def train(self, args):
with open(args.train_list, 'r') as train_list_file:
self.train_list = [line.strip() for line in train_list_file.readlines()]
self.eval_file = args.eval_file
self.num_train_sentences = args.num_train_sentences
self.batch_size = args.batch_size
self.lr = args.lr
self.max_epoch = args.max_epoch
self.model_path = args.model_path
self.log_path = args.log_path
self.fig_path = args.fig_path
self.eval_plot_num = args.eval_plot_num
self.eval_steps = args.eval_steps
self.resume_model = args.resume_model
self.wav_path = args.wav_path
self.train_wav_path = args.train_wav_path
self.tool_path = args.tool_path
# create a training dataset and an evaluation dataset
trainSet = TrainingDataset(self.train_list,
frame_size=self.frame_size,
frame_shift=self.frame_shift)
evalSet = EvalDataset(self.eval_file,
self.num_test_sentences)
# trainSet = evalSet
# create data loaders for training and evaluation
train_loader = DataLoader(trainSet,
batch_size=self.batch_size,
shuffle=True,
num_workers=16,
collate_fn=TrainCollate())
eval_loader = DataLoader(evalSet,
batch_size=1,
shuffle=False,
num_workers=4,
collate_fn=EvalCollate())
# create a network
print('model', self.model_name)
net = Net(device=self.device, L=self.frame_size, width=self.width)
# net = torch.nn.DataParallel(net)
net.to(self.device)
print('Number of learnable parameters: %d' % numParams(net))
print(net)
criterion = mse_loss()
criterion1 = stftm_loss(device=self.device)
optimizer = torch.optim.Adam(net.parameters(), lr=self.lr)
self.lr_list = [0.0002] * 3 + [0.0001] * 6 + [0.00005] * 3 + [0.00001] * 3
if self.resume_model:
print('Resume model from "%s"' % self.resume_model)
checkpoint = Checkpoint()
checkpoint.load(self.resume_model)
start_epoch = checkpoint.start_epoch
start_iter = checkpoint.start_iter
best_loss = checkpoint.best_loss
net.load_state_dict(checkpoint.state_dict)
optimizer.load_state_dict(checkpoint.optimizer)
else:
print('Training from scratch.')
start_epoch = 0
start_iter = 0
best_loss = np.inf
num_train_batches = self.num_train_sentences // self.batch_size
total_train_batch = self.max_epoch * num_train_batches
print('num_train_sentences', self.num_train_sentences)
print('batches_per_epoch', num_train_batches)
print('total_train_batch', total_train_batch)
print('batch_size', self.batch_size)
print('model_name', self.model_name)
batch_timings = 0.
counter = int(start_epoch * num_train_batches + start_iter)
counter1 = 0
print('counter', counter)
ttime = 0.
cnt = 0.
iteration = 0
print('best_loss', best_loss)
for epoch in range(start_epoch, self.max_epoch):
accu_train_loss = 0.0
net.train()
for param_group in optimizer.param_groups:
param_group['lr'] = self.lr_list[epoch]
start = timeit.default_timer()
for i, (features, labels, nframes, feat_size, label_size, get_filename) in enumerate(
train_loader): # features:torch.Size([4, 1, 250, 512])
iteration += 1
labels_cpu = labels
i += start_iter
features, labels = features.to(self.device), labels.to(self.device) # torch.Size([4, 1, 250, 512])
loss_mask = compLossMask(labels, nframes=nframes)
# forward + backward + optimize
optimizer.zero_grad()
outputs = net(features) # torch.Size([4, 1, 64256])
feature_maker = Fbank(sample_rate=16000, n_fft=400, n_mels=40)
loss_fbank = 0
for t in range(len(get_filename)):
reader = h5py.File(get_filename[t], 'r')
feature_asr = reader['noisy_raw'][:]
label_asr = reader['clean_raw'][:]
feat_asr_size = int(feat_size[t][0].item())
label_asr_size = int(label_size[t][0].item())
output_asr = self.train_asr_forward(feature_asr, net)
est_output_asr = output_asr[:feat_asr_size]
ideal_labels_asr = label_asr
# 保存train的wav
est_path = os.path.join(self.train_wav_path, '{}_est.wav'.format(t + 1))
ideal_path = os.path.join(self.train_wav_path, '{}_ideal.wav'.format(t + 1))
sf.write(est_path, normalize_wav(est_output_asr)[0], self.srate)
sf.write(ideal_path, normalize_wav(ideal_labels_asr)[0], self.srate)
# read wav
est_sig = sb.dataio.dataio.read_audio(est_path).unsqueeze(axis=0).to(self.device)
ideal_sig = sb.dataio.dataio.read_audio(ideal_path).unsqueeze(axis=0).to(self.device)
est_sig_feats = feature_maker(est_sig)
ideal_sig_feats = feature_maker(ideal_sig)
# fbank_loss
loss_fbank += F.mse_loss(est_sig_feats, ideal_sig_feats, True)
loss_fbank /= 100 * len(get_filename)
# print(loss_fbank)
# loss_fbank = 1 / (1 + math.exp(loss_fbank))
outputs = outputs[:, :, :labels.shape[-1]]
loss1 = criterion(outputs, labels, loss_mask, nframes)
loss2 = criterion1(outputs, labels, loss_mask, nframes)
# print(loss1)
# print(loss2)
# loss = 0.8 * loss1 + 0.2 * loss2
loss = 0.4 * loss1 + 0.1 * loss2 + 0.5 * loss_fbank
loss.backward()
optimizer.step()
# calculate losses
running_loss = loss.data.item()
accu_train_loss += running_loss
# train-loss show
summary.add_scalar('Train Loss', accu_train_loss, iteration)
cnt += 1.
