""" # 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.utils.data from src.dataset.audio2landmark.audio2landmark_dataset import Audio2landmark_Dataset from src.models.model_audio2landmark import * from util.utils import get_n_params import numpy as np import pickle 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.eval_data = Audio2landmark_Dataset(dump_dir='examples/dump', dump_name='random', status='val', num_window_frames=18, num_window_step=1) 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))) # Step 3: Load model self.G = Audio2landmark_pos(drop_out=0.5, spk_emb_enc_size=128, c_enc_hidden_size=256, transformer_d_model=32, N=2, heads=2, z_size=128, audio_dim=256) print('G: Running on {}, total num params = {:.2f}M'.format(device, get_n_params(self.G)/1.0e6)) 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 k.split('.')[0] not in ['comb_mlp']} model_dict.update(pretrained_dict) self.G.load_state_dict(model_dict) print('======== LOAD PRETRAINED FACE ID MODEL {} ========='.format(opt_parser.load_a2l_G_name)) self.G.to(device) ''' baseline model ''' self.C = Audio2landmark_content(num_window_frames=18, in_size=80, use_prior_net=True, bidirectional=False, drop_out=0.5) ckpt = torch.load(opt_parser.load_a2l_C_name) self.C.load_state_dict(ckpt['model_g_face_id']) # self.C.load_state_dict(ckpt['C']) print('======== LOAD PRETRAINED FACE ID 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, :] with open(os.path.join('examples', 'dump', 'emb.pickle'), 'rb') as fp: self.test_embs = pickle.load(fp) print('====================================') for key in self.test_embs.keys(): print(key) print('====================================') def __train_face_and_pos__(self, fls, aus, embs, face_id, smooth_win=31, close_mouth_ratio=.99): fls_without_traj = 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) baseline_face_id = face_id.detach() z = torch.tensor(torch.zeros(aus.shape[0], 128), requires_grad=False, dtype=torch.float).to(device) fl_dis_pred, _, spk_encode = self.G(aus, embs * 3.0, face_id, fls_without_traj, z, add_z_spk=False) # ADD CONTENT from scipy.signal import savgol_filter smooth_length = int(min(fl_dis_pred.shape[0]-1, smooth_win) // 2 * 2 + 1) fl_dis_pred = savgol_filter(fl_dis_pred.cpu().numpy(), smooth_length, 3, axis=0) # ''' ================ 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] + 0.5 * fl_dis_pred[:, index1] fl_dis_pred[:, 49:54] = mean_out * close_mouth_ratio + fl_dis_pred[:, 49:54] * (1 - close_mouth_ratio) fl_dis_pred[:, index1] = mean_out * close_mouth_ratio + fl_dis_pred[:, index1] * (1 - close_mouth_ratio) mean_in = 0.5 * (fl_dis_pred[:, 61:64] + fl_dis_pred[:, index2]) fl_dis_pred[:, 61:64] = mean_in * close_mouth_ratio + fl_dis_pred[:, 61:64] * (1 - close_mouth_ratio) fl_dis_pred[:, index2] = mean_in * close_mouth_ratio + fl_dis_pred[:, index2] * (1 - close_mouth_ratio) fl_dis_pred = fl_dis_pred.reshape(-1, 204) ''' ============================================================= ''' fl_dis_pred = torch.tensor(fl_dis_pred).to(device) * self.opt_parser.amp_pos residual_face_id = baseline_face_id # ''' CALIBRATION ''' baseline_pred_fls, _ = self.C(aus[:, 0:18, :], residual_face_id) baseline_pred_fls = self.