forked from EvelynFan/FaceFormer
-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
173 lines (149 loc) · 8.04 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import re, random, math
import numpy as np
import argparse
from tqdm import tqdm
import os, shutil
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from data_loader import get_dataloaders
from faceformer import Faceformer
def trainer(args, train_loader, dev_loader, model, optimizer, criterion, epoch=100):
print ("Create the save folder...")
save_path = os.path.join(args.dataset,args.save_path)
if os.path.exists(save_path):
shutil.rmtree(save_path)
os.makedirs(save_path)
print ("Start training...")
train_subjects_list = [i for i in args.train_subjects.split(" ")]
iteration = 0
for e in range(epoch+1):
loss_log = []
# train
model.train()
pbar = tqdm(enumerate(train_loader),total=len(train_loader))
optimizer.zero_grad()
for i, (audio, vertice, template, one_hot, file_name) in pbar:
iteration += 1
# to gpu
audio, vertice, template, one_hot = audio.to(device="cuda"), vertice.to(device="cuda"), template.to(device="cuda"), one_hot.to(device="cuda")
loss = model(audio, template, vertice, one_hot, criterion,teacher_forcing=False)
loss.backward()
loss_log.append(loss.item())
if i % args.gradient_accumulation_steps==0:
optimizer.step()
optimizer.zero_grad()
pbar.set_description("(Epoch {}, iteration {}) TRAIN LOSS:{:.7f}".format((e+1), iteration ,np.mean(loss_log)))
# validation
valid_loss_log = []
model.eval()
for audio, vertice, template, one_hot_all,file_name in dev_loader:
# to gpu
audio, vertice, template, one_hot_all= audio.to(device="cuda"), vertice.to(device="cuda"), template.to(device="cuda"), one_hot_all.to(device="cuda")
train_subject = "_".join(file_name[0].split("_")[:-1])
if train_subject in train_subjects_list:
condition_subject = train_subject
iter = train_subjects_list.index(condition_subject)
one_hot = one_hot_all[:,iter,:]
loss = model(audio, template, vertice, one_hot, criterion)
valid_loss_log.append(loss.item())
else:
for iter in range(one_hot_all.shape[-1]):
condition_subject = train_subjects_list[iter]
one_hot = one_hot_all[:,iter,:]
loss = model(audio, template, vertice, one_hot, criterion)
valid_loss_log.append(loss.item())
current_loss = np.mean(valid_loss_log)
if (e > 0 and e % 25 == 0) or e == args.max_epoch:
torch.save(model.state_dict(), os.path.join(save_path,'{}_model.pth'.format(e)))
print("epcoh: {}, current loss:{:.7f}".format(e+1,current_loss))
return model
@torch.no_grad()
def test(args, model, test_loader,epoch):
print ("Create the result folder...")
result_path = os.path.join(args.dataset,args.result_path)
if os.path.exists(result_path):
shutil.rmtree(result_path)
os.makedirs(result_path)
save_path = os.path.join(args.dataset,args.save_path)
train_subjects_list = [i for i in args.train_subjects.split(" ")]
print ("Load the model...")
print(os.path.join(save_path, '{}_model.pth'.format(epoch)))
breakpoint()
model.load_state_dict(torch.load(os.path.join(save_path, '{}_model.pth'.format(epoch))))
model = model.to(torch.device("cuda"))
model.eval()
print ("Start testing...")
test_subjects_list = [i for i in args.test_subjects.split(" ")]
test_loss_log = []
for audio, vertice, template, one_hot_all, file_name in test_loader:
# to gpu
audio, vertice, template, one_hot_all= audio.to(device="cuda"), vertice.to(device="cuda"), template.to(device="cuda"), one_hot_all.to(device="cuda")
train_subject = "_".join(file_name[0].split("_")[:-1])
if train_subject in train_subjects_list:
condition_subject = train_subject
iter = train_subjects_list.index(condition_subject)
one_hot = one_hot_all[:,iter,:]
prediction = model.predict(audio, template, one_hot)
prediction = prediction.squeeze() # (seq_len, V*3)
loss = torch.mean((prediction - vertice)**2)
test_loss_log.append(loss.item())
np.save(os.path.join(result_path, file_name[0].split(".")[0]+"_condition_"+condition_subject+".npy"), prediction.detach().cpu().numpy())
else:
for iter in range(one_hot_all.shape[-1]):
condition_subject = train_subjects_list[iter]
one_hot = one_hot_all[:,iter,:]
prediction = model.predict(audio, template, one_hot)
prediction = prediction.squeeze() # (seq_len, V*3)
loss = torch.mean((prediction - vertice)**2)
test_loss_log.append(loss.item())
np.save(os.path.join(result_path, file_name[0].split(".")[0]+"_condition_"+condition_subject+".npy"), prediction.detach().cpu().numpy())
print ("test loss: ", np.mean(test_loss_log))
np.save(os.path.join(result_path, "test_loss.npy"), np.mean(test_loss_log))
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def main():
parser = argparse.ArgumentParser(description='FaceFormer: Speech-Driven 3D Facial Animation with Transformers')
parser.add_argument("--lr", type=float, default=0.0001, help='learning rate')
parser.add_argument("--dataset", type=str, default="vocaset", help='vocaset or BIWI')
parser.add_argument("--vertice_dim", type=int, default=5023*3, help='number of vertices - 5023*3 for vocaset; 23370*3 for BIWI')
parser.add_argument("--feature_dim", type=int, default=64, help='64 for vocaset; 128 for BIWI')
parser.add_argument("--period", type=int, default=30, help='period in PPE - 30 for vocaset; 25 for BIWI')
parser.add_argument("--wav_path", type=str, default= "wav", help='path of the audio signals')
parser.add_argument("--vertices_path", type=str, default="vertices_npy", help='path of the ground truth')
parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help='gradient accumulation')
parser.add_argument("--max_epoch", type=int, default=100, help='number of epochs')
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--template_file", type=str, default="templates.pkl", help='path of the personalized templates')
parser.add_argument("--save_path", type=str, default="save", help='path of the trained models')
parser.add_argument("--result_path", type=str, default="result", help='path to the predictions')
parser.add_argument("--train_subjects", type=str, default="FaceTalk_170728_03272_TA"
" FaceTalk_170904_00128_TA FaceTalk_170725_00137_TA FaceTalk_170915_00223_TA"
" FaceTalk_170811_03274_TA FaceTalk_170913_03279_TA"
" FaceTalk_170904_03276_TA FaceTalk_170912_03278_TA")
parser.add_argument("--val_subjects", type=str, default="FaceTalk_170811_03275_TA"
" FaceTalk_170908_03277_TA")
parser.add_argument("--test_subjects", type=str, default="FaceTalk_170809_00138_TA"
" FaceTalk_170731_00024_TA")
parser.add_argument("--onlyTestFlag", type=bool, default=False)
args = parser.parse_args()
#build model
model = Faceformer(args)
print("model parameters: ", count_parameters(model))
# to cuda
assert torch.cuda.is_available()
model = model.to(torch.device("cuda"))
#load data
dataset = get_dataloaders(args)
# loss
criterion = nn.MSELoss()
if (args.onlyTestFlag == False):
# Train the model
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,model.parameters()), lr=args.lr)
model = trainer(args, dataset["train"], dataset["valid"],model, optimizer, criterion, epoch=args.max_epoch)
else:
print ("Only test the model...")
test(args, model, dataset["test"], epoch=args.max_epoch)
if __name__=="__main__":
main()