def train_experiment(device, engine=None):
with TemporaryDirectory() as logdir:
# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32)
# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}
# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])
# model training
runner = dl.SupervisedRunner(input_key="features",
output_key="logits",
target_key="targets",
loss_key="loss")
callbacks = [
dl.BatchTransformCallback(
transform="F.sigmoid",
scope="on_batch_end",
input_key="logits",
output_key="scores",
),
dl.MultilabelAccuracyCallback(input_key="scores",
target_key="targets",
threshold=0.5),
dl.MultilabelPrecisionRecallF1SupportCallback(
input_key="scores",
target_key="targets",
num_classes=num_classes),
]
if SETTINGS.amp_required and (engine is None or not isinstance(
engine,
(dl.AMPEngine, dl.DataParallelAMPEngine,
dl.DistributedDataParallelAMPEngine),
)):
callbacks.append(
dl.AUCCallback(input_key="scores", target_key="targets"))
runner.train(
engine=engine or dl.DeviceEngine(device),
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
logdir=logdir,
num_epochs=1,
valid_loader="valid",
valid_metric="accuracy",
minimize_valid_metric=False,
verbose=False,
callbacks=callbacks,
)
def train_experiment(device):
with TemporaryDirectory() as logdir:
# sample data
num_samples, num_features, num_classes = int(1e4), int(1e1), 4
X = torch.rand(num_samples, num_features)
y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32)
# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}
# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_classes)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])
# model training
runner = dl.SupervisedRunner(input_key="features",
output_key="logits",
target_key="targets",
loss_key="loss")
runner.train(
engine=dl.DeviceEngine(device),
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
logdir=logdir,
num_epochs=1,
valid_loader="valid",
valid_metric="accuracy",
minimize_valid_metric=False,
verbose=False,
callbacks=[
dl.AUCCallback(input_key="logits", target_key="targets"),
dl.MultilabelAccuracyCallback(input_key="logits",
target_key="targets",
threshold=0.5),
],
)