supporting pytorch 1.1.0

Author: ShichenLiu

README.md

@@ -41,7 +41,7 @@ Figure 2: CondenseNets with Fully Dense Connectivity and Increasing Growth Rate.
 ### Dependencies
 
 - [Python3](https://www.python.org/downloads/)
-- [PyTorch(0.1.12+)](http://pytorch.org)
+- [PyTorch(1.1.0)](http://pytorch.org)
 - [ImageNet](https://www.image-net.org/challenges/LSVRC/2012/)
 
 ### Train

main.py

@@ -295,7 +295,7 @@ def train(train_loader, model, criterion, optimizer, epoch):
         ### Measure data loading time
         data_time.update(time.time() - end)
 
-        target = target.cuda(async=True)
+        target = target.cuda(non_blocking=True)
         input_var = torch.autograd.Variable(input)
         target_var = torch.autograd.Variable(target)
 
@@ -312,9 +312,9 @@ def train(train_loader, model, criterion, optimizer, epoch):
 
         ### Measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
-        losses.update(loss.data[0], input.size(0))
-        top1.update(prec1[0], input.size(0))
-        top5.update(prec5[0], input.size(0))
+        losses.update(loss.item(), input.size(0))
+        top1.update(prec1.item(), input.size(0))
+        top5.update(prec5.item(), input.size(0))
 
         ### Compute gradient and do SGD step
         optimizer.zero_grad()
@@ -349,7 +349,7 @@ def validate(val_loader, model, criterion):
 
     end = time.time()
     for i, (input, target) in enumerate(val_loader):
-        target = target.cuda(async=True)
+        target = target.cuda(non_blocking=True)
         input_var = torch.autograd.Variable(input, volatile=True)
         target_var = torch.autograd.Variable(target, volatile=True)
 
@@ -359,9 +359,9 @@ def validate(val_loader, model, criterion):
 
         ### Measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
-        losses.update(loss.data[0], input.size(0))
-        top1.update(prec1[0], input.size(0))
-        top5.update(prec5[0], input.size(0))
+        losses.update(loss.data.item(), input.size(0))
+        top1.update(prec1.item(), input.size(0))
+        top5.update(prec5.item(), input.size(0))
 
         ### Measure elapsed time
         batch_time.update(time.time() - end)