counter += 1
counter1 += 1
del loss, loss_fbank, loss1, loss2, outputs, loss_mask, features, labels
end = timeit.default_timer()
curr_time = end - start
ttime += curr_time
mtime = ttime / counter1
print(
'iter = {}/{}, epoch = {}/{}, loss = {:.5f}, time/batch = {:.5f}, mtime/batch = {:.5f}'.format(
i + 1,
num_train_batches, epoch + 1, self.max_epoch, running_loss, curr_time, mtime))
start = timeit.default_timer()
if (i + 1) % self.eval_steps == 0:
start = timeit.default_timer()
avg_train_loss = accu_train_loss / cnt
avg_eval_loss = self.validate(net, eval_loader, iteration)
net.train()
print('Epoch [%d/%d], Iter [%d/%d] ( TrainLoss: %.4f | EvalLoss: %.4f )' % (
epoch + 1, self.max_epoch, i + 1, self.num_train_sentences // self.batch_size,
avg_train_loss,
avg_eval_loss))
is_best = True if avg_eval_loss < best_loss else False
best_loss = avg_eval_loss if is_best else best_loss
checkpoint = Checkpoint(epoch, i, avg_train_loss, avg_eval_loss, best_loss, net.state_dict(),
optimizer.state_dict())
model_name = self.model_name + '_latest.model'
best_model = self.model_name + '_best.model'
checkpoint.save(is_best, os.path.join(self.model_path, model_name),
os.path.join(self.model_path, best_model))
logging(self.log_path, self.model_name + '_loss_log.txt', checkpoint, self.eval_steps)
# metric_logging(self.log_path, self.model_name +'_metric_log.txt', epoch+1, [avg_st, avg_sn, avg_pe])
accu_train_loss = 0.0
cnt = 0.
net.train()
if (i + 1) % num_train_batches == 0:
break
avg_st, avg_sn, avg_pe = self.validate_with_metrics(net, eval_loader)
net.train()
print('#' * 50)
print('')
print('After {} epoch the performance on validation score is a s follows:'.format(epoch + 1))
print('')
print('STOI: {:.4f}'.format(avg_st))
print('SNR: {:.4f}'.format(avg_sn))
print('PESQ: {:.4f}'.format(avg_pe))
for param_group in optimizer.param_groups:
print('learning_rate', param_group['lr'])
print('')
print('#' * 50)
checkpoint = Checkpoint(epoch, 0, None, None, best_loss, net.state_dict(), optimizer.state_dict())
checkpoint.save(False, os.path.join(self.model_path, self.model_name + '-{}.model'.format(epoch + 1)),
os.path.join(self.model_path, best_model))
metric_logging(self.log_path, self.model_name + '_metric_log.txt', epoch, [avg_st, avg_sn, avg_pe])
start_iter = 0.
