denoising_relative_pca) {
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pca_threshold = -powf(10.0f, lerp(-8.0f, 0.0f, task.denoising_feature_strength));
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}
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else {
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pca_threshold = powf(10.0f, lerp(-5.0f, 3.0f, task.denoising_feature_strength));
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}
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render_buffer.pass_stride = task.pass_stride;
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render_buffer.offset = task.pass_denoising_data;
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target_buffer.pass_stride = task.pass_stride;
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target_buffer.denoising_clean_offset = task.pass_denoising_clean;
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functions.map_neighbor_tiles = function_bind(task.map_neighbor_tiles, _1, device);
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functions.unmap_neighbor_tiles = function_bind(task.unmap_neighbor_tiles, _1, device);
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}
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DenoisingTask::~DenoisingTask()
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{
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storage.XtWX.free();
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storage.XtWY.free();
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storage.transform.free();
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storage.rank.free();
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buffer.mem.free();
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buffer.temporary_mem.free();
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tile_info_mem.free();
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}
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void DenoisingTask::set_render_buffer(RenderTile *rtiles)
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{
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tile_info = (TileInfo*) tile_info_mem.alloc(sizeof(TileInfo)/sizeof(int));
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for(int i = 0; i < 9; i++) {
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tile_info->offsets[i] = rtiles[i].offset;
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tile_info->strides[i] = rtiles[i].stride;
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tile_info->buffers[i] = rtiles[i].buffer;
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}
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tile_info->x[0] = rtiles[3].x;
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tile_info->x[1] = rtiles[4].x;
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tile_info->x[2] = rtiles[5].x;
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tile_info->x[3] = rtiles[5].x + rtiles[5].w;
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tile_info->y[0] = rtiles[1].y;
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tile_info->y[1] = rtiles[4].y;
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tile_info->y[2] = rtiles[7].y;
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tile_info->y[3] = rtiles[7].y + rtiles[7].h;
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target_buffer.offset = rtiles[9].offset;
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target_buffer.stride = rtiles[9].stride;
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target_buffer.ptr = rtiles[9].buffer;
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tile_info_mem.copy_to_device();
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}
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void DenoisingTask::setup_denoising_buffer()
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{
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/* Expand filter_area by radius pixels and clamp the result to the extent of the neighboring tiles */
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rect = rect_from_shape(filter_area.x, filter_area.y, filter_area.z, filter_area.w);
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rect = rect_expand(rect, radius);
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rect = rect_clip(rect, make_int4(tile_info->x[0], tile_info->y[0], tile_info->x[3], tile_info->y[3]));
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buffer.passes = 14;
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buffer.width = rect.z - rect.x;
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buffer.stride = align_up(buffer.width, 4);
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buffer.h = rect.w - rect.y;
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int alignment_floats = divide_up(device->mem_sub_ptr_alignment(), sizeof(float));
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buffer.pass_stride = align_up(buffer.stride * buffer.h, alignment_floats);
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/* Pad the total size by four floats since the SIMD kernels might go a bit over the end. */
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int mem_size = align_up(buffer.pass_stride * buffer.passes + 4, alignment_floats);
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buffer.mem.alloc_to_device(mem_size, false);
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/* CPUs process shifts sequentially while GPUs process them in parallel. */
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int num_layers;
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if(buffer.gpu_temporary_mem) {
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/* Shadowing prefiltering uses a radius of 6, so allocate at least that much. */
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int max_radius = max(radius, 6);
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int num_shifts = (2*max_radius + 1) * (2*max_radius + 1);
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num_layers = 2*num_shifts + 1;
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}
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else {
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num_layers = 3;
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}
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/* Allocate two layers per shift as well as one for the weight accumulation. */
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buffer.temporary_mem.alloc_to_device(num_layers * buffer.pass_stride);
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}
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void DenoisingTask::prefilter_shadowing()
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{
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device_ptr null_ptr = (device_ptr) 0;
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device_sub_ptr unfiltered_a (buffer.mem, 0, buffer.pass_stride);
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device_sub_ptr unfiltered_b (buffer.mem, 1*buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr sample_var (buffer.mem, 2*buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr sample_var_var (buffer.mem, 3*buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr buffer_var (buffer.mem, 5*buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr filtered_var (buffer.mem, 6*buffer.pass_stride, buffer.pass_stride);
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/* Get the A/B unfiltered passes, the combined sample variance, the estimated variance of the sample variance and the buffer variance. */
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functions.divide_shadow(*unfiltered_a, *unfiltered_b, *sample_var, *sample_var_var, *buffer_var);
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/* Smooth the (generally pretty noisy) buffer variance using the spatial information from the sample variance. */
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nlm_state.set_parameters(6, 3, 4.0f, 1.0f);
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functions.non_local_means(*buffer_var, *sample_var, *sample_var_var, *filtered_var);
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/* Reuse memory, the previous data isn't needed anymore. */
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device_ptr filtered_a = *buffer_var,
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filtered_b = *sample_var;
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/* Use the smoothed variance to filter the two shadow half images using each other for weight calculation. */
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nlm_state.set_parameters(5, 3, 1.0f, 0.25f);
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functions.non_local_means(*unfiltered_a, *unfiltered_b, *filtered_var, filtered_a);
