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import torch
from tqdm import tqdm
import sys
def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10):
model.to(device)
model.train()
running_loss = 0
previous_running_loss = 0
with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch:
for i, batch in enumerate(tepoch):
# set gradients to zero
optimizer.zero_grad()
# push batch to device
for key in batch:
batch[key] = batch[key].to(device)
target = batch['target']
# calculate model output
output = model(batch['signals'].permute(0, 2, 1), batch['features'], batch['periods'], batch['numbits'])
# calculate loss
if isinstance(output, list):
loss = torch.zeros(1, device=device)
for y in output:
loss = loss + criterion(target, y.squeeze(1))
loss = loss / len(output)
else:
loss = criterion(target, output.squeeze(1))
# calculate gradients
loss.backward()
# update weights
optimizer.step()
# update learning rate
scheduler.step()
# sparsification
if hasattr(model, 'sparsifier'):
model.sparsifier()
# update running loss
running_loss += float(loss.cpu())
# update status bar
if i % log_interval == 0:
tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}")
previous_running_loss = running_loss
running_loss /= len(dataloader)
return running_loss
def evaluate(model, criterion, dataloader, device, log_interval=10):
model.to(device)
model.eval()
running_loss = 0
previous_running_loss = 0
with torch.no_grad():
with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch:
for i, batch in enumerate(tepoch):
# push batch to device
for key in batch:
batch[key] = batch[key].to(device)
target = batch['target']
# calculate model output
output = model(batch['signals'].permute(0, 2, 1), batch['features'], batch['periods'], batch['numbits'])
# calculate loss
loss = criterion(target, output.squeeze(1))
# update running loss
running_loss += float(loss.cpu())
# update status bar
if i % log_interval == 0:
tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}")
previous_running_loss = running_loss
running_loss /= len(dataloader)
return running_loss
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