Spaces:
Running
on
Zero
Running
on
Zero
| # SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: LicenseRef-NvidiaProprietary | |
| # | |
| # NVIDIA CORPORATION, its affiliates and licensors retain all intellectual | |
| # property and proprietary rights in and to this material, related | |
| # documentation and any modifications thereto. Any use, reproduction, | |
| # disclosure or distribution of this material and related documentation | |
| # without an express license agreement from NVIDIA CORPORATION or | |
| # its affiliates is strictly prohibited. | |
| from os import device_encoding | |
| from turtle import update | |
| import math | |
| import torch | |
| import numpy as np | |
| import torch.nn.functional as F | |
| import cv2 | |
| import torchvision | |
| from torch_utils import persistence | |
| from recon.models.stylegannext3D.networks_stylegan2_new import Generator as StyleGAN2Backbone_cond | |
| from recon.volumetric_rendering.renderer import ImportanceRenderer, ImportanceRenderer_bsMotion | |
| from recon.volumetric_rendering.ray_sampler import RaySampler, RaySampler_zxc | |
| import dnnlib | |
| from recon.volumetric_rendering.renderer import fill_mouth | |
| class TriPlaneGenerator(torch.nn.Module): | |
| def __init__(self, | |
| z_dim, # Input latent (Z) dimensionality. | |
| c_dim, # Conditioning label (C) dimensionality. | |
| w_dim, # Intermediate latent (W) dimensionality. | |
| img_resolution, # Output resolution. | |
| img_channels, # Number of output color channels. | |
| topology_path=None, # | |
| sr_num_fp16_res=0, | |
| mapping_kwargs={}, # Arguments for MappingNetwork. | |
| rendering_kwargs={}, | |
| sr_kwargs={}, | |
| **synthesis_kwargs, # Arguments for SynthesisNetwork. | |
| ): | |
| super().__init__() | |
| self.z_dim = z_dim | |
| self.c_dim = c_dim | |
| self.w_dim = w_dim | |
| self.img_resolution = img_resolution | |
| self.img_channels = img_channels | |
| self.renderer = ImportanceRenderer_bsMotion() | |
| self.ray_sampler = RaySampler_zxc() | |
| self.texture_backbone = StyleGAN2Backbone_cond(z_dim, c_dim, w_dim, img_resolution=256, img_channels=32, mapping_kwargs=mapping_kwargs, | |
| **synthesis_kwargs) # render neural texture | |
| self.face_backbone = StyleGAN2Backbone_cond(z_dim, c_dim, w_dim, img_resolution=256, img_channels=32, mapping_kwargs=mapping_kwargs, | |
| **synthesis_kwargs) | |
| self.backbone = StyleGAN2Backbone_cond(z_dim, c_dim, w_dim, img_resolution=256, img_channels=32 * 3, mapping_ws=self.texture_backbone.num_ws, | |
| mapping_kwargs=mapping_kwargs, **synthesis_kwargs) | |
| self.superresolution = dnnlib.util.construct_class_by_name(class_name='recon.models.stylegannext3D.superresolution.SuperresolutionHybrid8XDC', channels=32, | |
| img_resolution=img_resolution, sr_num_fp16_res=sr_num_fp16_res, | |
| sr_antialias=rendering_kwargs['sr_antialias'], **sr_kwargs) | |
| self.decoder = OSGDecoder(32, {'decoder_lr_mul': rendering_kwargs.get('decoder_lr_mul', 1), 'decoder_output_dim': 32}) | |
| self.neural_rendering_resolution = 128 | |
| self.rendering_kwargs = rendering_kwargs | |
| self.fill_mouth = True | |
| def mapping(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False): | |
| if self.rendering_kwargs['c_gen_conditioning_zero']: | |
| c = torch.zeros_like(c) | |
| c = c[:, :self.c_dim] # remove expression labels | |
| return self.backbone.mapping(z, c * self.rendering_kwargs.get('c_scale', 0), truncation_psi=truncation_psi, | |
| truncation_cutoff=truncation_cutoff, update_emas=update_emas) | |
| def visualize_mesh_condition(self, mesh_condition, to_imgs=False): | |
| uvcoords_image = mesh_condition['uvcoords_image'].clone().permute(0, 3, 1, 2) # [B, C, H, W] | |
| ori_alpha_image = uvcoords_image[:, 2:].clone() | |
| full_alpha_image, mouth_masks = fill_mouth(ori_alpha_image, blur_mouth_edge=False) | |
| # upper_mouth_mask = mouth_masks.clone() | |
| # upper_mouth_mask[:, :, :87] = 0 | |
