"""This script defines the face reconstruction model for Deep3DFaceRecon_pytorch """ import numpy as np import torch import trimesh from scipy.io import savemat from util import util from util.nvdiffrast import MeshRenderer from util.preprocess import estimate_norm_torch from . import networks from .base_model import BaseModel from .bfm import ParametricFaceModel from .losses import landmark_loss from .losses import perceptual_loss from .losses import photo_loss from .losses import reflectance_loss from .losses import reg_loss class FaceReconModel(BaseModel): @staticmethod def modify_commandline_options(parser, is_train=True): """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="BFM") parser.add_argument("--bfm_model", type=str, default="BFM_model_front.mat", help="bfm model") # renderer parameters parser.add_argument("--focal", type=float, default=1015.0) parser.add_argument("--center", type=float, default=112.0) parser.add_argument("--camera_d", type=float, default=10.0) parser.add_argument("--z_near", type=float, default=5.0) parser.add_argument("--z_far", type=float, default=15.0) parser.add_argument( "--use_opengl", type=util.str2bool, nargs="?", const=True, default=True, help="use opengl context or not" ) 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=10.0, 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=10.0, 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=1015.0, center=112.0, camera_d=10.0, use_last_fc=False, z_near=5.0, z_far=15.0) 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.net_recon = networks.define_net_recon( net_recon=opt.net_recon, use_last_fc=opt.use_last_fc, init_path=opt.init_path ) 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), use_opengl=opt.use_opengl, ) 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 = self.net_recon(self.input_img) self.facemodel.to(self.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 = 255.0 * 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 = 255.0 * 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 / 255.0, 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(255.0 * recon_color, 0, 255).astype(np.uint8), process=False, ) 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)