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functions.non_local_means(*unfiltered_b, *unfiltered_a, *filtered_var, filtered_b);
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device_ptr residual_var = *sample_var_var;
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/* Estimate the residual variance between the two filtered halves. */
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functions.combine_halves(filtered_a, filtered_b, null_ptr, residual_var, 2, rect);
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device_ptr final_a = *unfiltered_a,
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final_b = *unfiltered_b;
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/* Use the residual variance for a second filter pass. */
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nlm_state.set_parameters(4, 2, 1.0f, 0.5f);
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functions.non_local_means(filtered_a, filtered_b, residual_var, final_a);
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functions.non_local_means(filtered_b, filtered_a, residual_var, final_b);
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/* Combine the two double-filtered halves to a final shadow feature. */
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device_sub_ptr shadow_pass(buffer.mem, 4*buffer.pass_stride, buffer.pass_stride);
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functions.combine_halves(final_a, final_b, *shadow_pass, null_ptr, 0, rect);
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}
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void DenoisingTask::prefilter_features()
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{
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device_sub_ptr unfiltered (buffer.mem, 8*buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr variance (buffer.mem, 9*buffer.pass_stride, buffer.pass_stride);
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int mean_from[] = { 0, 1, 2, 12, 6, 7, 8 };
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int variance_from[] = { 3, 4, 5, 13, 9, 10, 11};
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int pass_to[] = { 1, 2, 3, 0, 5, 6, 7};
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for(int pass = 0; pass < 7; pass++) {
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device_sub_ptr feature_pass(buffer.mem, pass_to[pass]*buffer.pass_stride, buffer.pass_stride);
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/* Get the unfiltered pass and its variance from the RenderBuffers. */
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functions.get_feature(mean_from[pass], variance_from[pass], *unfiltered, *variance);
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/* Smooth the pass and store the result in the denoising buffers. */
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nlm_state.set_parameters(2, 2, 1.0f, 0.25f);
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functions.non_local_means(*unfiltered, *unfiltered, *variance, *feature_pass);
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}
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}
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void DenoisingTask::prefilter_color()
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{
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int mean_from[] = {20, 21, 22};
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int variance_from[] = {23, 24, 25};
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int mean_to[] = { 8, 9, 10};
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int variance_to[] = {11, 12, 13};
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int num_color_passes = 3;
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device_only_memory<float> temporary_color(device, "denoising temporary color");
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temporary_color.alloc_to_device(3*buffer.pass_stride, false);
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for(int pass = 0; pass < num_color_passes; pass++) {
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device_sub_ptr color_pass(temporary_color, pass*buffer.pass_stride, buffer.pass_stride);
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device_sub_ptr color_var_pass(buffer.mem, variance_to[pass]*buffer.pass_stride, buffer.pass_stride);
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functions.get_feature(mean_from[pass], variance_from[pass], *color_pass, *color_var_pass);
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}
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device_sub_ptr depth_pass (buffer.mem, 0, buffer.pass_stride);
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device_sub_ptr color_var_pass(buffer.mem, variance_to[0]*buffer.pass_stride, 3*buffer.pass_stride);
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device_sub_ptr output_pass (buffer.mem, mean_to[0]*buffer.pass_stride, 3*buffer.pass_stride);
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functions.detect_outliers(temporary_color.device_pointer, *color_var_pass, *depth_pass, *output_pass);
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}
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void DenoisingTask::construct_transform()
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{
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storage.w = filter_area.z;
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storage.h = filter_area.w;
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storage.transform.alloc_to_device(storage.w*storage.h*TRANSFORM_SIZE, false);
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storage.rank.alloc_to_device(storage.w*storage.h, false);
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functions.construct_transform();
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}
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void DenoisingTask::reconstruct()
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{
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storage.XtWX.alloc_to_device(storage.w*storage.h*XTWX_SIZE, false);
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storage.XtWY.alloc_to_device(storage.w*storage.h*XTWY_SIZE, false);
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reconstruction_state.filter_window = rect_from_shape(filter_area.x-rect.x, filter_area.y-rect.y, storage.w, storage.h);
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int tile_coordinate_offset = filter_area.y*target_buffer.stride + filter_area.x;
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reconstruction_state.buffer_params = make_int4(target_buffer.offset + tile_coordinate_offset,
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target_buffer.stride,
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target_buffer.pass_stride,
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target_buffer.denoising_clean_offset);
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reconstruction_state.source_w = rect.z-rect.x;
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reconstruction_state.source_h = rect.w-rect.y;
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device_sub_ptr color_ptr (buffer.mem, 8*buffer.pass_stride, 3*buffer.pass_stride);
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device_sub_ptr color_var_ptr(buffer.mem, 11*buffer.pass_stride, 3*buffer.pass_stride);
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functions.reconstruct(*color_ptr, *color_var_ptr, target_buffer.ptr);
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}
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void DenoisingTask::run_denoising(RenderTile *tile)
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{
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RenderTile rtiles[10];
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rtiles[4] = *tile;
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functions.map_neighbor_tiles(rtiles);
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set_render_buffer(rtiles);
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setup_denoising_buffer();
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prefilter_shadowing();
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prefilter_features();
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prefilter_color();
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construct_transform();
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reconstruct();
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functions.unmap_neighbor_tiles(rtiles);
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}
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CCL_NAMESPACE_END
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