""" # Copyright 2020 Adobe # All Rights Reserved. # NOTICE: Adobe permits you to use, modify, and distribute this file in # accordance with the terms of the Adobe license agreement accompanying # it. """ import os import torch.nn.parallel import torch.optim as optim import torch.utils.data import time import torch.nn as nn from src.dataset.audio2landmark.audio2landmark_dataset import Speaker_aware_branch_Dataset from src.models.model_audio2landmark_speaker_aware import Audio2landmark_speaker_aware from src.models.model_audio2landmark import Audio2landmark_content from util.utils import Record, get_n_params from tensorboardX import SummaryWriter from util.icp import icp import numpy as np from scipy.spatial.transform import Rotation as R from scipy.signal import savgol_filter device = torch.device("cuda" if torch.cuda.is_available() else "cpu") class Speaker_aware_branch(): def __init__(self, opt_parser): print('Run on device:', device) # Step 1 : load opt_parser for key in vars(opt_parser).keys(): print(key, ':', vars(opt_parser)[key]) self.opt_parser = opt_parser self.dump_dir = opt_parser.dump_dir self.std_face_id = np.loadtxt('dataset/utils/STD_FACE_LANDMARKS.txt') # self.std_face_id = np.loadtxt( # os.path.join(opt_parser.root_dir, 'puppets', '{}_face_close_mouth.txt'.format(opt_parser.puppet_name))) self.std_face_id = self.std_face_id.reshape(1, 204) self.std_face_id = torch.tensor(self.std_face_id, requires_grad=False, dtype=torch.float).to(device) if(not opt_parser.test_end2end): # Step 2: Load dataset (train/eval) self.train_data = Speaker_aware_branch_Dataset(dump_dir=self.dump_dir, dump_name=opt_parser.dump_file_name, num_window_frames=opt_parser.num_window_frames, num_window_step=opt_parser.num_window_step, status='train', use_11spk_only=opt_parser.use_11spk_only) self.train_dataloader = torch.utils.data.DataLoader(self.train_data, batch_size=opt_parser.batch_size, shuffle=False, num_workers=0, collate_fn=self.train_data.my_collate_in_segments) print('Train num videos: {}'.format(len(self.train_data))) self.eval_data = Speaker_aware_branch_Dataset(dump_dir=self.dump_dir, dump_name=opt_parser.dump_file_name, num_window_frames=opt_parser.num_window_frames, num_window_step=opt_parser.num_window_step, status='val', use_11spk_only=opt_parser.use_11spk_only) self.eval_dataloader = torch.utils.data.DataLoader(self.eval_data, batch_size=opt_parser.batch_size, shuffle=False, num_workers=0, collate_fn=self.eval_data.my_collate_in_segments) print('EVAL num videos: {}'.format(len(self.eval_data))) else: self.eval_data = Speaker_aware_branch_Dataset(dump_dir='examples/dump', dump_name='random', status='val', num_window_frames=18, num_window_step=1) # self.eval_data = Speaker_aware_branch_Dataset(dump_dir=r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2/dump', # dump_name='celeb_normrot', # num_window_frames=18, # num_window_step=1, # status='val', use_11spk_only=opt_parser.use_11spk_only) self.eval_dataloader = torch.utils.data.DataLoader(self.eval_data, batch_size=1, shuffle=False, num_workers=0, collate_fn=self.eval_data.my_collate_in_segments) print('EVAL num videos: {}'.format(len(self.eval_data))) # exit(0) # Step 3: Load model self.G = Audio2landmark_speaker_aware( spk_emb_enc_size=opt_parser.spk_emb_enc_size, transformer_d_model=opt_parser.transformer_d_model, N=opt_parser.transformer_N, heads=opt_parser.transformer_heads, pos_dim=opt_parser.pos_dim, use_prior_net=True) # self.G.apply(weight_init) for p in self.G.