def run(model):
min_loss = 1e10
patience = 0
for epoch in range(args.epochs):
print("Epoch{}:".format(epoch))
model.train()
for i, data in enumerate(train_loader):
# print("batch"+str(i))
data = data.to(args.device)
out = model(data)
loss = F.nll_loss(out, data.y)
# print("Training loss:{}".format(loss.item()))
loss.backward()
optimizer.step()
optimizer.zero_grad()
val_acc, val_loss = test(model, val_loader)
print("Validation loss:{}\taccuracy:{}".format(val_loss, val_acc))
if val_loss < min_loss:
torch.save(model.state_dict(), 'latest.pth')
print("Model saved at epoch{}".format(epoch))
min_loss = val_loss
patience = 0
else:
patience += 1
if patience > args.patience:
print("Early stop at epoch{}".format(epoch))
break
model = Net(args).to(args.device)
model.load_state_dict(torch.load('latest.pth'))
test_acc, test_loss = test(model, test_loader)
print("Test accuarcy:{}".format(test_acc))
def run_main():
"""
Will Train a network and save all the batch gradients in a file (pickled dict with :
key:value => conv1.weight : array [batch x chan x h x w]
:return:
"""
gradient_save_path = "gradients/test"
os.makedirs(gradient_save_path, exist_ok=True)
device = torch.device("cuda")
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=256,
shuffle=True)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=256,
shuffle=True)
model = Net(5).to(device)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
# save initial weights
torch.save(model.state_dict(),
os.path.join(gradient_save_path, "weights.pth"))
training_curve = []
for epoch in range(1, 6):
gradients = record_grad(model,
device,
train_loader,
optimizer,
n_steps=300)
# save epoch gradients
pickle.dump(
gradients,
open(
os.path.join(gradient_save_path, "epoch_{}.pkl".format(epoch)),
"bw"))
# save test results
loss, accuracy = test(model, device, test_loader)
training_curve.append([loss, accuracy])
np.savetxt(os.path.join(gradient_save_path, "train_original.txt"),
np.array(training_curve))
def __init__(self,
dataloader: DataLoader,
epoch: int,
optim_create_func: Optimizer,
lr: float,
loss_function: LOSS_FUNC,
device: torch.device = torch.device('cpu'),
pretrained_model_path: str = None,
visual_helper: VisualHelper = None,
model_saver: ModelSaver = None,
weight_init_func: WEIGHT_INIT_FUNC = None,
drop_rate=0,
*args,
**kwargs):
super(Trainer, self).__init__(*args, **kwargs)
cpu_device = torch.device('cpu')
self.model: Net = Net(drop_rate=drop_rate).to(cpu_device)
self.model.train()
self.dataloader = dataloader
if weight_init_func is not None:
self.model.apply(weight_init_func)
if pretrained_model_path is not None:
ret = self.model.load_encoder_weight(pretrained_model_path)
print(ret)
self.epoch = epoch
optim_create_func(self.model, lr)
self.optim = optim_create_func
self.device = device
self.loss_func = loss_function
self.visual_helper = visual_helper
self.model_saver = model_saver
self.start_epoch = 0
self.model.to(device)
def __init__(self, path=current_folder, learning_rate=1e-3, batch_size=128):
torch.manual_seed(12345)
self.path = path
self.batch_size = batch_size
self.data_creator = DataCreator(self.batch_size)
self.learning_rate = learning_rate
try:
self.net = torch.load(self.path + "/net.pth")
print("--------------------------------\n"
"Models were loaded successfully! \n"
"--------------------------------")
except:
print("-----------------------\n"
"No models were loaded! \n"
"-----------------------")
self.net = Net(input_dim=225, hidden_dim=450)
self.net.cuda()
def run_main():
"""
Load gradient and update a network weights (in open loop)
key:value => conv1.weight : array [batch x chan x h x w]
:return:
"""
gradient_path = "gradients/test"
epoch_files = [
os.path.join(gradient_path, x) for x in os.listdir(gradient_path)
if "epoch" in x
]
