Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
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batch_time = ExpoAverageMeter() # forward prop. + back prop. time
losses = ExpoAverageMeter() # loss (per word decoded)
accs = ExpoAverageMeter() # accuracy
start = time.time()
# Batches
for i_batch, (imgs, label_ids, attributes) in enumerate(train_loader):
# Zero gradients
optimizer.zero_grad()
# Set device options
imgs = imgs.to(device)
# print(img.size())
label_ids = label_ids.view(-1).to(device)
# print('label_ids: ' + str(label_ids))
# print('label_ids.size(): ' + str(label_ids.size()))
attributes = attributes.to(device)
# print('targets: ' + str(targets))
# print('targets.size(): ' + str(targets.size()))
X = model(imgs) # (batch_size, 2048)
preds = X.mm(W) # (batch_size, 123)
_, scores = batched_KNN(preds, 1, attributes_per_class)
# print('scores: ' + str(scores))
# print('scores.size(): ' + str(scores.size()))
# loss = criterion(preds, attributes)
# loss = torch.norm(torch.matmul(X, W) - attributes) ** 2 + 1.0 / lambda1 * torch.norm(
# X - torch.matmul(attributes, W.t())) ** 2
loss = (X.mm(W) - attributes).pow(2).mean() + 1.0 / lambda1 * (X - attributes.mm(W.t())).pow(2).mean()
loss.backward()
optimizer.step()
acc = accuracy(scores, label_ids)
# print('acc: ' + str(acc))
# Keep track of metrics
losses.update(loss.item())
batch_time.update(time.time() - start)
accs.update(acc)
start = time.time()
# Print status
if i_batch % print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Batch Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Accuracy {accs.val:.3f} ({accs.avg:.3f})'.format(epoch, i_batch, len(train_loader),
batch_time=batch_time,
loss=losses,
accs=accs))
def valid(val_loader, model, W, attributes_per_class):
model.eval() # eval mode (no dropout or batchnorm)
# Loss function
# criterion = nn.MSELoss().to(device)
batch_time = ExpoAverageMeter() # forward prop. + back prop. time
losses = ExpoAverageMeter() # loss (per word decoded)
accs = ExpoAverageMeter() # accuracy
start = time.time()
with torch.no_grad():
# Batches
for i_batch, (imgs, label_ids, attributes) in enumerate(val_loader):
# Set device options
imgs = imgs.to(device)
label_ids = label_ids.view(-1).to(device)
attributes = attributes.to(device) # (batch_size, 123)
X = model(imgs) # (batch_size, 2048)
preds = X.mm(W) # (batch_size, 123)
# loss = criterion(preds, attributes)
# loss = torch.norm(torch.matmul(X, W) - attributes) ** 2 + 1.0 / lambda1 * torch.norm(
# X - torch.matmul(attributes, W.t())) ** 2
loss = (X.mm(W) - attributes).pow(2).mean() + 1.0 / lambda1 * (X - attributes.mm(W.t())).pow(2).mean()
_, scores = batched_KNN(preds, 1, attributes_per_class)
acc = accuracy(scores, label_ids)
# Keep track of metrics
losses.update(loss.item())
batch_time.update(time.time() - start)
accs.update(acc)
start = time.time()
# Print status
if i_batch % print_freq == 0:
print('Validation: [{0}/{1}]\t'
'Batch Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'