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) print('G: Running on {}, total num params = {:.2f}M'.format(device, get_n_params(self.G)/1.0e6)) # self.D_L = Audio2landmark_pos_DL() # self.D_L.apply(weight_init) # print('D_L: Running on {}, total num params = {:.2f}M'.format(device, get_n_params(self.D_L)/1.0e6)) # # self.D_T = Audio2landmark_pos_DT(spk_emb_enc_size=opt_parser.spk_emb_enc_size, # transformer_d_model=opt_parser.transformer_d_model, # N=opt_parser.transformer_N, heads=opt_parser.transformer_heads) # for p in self.D_T.parameters(): # if p.dim() > 1: # nn.init.xavier_uniform_(p) # print('D_T: Running on {}, total num params = {:.2f}M'.format(device, get_n_params(self.D_T) / 1.0e6)) if (opt_parser.init_content_encoder.split('/')[-1] != ''): model_dict = self.G.state_dict() ckpt = torch.load(opt_parser.init_content_encoder) pretrained_dict = {k: v for k, v in ckpt['model_g_face_id'].items() if 'bilstm' in k or 'fc_prior' in k} model_dict.update(pretrained_dict) self.G.load_state_dict(model_dict) print('======== LOAD INIT POS MODEL {} ========='.format(opt_parser.init_content_encoder)) if (opt_parser.load_a2l_G_name.split('/')[-1] != ''): model_dict = self.G.state_dict() ckpt = torch.load(opt_parser.load_a2l_G_name) pretrained_dict = {k: v for k, v in ckpt['G'].items() if 'out.' not in k and 'out_pos_1.' not in k} model_dict.update(pretrained_dict) self.G.load_state_dict(model_dict) print('======== LOAD PRETRAINED SPEAKER AWARE MODEL {} ========='.format(opt_parser.load_a2l_G_name)) self.G.to(device) ''' Speech content model ''' self.C = Audio2landmark_content(num_window_frames=18, in_size=80, use_prior_net=True, bidirectional=False, drop_out=0.) ckpt = torch.load(opt_parser.load_a2l_C_name) self.C.load_state_dict(ckpt['model_g_face_id']) print('======== LOAD PRETRAINED FACE ID MODEL {} ========='.format(opt_parser.load_a2l_C_name)) self.C.to(device) self.loss_mse = torch.nn.MSELoss() self.loss_bce = torch.nn.BCELoss() self.opt_G = optim.Adam(self.G.parameters(), lr=opt_parser.lr, weight_decay=opt_parser.reg_lr) # # test embeddings: self.test_embs = {} if(not opt_parser.test_end2end): for i, batch in enumerate(self.eval_dataloader): global_id, video_name = self.eval_data[i][0][1][0], self.eval_data[i][0][1][1][:-4] if(video_name.split('_x_')[1] not in self.test_embs.keys()): inputs_fl, inputs_au, inputs_emb, _, _, _ = batch self.test_embs[video_name.split('_x_')[1]] = inputs_emb[0] else: self.emb_data = Speaker_aware_branch_Dataset(dump_dir='examples/dump', dump_name='celeb_normrot', status='val', num_window_frames=18, num_window_step=1, use_11spk_only=opt_parser.use_11spk_only) self.emb_dataloader = torch.utils.data.DataLoader(self.emb_data, batch_size=1, shuffle=False, num_workers=0, collate_fn=self.emb_data.my_collate_in_segments) for i, batch in enumerate(self.emb_dataloader): global_id, video_name = self.emb_data[i][0][1][0], self.emb_data[i][0][1][1][:-4] if(video_name.split('_x_')[1] not in self.test_embs.keys()): inputs_fl, inputs_au, inputs_emb, _, _, _ = batch self.test_embs[video_name.split('_x_')[1]] = inputs_emb[0] print(self.test_embs.keys(), len(self.test_embs.keys())) self.test_embs_dic = {key: i for i, key in enumerate(self.test_embs.keys())} if (opt_parser.write): self.writer = SummaryWriter(log_dir=os.path.join(opt_parser.log_dir, opt_parser.name)) self.writer_count = {'TRAIN_epoch': 0, 'TRAIN_batch': 0, 