epoch_files.sort()
gradient_save_path = "gradients/test"
os.makedirs(gradient_save_path, exist_ok=True)
device = torch.device("cuda")
model = Net(5).to(device)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=256,
shuffle=True)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
model.load_state_dict(
torch.load(os.path.join(gradient_path, "weights.pth")))
training_curve = []
for file in epoch_files:
grads = pkl.load(open(file, "br"))
train_grad(grads, model, device, optimizer)
loss, accuracy = test(model, device, test_loader)
training_curve.append([loss, accuracy])
np.savetxt(os.path.join(gradient_save_path, "train_loaded.txt"),
np.array(training_curve))
def run_main():
"""
Will Train a network and save all the batch gradients in a file (pickled dict with :
key:value => conv1.weight : array [batch x chan x h x w]
:return:
"""
device = torch.device("cuda")
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=256,
shuffle=True)
test_loader = torch.utils.data.DataLoader(datasets.MNIST(
'../data',
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
])),
batch_size=512,
shuffle=True)
model = Net(5).to(device)
model.train()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
grad_func = GradientFunc()
training_curve = []
for epoch in range(1, 6):
process(model, device, train_loader, optimizer, grad_func, n_steps=100)
# save epoch gradients
# save test results
loss, accuracy = test(model, device, test_loader)
def run_main():
device = torch.device("cuda")
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=256, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=256, shuffle=True)
model = Net(5).to(device)
summary(model, (1, 28, 28))
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
for epoch in range(1, 6):
train(model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
def __init__(self,
dataloader: DataLoader,
img_save_path: str,
model_pth_path: str,
device: torch.device = torch.device('cpu'),
label_trans_func: LABEL_TRANS_FUNC = lambda x: x,
*args,
**kwargs) -> None:
super(Tester, self).__init__(*args, **kwargs)
self._dataloader = dataloader
self._img_save_path = img_save_path
mkdirs(img_save_path)
self._device = device
self._label_transform_func = label_trans_func
model_dict = torch.load(model_pth_path, map_location='cpu')['model']
self._model = Net()
self._model.load_state_dict(model_dict)
del model_dict
self._model = self._model.to(device)
self._model.eval()
# transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
transforms.Normalize((0.1307,), (0.3081,))
])
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
train_loader = torch.utils.data.DataLoader(train_set,
batch_size = batch_size,
shuffle = True,
)
val_set = datasets.ImageFolder('../surrogate_dataset/unlab_dataset_035/val_set/',
transform = transforms.Compose([transforms.ToTensor(), normalize]))
val_loader = torch.utils.data.DataLoader(val_set,
batch_size = batch_size,
shuffle = False,
)
nb_classes = len(os.listdir('../surrogate_dataset/unlab_dataset_035/train_set/'))
print "Training with " + str(nb_classes) + " classes"
# define a CNN
net = Net(nb_classes).cuda()
print "Model defined"
print "Model to GPU"
initial_lr = 0.01
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr = initial_lr, momentum = 0.9, weight_decay = 1e-4)
lr = optimizer.param_groups[0]['lr']
print "Initial learning rate: " + str(lr)
# Start training
loss_history = []
accuracy_val_history = []
accuracy_train_history = []
val_loss_history = []
#save result in txt
def save_result(test_acc, save_path):
with open(os.path.join(save_path, 'result.txt'), 'a') as f:
test_acc *= 100
f.write(args.dataset + ";")
f.write("pooling_layer_type:" + args.pooling_layer_type + ";")
f.write("feature_fusion_type:" + args.feature_fusion_type + ";")
f.write(str(test_acc))
