parser.add_argument('--analysis',
choices=analysis.names,
help='Run analysis after each epoch')
parser.add_argument('--input-size',
type=int,
help='Set transform train and val. Samples are resized to '
'input-size + 32.')
parser.add_argument('--lr-decay-every', type=int, default=0)
data.custom.add_arguments(parser)
loss.add_arguments(parser)
analysis.add_arguments(parser)
args = parser.parse_args()
loss.set_default_values(args)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
def main():
maybe_install_wordnet()
datasets = data.cifar.names + data.imagenet.names + data.custom.names
parser = argparse.ArgumentParser(description="PyTorch CIFAR Training")
parser.add_argument("--batch-size",
default=512,
type=int,
help="Batch size used for training")
parser.add_argument(
"--epochs",
"-e",
default=200,
type=int,
help="By default, lr schedule is scaled accordingly",
)
parser.add_argument("--dataset", default="CIFAR10", choices=datasets)
parser.add_argument("--arch",
default="ResNet18",
choices=list(models.get_model_choices()))
parser.add_argument("--lr", default=0.1, type=float, help="learning rate")
parser.add_argument("--resume",
"-r",
action="store_true",
help="resume from checkpoint")
# extra general options for main script
parser.add_argument("--path-resume",
default="",
help="Overrides checkpoint path generation")
parser.add_argument(
"--name",
default="",
help="Name of experiment. Used for checkpoint filename")
parser.add_argument(
"--pretrained",
action="store_true",
help="Download pretrained model. Not all models support this.",
)
parser.add_argument("--eval", help="eval only", action="store_true")
parser.add_argument(
"--dataset-test",
choices=datasets,
help="If not set, automatically set to train dataset",
)
parser.add_argument(
"--disable-test-eval",
help="Allows you to run model inference on a test dataset "
" different from train dataset. Use an anlayzer to define "
"a metric.",
action="store_true",
)
# options specific to this project and its dataloaders
parser.add_argument("--loss",
choices=loss.names,
default=["CrossEntropyLoss"],
nargs="+")
parser.add_argument("--metric", choices=metrics.names, default="top1")
parser.add_argument("--analysis",
choices=analysis.names,
help="Run analysis after each epoch")
# other dataset, loss or analysis specific options
data.custom.add_arguments(parser)
T.add_arguments(parser)
loss.add_arguments(parser)
analysis.add_arguments(parser)
args = parser.parse_args()
loss.set_default_values(args)
device = "cuda" if torch.cuda.is_available() else "cpu"
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
# Data
print("==> Preparing data..")
dataset_train = getattr(data, args.dataset)
dataset_test = getattr(data, args.dataset_test or args.dataset)
transform_train = dataset_train.transform_train()
transform_test = dataset_test.transform_val()
dataset_train_kwargs = generate_kwargs(
args,
dataset_train,
name=f"Dataset {dataset_train.__class__.__name__}",
globals=locals(),
)
dataset_test_kwargs = generate_kwargs(
args,
dataset_test,
name=f"Dataset {dataset_test.__class__.__name__}",
globals=locals(),
)
trainset = dataset_train(
**dataset_train_kwargs,
root="./data",
train=True,
download=True,
transform=transform_train,
)
testset = dataset_test(
**dataset_test_kwargs,
root="./data",
train=False,
download=True,
transform=transform_test,
)
assert trainset.classes == testset.classes or args.disable_test_eval, (
trainset.classes,
testset.classes,
)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=2)
testloader = torch.utils.data.DataLoader(testset,
batch_size=100,
shuffle=False,
num_workers=2)
Colors.cyan(
f"Training with dataset {args.dataset} and {len(trainset.classes)} classes"
)
Colors.cyan(
f"Testing with dataset {args.dataset_test or args.dataset} and {len(testset.classes)} classes"
)
# Model
print("==> Building model..")
model = getattr(models, args.arch)
if args.pretrained:
print("==> Loading pretrained model..")
model = make_kwarg_optional(model, dataset=args.dataset)
net = model(pretrained=True, num_classes=len(trainset.classes))
else:
net = model(num_classes=len(trainset.classes))
net = net.to(device)
if device == "cuda":
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
checkpoint_fname = generate_checkpoint_fname(**vars(args))
checkpoint_path = "./checkpoint/{}.pth".format(checkpoint_fname)
print(f"==> Checkpoints will be saved to: {checkpoint_path}")
resume_path = args.path_resume or checkpoint_path
if args.resume:
# Load checkpoint.
print("==> Resuming from checkpoint..")
assert os.path.isdir(
"checkpoint"), "Error: no checkpoint directory found!"
if not os.path.exists(resume_path):
print("==> No checkpoint found. Skipping...")
else:
checkpoint = torch.load(resume_path,
map_location=torch.device(device))
if "net" in checkpoint:
load_state_dict(net, checkpoint["net"])
best_acc = checkpoint["acc"]
start_epoch = checkpoint["epoch"]
Colors.cyan(
f"==> Checkpoint found for epoch {start_epoch} with accuracy "
f"{best_acc} at {resume_path}")
else:
load_state_dict(net, checkpoint)
Colors.cyan(f"==> Checkpoint found at {resume_path}")
# hierarchy
tree = Tree.create_from_args(args, classes=trainset.classes)
# loss
criterion = None
for _loss in args.loss:
if criterion is None and not hasattr(nn, _loss):
criterion = nn.CrossEntropyLoss()
class_criterion = getattr(loss, _loss)
loss_kwargs = generate_kwargs(
args,
class_criterion,
name=f"Loss {args.loss}",
globals=locals(),
)
criterion = class_criterion(**loss_kwargs)
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(3 / 7.0 * args.epochs),
int(5 / 7.0 * args.epochs)])
class_analysis = getattr(analysis, args.analysis or "Noop")
analyzer_kwargs = generate_kwargs(
args,
class_analysis,
name=f"Analyzer {args.analysis}",
globals=locals(),
)
analyzer = class_analysis(**analyzer_kwargs)
metric = getattr(metrics, args.metric)()
# Training
@analyzer.train_function
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()
@analyzer.test_function
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
if args.disable_test_eval and (not args.analysis
or args.analysis == "Noop"):
Colors.red(
" * Warning: `disable_test_eval` is used but no custom metric "
"`--analysis` is supplied. I suggest supplying an analysis to perform "
" custom loss and accuracy calculation.")
if args.eval:
if not args.resume and not args.pretrained:
Colors.red(" * Warning: Model is not loaded from checkpoint. "
"Use --resume or --pretrained (if supported)")
with analyzer.epoch_context(0):
test(0, checkpoint=False)
else:
for epoch in range(start_epoch, args.epochs):
with analyzer.epoch_context(epoch):
train(epoch)
test(epoch)
print(f"Best accuracy: {best_acc} // Checkpoint name: {checkpoint_fname}")