'TRAIN_in_batch': 0, 'EVAL_epoch': 0, 'EVAL_batch': 0, 'EVAL_in_batch': 0} self.t_shape_idx = (27, 28, 29, 30, 33, 36, 39, 42, 45) self.anchor_t_shape = np.loadtxt('dataset/utils//STD_FACE_LANDMARKS.txt') self.anchor_t_shape = self.anchor_t_shape[self.t_shape_idx, :] def __train_speaker_aware__(self, fls, aus, embs, face_id, reg_fls, rot_trans, rot_quats, use_residual=False, is_training=True): fls_gt = fls[:, 0, :].detach().clone().requires_grad_(False) reg_fls_gt = reg_fls[:, 0, :].detach().clone().requires_grad_(False) if (face_id.shape[0] == 1): face_id = face_id.repeat(aus.shape[0], 1) face_id = face_id.requires_grad_(False) content_branch_face_id = face_id.detach() d_real_dt, d_fake_dt, d_real_dl, d_fake_dl, g_fake_dt, g_fake_dl, d_ano_dt = 0., 0., 0., 0., 0., 0., 0. # # ''' ====================================================== # Discriminator D_T # ====================================================== ''' # # if (self.opt_parser.train_DT > 0.0): # for name, p in self.G.named_parameters(): # p.requires_grad = False # for p in self.D_T.parameters(): # p.requires_grad = True # for p in self.D_L.parameters(): # p.requires_grad = False # self.opt_D_T.zero_grad() # # # real # fl_dis_pred, _, spk_encode = self.G(aus, embs * self.opt_parser.emb_coef, face_id, fls_without_traj, z, add_z_spk=True) # # d_real = self.D_T(fls_without_traj, spk_encode.detach()) # d_real_dt = self.loss_mse(d_real, torch.ones_like(d_real).to(device)) * 2.0 * self.opt_parser.train_DT # if (is_training): # d_real_dt.backward() # # # fake # d_fake = self.D_T(fl_dis_pred.detach() + face_id, spk_encode.detach()) # d_fake_dt = self.loss_mse(d_fake, torch.zeros_like(d_fake).to(device)) * self.opt_parser.train_DT # if (is_training): # d_fake_dt.backward() # # # # another embedding # # another_emb = np.tile(another_emb, (embs.shape[0], 1)) # # another_emb = torch.tensor(another_emb, dtype=torch.float, requires_grad=False).to(device) # # d_ano = self.D_T(another_emb * self.opt_parser.emb_coef, fls_without_traj) # # d_ano_dt = self.loss_mse(d_ano, torch.zeros_like(d_ano).to(device)) # # if (is_training): # # d_ano_dt.backward() # # self.opt_D_T.step() # # ''' ====================================================== # Discriminator D_L # ====================================================== ''' # if (self.opt_parser.train_DL>0.0): # for p in self.D_L.parameters(): # p.requires_grad = True # for p in self.D_T.parameters(): # p.requires_grad = False # self.opt_D_L.zero_grad() # # # real # fl_dis_pred, _, spk_encode = self.G(aus, embs * self.opt_parser.emb_coef, face_id, fls_without_traj, z, add_z_spk=True) # # d_real = self.D_L(fls_without_traj) # d_real_dl = self.loss_mse(d_real, torch.ones_like(d_real).to(device)) * 1.0 * self.opt_parser.train_DL # if (is_training): # d_real_dl.backward() # # # fake # d_fake = self.D_L(fl_dis_pred + face_id) # d_fake_dl = self.loss_mse(d_fake, torch.zeros_like(d_fake).to(device)) * self.opt_parser.train_DL # if (is_training): # d_fake_dl.backward() # # self.opt_D_L.step() ''' ====================================================== Generator G ====================================================== ''' for name, p in self.G.named_parameters(): p.requires_grad = True fl_dis_pred, pos_pred, _, spk_encode = self.G(aus, embs * self.opt_parser.emb_coef, face_id, add_z_spk=True) if (use_residual): baseline_pred_fls, _ = self.C(aus[:, 0:18, :], content_branch_face_id) ''' CALIBRATION in TEST TIME ''' if (not