f.write('\r\n')
#training configuration
train_loader, val_loader, test_loader = data_builder(args)
model = Net(args).to(args.device)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
#training steps
patience = 0
min_loss = args.min_loss
for epoch in range(args.epochs):
model.train()
for i, data in enumerate(train_loader):
data = data.to(args.device)
out = model(data)
loss = F.nll_loss(out, data.y)
print("Training loss:{}".format(loss.item()))
loss.backward()
#!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
import sys
sys.path.insert(-1,'../../maestroflame')
from networks import Net, OC_Net
## Read Model file
filename = "example_model.pt"
model = Net(16, 64, 128, 64, 14)
model.load_state_dict(torch.load(filename))
# model.eval()
print(model)
## Converting to Torch Script (Annotation)
# Using annotation
net_module = torch.jit.script(model)
net_module.save("ts_model.pt")
print(net_module.code)
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[
0.229, 0.224, 0.225])
])
training_data = TripletVeriDataset(
root_dir=opt.train_images, xml_path=opt.train_annotation_path, transform=train_transform)
validation_data = TripletVeriDataset(
root_dir=opt.test_images, xml_path=opt.test_annotation_path, transform=test_transform)
train_loader = torch.utils.data.DataLoader(training_data,
batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers)
val_loader = torch.utils.data.DataLoader(validation_data,
batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers)
embedding_net = Net()
# embedding_net = MobileNetv2()
model = TripletNet(embedding_net).to(device)
loss_fn = nn.TripletMarginLoss(margin=0.5)
# loss_fn = TripletLoss(0.5)
# optimizer = optim.Adadelta(
# model.parameters(), lr=opt.learning_rate, weight_decay=5e-4)
optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9)
# scheduler = StepLR(optimizer, step_size=1, gamma=0.1)
scheduler = ReduceLROnPlateau(
optimizer, 'min', patience=5)
if opt.resume_path:
print('loading checkpoint {}'.format(opt.resume_path))
index_test = torch.from_numpy(index_test).to(torch.long)
train_validation = dataset[index_train_validation]
test_set = dataset[index_test]
num_training = int(len(train_validation) * 0.9)
num_val = len(train_validation) - num_training
training_set, validation_set = random_split(
train_validation, [num_training, num_val])
train_loader = DataLoader(training_set,
batch_size=args.batch_size,
shuffle=True)
val_loader = DataLoader(validation_set,
batch_size=args.batch_size,
shuffle=False)
test_loader = DataLoader(test_set, batch_size=1, shuffle=False)
model = Net(args).to(args.device)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
run(model)
else: # one random fold validation
num_training = int(len(dataset) * 0.9)
num_test = len(dataset) - (num_training)
training_set, test_set = random_split(dataset,
[num_training, num_test])
train_loader = DataLoader(training_set,
batch_size=args.batch_size,
shuffle=True)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
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()
class Model:
def __init__(self, path=current_folder, learning_rate=1e-3, batch_size=128):
torch.manual_seed(12345)
self.path = path
self.batch_size = batch_size
self.data_creator = DataCreator(self.batch_size)
self.learning_rate = learning_rate
try:
self.net = torch.load(self.path + "/net.pth")
print("--------------------------------\n"
"Models were loaded successfully! \n"
"--------------------------------")
except:
print("-----------------------\n"
"No models were loaded! \n"
"-----------------------")
self.net = Net(input_dim=225, hidden_dim=450)
self.net.cuda()
def predict_signal(self, ticker):
signals = ['SELL', 'BUY', 'HOLD']
_, data = get_daily_data(ticker, compact=True)
self.net.train(False)
with torch.no_grad():
