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"""
# 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
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