is_training): smooth_length = int(min(fl_dis_pred.shape[0] - 1, 51) // 2 * 2 + 1) fl_dis_pred = savgol_filter(fl_dis_pred.cpu().numpy(), smooth_length, 3, axis=0) fl_dis_pred *= self.opt_parser.amp_pos # close pose-branch mouth fl_dis_pred = fl_dis_pred.reshape((-1, 68, 3)) index1 = list(range(60-1, 55-1, -1)) index2 = list(range(68-1, 65-1, -1)) mean_out = 0.5 * (fl_dis_pred[:, 49:54] + fl_dis_pred[:, index1]) mean_in = 0.5 * (fl_dis_pred[:, 61:64] + fl_dis_pred[:, index2]) fl_dis_pred[:, 49:54] = fl_dis_pred[:, index1] = mean_out fl_dis_pred[:, 61:64] = fl_dis_pred[:, index2] = mean_in fl_dis_pred = fl_dis_pred.reshape(-1, 204) fl_dis_pred = torch.tensor(fl_dis_pred).to(device) * self.opt_parser.amp_pos mean_face_id = torch.mean(baseline_pred_fls.detach(), dim=0, keepdim=True) # option 1 content_branch_face_id -= mean_face_id.view(1, 204) * 1.0 baseline_pred_fls, _ = self.C(aus[:, 0:18, :], content_branch_face_id) # option 2 # baseline_pred_fls -= mean_face_id baseline_pred_fls[:, 48 * 3::3] *= self.opt_parser.amp_lip_x # mouth x baseline_pred_fls[:, 48 * 3 + 1::3] *= self.opt_parser.amp_lip_y # mouth y fl_dis_pred += baseline_pred_fls.detach() # reconstruct face through pos fl_dis_pred = fl_dis_pred + face_id[0:1].detach() # reg fls loss loss_reg_fls = torch.nn.functional.l1_loss(fl_dis_pred, reg_fls_gt) # reg fls laplacian ''' use laplacian smooth loss ''' loss_laplacian = 0. if (self.opt_parser.lambda_laplacian_smooth_loss > 0.0): n1 = [1] + list(range(0, 16)) + [18] + list(range(17, 21)) + [23] + list(range(22, 26)) + \ [28] + list(range(27, 35)) + [41] + list(range(36, 41)) + [47] + list(range(42, 47)) + \ [59] + list(range(48, 59)) + [67] + list(range(60, 67)) n2 = list(range(1, 17)) + [15] + list(range(18, 22)) + [20] + list(range(23, 27)) + [25] + \ list(range(28, 36)) + [34] + list(range(37, 42)) + [36] + list(range(43, 48)) + [42] + \ list(range(49, 60)) + [48] + list(range(61, 68)) + [60] V = (fl_dis_pred + face_id[0:1]).view(-1, 68, 3) L_V = V - 0.5 * (V[:, n1, :] + V[:, n2, :]) G = reg_fls_gt.view(-1, 68, 3) L_G = G - 0.5 * (G[:, n1, :] + G[:, n2, :]) loss_laplacian = torch.nn.functional.l1_loss(L_V, L_G) # pos loss if(self.opt_parser.pos_dim == 7): pos_gt = torch.cat([rot_quats[:, 0], rot_trans[:, 0, :, 3]], dim=1) # pos_pred[:, 0:4] = torch.nn.functional.normalize(pos_pred[:, 0:4], p=2, dim=1) loss_pos = torch.nn.functional.l1_loss(pos_pred, pos_gt) else: pos_gt = rot_trans[:, 0].view(-1, 12) loss_pos = torch.nn.functional.l1_loss(pos_pred, pos_gt) loss = loss_reg_fls + loss_laplacian * self.opt_parser.lambda_laplacian_smooth_loss + loss_pos # loss = loss_pos if(is_training): self.opt_G.zero_grad() loss.backward() self.opt_G.step() if (self.opt_parser.pos_dim == 7): pos_pred[:, 0:4] = torch.nn.functional.normalize(pos_pred[:, 0:4], p=2, dim=1) return fl_dis_pred, pos_pred, face_id[0:1, :], (loss, loss_reg_fls, loss_laplacian, loss_pos) def __train_pass__(self, epoch, log_loss, is_training=True): st_epoch = time.time() # Step 1: init setup if (is_training): self.G.train() self.C.train() data = self.train_data dataloader = self.train_dataloader status = 'TRAIN' else: self.G.eval() self.C.eval() data = self.eval_data dataloader = self.eval_dataloader status = 'EVAL' # random_clip_index = np.random.randint(0, len(dataloader)-1, 4) # random_clip_index = np.random.randint(0, 64, 4) random_clip_index = list(range(len(dataloader))) print('random_clip_index', random_clip_index) # Step 