input = torch.tensor(data.to_numpy()[-1]).float().cuda()
output = F.softmax(self.net(input), dim=-1).cpu().numpy()
signal_idx = np.argmax(output)
return signals[int(signal_idx)], 100*output[signal_idx]
def test(self):
losses = []
accuracies = []
buy_accuracies = []
sell_accuracies = []
hold_accuracies = []
data_loader = self.data_creator.provide_testing_stock()
criterion = nn.CrossEntropyLoss()
self.net.train(False)
with torch.no_grad():
for i, (batch_x, batch_y) in enumerate(data_loader):
batch_x = batch_x.float().cuda()
batch_y = batch_y.long().cuda()
output = self.net(batch_x)
loss = criterion(output, batch_y)
output_metric = np.argmax(F.softmax(output, dim=1).cpu().numpy(), axis=1)
batch_size = batch_y.size()[0]
batch_y = batch_y.cpu().numpy()
sell_mask_label = batch_y == 0
sell_mask_output = output_metric == 0
sell_accuracies.append(100*(sell_mask_label == sell_mask_output).sum()/batch_size)
buy_mask_label = batch_y == 1
buy_mask_output = output_metric == 1
buy_accuracies.append(100*(buy_mask_label == buy_mask_output).sum()/batch_size)
hold_mask_label = batch_y == 2
hold_mask_output = output_metric == 2
hold_accuracies.append(100*(hold_mask_label == hold_mask_output).sum()/batch_size)
losses.append((loss.item()))
accuracy = 100 * sum(1 if output_metric[k] == batch_y[k] else 0 for k in
range(batch_size)) / batch_size
accuracies.append(accuracy)
print("Average loss: ", np.mean(losses))
print("Average accuracy: ", np.mean(accuracies))
print("Buy-Average accuracy: ", np.mean(buy_accuracies))
print("Sell-Average accuracy: ", np.mean(sell_accuracies))
print("Hold-Average accuracy: ", np.mean(hold_accuracies))
def train(self, epochs):
rocs_aucs = []
baseline_rocs_aucs = []
losses = []
accuracies = []
data_loader, class_weights = self.data_creator.provide_training_stock()
criterion = nn.CrossEntropyLoss()
optimiser = optim.AdamW(self.net.parameters(), lr=self.learning_rate, weight_decay=1e-5, amsgrad=True)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimiser, patience=220, min_lr=1e-9)
self.net.train(True)
pbar = tqdm(total=epochs)
# train the network
for epoch in range(epochs):
for i, (batch_x, batch_y) in enumerate(data_loader):
batch_x = batch_x.float().cuda()
batch_y = batch_y.long().cuda()
self.net.zero_grad()
output = self.net(batch_x)
loss = criterion(output, batch_y)
loss.backward()
optimiser.step()
scheduler.step(loss.item())
# Print some loss stats
if i % 2 == 0:
output_metric = F.softmax(output.detach().cpu(), dim=1).numpy()
random_metric = relabel_data(np.random.choice([0, 1, 2], size=(1, self.batch_size), p=[1/3, 1/3, 1/3]))
label_metric = relabel_data(batch_y.detach().cpu().numpy())
losses.append((loss.item()))
rocs_aucs.append(roc_auc_score(label_metric, output_metric, multi_class='ovo'))
baseline_rocs_aucs.append(roc_auc_score(label_metric, random_metric, multi_class='ovo'))
accuracy = 100 * sum(1 if np.argmax(output_metric[k]) == np.argmax(label_metric[k]) else 0 for k in
range(self.batch_size)) / self.batch_size
accuracies.append(accuracy)
pbar.update(1)
pbar.close()
fig, axs = plt.subplots(1, 3)
axs[0].plot(np.convolve(losses, (1/25)*np.ones(25), mode='valid'))
axs[1].plot(np.convolve(rocs_aucs, (1/25)*np.ones(25), mode='valid'))
axs[1].plot(np.convolve(baseline_rocs_aucs, (1/25)*np.ones(25), mode='valid'))
axs[1].legend(['Net', 'Baseline'])
axs[2].plot(np.convolve(accuracies, (1/25)*np.ones(25), mode='valid'))
plt.show()
def save(self):
torch.save(self.net, self.path + "/net.pth")
test_min_dir = '/scratch/additya/graph_data/DB2_A_cleaned_processed_min'
# # im_dir = test_im_dir
# # min_dir = test_min_dir
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,
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(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.
