def evaluate(self, loader, verbose=False, write_summary=False, epoch=None):
self.eval()
valid_loss = 0
all_class_probs = []
all_labels = []
with torch.no_grad():
for batch_idx, sample in enumerate(loader):
signal, labels = (
sample["signal"].to(self.device),
sample["labels"].to(self.device).float()
)
outputs = self(signal)
class_logits = outputs["class_logits"]
loss = (
lsep_loss(
class_logits,
labels,
)
).item()
multiplier = len(labels) / len(loader.dataset)
valid_loss += loss * multiplier
class_probs = torch.sigmoid(class_logits).data.cpu().numpy()
labels = labels.data.cpu().numpy()
all_class_probs.extend(class_probs)
all_labels.extend(labels)
all_class_probs = np.asarray(all_class_probs)
all_labels = np.asarray(all_labels)
metric = lwlrap(all_labels, all_class_probs)
if write_summary:
self.add_scalar_summaries(
valid_loss,
metric,
writer=self.valid_writer, global_step=self.global_step
)
if verbose:
print("\nValidation loss: {:.4f}".format(valid_loss))
print("Validation metric: {:.4f}".format(metric))
return metric
def train_epoch(self, train_loader,
epoch, log_interval, write_summary=True):
self.train()
print(
"\n" + " " * 10 + "****** Epoch {epoch} ******\n"
.format(epoch=epoch)
)
training_losses = []
history = deque(maxlen=30)
self.optimizer.zero_grad()
accumulated_loss = 0
with tqdm(total=len(train_loader), ncols=80) as pb:
for batch_idx, sample in enumerate(train_loader):
self.global_step += 1
make_step(self.scheduler, step=self.global_step)
signal, labels, is_noisy = (
sample["signal"].to(self.device),
sample["labels"].to(self.device).float(),
sample["is_noisy"].to(self.device).float()
)
outputs = self(signal)
class_logits = outputs["class_logits"]
loss = (
lsep_loss(
class_logits,
labels,
average=False
)
) / self.config.train.accumulation_steps
training_losses.extend(loss.data.cpu().numpy())
loss = loss.mean()
loss.backward()
accumulated_loss += loss
if batch_idx % self.config.train.accumulation_steps == 0:
self.optimizer.step()
accumulated_loss = 0
self.optimizer.zero_grad()
probs = torch.sigmoid(class_logits).data.cpu().numpy()
labels = labels.data.cpu().numpy()
metric = lwlrap(labels, probs)
history.append(metric)
pb.update()
pb.set_description(
"Loss: {:.4f}, Metric: {:.4f}".format(
loss.item(), np.mean(history)))
if batch_idx % log_interval == 0:
self.add_scalar_summaries(
loss.item(), metric, self.train_writer, self.global_step)
if batch_idx == 0:
self.add_image_summaries(
signal, self.global_step, self.train_writer)
self.add_histogram_summaries(
training_losses, self.train_writer, self.global_step)