def test(self, args):
with open(args.test_list, 'r') as test_list_file:
self.test_list = [line.strip() for line in test_list_file.readlines()]
self.model_name = args.model_name
self.model_file = args.model_file
self.test_mixture_path = args.test_mixture_path
self.prediction_path = args.prediction_path
# create a network
print('model', self.model_name)
net = Net(device=self.device, L=self.frame_size, width=self.width)
# net = torch.nn.DataParallel(net)
net.to(self.device)
print('Number of learnable parameters: %d' % numParams(net))
print(net)
# loss and optimizer
criterion = mse_loss()
net.eval()
print('Load model from "%s"' % self.model_file)
checkpoint = Checkpoint()
checkpoint.load(self.model_file)
net.load_state_dict(checkpoint.state_dict)
with torch.no_grad():
for i in range(len(self.test_list)):
# read the mixture for resynthesis
filename_input = self.test_list[i].split('/')[-1]
start1 = timeit.default_timer()
print('{}/{}, Started working on {}.'.format(i + 1, len(self.test_list), self.test_list[i]))
print('')
filename_mix = filename_input.replace('.samp', '_mix.dat')
filename_s_ideal = filename_input.replace('.samp', '_s_ideal.dat')
filename_s_est = filename_input.replace('.samp', '_s_est.dat')
# print(filename_mix)
# sys.exit()
f_mix = h5py.File(os.path.join(self.test_mixture_path, filename_mix), 'r')
f_s_ideal = h5py.File(os.path.join(self.prediction_path, filename_s_ideal), 'w')
f_s_est = h5py.File(os.path.join(self.prediction_path, filename_s_est), 'w')
# create a test dataset
testSet = EvalDataset(os.path.join(self.test_mixture_path, self.test_list[i]),
self.num_test_sentences)
# create a data loader for test
test_loader = DataLoader(testSet,
batch_size=1,
shuffle=False,
num_workers=2,
collate_fn=EvalCollate())
# print '\n[%d/%d] Predict on %s' % (i+1, len(self.test_list), self.test_list[i])
accu_test_loss = 0.0
accu_test_nframes = 0
ttime = 0.
mtime = 0.
cnt = 0.
for k, (mix_raw, cln_raw) in enumerate(test_loader):
start = timeit.default_timer()
est_s = self.eval_forward(mix_raw, net)
est_s = est_s[:mix_raw.size]
mix = f_mix[str(k)][:]
ideal_s = cln_raw
f_s_ideal.create_dataset(str(k), data=ideal_s.astype(np.float32), chunks=True)
f_s_est.create_dataset(str(k), data=est_s.astype(np.float32), chunks=True)
# compute eval_loss
test_loss = np.mean((est_s - ideal_s) ** 2)
accu_test_loss += test_loss
cnt += 1
end = timeit.default_timer()
curr_time = end - start
ttime += curr_time
mtime = ttime / cnt
mtime = (mtime * (k) + (end - start)) / (k + 1)
print('{}/{}, test_loss = {:.4f}, time/utterance = {:.4f}, '
'mtime/utternace = {:.4f}'.format(k + 1, self.num_test_sentences, test_loss, curr_time,
mtime))
avg_test_loss = accu_test_loss / cnt
# bar.update(k,test_loss=avg_test_loss)
# bar.finish()
end1 = timeit.default_timer()
print('********** Finisehe working on {}. time taken = {:.4f} **********'.format(filename_input,
end1 - start1))
print('')
f_mix.close()
f_s_est.close()
f_s_ideal.close()
test_transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = MNIST(root="./mnist", download=True, transform=train_transform)
test_dataset = MNIST(root="./mnist", train=False, download=True, transform=test_transform)
train_loader = data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = data.DataLoader(test_dataset, batch_size=64)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = Net()
if torch.cuda.device_count() > 1:
print(f"Using {torch.cuda.device_count()} GPUs...")
net = nn.DataParallel(net)
net.to(device)
loss_fn = nn.CrossEntropyLoss()
lr = 0.003
optimizer = optim.Adam(net.parameters(), weight_decay=1e-6, lr=lr)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.95)
# writer = SummaryWriter()
def compute_confusion_matrix(init_cm, y_true, y_pred):
for i in range(len(y_true)):
init_cm[y_true[i]][y_pred[i]] += 1
def train(epoch):
net.train()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_ids = os.environ['CUDA_VISIBLE_DEVICES'].split(',')
device_ids = [int(device_id) for device_id in device_ids]
print('GPU configuration:', device, device_ids)
# # device = torch.device('cuda')
# # encoder = NodeEncoder().to(device)
encoder = resnet18(pretrained=True)
encoder.fc = torch.nn.Linear(512, 512)
propagator = GraphPropagator().to(device)
gencoder = GraphEncoder().to(device)
model = Net(encoder, propagator, gencoder)
# model = torch.nn.DataParallel(model, device_ids=device_ids).to(device)
# clf = torch.nn.Linear(256, 2000)
# clf = torch.nn.DataParallel(clf, device_ids=device_ids).to(device)
model = model.to(device)
clf = torch.nn.Linear(256, 2000).to(device)
criterion = torch.nn.CrossEntropyLoss()