| # alpha_image = torch.clamp(ori_alpha_image + upper_mouth_mask, min=0, max=1) | |
| if to_imgs: | |
| uvcoords_image[full_alpha_image.expand(-1, 3, -1, -1) == 0] = -1 | |
| uvcoords_image = ((uvcoords_image+1)*127.5).to(dtype=torch.uint8).cpu() | |
| vis_images = [] | |
| for vis_uvcoords in uvcoords_image: | |
| vis_images.append(torchvision.transforms.ToPILImage()(vis_uvcoords)) | |
| return vis_images | |
| else: | |
| return uvcoords_image | |
| def synthesis(self, ws, c, mesh_condition, neural_rendering_resolution=None, update_emas=False, cache_backbone=False, use_cached_backbone=False, | |
| return_featmap=False, evaluation=False, **synthesis_kwargs): | |
| batch_size = ws.shape[0] | |
| # cam = c[:, -25:] | |
| cam = c | |
| cam2world_matrix = cam[:, :16].view(-1, 4, 4) | |
| intrinsics = cam[:, 16:25].view(-1, 3, 3) | |
| if neural_rendering_resolution is None: | |
| neural_rendering_resolution = self.neural_rendering_resolution | |
| else: | |
| self.neural_rendering_resolution = neural_rendering_resolution | |
| # print(self.neural_rendering_resolution) | |
| # Create a batch of rays for volume rendering | |
| ray_origins, ray_directions = self.ray_sampler(cam2world_matrix, intrinsics, neural_rendering_resolution) | |
| # Create triplanes by running StyleGAN backbone | |
| N, M, _ = ray_origins.shape | |
| texture_feats = self.texture_backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) | |
| static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) | |
| static_plane = static_feats[-1] | |
| static_plane = static_plane.view(len(static_plane), 3, 32, static_plane.shape[-2], static_plane.shape[-1]) | |
| static_feats[0] = static_feats[0].view(len(static_plane), 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0] | |
| static_feats[-1] = static_plane[:, 0] | |
| assert len(static_feats) == len(texture_feats) | |
| bbox_256 = [57, 185, 64, 192] # the face region is the center-crop result from the frontal triplane. | |
| rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition , static_feats, bbox_256) | |
| rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) | |
| rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) | |
| rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', antialias=True) | |
| full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', antialias=True) | |
| full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ | |
| # blend features of neural texture and tri-plane | |
| full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) | |
| rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) | |
| rendering_stitch = rendering_stitch.view(*static_plane.shape) | |
| blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) | |
| # Perform volume rendering | |
| if evaluation: | |
| assert 'noise_mode' in synthesis_kwargs.keys() and synthesis_kwargs['noise_mode'] == 'const', \ | |
| ('noise_mode' in synthesis_kwargs.keys(), synthesis_kwargs['noise_mode'] == 'const') | |
| feature_samples, depth_samples, weights_samples = self.renderer(blended_planes, self.decoder, ray_origins, ray_directions, | |
| self.rendering_kwargs, evaluation=evaluation) | |
| # Reshape into 'raw' neural-rendered image | |
| H = W = self.neural_rendering_resolution | |
| feature_image = feature_samples.permute(0, 2, 1).reshape(N, feature_samples.shape[-1], H, W).contiguous() | |
| depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W) | |
| # Run superresolution to get final image | |
| rgb_image = feature_image[:, :3] | |
| # rgb_image = weights_samples * rgb_image + (1 - weights_samples) * torch.ones_like(rgb_image) | |
| sr_image = self.superresolution(rgb_image, feature_image, ws, noise_mode=self.rendering_kwargs['superresolution_noise_mode'], | |
| **{k: synthesis_kwargs[k] for k in synthesis_kwargs.keys() if k != 'noise_mode'}) | |
| if return_featmap: | |
| return {'image': sr_image, 'image_raw': rgb_image, 'image_depth': depth_image, | |
