def valid_fn(
model: nn.Module,
loader: DataLoader,
device: str,
loss_fn: nn.Module,
verbose: bool = True,
) -> dict:
"""Validation step.
Args:
model (nn.Module): model to train
loader (DataLoader): loader with data
device (str): device to use for placing batches
loss_fn (nn.Module): loss function, should be callable
verbose (bool, optional): verbosity mode.
Default is True.
Returns:
dict with metics computed during the validation on loader
"""
model.eval()
metrics = {
"loss": [],
"gap": [],
"accuracy": [],
}
with torch.no_grad(), tqdm(
total=len(loader), desc="valid", disable=not verbose
) as progress:
for _idx, batch in enumerate(loader):
inputs, targets = t2d(batch, device)
outputs = model(inputs, targets)
loss = loss_fn(outputs, targets)
_loss = loss.detach().item()
metrics["loss"].append(_loss)
classes = torch.argmax(outputs, 1)
_acc = (classes == targets).float().mean().detach().item()
metrics["accuracy"].append(_acc)
confidences, predictions = torch.max(outputs, dim=1)
_gap = gap(predictions, confidences, targets)
metrics["gap"].append(_gap)
progress.set_postfix_str(
f"loss {_loss:.4f}, gap {_gap:.4f}, accuracy - {_acc}"
)
progress.update(1)
if _idx == DEBUG:
break
metrics["loss"] = np.mean(metrics["loss"])
metrics["gap"] = np.mean(metrics["gap"])
metrics["accuracy"] = np.mean(metrics["accuracy"])
return metrics
def train_fn(
model: nn.Module,
loader: DataLoader,
device: str,
loss_fn: nn.Module,
optimizer: optim.Optimizer,
scheduler=None,
accumulation_steps: int = 1,
verbose: bool = True,
) -> dict:
"""Train step.
Args:
model (nn.Module): model to train
loader (DataLoader): loader with data
device (str): device to use for placing batches
loss_fn (nn.Module): loss function, should be callable
optimizer (optim.Optimizer): model parameters optimizer
scheduler ([type], optional): batch scheduler to use.
Default is `None`.
accumulation_steps (int, optional): number of steps to accumulate gradients.
Default is `1`.
verbose (bool, optional): verbosity mode.
Default is True.
Returns:
dict with metics computed during the training on loader
"""
model.train()
metrics = {
"loss": [],
"gap": [],
"accuracy": [],
}
with tqdm(total=len(loader), desc="train",
disable=not verbose) as progress:
for _idx, batch in enumerate(loader):
inputs, targets = t2d(batch, device)
zero_grad(optimizer)
outputs = model(inputs, targets)
loss = loss_fn(outputs, targets)
_loss = loss.detach().item()
metrics["loss"].append(_loss)
classes = torch.argmax(outputs, 1)
_acc = (classes == targets).float().mean().detach().item()
metrics["accuracy"].append(_acc)
confidences, predictions = torch.max(outputs, dim=1)
_gap = gap(predictions, confidences, targets)
metrics["gap"].append(_gap)
loss.backward()
progress.set_postfix_str(
f"loss {_loss:.4f}, gap {_gap:.4f}, accuracy {_acc:.4f}")
if (_idx + 1) % accumulation_steps == 0:
optimizer.step()
if scheduler is not None:
scheduler.step()
progress.update(1)
if _idx == DEBUG:
break
metrics["loss"] = np.mean(metrics["loss"])
metrics["gap"] = np.mean(metrics["gap"])
metrics["accuracy"] = np.mean(metrics["accuracy"])
return metrics