# # criterion = OnlineTripletLoss(2.0, SemihardNegativeTripletSelector(2.0))
# print("Net done!")
train_dataset = MinutiaeDataset(im_dir, min_dir, 'train')
# # sampler = BalancedBatchSampler(dataset.labels, n_classes=10, n_samples=5)
# # train_loader = DataLoader(dataset, num_workers=10, batch_sampler=sampler)
train_loader = DataLoader(train_dataset,
batch_size=64,
shuffle=False,
num_workers=10)
def train(self, args):
with open(args.train_list, 'r') as train_list_file:
self.train_list = [
line.strip() for line in train_list_file.readlines()
]
self.eval_file = args.eval_file
self.num_train_sentences = args.num_train_sentences
self.batch_size = args.batch_size
self.lr = args.lr
self.max_epoch = args.max_epoch
self.model_path = args.model_path
self.log_path = args.log_path
self.fig_path = args.fig_path
self.eval_plot_num = args.eval_plot_num
self.eval_steps = args.eval_steps
self.resume_model = args.resume_model
self.wav_path = args.wav_path
self.tool_path = args.tool_path
# create a training dataset and an evaluation dataset
trainSet = TrainingDataset(self.train_list,
frame_size=self.frame_size,
frame_shift=self.frame_shift)
evalSet = EvalDataset(self.eval_file, self.num_test_sentences)
#trainSet = evalSet
# create data loaders for training and evaluation
train_loader = DataLoader(trainSet,
batch_size=self.batch_size,
shuffle=True,
num_workers=16,
collate_fn=TrainCollate())
eval_loader = DataLoader(evalSet,
batch_size=1,
shuffle=False,
num_workers=4,
collate_fn=EvalCollate())
# create a network
print('model', self.model_name)
net = Net(device=self.device, L=self.frame_size, width=self.width)
#net = torch.nn.DataParallel(net)
net.to(self.device)
print('Number of learnable parameters: %d' % numParams(net))
print(net)
criterion = mse_loss()
criterion1 = stftm_loss(device=self.device)
optimizer = torch.optim.Adam(net.parameters(), lr=self.lr)
self.lr_list = [0.0002] * 3 + [0.0001] * 6 + [0.00005] * 3 + [0.00001
] * 3
if self.resume_model:
print('Resume model from "%s"' % self.resume_model)
checkpoint = Checkpoint()
checkpoint.load(self.resume_model)
start_epoch = checkpoint.start_epoch
start_iter = checkpoint.start_iter
best_loss = checkpoint.best_loss
net.load_state_dict(checkpoint.state_dict)
optimizer.load_state_dict(checkpoint.optimizer)
else:
print('Training from scratch.')
start_epoch = 0
start_iter = 0
best_loss = np.inf
num_train_batches = self.num_train_sentences // self.batch_size
total_train_batch = self.max_epoch * num_train_batches
print('num_train_sentences', self.num_train_sentences)
print('batches_per_epoch', num_train_batches)
print('total_train_batch', total_train_batch)
print('batch_size', self.batch_size)
print('model_name', self.model_name)
batch_timings = 0.
counter = int(start_epoch * num_train_batches + start_iter)
counter1 = 0
print('counter', counter)
ttime = 0.
cnt = 0.
print('best_loss', best_loss)
for epoch in range(start_epoch, self.max_epoch):
accu_train_loss = 0.0
net.train()
for param_group in optimizer.param_groups:
param_group['lr'] = self.lr_list[epoch]
start = timeit.default_timer()
for i, (features, labels, nframes) in enumerate(train_loader):
i += start_iter
features, labels = features.to(self.device), labels.to(
self.device)
loss_mask = compLossMask(labels, nframes=nframes)
# forward + backward + optimize
optimizer.zero_grad()
outputs = net(features)
outputs = outputs[:, :, :labels.shape[-1]]
loss1 = criterion(outputs, labels, loss_mask, nframes)
loss2 = criterion1(outputs, labels, loss_mask, nframes)
loss = 0.8 * loss1 + 0.2 * loss2
loss.backward()
optimizer.step()
# calculate losses
running_loss = loss.data.item()
accu_train_loss += running_loss
cnt += 1.
