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Network_Training_MTFAA_full.py
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from typing import OrderedDict
from unicodedata import name
import torch
import torch.optim as optim
import numpy as np
from torch.optim.lr_scheduler import StepLR
from tqdm import tqdm
import soundfile as sf
from Dataloader import Dataset, collate_fn
from MTFAA_Net_full import MTFAA_Net as MTFAA
from MTFAA_Net_full_F_ASqbi import MTFAA_Net as MTFAA_ASqBi
from MTFAA_Net_full_local_atten import MTFAA_Net as MTFAA_LSA
torch.set_default_tensor_type(torch.FloatTensor)
from signal_processing import iSTFT_module_1_8
WINDOW = torch.sqrt(torch.hann_window(1536,device=device) + 1e-8)
import argparse
import librosa
from collections import OrderedDict
import os
class NoamOpt:
"Optim wrapper that implements rate."
def __init__(self, model_size, factor, warmup, optimizer):
self.optimizer = optimizer
self._step = 0
self.warmup = warmup
self.factor = factor
self.model_size = model_size
self._rate = 0
def step(self):
"Update parameters and rate"
self._step += 1
rate = self.rate()
for p in self.optimizer.param_groups:
p['lr'] = rate
self._rate = rate
self.optimizer.step()
def rate(self, step = None):
"Implement `lrate` above"
if step is None:
step = self._step
return self.factor * \
(self.model_size ** (-0.5) *
min(step ** (-0.5), step * self.warmup ** (-1.5)))
def main(args):
def Loss(y_pred, y_true, train = True, idx = -1, epoch = 0):
snr = torch.div(torch.mean(torch.square(y_pred - y_true), dim=1, keepdim=True),(torch.mean(torch.square(y_true), dim=1, keepdim=True) + 1e-7))
snr_loss = 10 * torch.log10(snr + 1e-7)
pred_stft = torch.stft(y_pred,1536,384,win_length=1536,window=WINDOW,center=True)
true_stft = torch.stft(y_true,1536,384,win_length=1536,window=WINDOW,center=True)
pred_stft_real, pred_stft_imag = pred_stft[:,:,:,0], pred_stft[:,:,:,1]
true_stft_real, true_stft_imag = true_stft[:,:,:,0], true_stft[:,:,:,1]
pred_mag = torch.sqrt(pred_stft_real**2 + pred_stft_imag**2 + 1e-12)
true_mag = torch.sqrt(true_stft_real**2 + true_stft_imag**2 + 1e-12)
pred_real_c = pred_stft_real / (pred_mag**(0.7))
pred_imag_c = pred_stft_imag / (pred_mag**(0.7))
true_real_c = true_stft_real / (true_mag**(0.7))
true_imag_c = true_stft_imag / (true_mag**(0.7))
real_loss = torch.mean((pred_real_c - true_real_c)**2)
imag_loss = torch.mean((pred_imag_c - true_imag_c)**2)
mag_loss = torch.mean((pred_mag**(0.3)-true_mag**(0.3))**2)
return 0.3*(real_loss + imag_loss) + 0.7*mag_loss, snr_loss
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
'''model'''
if args.model == 'MTFAA':
model = MTFAA()
elif args.model == 'MTFAA_ASqBi'
model = MTFAA_ASqBi()
elif args.model == 'MTFAA_LSA':
model = MTFAA_LSA()
''' train from checkpoints'''
# checkpoint = torch.load('./.pth',map_location=device)
# model.load_state_dict(checkpoint['state_dict'])
model = model.to(device)
'''optimizer & lr_scheduler'''
optimizer = NoamOpt(model_size=32, factor=1., warmup=6000,
optimizer=torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
'''load train data'''
dataset = Dataset(length_in_seconds=3.2, num_clip_per_epoch=4000, random_start_point=True, train=True)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=3, shuffle=True, drop_last=True, num_workers=2)
'''start train'''
for epoch in range(args.epochs):
train_loss = []
asnr_loss = []
model.train()
dataset.sample()
dataset.train = True
dataset.random_start_point = True
idx = 0
'''train'''
print('epoch %s--training' %(epoch))
for i, data in enumerate(tqdm(data_loader)):
noisy, clean = data
noisy = noisy.to(device)
clean = clean.to(device)
optimizer.optimizer.zero_grad()
noisy_stft = torch.stft(noisy,1536,384,win_length=1536,window=WINDOW,center=True,return_complex=True)
enh_stft = model(noisy_stft)
enh_s = iSTFT_module_1_8(n_fft=1536, hop_length=384, win_length=1536,window=WINDOW,center = True,length = noisy.shape[-1])(enh_stft.permute([0, 3, 2, 1]).contiguous())
stft_loss, snr_loss = Loss(enh_s, clean, train=True, idx = idx, epoch = epoch)
loss_overall = stft_loss
loss_overall.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=3)
optimizer.step()
train_loss.append(loss_overall.cpu().detach().numpy())
idx += 1
train_loss = np.mean(train_loss)
asnr_loss = np.mean(asnr_loss)
lr_scheduler.step()
'''eval'''
valid_loss = []
model.eval()
print('epoch %s--validating' %(epoch))
dataset.train = False
dataset.random_start_point = False
with torch.no_grad():
for i, data in enumerate(tqdm(data_loader)):
noisy, clean = data
noisy = noisy.to(device)
clean = clean.to(device)
noisy_stft = torch.stft(noisy,1536,384,win_length=1536,window=WINDOW,center=True, return_complex=True)
enh_stft = model(noisy_stft)
enh_s = iSTFT_module_1_8(n_fft=1536, hop_length=384, win_length=1536,window=WINDOW,center = True,length = noisy.shape[-1])(enh_stft.permute([0, 3, 2, 1]).contiguous())
stft_loss, snr_loss = Loss(enh_s, clean, train=True, idx = idx, epoch = epoch)
loss_overall = stft_loss
valid_loss.append(loss_overall.cpu().detach().numpy())
asnr_loss.append(snr_loss.cpu().detach().numpy())
valid_loss = np.mean(valid_loss)
asnr_loss = np.mean(asnr_loss)
print('train loss: %s, valid loss %s, snr loss: %s' %(train_loss, valid_loss, asnr_loss))
torch.save(
{'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.optimizer.state_dict()},
args.chkpt_path + '/Epoch_%s_trainloss_%s_validloss_%s.pth' %(str(epoch), str(train_loss), str(valid_loss)))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-m', "--model", default='MTFAA',
help='Choose the model you wanna train: MTFAA, MTFAA_LSA or MTFAA_ASqBi')
parser.add_argument('-c', "--chkpt_path", default=None, help='Dir to save the checkpoint files')
parser.add_argument('-e', "--epochs", default='300',
help='Epochs for training')
parser.add_argument('-d', "--device", default='cuda:0',
help='Device used for training')
args = parser.parse_args()
main(args)