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Refactor Outdated PyTorch Operations for AlexNet Training on CIFAR #57

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Refactor Outdated PyTorch Operations for AlexNet Training on CIFAR #57

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5c70d06
asynch=True argument removed from Pytorch 1.0.0.
KaledDahleh Jun 15, 2024
d378d87
Fix tensor reshaping: replace view with reshape in accuracy function
KaledDahleh Jun 15, 2024
c570bd6
fixed index error accessing 0-dim tensors in training metrics update
KaledDahleh Jun 15, 2024
2935578
fixed remaining index errors accessing 0-dim tensors
KaledDahleh Jun 15, 2024
4d393ff
refactor deprecated volatile=true to torch.no_grad() for better compa…
KaledDahleh Jun 15, 2024
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18 changes: 10 additions & 8 deletions cifar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -245,7 +245,7 @@ def train(trainloader, model, criterion, optimizer, epoch, use_cuda):
data_time.update(time.time() - end)

if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda(async=True)
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)

# compute output
Expand All @@ -254,9 +254,9 @@ def train(trainloader, model, criterion, optimizer, epoch, use_cuda):

# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.data[0], inputs.size(0))
top1.update(prec1[0], inputs.size(0))
top5.update(prec5[0], inputs.size(0))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))

# compute gradient and do SGD step
optimizer.zero_grad()
Expand Down Expand Up @@ -303,17 +303,19 @@ def test(testloader, model, criterion, epoch, use_cuda):

if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = torch.autograd.Variable(inputs, volatile=True), torch.autograd.Variable(targets)
with torch.no_grad():
inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)


# compute output
outputs = model(inputs)
loss = criterion(outputs, targets)

# measure accuracy and record loss
prec1, prec5 = accuracy(outputs.data, targets.data, topk=(1, 5))
losses.update(loss.data[0], inputs.size(0))
top1.update(prec1[0], inputs.size(0))
top5.update(prec5[0], inputs.size(0))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))

# measure elapsed time
batch_time.update(time.time() - end)
Expand Down
4 changes: 2 additions & 2 deletions utils/eval.py
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Expand Up @@ -9,10 +9,10 @@ def accuracy(output, target, topk=(1,)):

_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
correct = pred.eq(target.reshape(1, -1).expand_as(pred))

res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
correct_k = correct[:k].reshape(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res