counter += 1
counter1 += 1
del loss, loss1, loss2, outputs, loss_mask, features, labels
end = timeit.default_timer()
curr_time = end - start
ttime += curr_time
mtime = ttime / counter1
print(
'iter = {}/{}, epoch = {}/{}, loss = {:.5f}, time/batch = {:.5f}, mtime/batch = {:.5f}'
.format(i + 1, num_train_batches, epoch + 1,
self.max_epoch, running_loss, curr_time, mtime))
start = timeit.default_timer()
if (i + 1) % self.eval_steps == 0:
start = timeit.default_timer()
avg_train_loss = accu_train_loss / cnt
avg_eval_loss = self.validate(net, eval_loader)
net.train()
print(
'Epoch [%d/%d], Iter [%d/%d] ( TrainLoss: %.4f | EvalLoss: %.4f )'
% (epoch + 1, self.max_epoch, i + 1,
self.num_train_sentences // self.batch_size,
avg_train_loss, avg_eval_loss))
is_best = True if avg_eval_loss < best_loss else False
best_loss = avg_eval_loss if is_best else best_loss
checkpoint = Checkpoint(epoch, i, avg_train_loss,
avg_eval_loss, best_loss,
net.state_dict(),
optimizer.state_dict())
model_name = self.model_name + '_latest.model'
best_model = self.model_name + '_best.model'
checkpoint.save(is_best,
os.path.join(self.model_path, model_name),
os.path.join(self.model_path, best_model))
logging(self.log_path, self.model_name + '_loss_log.txt',
checkpoint, self.eval_steps)
#metric_logging(self.log_path, self.model_name +'_metric_log.txt', epoch+1, [avg_st, avg_sn, avg_pe])
accu_train_loss = 0.0
cnt = 0.
net.train()
if (i + 1) % num_train_batches == 0:
break
avg_st, avg_sn, avg_pe = self.validate_with_metrics(
net, eval_loader)
net.train()
print('#' * 50)
print('')
print(
'After {} epoch the performance on validation score is a s follows:'
.format(epoch + 1))
print('')
print('STOI: {:.4f}'.format(avg_st))
print('SNR: {:.4f}'.format(avg_sn))
print('PESQ: {:.4f}'.format(avg_pe))
for param_group in optimizer.param_groups:
print('learning_rate', param_group['lr'])
print('')
print('#' * 50)
checkpoint = Checkpoint(epoch, 0, None, None, best_loss,
net.state_dict(), optimizer.state_dict())
checkpoint.save(
False,
os.path.join(self.model_path,
self.model_name + '-{}.model'.format(epoch + 1)),
os.path.join(self.model_path, best_model))
metric_logging(self.log_path, self.model_name + '_metric_log.txt',
epoch, [avg_st, avg_sn, avg_pe])
start_iter = 0.
def train(args):
torch.manual_seed(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Network()
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.MSELoss()
fb = FlappyBird()
image, reward, terminal, score = fb.next_frame(randint(0, 1))[:-1]
img = image_pre_processing(image, args.img_size, args.img_size)
img = torch.from_numpy(img)
img.to(device)
state = torch.cat(tuple(img for _ in range(4)))[None, :, :, :]
replay_memory = []
episode = 0
while 1:
prediction = model(state)[0]
epsilon = args.initial_epsilon
if np.random.uniform() > epsilon:
action = torch.argmax(prediction)
else:
action = randint(0, 1)
next_image, reward, terminal = fb.next_frame(action, '')[:3]
if terminal:
fb.__init__()
next_image, reward, terminal = fb.next_frame(action, '')[:3]
next_img = image_pre_processing(next_image, args.img_size,
args.img_size)
next_img = torch.from_numpy(next_img)
next_state = torch.cat((state[0, 1:, :, :], next_img))[None, :, :, :]
replay_memory.append([state, action, reward, next_state, terminal])
if len(replay_memory) > args.replay_memory_size:
del replay_memory[0]
batch = sample(replay_memory, min(len(replay_memory), args.batch_size))
state_batch, action_batch, reward_batch, next_state_batch, terminal_batch = zip(
*batch)
state_batch = torch.cat(tuple(state for state in state_batch))
action_batch = torch.from_numpy(
np.array([[1, 0] if action == 0 else [0, 1]
for action in action_batch],
dtype=np.float32))
reward_batch = torch.from_numpy(
np.array(reward_batch, dtype=np.float32)[:, None])
next_state_batch = torch.cat(tuple(state
for state in next_state_batch))
state_batch.to(device)
action_batch.to(device)
reward_batch.to(device)
next_state_batch.to(device)
current_prediction_batch = model(state_batch)
next_prediction_batch = model(next_state_batch)
y_batch = torch.cat(
tuple(reward if terminal else reward + args.gamma * torch.max(prediction) for reward, terminal, prediction in \
zip(reward_batch, terminal_batch, next_prediction_batch))
)
q_value = torch.sum(current_prediction_batch * action_batch, dim=1)
optimizer.zero_grad()
loss = criterion(q_value, y_batch)
loss.backward()
optimizer.step()
state = next_state
episode += 1