2: train for each batch for i, batch in enumerate(dataloader): # if(i>=64): # break st = time.time() global_id, video_name = data[i][0][1][0], data[i][0][1][1][:-4] # Step 2.1: load batch data from dataloader (in segments) inputs_fl, inputs_au, inputs_emb, inputs_reg_fl, inputs_rot_tran, inputs_rot_quat = batch # inputs_emb = torch.zeros(size=(inputs_au.shape[0], len(self.test_embs_dic.keys()))) # this_emb = video_name.split('_x_')[1] # inputs_emb[:, self.test_embs_dic[this_emb]] = 1. if (is_training): rand_start = np.random.randint(0, inputs_fl.shape[0] // 5, 1).reshape(-1) inputs_fl = inputs_fl[rand_start[0]:] inputs_au = inputs_au[rand_start[0]:] inputs_emb = inputs_emb[rand_start[0]:] inputs_reg_fl = inputs_reg_fl[rand_start[0]:] inputs_rot_tran = inputs_rot_tran[rand_start[0]:] inputs_rot_quat = inputs_rot_quat[rand_start[0]:] inputs_fl, inputs_au, inputs_emb = inputs_fl.to(device), inputs_au.to(device), inputs_emb.to(device) inputs_reg_fl, inputs_rot_tran, inputs_rot_quat = inputs_reg_fl.to(device), inputs_rot_tran.to(device), inputs_rot_quat.to(device) std_fls_list, fls_pred_face_id_list, fls_pred_pos_list = [], [], [] seg_bs = self.opt_parser.segment_batch_size close_fl_list = inputs_fl[::10, 0, :] idx = self.__close_face_lip__(close_fl_list.detach().cpu().numpy()) input_face_id = close_fl_list[idx:idx + 1, :] ''' register face ''' if (self.opt_parser.use_reg_as_std): landmarks = input_face_id.detach().cpu().numpy().reshape(68, 3) frame_t_shape = landmarks[self.t_shape_idx, :] T, distance, itr = icp(frame_t_shape, self.anchor_t_shape) landmarks = np.hstack((landmarks, np.ones((68, 1)))) registered_landmarks = np.dot(T, landmarks.T).T input_face_id = torch.tensor(registered_landmarks[:, 0:3].reshape(1, 204), requires_grad=False, dtype=torch.float).to(device) for j in range(0, inputs_fl.shape[0], seg_bs): # Step 3.1: load segments inputs_fl_segments = inputs_fl[j: j + seg_bs] inputs_au_segments = inputs_au[j: j + seg_bs] inputs_emb_segments = inputs_emb[j: j + seg_bs] inputs_reg_fl_segments = inputs_reg_fl[j: j + seg_bs] inputs_rot_tran_segments = inputs_rot_tran[j: j + seg_bs] inputs_rot_quat_segments = inputs_rot_quat[j: j + seg_bs] if(inputs_fl_segments.shape[0] < 10): continue if(self.opt_parser.test_emb): input_face_id = self.std_face_id fl_dis_pred_pos, pos_pred, input_face_id, (loss, loss_reg_fls, loss_laplacian, loss_pos) = \ self.__train_speaker_aware__(inputs_fl_segments, inputs_au_segments, inputs_emb_segments, input_face_id, inputs_reg_fl_segments, inputs_rot_tran_segments, inputs_rot_quat_segments, is_training=is_training, use_residual=self.opt_parser.use_residual) fl_dis_pred_pos = fl_dis_pred_pos.data.cpu().numpy() pos_pred = pos_pred.data.cpu().numpy() fl_std = inputs_reg_fl_segments[:, 0, :].data.cpu().numpy() pos_std = inputs_rot_tran_segments[:, 0, :].data.cpu().numpy() ''' solve inverse lip ''' if(not is_training): fl_dis_pred_pos = self.__solve_inverse_lip2__(fl_dis_pred_pos) fl_dis_pred_pos = fl_dis_pred_pos.reshape((-1, 68, 3)) fl_std = fl_std.reshape((-1, 68, 3)) if(self.opt_parser.pos_dim == 12): pos_pred = pos_pred.reshape((-1, 3, 4)) for k in range(fl_dis_pred_pos.shape[0]): fl_dis_pred_pos[k] = np.dot(pos_pred[k, :3, :3].T + np.eye(3), (fl_dis_pred_pos[k] - pos_pred[k, :, 3].T).T).T pos_std = pos_std.reshape((-1, 3, 4)) for k in range(fl_std.shape[0]): fl_std[k] = np.dot(pos_std[k, :3, :3].T + np.eye(3), (fl_std[k] - pos_std[k, :, 3].T).T).T else: if(not is_training): smooth_length = int(min(pos_pred.shape[0] - 1, 27) // 2 * 2 + 1) pos_pred = savgol_filter(pos_pred, smooth_length, 3, axis=0) quat = pos_pred[:, :4] trans = pos_pred[:, 4:] for k in range(fl_dis_pred_pos.shape[0]): fl_dis_pred_pos[k] = np.dot(R.from_quat(quat[k]).as_matrix().T, (fl_dis_pred_pos[k] - trans[k:k+1]).T).T pos_std = pos_std.reshape((-1, 3, 4)) for k in range(fl_std.shape[0]): fl_std[k] = np.dot(pos_std[k, :3, :3].T + np.eye(3), (fl_std[k] - pos_std[k, :, 3].T).T).T fls_pred_pos_list += [fl_dis_pred_pos.reshape((-1, 204))] std_fls_list += [fl_std.reshape((-1, 204))] for key in log_loss.keys(): if (key not in locals().keys()): continue if (type(locals()[key]) == float): log_loss[key].add(locals()[key]) else: log_loss[key].add(locals()[key].data.cpu().numpy()) if (epoch % self.opt_parser.jpg_freq == 0 and i in random_clip_index): def save_fls_av(fake_fls_list, postfix='', ifsmooth=True): fake_fls_np = np.concatenate(fake_fls_list) filename = 'fake_fls_{}_{}_{}.txt'.format(epoch, video_name, postfix) np.savetxt( os.path.join(self.opt_parser.dump_dir, '../nn_result', self.opt_parser.name, filename), fake_fls_np, fmt='%.6f') audio_filename = '{:05d}_{}_audio.wav'.format(global_id, video_name) from util.vis import Vis_old Vis_old(run_name=self.opt_parser.name, pred_fl_filename=filename, audio_filename=audio_filename, fps=62.5, av_name='e{:04d}_{}_{}'.format(epoch, i, postfix), postfix=postfix, root_dir=self.opt_parser.root_dir, ifsmooth=ifsmooth) if (True): if (self.opt_parser.show_animation): print('show animation ....') save_fls_av(fls_pred_pos_list, 'pred', ifsmooth=True) save_fls_av(std_fls_list, 'std', ifsmooth=False) if (self.opt_parser.verbose <= 1): print('{} Epoch: #{} batch #{}/{}'.format(status, epoch, i, len(dataloader)), end=': ') for key in log_loss.keys(): print(key, '{:.5f}'.format(log_loss[key].per('batch')), end=', ') print('') self.__tensorboard_write__(status, log_loss, 'batch') if (self.opt_parser.verbose <= 2): print('==========================================================') print('{} Epoch: #{}'.format(status, epoch), end=':') for key in log_loss.keys(): print(key, '{:.4f}'.format(log_loss[key].per('epoch')), end=', ') print('Epoch time usage: {:.2f} sec\n==========================================================\n'.format(time.time() - st_epoch)) self.__save_model__(save_type='last_epoch', epoch=epoch) if(epoch % self.opt_parser.ckpt_epoch_freq == 0): self.__save_model__(save_type='e_{}'.format(epoch), epoch=epoch) self.__tensorboard_write__(status, log_loss, 'epoch') def test_end2end(self, jpg_shape): self.G.eval() self.C.eval() data = self.eval_data dataloader = self.eval_dataloader for i, batch in enumerate(dataloader): global_id, video_name = data[i][0][1][0], data[i][0][1][1][:-4] inputs_fl, inputs_au, inputs_emb, inputs_reg_fl, inputs_rot_tran, inputs_rot_quat = batch for key in ['irx71tYyI-Q', 'J-NPsvtQ8lE', 'Z7WRt--g-h4', 'E0zgrhQ0QDw', 'bXpavyiCu10', 'W6uRNCJmdtI', 'sxCbrYjBsGA', 'wAAMEC1OsRc', '_ldiVrXgZKc', '48uYS3bHIA8', 'E_kmpT-EfOg']: emb_val = self.test_embs[key] inputs_emb = np.tile(emb_val, (inputs_emb.shape[0], 1)) inputs_emb = torch.tensor(inputs_emb, dtype=torch.float, requires_grad=False) # this_emb = key # inputs_emb = torch.zeros(size=(inputs_au.shape[0], len(self.test_embs_dic.keys()))) # inputs_emb[:, self.test_embs_dic[this_emb]] = 1. inputs_fl, inputs_au, inputs_emb = inputs_fl.to(device), inputs_au.to(device), inputs_emb.to(device) inputs_reg_fl, inputs_rot_tran, inputs_rot_quat = inputs_reg_fl.to(device), inputs_rot_tran.to(device), inputs_rot_quat.to(device) std_fls_list, fls_pred_face_id_list, fls_pred_pos_list = [], [], [] seg_bs = self.opt_parser.segment_batch_size # input_face_id = self.std_face_id input_face_id = torch.tensor(jpg_shape.reshape(1, 204), requires_grad=False, dtype=torch.float).to(device) ''' register face ''' if (True): landmarks = input_face_id.detach().cpu().numpy().reshape(68, 3) frame_t_shape = landmarks[self.t_shape_idx, :] T, distance, itr = icp(frame_t_shape, self.anchor_t_shape) landmarks = np.hstack((landmarks, np.ones((68, 1)))) registered_landmarks = np.dot(T, landmarks.T).T input_face_id = torch.tensor(registered_landmarks[:, 0:3].reshape(1, 204), requires_grad=False, dtype=torch.float).to(device) for j in range(0, inputs_fl.shape[0], seg_bs): # Step 3.1: load segments inputs_fl_segments = inputs_fl[j: j + seg_bs] inputs_au_segments = inputs_au[j: j + seg_bs] inputs_emb_segments = inputs_emb[j: j + seg_bs] inputs_reg_fl_segments = inputs_reg_fl[j: j + seg_bs] inputs_rot_tran_segments = inputs_rot_tran[j: j + seg_bs] inputs_rot_quat_segments = inputs_rot_quat[j: j + seg_bs] if(inputs_fl_segments.shape[0] < 10): continue fl_dis_pred_pos, pos_pred, input_face_id, (loss, loss_reg_fls, loss_laplacian, loss_pos) = \ self.__train_speaker_aware__(inputs_fl_segments, inputs_au_segments, inputs_emb_segments, input_face_id, inputs_reg_fl_segments, inputs_rot_tran_segments, inputs_rot_quat_segments, is_training=False, use_residual=True) fl_dis_pred_pos = fl_dis_pred_pos.data.cpu().numpy() pos_pred = pos_pred.data.cpu().numpy() fl_std = inputs_reg_fl_segments[:, 0, :].data.cpu().numpy() pos_std = inputs_rot_tran_segments[:, 0, :].data.cpu().numpy() ''' solve inverse lip ''' fl_dis_pred_pos = self.__solve_inverse_lip2__(fl_dis_pred_pos) fl_dis_pred_pos = fl_dis_pred_pos.reshape((-1, 68, 3)) fl_std = fl_std.reshape((-1, 68, 3)) if(self.opt_parser.pos_dim == 12): pos_pred = pos_pred.reshape((-1, 3, 4)) for k in range(fl_dis_pred_pos.shape[0]): fl_dis_pred_pos[k] = np.dot(pos_pred[k, :3, :3].T + np.eye(3), (fl_dis_pred_pos[k] - pos_pred[k, :, 3].T).T).T pos_std = pos_std.reshape((-1, 3, 4)) for k in range(fl_std.shape[0]): fl_std[k] = np.dot(pos_std[k, :3, :3].T + np.eye(3), (fl_std[k] - pos_std[k, :, 3].T).T).T else: smooth_length = int(min(pos_pred.shape[0] - 1, 27) // 2 * 2 + 1) pos_pred = savgol_filter(pos_pred, smooth_length, 3, axis=0) quat = pos_pred[:, :4] trans = pos_pred[:, 4:] for k in range(fl_dis_pred_pos.shape[0]): fl_dis_pred_pos[k] = np.dot(R.from_quat(quat[k]).as_matrix().T, (fl_dis_pred_pos[k] - trans[k:k+1]).T).T pos_std = pos_std.reshape((-1, 3, 4)) for k in range(fl_std.shape[0]): fl_std[k] = np.dot(pos_std[k, :3, :3].T + np.eye(3), (fl_std[k] - pos_std[k, :, 3].T).T).T fls_pred_pos_list += [fl_dis_pred_pos.reshape((-1, 204))] std_fls_list += [fl_std.reshape((-1, 204))] fake_fls_np = np.concatenate(fls_pred_pos_list) filename = 'pred_fls_{}_{}.txt'.format(video_name.split('/')[-1], key) np.savetxt(os.path.join('examples', filename), fake_fls_np, fmt='%.6f') def __close_face_lip__(self, fl): facelandmark = fl.reshape(-1, 68, 3) from util.geo_math import area_of_polygon min_area_lip, idx = 999, 0 for i, fls in enumerate(facelandmark): area_of_mouth = area_of_polygon(fls[list(range(60, 68)), 0:2]) if (area_of_mouth < min_area_lip): min_area_lip = area_of_mouth idx = i return idx def train(self): train_loss = {key: Record(['epoch', 'batch']) for key in ['loss','loss_laplacian', 'loss_reg_fls', 'loss_pos']} eval_loss = {key: Record(['epoch', 'batch']) for key in ['loss','loss_laplacian', 'loss_reg_fls', 'loss_pos']} for epoch in range(self.opt_parser.nepoch): self.