num_training = int(len(dataset) * 0.8)
num_val = int(len(dataset) * 0.1)
num_test = len(dataset) - (num_training + num_val)
training_set, validation_set, test_set = random_split(
dataset, [num_training, num_val, num_test])
train_loader = DataLoader(training_set,
batch_size=args.batch_size,
shuffle=True)
val_loader = DataLoader(validation_set,
batch_size=args.batch_size,
shuffle=False)
test_loader = DataLoader(test_set, batch_size=1, shuffle=False)
max_nodes = count_max_nodes(dataset)
model = Net(dataset.num_features, 64, dataset.num_classes, max_nodes)
trainer = Trainer(model)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
criterion = torch.nn.CrossEntropyLoss()
# def eval(model,loader):
# model.eval()
# correct = 0.
# loss = 0.
# for data in loader:
# data = data.to(args.device)
# out = model(data)
# pred = out.max(dim=1)[1]
# correct += pred.eq(data.y).sum().item()
def train(ini_file):
''' Performs training according to .ini file
:param ini_file: (String) the path of .ini file
:return best_c_index: the best c-index
'''
# reads configuration from .ini file
config = read_config(ini_file)
# builds network|criterion|optimizer based on configuration
model = Net(config['network']).to(device)
criterion = Criterion(config['network'], device).to(device)
optimizer = eval('optim.{}'.format(config['train']['optimizer']))(
model.parameters(), lr=config['train']['learning_rate'])
# constructs data loaders based on configuration
train_dataset = MakeDataset(config['train']['h5_file'], is_train=True, device=device)
test_dataset = MakeDataset(config['train']['h5_file'], is_train=False, device=device)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=train_dataset.__len__())
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=test_dataset.__len__())
# training
_best_acc = 0.70
best_acc = 0.65
best_ep = 0
flag = 0
_best_auc = 0
best_auc = 0
best_roc = None
for epoch in range(1, config['train']['epochs'] + 1):
# adjusts learning rate
lr = adjust_learning_rate(optimizer, epoch,
config['train']['learning_rate'],
config['train']['lr_decay_rate'])
# train step
model.train()
for X, y in train_loader:
# makes predictions
pred = model(X)
train_loss = criterion(pred, y, model)
train_FPR, train_TPR, train_ACC, train_roc, train_roc_auc, _, _, _, _ = Auc(pred, y)
# updates parameters
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
# valid step
model.eval()
for X, y in test_loader:
# makes predictions
with torch.no_grad():
pred = model(X)
# print(pred, y)
valid_loss = criterion(pred, y, model)
valid_FPR, valid_TPR, valid_ACC, valid_roc, valid_roc_auc, _, _, _, _ = Auc(pred, y)
if valid_ACC > best_acc and train_ACC > _best_acc:
flag = 0
best_acc = valid_ACC
_best_acc = train_ACC
best_ep = epoch
best_auc = valid_roc_auc
_best_auc = train_roc_auc
best_roc = valid_roc
# saves the best model
torch.save({
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch}, os.path.join(models_dir, ini_file.split('\\')[-1] + '.pth'))
else:
flag += 1
if flag >= patience:
print('epoch: {}\t{:.8f}({:.8f})'.format(best_ep, _best_acc, best_acc))
if best_roc is not None:
plt.plot(best_roc[:, 0], best_roc[:, 1])
plt.title('ep:{} AUC: {:.4f}({:.4f}) ACC: {:.4f}({:.4f})'.format(best_ep, _best_auc, best_auc, _best_acc, best_acc))
plt.show()
return best_acc, _best_acc
# notes that, train loader and valid loader both have one batch!!!
print('\rEpoch: {}\tLoss: {:.8f}({:.8f})\tACC: {:.8f}({:.8f})\tAUC: {}({})\tFPR: {:.8f}({:.8f})\tTPR: {:.8f}({:.8f})\tlr: {:g}\n'.format(
epoch, train_loss.item(), valid_loss.item(), train_ACC, valid_ACC, train_roc_auc, valid_roc_auc, train_FPR, valid_FPR, train_TPR, valid_TPR, lr), end='', flush=False)
return best_acc, _best_acc
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
