Fix meters

The meters in DARTS were updated when they were moved to common,
but the logging in P3B5 was not updated.

Author: yngtodd

Pilot3/P3B5/p3b5_baseline_pytorch.py

@@ -97,7 +97,7 @@ def run(params):
         print(f'Genotype: {genotype}')
 
         # training
-        train_acc, train_loss = train(
+        train(
             trainloader,
             validloader,
             model,
@@ -112,16 +112,20 @@ def run(params):
         )
 
         # validation
-        valid_acc, valid_loss = infer(validloader, model, criterion, args, tasks, device, valid_meter)
+        valid_loss = infer(
+            validloader,
+            model,
+            criterion,
+            args,
+            tasks,
+            device,
+            valid_meter
+        )
 
         if valid_loss < min_loss:
             genotype_store.save_genotype(genotype)
             min_loss = valid_loss
 
-        print(f'\nEpoch {epoch} stats:')
-       # darts.log_accuracy(train_acc, 'train')
-       # darts.log_accuracy(valid_acc, 'valid')
-
 
 def main():
     params = initialize_parameters()

Pilot3/P3B5/p3b5_darts.py

@@ -66,12 +66,10 @@ def train(trainloader, validloader, model, architecture, criterion, optimizer, l
         meter.update_batch_accuracy(prec1, batch_size)
 
         if step % args.log_interval == 0:
-            print(f'Step: {step} loss: {losses.avg:.4}')
-            #darts.log_accuracy(top1)
+            print(f'Step: {step} loss: {meter.loss_meter.avg:.4}')
 
     meter.update_epoch()
     meter.save(args.savepath)
-    return top1, losses.avg
 
 
 def infer(validloader, model, criterion, args, tasks, device, meter):
@@ -94,12 +92,12 @@ def infer(validloader, model, criterion, args, tasks, device, meter):
             meter.update_batch_accuracy(prec1, batch_size)
 
             if step % args.log_interval == 0:
-                print(f'>> Validation: {step} loss: {losses.avg:.4}')
-                #darts.log_accuracy(top1, 'valid')
+                print(f'>> Validation: {step} loss: {meter.loss_meter.avg:.4}')
 
     meter.update_epoch()
     meter.save(args.savepath)
-    return top1, losses.avg
+
+    return meter.loss_meter.avg
 
 
 if __name__=='__main__':