__calib_baseline_pred_fls__(baseline_pred_fls) fl_dis_pred += baseline_pred_fls return fl_dis_pred, face_id[0:1, :] def __calib_baseline_pred_fls_old_(self, baseline_pred_fls, residual_face_id, aus): mean_face_id = torch.mean(baseline_pred_fls.detach(), dim=0, keepdim=True) residual_face_id -= mean_face_id.view(1, 204) * 1. baseline_pred_fls, _ = self.C(aus, residual_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 return baseline_pred_fls def __calib_baseline_pred_fls__(self, baseline_pred_fls, ratio=0.5): np_fl_dis_pred = baseline_pred_fls.detach().cpu().numpy() K = int(np_fl_dis_pred.shape[0] * ratio) 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 baseline_pred_fls = torch.tensor(np_fl_dis_pred, requires_grad=False).to(device) 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 return baseline_pred_fls def __train_pass__(self, au_emb=None, centerize_face=False, no_y_rotation=False, vis_fls=False): # Step 1: init setup self.G.eval() self.C.eval() data = self.eval_data dataloader = self.eval_dataloader # 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] # Step 2.1: load batch data from dataloader (in segments) inputs_fl, inputs_au, inputs_emb = batch keys = self.opt_parser.reuse_train_emb_list if(len(keys) == 0): keys = ['audio_embed'] for key in keys: # ['45hn7-LXDX8']: #['sxCbrYjBsGA']:# # load saved emb if(au_emb is None): emb_val = self.test_embs[key] else: emb_val = au_emb[i] inputs_emb = np.tile(emb_val, (inputs_emb.shape[0], 1)) inputs_emb = torch.tensor(inputs_emb, dtype=torch.float, requires_grad=False) inputs_fl, inputs_au, inputs_emb = inputs_fl.to(device), inputs_au.to(device), inputs_emb.to(device) std_fls_list, fls_pred_face_id_list, fls_pred_pos_list = [], [], [] seg_bs = 512 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] if(inputs_fl_segments.shape[0] < 10): continue input_face_id = self.std_face_id fl_dis_pred_pos, input_face_id = \ self.__train_face_and_pos__(inputs_fl_segments, inputs_au_segments, inputs_emb_segments, input_face_id) 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] fake_fls_np = np.concatenate(fls_pred_pos_list) # revise nose top point fake_fls_np[:, 27 * 3:28 * 3] = fake_fls_np[:, 28 * 3:29 * 3] * 2 - fake_fls_np[:, 29 * 3:30 * 3] # fake_fls_np[:, 48*3+1::3] += 0.1 # smooth from scipy.signal import savgol_filter fake_fls_np = savgol_filter(fake_fls_np, 5, 3, axis=0) if(centerize_face): std_m = np.mean(self.std_face_id.detach().cpu().numpy().reshape((1, 68, 3)), axis=1, keepdims=True) fake_fls_np = fake_fls_np.reshape((-1, 68, 3)) fake_fls_np = fake_fls_np - np.mean(fake_fls_np, axis=1, keepdims=True) + std_m fake_fls_np = fake_fls_np.reshape((-1, 68 * 3)) if(no_y_rotation): std = self.std_face_id.detach().cpu().numpy().reshape(68, 3) std_t_shape = std[self.t_shape_idx, :] fake_fls_np = fake_fls_np.reshape((fake_fls_np.shape[0], 68, 3)) frame_t_shape = fake_fls_np[:, self.t_shape_idx, :] from util.icp import icp from scipy.spatial.transform import Rotation as R for i in range(frame_t_shape.shape[0]): T, distance, itr = icp(frame_t_shape[i], std_t_shape) landmarks = np.hstack((frame_t_shape[i], np.ones((9, 1)))) rot_mat = T[:3, :3] r = R.from_dcm(rot_mat).as_euler('xyz') r = [0., r[1], r[2]] r = R.from_euler('xyz', r).as_dcm() # print(frame_t_shape[i, 0], r) landmarks = np.hstack((fake_fls_np[i] - T[:3, 3:4].T, np.ones((68, 1)))) T2 = np.hstack((r, T[:3, 3:4])) fake_fls_np[i] = np.dot(T2, landmarks.T).T # print(frame_t_shape[i, 0]) fake_fls_np = fake_fls_np.reshape((-1, 68 * 3)) filename = 'pred_fls_{}_{}.txt'.format(video_name.split('\\')[-1].split('/')[-1], key) np.savetxt(os.path.join(self.opt_parser.output_folder, filename), fake_fls_np, fmt='%.6f') # ''' Visualize result in landmarks ''' if(vis_fls): from util.vis import Vis Vis(fls=fake_fls_np, filename=video_name.split('\\')[-1].split('/')[-1], fps=62.5, audio_filenam=os.path.join('examples', video_name.split('\\')[-1].split('/')[-1]+'.wav')) 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, au_emb=None): with torch.no_grad(): self.__train_pass__(au_emb, vis_fls=True) 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