| 'image_feature': feature_image, 'triplane': blended_planes, 'texture': texture_feats, 'static_plane': static_plane, 'rendering_stitch': rendering_stitch}#static_plane, 'texture_map': texture_feats[-2]} | |
| else: | |
| return {'image': sr_image, 'image_raw': rgb_image, 'image_depth': depth_image} | |
| def synthesis_withTexture(self, ws, texture_feats, c, mesh_condition, static_feats=None, neural_rendering_resolution=None, update_emas=False, | |
| cache_backbone=False, use_cached_backbone=False, evaluation=False, **synthesis_kwargs): | |
| bs = ws.shape[0] | |
| # eg3d_ws, texture_ws = ws[:, :self.texture_backbone.num_ws], ws[:, self.texture_backbone.num_ws:] | |
| # cam = c[:, :25] | |
| cam = c[:, -25:] | |
| cam2world_matrix = cam[:, :16].view(-1, 4, 4) | |
| intrinsics = cam[:, 16:25].view(-1, 3, 3) | |
| if neural_rendering_resolution is None: | |
| neural_rendering_resolution = self.neural_rendering_resolution | |
| else: | |
| self.neural_rendering_resolution = neural_rendering_resolution | |
| # Create a batch of rays for volume rendering | |
| ray_origins, ray_directions = self.ray_sampler(cam2world_matrix, intrinsics, neural_rendering_resolution) | |
| # Create triplanes by running StyleGAN backbone | |
| N, M, _ = ray_origins.shape | |
| if static_feats is None: | |
| static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) | |
| static_plane = static_feats[-1].view(bs, 3, 32, static_feats[-1].shape[-2], static_feats[-1].shape[-1]) | |
| assert len(static_feats) == len(texture_feats), (len(static_feats), len(texture_feats)) | |
| bbox_256 = [57, 185, 64, 192] | |
| rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition['uvcoords_image'], bbox_256=bbox_256, | |
| static_feats=[static_feats[0].view(bs, 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0]] + | |
| static_feats[1:-1] + [static_plane[:, 0]]) | |
| rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) | |
| # upper_mouth_mask = mouth_masks.clone() | |
| # upper_mouth_mask[:, :, :87] = 0 | |
| # rendering_stitch = F.interpolate(static_plane[:, 0, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]], size=(256, 256), mode='bilinear', | |
| # antialias=True) * upper_mouth_mask + rendering_stitch * (1 - upper_mouth_mask) | |
| rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) | |
| rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', antialias=True) | |
| full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', antialias=True) | |
| full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ | |
| # blend features of neural texture and tri-plane | |
| full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) | |
| rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) | |
| rendering_stitch = rendering_stitch.view(*static_plane.shape) | |
| blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) | |
| # if flag is not False: | |
| # import cv2 | |
| # with torch.no_grad(): | |
| # if not hasattr(self, 'weight'): | |
| # self.weight = torch.nn.Conv2d(32, 3, 1).weight.cuda() | |
| # weight = self.weight | |
| # vis = torch.nn.functional.conv2d((rendering_stitch * full_alpha_image)[:, 0, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]], weight) | |
| # max_ = [torch.max(torch.abs(vis[:, i])) for i in range(3)] | |
| # for i in range(3): vis[:, i] /= max_[i] | |
| # print('rendering_stitch', vis.max().item(), vis.min().item()) | |
| # vis = torch.cat([vis[i] for i in range(blended_planes.shape[0])], dim=-1) | |
| # vis = (vis.permute(1, 2, 0).clamp(min=-1.0, max=1.0) + 1.) * 127.5 | |
| # cv2.imwrite('vis_%s_rendering_stitch.png' % flag, vis.cpu().numpy().astype(np.uint8)[..., ::-1]) | |
| # vis = torch.nn.functional.conv2d((static_plane * (1 - full_alpha_image))[:, 0], weight) | |
| # for i in range(3): vis[:, i] /= max_[i] | |
| # print('static_plane', vis.max().item(), vis.min().item()) | |
| # vis = torch.cat([vis[i] for i in range(blended_planes.shape[0])], dim=-1) | |
| # vis = (vis.permute(1, 2, 0).clamp(min=-1.0, max=1.0) + 1.) * 127.5 | |
