""" # 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 from src.dataset.audio2landmark.audio2landmark_dataset import Audio2landmark_Dataset from src.models.model_audio2landmark import Audio2landmark_content from util.utils import Record from util.icp import icp import numpy as np device = torch.device("cuda" if torch.cuda.is_available() else "cpu") class Audio2landmark_model(): def __init__(self, opt_parser, jpg_shape=None): ''' Init model with opt_parser ''' print('Run on device:', device) # Step 1 : load opt_parser self.opt_parser = opt_parser self.std_face_id = np.loadtxt('src/dataset/utils/STD_FACE_LANDMARKS.txt') if(jpg_shape is not None): self.std_face_id = jpg_shape 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) self.train_data = Audio2landmark_Dataset(dump_dir=opt_parser.dump_dir, dump_name='autovc_retrain_mel', status='train', num_window_frames=opt_parser.num_window_frames, num_window_step=opt_parser.num_window_step) 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_noemb) print('TRAIN num videos: {}'.format(len(self.train_data))) self.eval_data = Audio2landmark_Dataset(dump_dir=opt_parser.dump_dir, dump_name='autovc_retrain_mel', status='test', num_window_frames=opt_parser.num_window_frames, num_window_step=opt_parser.num_window_step) 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_noemb) print('EVAL num videos: {}'.format(len(self.eval_data))) # Step 3: Load model self.C = Audio2landmark_content(num_window_frames=opt_parser.num_window_frames, hidden_size=opt_parser.hidden_size, in_size=opt_parser.in_size, use_prior_net=opt_parser.use_prior_net, bidirectional=False, drop_out=opt_parser.drop_out) if(opt_parser.load_a2l_C_name.split('/')[-1] != ''): ckpt = torch.load(opt_parser.load_a2l_C_name) self.C.load_state_dict(ckpt['model_g_face_id']) print('======== LOAD PRETRAINED CONTENT BRANCH MODEL {} ========='.format(opt_parser.load_a2l_C_name)) self.C.to(device) self.t_shape_idx = (27, 28, 29, 30, 33, 36, 39, 42, 45) self.anchor_t_shape = np.loadtxt('src/dataset/utils/STD_FACE_LANDMARKS.txt') self.anchor_t_shape = self.anchor_t_shape[self.t_shape_idx, :] self.opt_C = optim.Adam(self.C.parameters(), lr=opt_parser.lr, weight_decay=opt_parser.reg_lr) self.loss_mse = torch.nn.MSELoss() def __train_content__(self, fls, aus, face_id, is_training=True): fls_gt = 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) fl_dis_pred, _ = self.C(aus, face_id) ''' lip region weight ''' w = torch.abs(fls[:, 0, 66 * 3 + 1] - fls[:, 0, 62 * 3 + 1]) w = torch.tensor([1.0]).to(device) / (w * 4.0 + 0.1) w = w.unsqueeze(1) lip_region_w = torch.ones((fls.shape[0], 204)).to(device) lip_region_w[:, 48*3:] = torch.cat([w] * 60, dim=1) lip_region_w = lip_region_w.detach().clone().requires_grad_(False) if (self.opt_parser.use_lip_weight): # loss = torch.mean(torch.mean((fl_dis_pred + face_id - fls[:, 0, :]) ** 2, dim=1) * w) loss = torch.mean(torch.abs(fl_dis_pred +face_id[0:1].detach() - fls_gt) * lip_region_w) else: # loss = self.loss_mse(fl_dis_pred + face_id, fls[:, 0, :]) loss = torch.nn.functional.l1_loss(fl_dis_pred+face_id[0:1].detach(), fls_gt) if (self.opt_parser.use_motion_loss): pred_motion = fl_dis_pred[:-1] - fl_dis_pred[1:] gt_motion = fls_gt[:-1] - fls_gt[1:] loss += torch.nn.functional.l1_loss(pred_motion, gt_motion) ''' use laplacian smooth loss ''' 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].detach()).view(-1, 68, 3) L_V = V - 0.5 * (V[:, n1, :] + V[:, n2, :]) G = 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) loss += loss_laplacian if(is_training): self.opt_C.zero_grad() loss.backward() self.opt_C.step() if(not is_training): # ''' CALIBRATION ''' np_fl_dis_pred = fl_dis_pred.detach().cpu().numpy() K = int(np_fl_dis_pred.shape[0] * 0.5) for calib_i in range(204): min_k_idx = np.argpartition(np_fl_dis_pred[:, calib_i], K) m = np.mean(np_fl_dis_pred[min_k_idx[:K], calib_i]) np_fl_dis_pred[:, calib_i] = np_fl_dis_pred[:, calib_i] - m