def test(epoch, analyzer):
analyzer.start_test(epoch)
net.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
if device == 'cuda':
predicted = predicted.cpu()
targets = targets.cpu()
predicted_nbdt = analyzer.update_batch(outputs, targets)
print(predicted.eq(targets).sum())
print(predicted_nbdt.eq(targets).sum())
progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d) '
% (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
acc = 100.*correct/total
print("Accuracy: {}, {}/{}".format(acc, correct, total))
analyzer.end_test(epoch)
def train(epoch):
if hasattr(criterion, "set_epoch"):
criterion.set_epoch(epoch, args.epochs)
print("\nEpoch: %d / LR: %.04f" % (epoch, scheduler.get_last_lr()[0]))
net.train()
train_loss = 0
metric.clear()
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
metric.forward(outputs, targets)
transform = trainset.transform_val_inverse().to(device)
stat = analyzer.update_batch(outputs, targets, transform(inputs))
progress_bar(
batch_idx,
len(trainloader),
"Loss: %.3f | Acc: %.3f%% (%d/%d) %s" % (
train_loss / (batch_idx + 1),
100.0 * metric.report(),
metric.correct,
metric.total,
f"| {analyzer.name}: {stat}" if stat else "",
),
)
scheduler.step()
def train(epoch, analyzer):
analyzer.start_train(epoch)
lr = adjust_learning_rate(epoch, args.lr)
optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
stat = analyzer.update_batch(outputs, targets)
extra = f'| {stat}' if stat else ''
progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d) %s'
% (train_loss/(batch_idx+1), 100.*correct/total, correct, total, extra))
analyzer.end_train(epoch)
def test(epoch, analyzer, checkpoint=True):
analyzer.start_test(epoch)
global best_acc
net.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
if device == 'cuda':
predicted = predicted.cpu()
targets = targets.cpu()
stat = analyzer.update_batch(outputs, targets)
extra = f'| {stat}' if stat else ''
progress_bar(
batch_idx, len(testloader),
'Loss: %.3f | Acc: %.3f%% (%d/%d) %s' %
(test_loss / (batch_idx + 1), 100. * correct / total, correct,
total, extra))
# Save checkpoint.
acc = 100. * correct / total
print("Accuracy: {}, {}/{}".format(acc, correct, total))
if acc > best_acc and checkpoint:
state = {
'net': net.state_dict(),
'acc': acc,
'epoch': epoch,
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
print(f'Saving to {checkpoint_fname} ({acc})..')
torch.save(state, f'./checkpoint/{checkpoint_fname}.pth')
best_acc = acc
analyzer.end_test(epoch)
def test(epoch, checkpoint=True):
nonlocal best_acc
net.eval()
test_loss = 0
metric.clear()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
if not args.disable_test_eval:
loss = criterion(outputs, targets)
test_loss += loss.item()
metric.forward(outputs, targets)
transform = testset.transform_val_inverse().to(device)
stat = analyzer.update_batch(outputs, targets,
transform(inputs))
progress_bar(
batch_idx,
len(testloader),
"Loss: %.3f | Acc: %.3f%% (%d/%d) %s" % (
test_loss / (batch_idx + 1),
100.0 * metric.report(),
metric.correct,
metric.total,
f"| {analyzer.name}: {stat}" if stat else "",
),
)
# Save checkpoint.
acc = 100.0 * metric.report()
print("Accuracy: {}, {}/{} | Best Accurracy: {}".format(
acc, metric.correct, metric.total, best_acc))
if acc > best_acc and checkpoint:
Colors.green(f"Saving to {checkpoint_fname} ({acc})..")
state = {
"net": net.state_dict(),
"acc": acc,
"epoch": epoch,
}
os.makedirs("checkpoint", exist_ok=True)
torch.save(state, f"./checkpoint/{checkpoint_fname}.pth")
best_acc = acc