| # cv2.imwrite('vis_%s_static_plane.png' % flag, vis.cpu().numpy().astype(np.uint8)[..., ::-1]) | |
| # vis = torch.nn.functional.conv2d(blended_planes[:, 0], weight) | |
| # for i in range(3): vis[:, i] /= max_[i] | |
| # print('blended_planes', vis.max().item(), vis.min().item()) | |
| # vis = torch.cat([vis[i] for i in range(blended_planes.shape[0])], dim=-1) | |
| # vis = (vis.permute(1, 2, 0).clamp(min=-1.0, max=1.0) + 1.) * 127.5 | |
| # cv2.imwrite('vis_%s_blended_planes.png' % flag, vis.cpu().numpy().astype(np.uint8)[..., ::-1]) | |
| # Perform volume rendering | |
| if evaluation: | |
| assert 'noise_mode' in synthesis_kwargs.keys() and synthesis_kwargs['noise_mode']=='const',\ | |
| ('noise_mode' in synthesis_kwargs.keys(), synthesis_kwargs['noise_mode']=='const') | |
| feature_samples, depth_samples, weights_samples = self.renderer(blended_planes, self.decoder, ray_origins, ray_directions, | |
| self.rendering_kwargs, evaluation=evaluation) | |
| # Reshape into 'raw' neural-rendered image | |
| H = W = self.neural_rendering_resolution | |
| feature_image = feature_samples.permute(0, 2, 1).reshape(N, feature_samples.shape[-1], H, W).contiguous() | |
| depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W) | |
| # Run superresolution to get final image | |
| rgb_image = feature_image[:, :3] | |
| sr_image = self.superresolution(rgb_image, feature_image, ws, noise_mode=self.rendering_kwargs['superresolution_noise_mode'], | |
| **{k: synthesis_kwargs[k] for k in synthesis_kwargs.keys() if k != 'noise_mode'}) | |
| return {'image': sr_image, 'image_raw': rgb_image, 'image_depth': depth_image, | |
| 'feature_image': feature_image, 'triplane': blended_planes}#static_plane, 'texture_map': texture_feats[-2]} | |
| def synthesis_withCondition(self, ws, c, mesh_condition, gt_texture_feats=None, gt_static_feats=None, texture_feats_conditions=None, | |
| static_feats_conditions=None, neural_rendering_resolution=None, update_emas=False, cache_backbone=False, | |
| use_cached_backbone=False, only_image=False, return_feats=False, **synthesis_kwargs): | |
| bs = ws.shape[0] | |
| cam = c[:, -25:] | |
| cam2world_matrix = cam[:, :16].view(-1, 4, 4) | |
| intrinsics = cam[:, 16:25].view(-1, 3, 3) | |
| if neural_rendering_resolution is None: | |
| neural_rendering_resolution = self.neural_rendering_resolution | |
| else: | |
| self.neural_rendering_resolution = neural_rendering_resolution | |
| # Create a batch of rays for volume rendering | |
| ray_origins, ray_directions = self.ray_sampler(cam2world_matrix, intrinsics, neural_rendering_resolution) | |
| # Create triplanes by running StyleGAN backbone | |
| N, M, _ = ray_origins.shape | |
| if gt_texture_feats is None: | |
| texture_feats = self.texture_backbone.synthesis(ws, cond_list=None, return_list=True, feat_conditions=texture_feats_conditions, | |
| update_emas=update_emas, **synthesis_kwargs) | |
| if gt_static_feats is None: | |
| static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, feat_conditions=static_feats_conditions, | |
| update_emas=update_emas, **synthesis_kwargs) | |
| static_plane = static_feats[-1].view(bs, 3, 32, static_feats[-1].shape[-2], static_feats[-1].shape[-1]) | |
| assert len(static_feats) == len(texture_feats) | |
| bbox_256 = [57, 185, 64, 192] | |
| rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition['uvcoords_image'], bbox_256=bbox_256, | |
| static_feats=[static_feats[0].view(bs, 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0]] + | |
| static_feats[1:-1] + [static_plane[:, 0]]) | |
| rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) | |
| rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) | |
| rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', antialias=True) | |
| full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', antialias=True) | |
| full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ | |
| # blend features of neural texture and tri-plane | |
