Fixes to validation in imagenet example scripts. Precision and loss reporting...

Fixes to validation in imagenet example scripts. Precision and loss reporting modified to be consistent with train.

examples/imagenet/main.py

@@ -377,13 +377,15 @@ def validate(val_loader, model, criterion):
             output = model(input_var)
             loss = criterion(output, target_var)
 
-        reduced_loss = reduce_tensor(loss.data)
-
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
 
-        reduced_prec1 = reduce_tensor(prec1)
-        reduced_prec5 = reduce_tensor(prec5)
+        if args.distributed:
+            reduced_loss = reduce_tensor(loss.data)
+            prec1 = reduce_tensor(prec1)
+            prec5 = reduce_tensor(prec5)
+        else:
+            reduced_loss = loss.data
 
         losses.update(to_python_float(reduced_loss), input.size(0))
         top1.update(to_python_float(prec1), input.size(0))

examples/imagenet/main_fp16_optimizer.py

@@ -338,13 +338,15 @@ def validate(val_loader, model, criterion):
             output = model(input_var)
             loss = criterion(output, target_var)
 
-        reduced_loss = reduce_tensor(loss.data)
-
         # measure accuracy and record loss
         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
 
-        reduced_prec1 = reduce_tensor(prec1)
-        reduced_prec5 = reduce_tensor(prec5)
+        if args.distributed:
+            reduced_loss = reduce_tensor(loss.data)
+            prec1 = reduce_tensor(prec1)
+            prec5 = reduce_tensor(prec5)
+        else:
+            reduced_loss = loss.data
 
         losses.update(to_python_float(reduced_loss), input.size(0))
         top1.update(to_python_float(prec1), input.size(0))