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main.py
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import numpy as np
import argparse
from tqdm import tqdm
import os, shutil
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from data_loader import get_dataloaders
from faceXhubert import FaceXHuBERT
def plot_losses(train_losses, val_losses):
plt.plot(train_losses, label="Training loss")
plt.plot(val_losses, label="Validation loss")
plt.legend()
plt.title("Losses")
plt.savefig("losses.png")
plt.close()
def trainer(args, train_loader, dev_loader, model, optimizer, criterion, epoch=100):
train_losses = []
val_losses = []
save_path = os.path.join(args.save_path)
if os.path.exists(save_path):
shutil.rmtree(save_path)
os.makedirs(save_path)
train_subjects_list = [i for i in args.train_subjects.split(" ")]
iteration = 0
for e in range(epoch+1):
loss_log = []
model.train()
pbar = tqdm(enumerate(train_loader),total=len(train_loader))
optimizer.zero_grad()
for i, (audio, vertice, template, one_hot, file_name, emo_one_hot) in pbar:
iteration += 1
vertice = str(vertice[0])
vertice = np.load(vertice,allow_pickle=True)
vertice = vertice.astype(np.float32)
vertice = torch.from_numpy(vertice)
vertice = torch.unsqueeze(vertice,0)
audio, vertice, template, one_hot, emo_one_hot = audio.to(device="cuda"), vertice.to(device="cuda"), template.to(device="cuda"), one_hot.to(device="cuda"), emo_one_hot.to(device="cuda")
loss = model(audio, template, vertice, one_hot, emo_one_hot, criterion)
loss.backward()
loss_log.append(loss.item())
if i % args.gradient_accumulation_steps==0:
optimizer.step()
optimizer.zero_grad()
del audio, vertice, template, one_hot, emo_one_hot
torch.cuda.empty_cache()
pbar.set_description("(Epoch {}, iteration {}) TRAIN LOSS:{:.8f}".format((e+1), iteration ,np.mean(loss_log)))
train_losses.append(np.mean(loss_log))
valid_loss_log = []
model.eval()
for audio, vertice, template, one_hot_all,file_name, emo_one_hot in dev_loader:
# to gpu
vertice = str(vertice[0])
vertice = np.load(vertice,allow_pickle=True)
vertice = vertice.astype(np.float32)
vertice = torch.from_numpy(vertice)
vertice = torch.unsqueeze(vertice,0)
audio, vertice, template, one_hot_all, emo_one_hot= audio.to(device="cuda"), vertice.to(device="cuda"), template.to(device="cuda"), one_hot_all.to(device="cuda"), emo_one_hot.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, emo_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, emo_one_hot, criterion)
valid_loss_log.append(loss.item())
current_loss = np.mean(valid_loss_log)
val_losses.append(current_loss)
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:{:.8f}".format(e+1,current_loss))
plot_losses(train_losses, val_losses)
return model
@torch.no_grad()
def test(args, model, test_loader,epoch):
result_path = os.path.join(args.result_path)
if os.path.exists(result_path):
shutil.rmtree(result_path)
os.makedirs(result_path)
save_path = os.path.join(args.save_path)
train_subjects_list = [i for i in args.train_subjects.split(" ")]
model.load_state_dict(torch.load(os.path.join(save_path, '{}_model.pth'.format(epoch))))
model = model.to(torch.device("cuda"))
model.eval()
for audio, vertice, template, one_hot_all, file_name, emo_one_hot in test_loader:
vertice = str(vertice[0])
vertice = np.load(vertice,allow_pickle=True)
vertice = vertice.astype(np.float32)
vertice = torch.from_numpy(vertice)
vertice = torch.unsqueeze(vertice,0)
audio, vertice, template, one_hot_all, emo_one_hot= audio.to(device="cuda"), vertice.to(device="cuda"), template.to(device="cuda"), one_hot_all.to(device="cuda"), emo_one_hot.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, emo_one_hot)
prediction = prediction.squeeze()
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()
np.save(os.path.join(result_path, file_name[0].split(".")[0]+"_condition_"+condition_subject+".npy"), prediction.detach().cpu().numpy())
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def main():
parser = argparse.ArgumentParser(description='FaceXHuBERT: Text-less Speech-driven E(X)pressive 3D Facial Animation Synthesis using Self-Supervised Speech Representation Learning')
parser.add_argument("--lr", type=float, default=0.0001, help='learning rate')
parser.add_argument("--dataset", type=str, default="BIWI", help='Name of the dataset folder. eg: BIWI')
parser.add_argument("--vertice_dim", type=int, default=70110, help='number of vertices - 23370*3 for BIWI dataset')
parser.add_argument("--feature_dim", type=int, default=256, help='GRU Vertex decoder hidden size')
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_scaled.pkl", help='path of the train subject 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="F1 F2 F3 F4 F5 F6 F7 F8 M1 M2 M3 M4 M5 M6")
parser.add_argument("--val_subjects", type=str, default="F1 F2 F3 F4 F5 F6 F7 F8 M1 M2 M3 M4 M5 M6")
parser.add_argument("--test_subjects", type=str, default="F1 F2 F3 F4 F5 F6 F7 F8 M1 M2 M3 M4 M5 M6")
parser.add_argument("--input_fps", type=int, default=50, help='HuBERT last hidden state produces 50 fps audio representation')
parser.add_argument("--output_fps", type=int, default=25, help='fps of the visual data, BIWI was captured in 25 fps')
args = parser.parse_args()
model = FaceXHuBERT(args)
print("model parameters: ", count_parameters(model))
assert torch.cuda.is_available()
model = model.to(torch.device("cuda"))
dataset = get_dataloaders(args)
criterion = nn.HuberLoss()
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)
test(args, model, dataset["test"], epoch=args.max_epoch)
print(model)
if __name__ == "__main__":
main()