| full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) | |
| rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) | |
| rendering_stitch = rendering_stitch.view(*static_plane.shape) | |
| blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) | |
| # Perform volume rendering | |
| evaluation = 'noise_mode' in synthesis_kwargs.keys() and synthesis_kwargs['noise_mode']=='const' | |
| feature_samples, depth_samples, weights_samples = self.renderer(blended_planes, self.decoder, ray_origins, ray_directions, | |
| self.rendering_kwargs, evaluation=evaluation) | |
| # Reshape into 'raw' neural-rendered image | |
| H = W = self.neural_rendering_resolution | |
| feature_image = feature_samples.permute(0, 2, 1).reshape(N, feature_samples.shape[-1], H, W).contiguous() | |
| depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W) | |
| # Run superresolution to get final image | |
| rgb_image = feature_image[:, :3] | |
| sr_image = self.superresolution(rgb_image, feature_image, ws, noise_mode=self.rendering_kwargs['superresolution_noise_mode'], | |
| **{k: synthesis_kwargs[k] for k in synthesis_kwargs.keys() if k != 'noise_mode'}) | |
| if only_image: | |
| return {'image': sr_image} | |
| out = {'image': sr_image, 'image_raw': rgb_image, 'image_depth': depth_image, 'feature_image': feature_image, 'triplane': blended_planes} | |
| if return_feats: | |
| out['static'] = static_feats | |
| out['texture'] = texture_feats | |
| return out | |
| def rasterize(self, texture_feats, uvcoords_image, static_feats, bbox_256): | |
| ''' | |
| uvcoords_image [B, H, W, C] | |
| ''' | |
| if not uvcoords_image.dtype == torch.float32: uvcoords_image = uvcoords_image.float() | |
| grid, alpha_image = uvcoords_image[..., :2], uvcoords_image[..., 2:].permute(0, 3, 1, 2) | |
| full_alpha_image, mouth_masks = fill_mouth(alpha_image.clone(), blur_mouth_edge=False) | |
| upper_mouth_mask = mouth_masks.clone() | |
| upper_mouth_mask[:, :, :87] = 0 | |
| upper_mouth_alpha_image = torch.clamp(alpha_image + upper_mouth_mask, min=0, max=1) | |
| rendering_images = [] | |
| for idx, texture in enumerate(texture_feats): | |
| res = texture.shape[2] | |
| bbox = [round(i * res / 256) for i in bbox_256] | |
| rendering_image = F.grid_sample(texture, grid, align_corners=False) | |
| rendering_feat = F.interpolate(rendering_image, size=(res, res), mode='bilinear', antialias=True) | |
| alpha_image_ = F.interpolate(alpha_image, size=(res, res), mode='bilinear', antialias=True) | |
| static_feat = F.interpolate(static_feats[idx][:, :, bbox[0]:bbox[1], bbox[2]:bbox[3]], size=(res, res), mode='bilinear', antialias=True) | |
| rendering_images.append(torch.cat([ | |
| rendering_feat * alpha_image_ + static_feat * (1 - alpha_image_), | |
| F.interpolate(upper_mouth_alpha_image, size=(res, res), mode='bilinear', antialias=True)], dim=1)) | |
| # print('rendering_images', grid.shape, rendering_images[-1].shape) | |
| return rendering_images, full_alpha_image, mouth_masks | |
| def sample(self, coordinates, directions, z, c, mesh_condition, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): | |
| # Compute RGB features, density for arbitrary 3D coordinates. Mostly used for extracting shapes. | |
| ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) | |
| batch_size = ws.shape[0] | |
| texture_feats = self.texture_backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) | |
| static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) | |
| static_plane = static_feats[-1] | |
| static_plane = static_plane.view(len(static_plane), 3, 32, static_plane.shape[-2], static_plane.shape[-1]) | |
| static_feats[0] = static_feats[0].view(len(static_plane), 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0] | |
| static_feats[-1] = static_plane[:, 0] | |
| assert len(static_feats) == len(texture_feats) | |
| bbox_256 = [57, 185, 64, 192] | |
| rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition['uvcoords_image'], static_feats, bbox_256) | |
| rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) | |
| rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) | |
| rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', | |
| antialias=True) | |
| full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', | |
| antialias=True) | |
| full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ | |
| # blend features of neural texture and tri-plane | |
| full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) | |
| rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) | |
| rendering_stitch = rendering_stitch.view(*static_plane.shape) | |
| blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) | |
| return self.renderer.run_model(blended_planes, self.decoder, coordinates, directions, self.rendering_kwargs) | |
| def sample_mixed(self, coordinates, directions, ws, mesh_condition, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): | |
| # Same as sample, but expects latent vectors 'ws' instead of Gaussian noise 'z' | |
| batch_size = ws.shape[0] | |
| texture_feats = self.texture_backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) | |
| static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) | |
| static_plane = static_feats[-1] | |
| static_plane = static_plane.view(len(static_plane), 3, 32, static_plane.shape[-2], static_plane.shape[-1]) | |
| static_feats[0] = static_feats[0].view(len(static_plane), 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0] | |
| static_feats[-1] = static_plane[:, 0] | |
| assert len(static_feats) == len(texture_feats) | |
| bbox_256 = [57, 185, 64, 192] | |
| rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition['uvcoords_image'], static_feats, bbox_256) | |
| rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) | |
| rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) | |
| rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', | |
| antialias=True) | |
| full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', | |
| antialias=True) | |
| full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ | |
| # blend features of neural texture and tri-plane | |
| full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) | |
| rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) | |
| rendering_stitch = rendering_stitch.view(*static_plane.shape) | |
| blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) | |
| return self.renderer.run_model(blended_planes, self.decoder, coordinates, directions, self.rendering_kwargs) | |
| def forward(self, z, c, v, truncation_psi=1, truncation_cutoff=None, neural_rendering_resolution=None, update_emas=False, cache_backbone=False, | |
| use_cached_backbone=False, **synthesis_kwargs): | |
| # Render a batch of generated images. | |
| ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) | |
| return self.synthesis(ws, c, v, update_emas=update_emas, neural_rendering_resolution=neural_rendering_resolution, | |
| cache_backbone=cache_backbone, use_cached_backbone=use_cached_backbone, **synthesis_kwargs) | |
| from training.networks_stylegan2 import FullyConnectedLayer | |
| class OSGDecoder(torch.nn.Module): | |
| def __init__(self, n_features, options): | |
| super().__init__() | |
| self.hidden_dim = 64 | |
| self.net = torch.nn.Sequential( | |
| FullyConnectedLayer(n_features, self.hidden_dim, lr_multiplier=options['decoder_lr_mul']), | |
| torch.nn.Softplus(), | |
| FullyConnectedLayer(self.hidden_dim, 1 + options['decoder_output_dim'], lr_multiplier=options['decoder_lr_mul']) | |
| ) | |
| def forward(self, sampled_features, ray_directions, sampled_embeddings=None): | |
| # Aggregate features | |
| sampled_features = sampled_features.mean(1) | |
| x = sampled_features | |
| N, M, C = x.shape | |
| x = x.view(N * M, C) | |
| x = self.net(x) | |
| x = x.view(N, M, -1) | |
| rgb = torch.sigmoid(x[..., 1:]) * (1 + 2 * 0.001) - 0.001 # Uses sigmoid clamping from MipNeRF | |
| sigma = x[..., 0:1] | |
| return {'rgb': rgb, 'sigma': sigma} |