/* Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved. * * SPDX-License-Identifier: Apache-2.0 * * 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. * * ------------------------------------------------------------------------ * * THIS IS A MODIFIED VERSION OF THE ORIGINAL FILE * * The original file, along with the original Apache-2.0 LICENSE can be found at: * https://github.com/google-research/jax3d/tree/main/jax3d/projects/mobilenerf * * Modification details: Shader code was updated to work on Vulkan (originally * built for WebGL) * Contributor: (Qualcomm) Rodrigo Holztrattner - quic_rholztra@quicinc.com */ #version 460 #extension GL_EXT_nonuniform_qualifier : enable layout (input_attachment_index = 0, binding = 0) uniform subpassInput inputFeature_0; layout (input_attachment_index = 1, binding = 1) uniform subpassInput inputFeature_1; layout (input_attachment_index = 2, binding = 2) uniform subpassInput rayDirectionIn; layout (input_attachment_index = 3, binding = 3) uniform usubpassInput weightsIndex; layout(location = 0) out vec4 o_color; // Try defining constants in the shader itself precision highp float; #define WEIGHTS_0_COUNT (176) #define WEIGHTS_1_COUNT (256) // The third layer's size is changed from 48 to 64 to make sure a 16 bytes alignement //#define WEIGHTS_2_COUNT (48) #define WEIGHTS_2_COUNT (64) #define BIAS_0_COUNT (16) #define BIAS_1_COUNT (16) // The third layer bias' size is changed from 3 to 4 to make sure a 16 bytes alignement #define BIAS_2_COUNT (4) layout(binding = 4) uniform mlp_weights { vec4 data[(WEIGHTS_0_COUNT + WEIGHTS_1_COUNT + WEIGHTS_2_COUNT + BIAS_0_COUNT + BIAS_1_COUNT + BIAS_2_COUNT)/4]; // Array of floats } weights_arr[]; vec3 evaluateNetwork( vec4 f0, vec4 f1, vec4 viewdir, uint idx) { vec3 res; int bias_0_ind = WEIGHTS_0_COUNT + WEIGHTS_1_COUNT + WEIGHTS_2_COUNT; vec4 intermediate_one[4] = vec4[]( weights_arr[nonuniformEXT(idx)].data[bias_0_ind/4], weights_arr[nonuniformEXT(idx)].data[bias_0_ind/4 + 1], weights_arr[nonuniformEXT(idx)].data[bias_0_ind/4 + 2], weights_arr[nonuniformEXT(idx)].data[bias_0_ind/4 + 3] ); #define APPLY_WEIGHTS_0(multiplier, weightFirstInd) \ intermediate_one[ 0] += (multiplier) * weights_arr[nonuniformEXT(idx)].data[ weightFirstInd/4]; \ intermediate_one[ 1] += (multiplier) * weights_arr[nonuniformEXT(idx)].data[ weightFirstInd/4 + 1]; \ intermediate_one[ 2] += (multiplier) * weights_arr[nonuniformEXT(idx)].data[ weightFirstInd/4 + 2]; \ intermediate_one[ 3] += (multiplier) * weights_arr[nonuniformEXT(idx)].data[ weightFirstInd/4 + 3]; APPLY_WEIGHTS_0( f0.r, 0) APPLY_WEIGHTS_0( f0.g, 16) APPLY_WEIGHTS_0( f0.b, 32) APPLY_WEIGHTS_0( f0.a, 48) APPLY_WEIGHTS_0( f1.r, 64) APPLY_WEIGHTS_0( f1.g, 80) APPLY_WEIGHTS_0( f1.b, 96) APPLY_WEIGHTS_0( f1.a, 112) // For models form original mobile nerf, use the original code APPLY_WEIGHTS_0( viewdir.r, 128) APPLY_WEIGHTS_0( -viewdir.b, 144) APPLY_WEIGHTS_0( viewdir.g, 160) int bias_1_ind = WEIGHTS_0_COUNT + WEIGHTS_1_COUNT + WEIGHTS_2_COUNT + BIAS_0_COUNT; vec4 intermediate_two[4] = vec4[]( weights_arr[nonuniformEXT(idx)].data[bias_1_ind/4], weights_arr[nonuniformEXT(idx)].data[bias_1_ind/4 + 1], weights_arr[nonuniformEXT(idx)].data[bias_1_ind/4 + 2], weights_arr[nonuniformEXT(idx)].data[bias_1_ind/4 + 3] ); #define APPLY_WEIGHTS_1(intermediate, oneInd) \ if(intermediate > 0.0f){ \ intermediate_two[ 0] += intermediate * weights_arr[nonuniformEXT(idx)].data[ WEIGHTS_0_COUNT/4 + oneInd * 4 + 0]; \ intermediate_two[ 1] += intermediate * weights_arr[nonuniformEXT(idx)].data[ WEIGHTS_0_COUNT/4 + oneInd * 4 + 1]; \ intermediate_two[ 2] += intermediate * weights_arr[nonuniformEXT(idx)].data[ WEIGHTS_0_COUNT/4 + oneInd * 4 + 2]; \ intermediate_two[ 3] += intermediate * weights_arr[nonuniformEXT(idx)].data[ WEIGHTS_0_COUNT/4 + oneInd * 4 + 3]; \ } APPLY_WEIGHTS_1( intermediate_one[0].r, 0) APPLY_WEIGHTS_1( intermediate_one[0].g, 1) APPLY_WEIGHTS_1( intermediate_one[0].b, 2) APPLY_WEIGHTS_1( intermediate_one[0].a, 3) APPLY_WEIGHTS_1( intermediate_one[1].r, 4) APPLY_WEIGHTS_1( intermediate_one[1].g, 5) APPLY_WEIGHTS_1( intermediate_one[1].b, 6) APPLY_WEIGHTS_1( intermediate_one[1].a, 7) APPLY_WEIGHTS_1( intermediate_one[2].r, 8) APPLY_WEIGHTS_1( intermediate_one[2].g, 9) APPLY_WEIGHTS_1( intermediate_one[2].b, 10) APPLY_WEIGHTS_1( intermediate_one[2].a, 11) APPLY_WEIGHTS_1( intermediate_one[3].r, 12) APPLY_WEIGHTS_1( intermediate_one[3].g, 13) APPLY_WEIGHTS_1( intermediate_one[3].b, 14) APPLY_WEIGHTS_1( intermediate_one[3].a, 15) int bias_2_ind = WEIGHTS_0_COUNT + WEIGHTS_1_COUNT + WEIGHTS_2_COUNT + BIAS_0_COUNT + BIAS_1_COUNT; vec4 result = weights_arr[nonuniformEXT(idx)].data[bias_2_ind/4]; #define APPLY_WEIGHTS_2(intermediate, oneInd) \ if(intermediate > 0.0f){ \ result += intermediate * weights_arr[nonuniformEXT(idx)].data[ WEIGHTS_0_COUNT/4 + WEIGHTS_1_COUNT/4 + oneInd]; \ } APPLY_WEIGHTS_2(intermediate_two[0].r, 0) APPLY_WEIGHTS_2(intermediate_two[0].g, 1) APPLY_WEIGHTS_2(intermediate_two[0].b, 2) APPLY_WEIGHTS_2(intermediate_two[0].a, 3) APPLY_WEIGHTS_2(intermediate_two[1].r, 4) APPLY_WEIGHTS_2(intermediate_two[1].g, 5) APPLY_WEIGHTS_2(intermediate_two[1].b, 6) APPLY_WEIGHTS_2(intermediate_two[1].a, 7) APPLY_WEIGHTS_2(intermediate_two[2].r, 8) APPLY_WEIGHTS_2(intermediate_two[2].g, 9) APPLY_WEIGHTS_2(intermediate_two[2].b,10) APPLY_WEIGHTS_2(intermediate_two[2].a,11) APPLY_WEIGHTS_2(intermediate_two[3].r,12) APPLY_WEIGHTS_2(intermediate_two[3].g,13) APPLY_WEIGHTS_2(intermediate_two[3].b,14) APPLY_WEIGHTS_2(intermediate_two[3].a,15) result = 1.0 / (1.0 + exp(-result)); return vec3(result * viewdir.a+(1.0-viewdir.a)); } ////////////////////////////////////////////////////////////// // MLP was trained with gamma-corrected values // // convert to linear so sRGB conversion isn't applied twice // ////////////////////////////////////////////////////////////// float Convert_sRGB_ToLinear(float value) { return value <= 0.04045 ? value / 12.92 : pow((value + 0.055) / 1.055, 2.4); } vec3 Convert_sRGB_ToLinear(vec3 value) { return vec3(Convert_sRGB_ToLinear(value.x), Convert_sRGB_ToLinear(value.y), Convert_sRGB_ToLinear(value.z)); } ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////// void main(void) { vec4 feature_0 = subpassLoad(inputFeature_0).rgba; vec4 feature_1 = subpassLoad(inputFeature_1).rgba; vec4 rayDirection = subpassLoad(rayDirectionIn).rgba; uint idx = subpassLoad(weightsIndex).r; if (rayDirection.a < 0.6) discard; //deal with iphone feature_0.a = feature_0.a*2.0-1.0; feature_1.a = feature_1.a*2.0-1.0; rayDirection.a = rayDirection.a*2.0-1.0; // Original o_color.rgb = Convert_sRGB_ToLinear(evaluateNetwork(feature_0,feature_1,rayDirection,idx)); o_color.a = 1.0; }