batch_size=batch_size,
sampler=test_sample)
# GPU setup
device = torch.device('cuda')
net = AlexNet(in_channel=2, classes=10).to(device=device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001) #, momentum=0.9)
# Train loop
num_epochs = 25
for epoch in range(num_epochs):
print("Epoch: {} - Train".format(epoch))
net.train()
running_loss = 0.
# Train:
for batch_index, (signals, labels) in enumerate(tqdm(train_loader)):
signals, labels = signals.to(device=device), labels.to(device=device)
optimizer.zero_grad()
outputs = net(signals)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
#if batch_index > 50:
def main(args):
logs = []
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
trainloader, testloader = get_dataset(args, transform)
net = AlexNet()
if args.no_distributed:
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.0)
else:
optimizer = DownpourSGD(net.parameters(), lr=args.lr, n_push=args.num_push, n_pull=args.num_pull, model=net)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=1, verbose=True, min_lr=1e-3)
# train
net.train()
if args.cuda:
net = net.cuda()
for epoch in range(args.epochs): # loop over the dataset multiple times
print("Training for epoch {}".format(epoch))
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
if args.cuda:
inputs, labels = inputs.cuda(), labels.cuda()
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimizer.step()
_, predicted = torch.max(outputs, 1)
accuracy = accuracy_score(predicted, labels)
log_obj = {
'timestamp': datetime.now(),
'iteration': i,
'training_loss': loss.item(),
'training_accuracy': accuracy,
}
if i % args.log_interval == 0 and i > 0: # print every n mini-batches
log_obj['test_loss'], log_obj['test_accuracy']= evaluate( net, testloader, args)
print("Timestamp: {timestamp} | "
"Iteration: {iteration:6} | "
"Loss: {training_loss:6.4f} | "
"Accuracy : {training_accuracy:6.4f} | "
"Test Loss: {test_loss:6.4f} | "
"Test Accuracy: {test_accuracy:6.4f}".format(**log_obj))
logs.append(log_obj)
val_loss, val_accuracy = evaluate(net, testloader, args, verbose=True)
scheduler.step(val_loss)
df = pd.DataFrame(logs)
print(df)
if args.no_distributed:
if args.cuda:
df.to_csv('log/gpu.csv', index_label='index')
else:
df.to_csv('log/single.csv', index_label='index')
else:
df.to_csv('log/node{}.csv'.format(dist.get_rank()), index_label='index')
print('Finished Training')
else:
# GoogLeNet
output, _1, _2 = model(image)
_, pred = torch.max(output.data, 1)
correct = (pred == label).sum()
loss = loss_func(output, label.long())
total_loss += loss.item()
total_acc += correct.cpu().numpy()
step += 1
total_loss /= step
total_acc /= 10000
return total_acc, total_loss
# train model
model.train()
steps = len(train_loader)
bar = ProgressBar(maxStep=steps) # initialize progress bar
for epoch in range(epochs):
epoch_loss = 0.0
epoch_acc = 0.0
for i, (image, label) in enumerate(train_loader):
image, label = image.to(Device), label.to(Device) # step 1.
optimizer.zero_grad() # step 2.
if modelName != 'GoogLeNet':
# Others
output = model(image) # step 3.
loss = loss_func(output, label.long()) # step 4.
else:
# GoogLeNet
output, auxOut_1, auxOut_2 = model(image)
