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| """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): | |
| 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 <model.setup> 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) | |