def run(net, logger, hps):
print("Running simple training loop")
# Create dataloaders
trainloader, valloader, testloader = get_dataloaders()
net = net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
print("Training", hps['name'], "on", device)
for epoch in range(hps['start_epoch'], 100):
acc_tr, loss_tr = train(net, trainloader, criterion, optimizer)
logger.loss_train.append(loss_tr)
logger.acc_train.append(acc_tr)
acc_v, loss_v = evaluate(net, valloader, criterion)
logger.loss_val.append(loss_v)
logger.acc_val.append(acc_v)
if (epoch + 1) % 20 == 0:
save(net, logger, hps, epoch + 1)
logger.save_plt(hps)
print('Epoch %2d' % (epoch + 1),
'Train Accuracy: %2.2f %%' % acc_tr,
'Val Accuracy: %2.2f %%' % acc_v,
sep='\t\t')
# Reduce Learning Rate
print("Reducing learning rate from 0.001 to 0.0001")
for param_group in optimizer.param_groups:
param_group['lr'] = 0.0001
# Train for 20 extra epochs
for epoch in range(epoch, 120):
acc_tr, loss_tr = train(net, trainloader, criterion, optimizer)
logger.loss_train.append(loss_tr)
logger.acc_train.append(acc_tr)
acc_v, loss_v = evaluate(net, valloader, criterion)
logger.loss_val.append(loss_v)
logger.acc_val.append(acc_v)
if (epoch + 1) % 20 == 0:
save(net, logger, hps, epoch + 1)
logger.save_plt(hps)
print('Epoch %2d' % (epoch + 1),
'Train Accuracy: %2.2f %%' % acc_tr,
'Val Accuracy: %2.2f %%' % acc_v,
sep='\t\t')
acc_test, loss_test = evaluate(net, testloader, criterion)
print('Test Accuracy: %2.2f %%' % acc_test,
'Test Loss: %2.6f %%' % loss_test,
sep='\t\t')
def run(net, logger, hps):
# Create dataloaders
trainloader, valloader, testloader = get_dataloaders()
net = net.to(device)
learning_rate = float(hps['lr'])
optimizer = torch.optim.SGD(net.parameters(),
lr=learning_rate,
momentum=0.9,
nesterov=True,
weight_decay=0.0001)
scheduler = ReduceLROnPlateau(optimizer,
mode='max',
factor=0.5,
patience=10,
verbose=True)
criterion = nn.CrossEntropyLoss()
print("Training", hps['name'], "on", device)
for epoch in range(hps['start_epoch'], hps['n_epochs']):
acc_tr, loss_tr = train(net, trainloader, criterion, optimizer)
logger.loss_train.append(loss_tr)
logger.acc_train.append(acc_tr)
acc_v, loss_v = evaluate(net, valloader, criterion)
logger.loss_val.append(loss_v)
logger.acc_val.append(acc_v)
# Update learning rate if plateau
scheduler.step(acc_v)
if (epoch + 1) % hps['save_freq'] == 0:
save(net, logger, hps, epoch + 1)
logger.save_plt(hps)
print('Epoch %2d' % (epoch + 1),
'Train Accuracy: %2.2f %%' % acc_tr,
'Val Accuracy: %2.2f %%' % acc_v,
sep='\t\t')
acc_test, loss_test = evaluate(net, testloader, criterion)
print('Test Accuracy: %2.2f %%' % acc_test,
'Test Loss: %2.6f %%' % loss_test,
sep='\t\t')
print("Loss: %2.6f" % loss)
print("Precision: %2.6f" % precision_score(y_gt, y_pred, average='micro'))
print("Recall: %2.6f" % recall_score(y_gt, y_pred, average='micro'))
print("F1 Score: %2.6f" % f1_score(y_gt, y_pred, average='micro'))
print("Confusion Matrix:\n", confusion_matrix(y_gt, y_pred), '\n')
if __name__ == "__main__":
# Important parameters
hps = setup_hparams(sys.argv[1:])
# build network
logger, net = setup_network(hps)
net = net.to(device)
print(net)
criterion = nn.CrossEntropyLoss()
# Get data with no augmentation
trainloader, valloader, testloader = get_dataloaders(augment=False)
print("Train")
evaluate(net, trainloader, criterion)
print("Val")
evaluate(net, valloader, criterion)
print("Test")
evaluate(net, testloader, criterion)
def run(net, logger, hps):
# Create dataloaders
trainloader, valloader, testloader = get_dataloaders(bs=hps['bs'])
net = net.to(device)
learning_rate = float(hps['lr'])
scaler = GradScaler()
# optimizer = torch.optim.Adadelta(net.parameters(), lr=learning_rate, weight_decay=0.0001)
# optimizer = torch.optim.Adagrad(net.parameters(), lr=learning_rate, weight_decay=0.0001)
# optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, weight_decay=0.0001, amsgrad=True)
# optimizer = torch.optim.ASGD(net.parameters(), lr=learning_rate, weight_decay=0.0001)
optimizer = torch.optim.SGD(net.parameters(),
lr=learning_rate,
momentum=0.9,
nesterov=True,
weight_decay=0.0001)
scheduler = ReduceLROnPlateau(optimizer,
mode='max',
factor=0.75,
patience=5,
verbose=True)
# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 20, gamma=0.5, last_epoch=-1, verbose=True)
# scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=len(trainloader), epochs=hps['n_epochs'])
# scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=1, eta_min=1e-6, last_epoch=-1, verbose=True)
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10, eta_min=1e-6, last_epoch=-1, verbose=False)
criterion = nn.CrossEntropyLoss()
best_acc = 0.0
print("Training", hps['name'], "on", device)
for epoch in range(hps['start_epoch'], hps['n_epochs']):
acc_tr, loss_tr = train(net, trainloader, criterion, optimizer, scaler)
logger.loss_train.append(loss_tr)
logger.acc_train.append(acc_tr)
acc_v, loss_v = evaluate(net, valloader, criterion)
logger.loss_val.append(loss_v)
logger.acc_val.append(acc_v)
# Update learning rate
scheduler.step(acc_v)
if acc_v > best_acc:
best_acc = acc_v
save(net, logger, hps, epoch + 1)
logger.save_plt(hps)
if (epoch + 1) % hps['save_freq'] == 0:
save(net, logger, hps, epoch + 1)
logger.save_plt(hps)
print('Epoch %2d' % (epoch + 1),
'Train Accuracy: %2.4f %%' % acc_tr,
'Val Accuracy: %2.4f %%' % acc_v,
sep='\t\t')
# Calculate performance on test set
acc_test, loss_test = evaluate(net, testloader, criterion)
print('Test Accuracy: %2.4f %%' % acc_test,
'Test Loss: %2.6f' % loss_test,
sep='\t\t')