"""This script defines the face reconstruction model for Deep3DFaceRecon_pytorch """ import numpy as np import torch from src.face3d.models.base_model import BaseModel from src.face3d.models import networks from src.face3d.models.bfm import ParametricFaceModel from src.face3d.models.losses import perceptual_loss, photo_loss, reg_loss, reflectance_loss, landmark_loss from src.face3d.util import util from src.face3d.util.nvdiffrast import MeshRenderer # from src.face3d.util.preprocess import estimate_norm_torch import trimesh from scipy.io import savemat class FaceReconModel(BaseModel): @staticmethod def modify_commandline_options(parser, is_train=False): """ Configures options specific for CUT model """ # net structure and parameters parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='network structure') parser.add_argument('--init_path', type=str, default='./checkpoints/init_model/resnet50-0676ba61.pth') parser.add_argument('--use_last_fc', type=util.str2bool, nargs='?', const=True, default=False, help='zero initialize the last fc') parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/') parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model') # renderer parameters parser.add_argument('--focal', type=float, default=1015.) parser.add_argument('--center', type=float, default=112.) parser.add_argument('--camera_d', type=float, default=10.) parser.add_argument('--z_near', type=float, default=5.) parser.add_argument('--z_far', type=float, default=15.) if is_train: # training parameters parser.add_argument('--net_recog', type=str, default='r50', choices=['r18', 'r43', 'r50'], help='face recog network structure') parser.add_argument('--net_recog_path', type=str, default='checkpoints/recog_model/ms1mv3_arcface_r50_fp16/backbone.pth') parser.add_argument('--use_crop_face', type=util.str2bool, nargs='?', const=True, default=False, help='use crop mask for photo loss') parser.add_argument('--use_predef_M', type=util.str2bool, nargs='?', const=True, default=False, help='use predefined M for predicted face') # augmentation parameters parser.add_argument('--shift_pixs', type=float, default=10., help='shift pixels') parser.add_argument('--scale_delta', type=float, default=0.1, help='delta scale factor') parser.add_argument('--rot_angle', type=float, default=10., help='rot angles, degree') # loss weights parser.add_argument('--w_feat', type=float, default=0.2, help='weight for feat loss') parser.add_argument('--w_color', type=float, default=1.92, help='weight for loss loss') parser.add_argument('--w_reg', type=float, default=3.0e-4, help='weight for reg loss') parser.add_argument('--w_id', type=float, default=1.0, help='weight for id_reg loss') parser.add_argument('--w_exp', type=float, default=0.8, help='weight for exp_reg loss') parser.add_argument('--w_tex', type=float, default=1.7e-2, help='weight for tex_reg loss') parser.add_argument('--w_gamma', type=float, default=10.0, help='weight for gamma loss') parser.add_argument('--w_lm', type=float, default=1.6e-3, help='weight for lm loss') parser.add_argument('--w_reflc', type=float, default=5.0, help='weight for reflc loss') opt, _ = parser.parse_known_args() parser.set_defaults( focal=1015., center=112., camera_d=10., use_last_fc=False, z_near=5., z_far=15. ) if is_train: parser.set_defaults( use_crop_face=True, use_predef_M=False ) return parser def __init__(self, opt): """Initialize this model class. Parameters: opt -- training/test options A few things can be done here. - (required) call the initialization function of BaseModel - define loss function, visualization images, model names, and optimizers """ BaseModel.__init__(self, opt) # call the initialization method of BaseModel self.visual_names = ['output_vis'] self.model_names = ['net_recon'] self.parallel_names = self.model_names + ['renderer'] self.facemodel = ParametricFaceModel( bfm_folder=opt.bfm_folder, camera_distance=opt.camera_d, focal=opt.focal, center=opt.center, is_train=self.isTrain, default_name=opt.bfm_model ) fov = 2 * np.arctan(opt.center / opt.focal) * 180 / np.pi self.renderer = MeshRenderer( rasterize_fov=fov, znear=opt.z_near, zfar=opt.z_far, rasterize_size=int(2 * opt.center) ) if self.isTrain: self.loss_names = ['all', 'feat', 'color', 'lm', 'reg', 'gamma', 'reflc'] self.net_recog = networks.define_net_recog( net_recog=opt.net_recog, pretrained_path=opt.net_recog_path ) # loss func name: (compute_%s_loss) % loss_name self.compute_feat_loss = perceptual_loss self.comupte_color_loss = photo_loss self.compute_lm_loss = landmark_loss self.compute_reg_loss = reg_loss self.compute_reflc_loss = reflectance_loss self.optimizer = torch.optim.Adam(self.net_recon.parameters(), lr=opt.lr) self.optimizers = [self.optimizer] self.parallel_names += ['net_recog'] # Our program will automatically call to define schedulers, load networks, and print networks def set_input(self, input): """Unpack input data from the dataloader and perform necessary pre-processing steps. Parameters: input: a dictionary that contains the data itself and its metadata information. """ self.input_img = input['imgs'].to(self.device) self.atten_mask = input['msks'].to(self.device) if 'msks' in input else None self.gt_lm = input['lms'].to(self.device) if 'lms' in input else None self.trans_m = input['M'].to(self.device) if 'M' in input else None self.image_paths = input['im_paths'] if 'im_paths' in input else None def forward(self, output_coeff, device): self.facemodel.to(device) self.pred_vertex, self.pred_tex, self.pred_color, self.pred_lm = \ self.facemodel.compute_for_render(output_coeff) self.pred_mask, _, self.pred_face = self.renderer( self.pred_vertex, self.facemodel.face_buf, feat=self.pred_color) self.pred_coeffs_dict = self.facemodel.split_coeff(output_coeff) def compute_losses(self): """Calculate losses, gradients, and update network weights; called in every training iteration""" assert self.net_recog.training == False trans_m = self.trans_m if not self.opt.use_predef_M: trans_m = estimate_norm_torch(self.pred_lm, self.input_img.shape[-2]) pred_feat = self.net_recog(self.pred_face, trans_m) gt_feat = self.net_recog(self.input_img, self.trans_m) self.loss_feat = self.opt.w_feat * self.compute_feat_loss(pred_feat, gt_feat) face_mask = self.pred_mask if self.opt.use_crop_face: face_mask, _, _ = self.renderer(self.pred_vertex, self.facemodel.front_face_buf) face_mask = face_mask.detach() self.loss_color = self.opt.w_color * self.comupte_color_loss( self.pred_face, self.input_img, self.atten_mask * face_mask) loss_reg, loss_gamma = self.compute_reg_loss(self.pred_coeffs_dict, self.opt) self.loss_reg = self.opt.w_reg * loss_reg self.loss_gamma = self.opt.w_gamma * loss_gamma self.loss_lm = self.opt.w_lm * self.compute_lm_loss(self.pred_lm, self.gt_lm) self.loss_reflc = self.opt.w_reflc * self.compute_reflc_loss(self.pred_tex, self.facemodel.skin_mask) self.loss_all = self.loss_feat + self.loss_color + self.loss_reg + self.loss_gamma \ + self.loss_lm + self.loss_reflc def optimize_parameters(self, isTrain=True): self.forward() self.compute_losses() """Update network weights; it will be called in every training iteration.""" if isTrain: self.optimizer.zero_grad() self.loss_all.backward() self.optimizer.step() def compute_visuals(self): with torch.no_grad(): input_img_numpy = 255. * self.input_img.detach().cpu().permute(0, 2, 3, 1).numpy() output_vis = self.pred_face * self.pred_mask + (1 - self.pred_mask) * self.input_img output_vis_numpy_raw = 255. * output_vis.detach().cpu().permute(0, 2, 3, 1).numpy() if self.gt_lm is not None: gt_lm_numpy = self.gt_lm.cpu().numpy() pred_lm_numpy = self.pred_lm.detach().cpu().numpy() output_vis_numpy = util.draw_landmarks(output_vis_numpy_raw, gt_lm_numpy, 'b') output_vis_numpy = util.draw_landmarks(output_vis_numpy, pred_lm_numpy, 'r') output_vis_numpy = np.concatenate((input_img_numpy, output_vis_numpy_raw, output_vis_numpy), axis=-2) else: output_vis_numpy = np.concatenate((input_img_numpy, output_vis_numpy_raw), axis=-2) self.output_vis = torch.tensor( output_vis_numpy / 255., dtype=torch.float32 ).permute(0, 3, 1, 2).to(self.device) def save_mesh(self, name): recon_shape = self.pred_vertex # get reconstructed shape recon_shape[..., -1] = 10 - recon_shape[..., -1] # from camera space to world space recon_shape = recon_shape.cpu().numpy()[0] recon_color = self.pred_color recon_color = recon_color.cpu().numpy()[0] tri = self.facemodel.face_buf.cpu().numpy() mesh = trimesh.Trimesh(vertices=recon_shape, faces=tri, vertex_colors=np.clip(255. * recon_color, 0, 255).astype(np.uint8)) mesh.export(name) def save_coeff(self,name): pred_coeffs = {key:self.pred_coeffs_dict[key].cpu().numpy() for key in self.pred_coeffs_dict} pred_lm = self.pred_lm.cpu().numpy() pred_lm = np.stack([pred_lm[:,:,0],self.input_img.shape[2]-1-pred_lm[:,:,1]],axis=2) # transfer to image coordinate pred_coeffs['lm68'] = pred_lm savemat(name,pred_coeffs)