fl_dis_pred = torch.tensor(np_fl_dis_pred, requires_grad=False).to(device) return fl_dis_pred, face_id[0:1, :], loss def __train_pass__(self, epoch, log_loss, is_training=True): st_epoch = time.time() # Step 1: init setup if(is_training): self.C.train() data = self.train_data dataloader = self.train_dataloader status = 'TRAIN' else: self.C.eval() data = self.eval_data dataloader = self.eval_dataloader status = 'EVAL' random_clip_index = np.random.permutation(len(dataloader))[0:self.opt_parser.random_clip_num] print('random visualize clip index', random_clip_index) # Step 2: train for each batch 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 = batch inputs_fl_ori, inputs_au_ori = inputs_fl.to(device), inputs_au.to(device) std_fls_list, fls_pred_face_id_list, fls_pred_pos_list = [], [], [] seg_bs = 512 ''' pick a most closed lip frame from entire clip data ''' close_fl_list = inputs_fl_ori[::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 in_batch in range(self.opt_parser.in_batch_nepoch): std_fls_list, fls_pred_face_id_list, fls_pred_pos_list = [], [], [] if (is_training): rand_start = np.random.randint(0, inputs_fl_ori.shape[0] // 5, 1).reshape(-1) inputs_fl = inputs_fl_ori[rand_start[0]:] inputs_au = inputs_au_ori[rand_start[0]:] else: inputs_fl = inputs_fl_ori inputs_au = inputs_au_ori 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] fl_std = inputs_fl_segments[:, 0, :].data.cpu().numpy() if(inputs_fl_segments.shape[0] < 10): continue fl_dis_pred_pos, input_face_id, loss = \ self.__train_content__(inputs_fl_segments, inputs_au_segments, input_face_id, is_training) fl_dis_pred_pos = (fl_dis_pred_pos + input_face_id).data.cpu().numpy() ''' solve inverse lip ''' fl_dis_pred_pos = self.__solve_inverse_lip2__(fl_dis_pred_pos) 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 or in_batch % self.opt_parser.jpg_freq == 1)): 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, in_batch, postfix), postfix=postfix, root_dir=self.opt_parser.root_dir, ifsmooth=ifsmooth) if (self.opt_parser.show_animation and not is_training): print('show animation ....') save_fls_av(fls_pred_pos_list, 'pred_{}'.format(i), ifsmooth=True) save_fls_av(std_fls_list, 'std_{}'.format(i), ifsmooth=False) from util.vis import Vis_comp Vis_comp(run_name=self.opt_parser.name, pred1='fake_fls_{}_{}_{}.txt'.format(epoch, video_name, 'pred_{}'.format(i)), pred2='fake_fls_{}_{}_{}.txt'.format(epoch, video_name, 'std_{}'.format(i)), audio_filename='{:05d}_{}_audio.wav'.format(global_id, video_name), fps=62.5, av_name='e{:04d}_{}_{}'.format(epoch, in_batch, 'comp_{}'.format(i)), postfix='comp_{}'.format(i), root_dir=self.opt_parser.root_dir, ifsmooth=False) self.__save_model__(save_type='last_inbatch', epoch=epoch) if (self.opt_parser.verbose <= 1): print('{} Epoch: #{} batch #{}/{} inbatch #{}/{}'.format( status, epoch, i, len(dataloader), in_batch, self.opt_parser.in_batch_nepoch), end=': ') for key in log_loss.keys(): print(key, '{:.5f}'.format(log_loss[key].per('batch')), end=', ') print('') 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) 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 test(self): eval_loss = {key: Record(['epoch', 'batch']) for key in ['loss']} with torch.no_grad(): self.__train_pass__(0, eval_loss, is_training=False) def train(self): train_loss = {key: Record(['epoch', 'batch']) for key in ['loss']} eval_loss = {key: Record(['epoch', 'batch']) for key in ['loss']} for epoch in range(self.opt_parser.nepoch): self.__train_pass__(epoch=epoch, log_loss=train_loss) with torch.no_grad(): self.__train_pass__(epoch, eval_loss, is_training=False) 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 def __save_model__(self, save_type, epoch): if (self.opt_parser.write): torch.save({ 'model_g_face_id': self.C.state_dict(), 'epoch': epoch }, os.path.join(self.opt_parser.ckpt_dir, 'ckpt_{}.pth'.format(save_type)))