__train_pass__(epoch=epoch, log_loss=train_loss, is_training=True) # with torch.no_grad(): # self.__train_pass__(epoch=epoch, log_loss=eval_loss, is_training=False) def test(self): train_loss = {key: Record(['epoch', 'batch', 'in_batch']) for key in ['loss', 'loss_g', 'loss_laplacian']} eval_loss = {key: Record(['epoch', 'batch', 'in_batch']) for key in ['loss_pos', 'loss_g', 'loss_laplacian']} with torch.no_grad(): self.__train_pass__(epoch=0, log_loss=eval_loss, is_training=False) def __tensorboard_write__(self, status, loss, t): if (self.opt_parser.write): for key in loss.keys(): self.writer.add_scalar('{}_loss_{}_{}'.format(status, t, key), loss[key].per(t), self.writer_count[status + '_' + t]) loss[key].clean(t) self.writer_count[status + '_' + t] += 1 else: for key in loss.keys(): loss[key].clean(t) def __save_model__(self, save_type, epoch): if (self.opt_parser.write): torch.save({ 'G': self.G.state_dict(), 'epoch': epoch }, os.path.join(self.opt_parser.ckpt_dir, 'ckpt_{}.pth'.format(save_type))) def adjust_learning_rate(self, optimizer, epoch): """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" lr = self.opt_parser.lr * (0.3 ** (np.max((0, epoch + 0)) // 50)) lr = np.max((lr, 1e-5)) print('###### ==== > Adjust learning rate to ', lr) for param_group in optimizer.param_groups: param_group['lr'] = lr # print('lr:', param_group['lr']) def __solve_inverse_lip2__(self, fl_dis_pred_pos_numpy): for j in range(fl_dis_pred_pos_numpy.shape[0]): # init_face = self.std_face_id.detach().cpu().numpy() from util.geo_math import area_of_signed_polygon fls = fl_dis_pred_pos_numpy[j].reshape(68, 3) area_of_mouth = area_of_signed_polygon(fls[list(range(60, 68)), 0:2]) if (area_of_mouth < 0): fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3] = 0.5 *(fl_dis_pred_pos_numpy[j, 63 * 3:64 * 3] + fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3]) fl_dis_pred_pos_numpy[j, 63 * 3:64 * 3] = fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3] fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3] = 0.5 *(fl_dis_pred_pos_numpy[j, 62 * 3:63 * 3] + fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3]) fl_dis_pred_pos_numpy[j, 62 * 3:63 * 3] = fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3] fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3] = 0.5 *(fl_dis_pred_pos_numpy[j, 61 * 3:62 * 3] + fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3]) fl_dis_pred_pos_numpy[j, 61 * 3:62 * 3] = fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3] p = max([j-1, 0]) fl_dis_pred_pos_numpy[j, 55 * 3+1:59 * 3+1:3] = fl_dis_pred_pos_numpy[j, 64 * 3+1:68 * 3+1:3] \ + fl_dis_pred_pos_numpy[p, 55 * 3+1:59 * 3+1:3] \ - fl_dis_pred_pos_numpy[p, 64 * 3+1:68 * 3+1:3] fl_dis_pred_pos_numpy[j, 59 * 3+1:60 * 3+1:3] = fl_dis_pred_pos_numpy[j, 60 * 3+1:61 * 3+1:3] \ + fl_dis_pred_pos_numpy[p, 59 * 3+1:60 * 3+1:3] \ - fl_dis_pred_pos_numpy[p, 60 * 3+1:61 * 3+1:3] fl_dis_pred_pos_numpy[j, 49 * 3+1:54 * 3+1:3] = fl_dis_pred_pos_numpy[j, 60 * 3+1:65 * 3+1:3] \ + fl_dis_pred_pos_numpy[p, 49 * 3+1:54 * 3+1:3] \ - fl_dis_pred_pos_numpy[p, 60 * 3+1:65 * 3+1:3] return fl_dis_pred_pos_numpy