updates for beta

exps/algos/ENAS.py

@@ -62,7 +62,7 @@ def train_controller(xloader, shared_cnn, controller, criterion, optimizer, conf
   # config. (containing some necessary arg)
   #   baseline: The baseline score (i.e. average val_acc) from the previous epoch
   data_time, batch_time = AverageMeter(), AverageMeter()
-  GradnormMeter, LossMeter, ValAccMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time()
+  GradnormMeter, LossMeter, ValAccMeter, EntropyMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time()
   
   shared_cnn.eval()
   controller.train()
@@ -96,8 +96,9 @@ def train_controller(xloader, shared_cnn, controller, criterion, optimizer, conf
     # account
     RewardMeter.update(reward.item())
     BaselineMeter.update(baseline.item())
-    ValAccMeter.update(val_top1.item())
+    ValAccMeter.update(val_top1.item()*100)
     LossMeter.update(loss.item())
+    EntropyMeter.update(entropy.item())
   
     # Average gradient over controller_num_aggregate samples
     loss = loss / config.ctl_num_aggre
@@ -116,7 +117,8 @@ def train_controller(xloader, shared_cnn, controller, criterion, optimizer, conf
       Sstr = '*Train-Controller* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, config.ctl_train_steps * config.ctl_num_aggre)
       Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
       Wstr = '[Loss {loss.val:.3f} ({loss.avg:.3f})  Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Reward {reward.val:.2f} ({reward.avg:.2f})] Baseline {basel.val:.2f} ({basel.avg:.2f})'.format(loss=LossMeter, top1=ValAccMeter, reward=RewardMeter, basel=BaselineMeter)
-      logger.log(Sstr + ' ' + Tstr + ' ' + Wstr)
+      Estr = 'Entropy={:.4f} ({:.4f})'.format(EntropyMeter.val, EntropyMeter.avg)
+      logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Estr)
 
   return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg, baseline.item()
 
@@ -250,7 +252,7 @@ def main(xargs):
     w_scheduler.update(epoch, 0.0)
     need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) )
     epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
-    logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
+    logger.log('\n[Search the {:}-th epoch] {:}, LR={:}, baseline={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()), baseline))
 
     cnn_loss, cnn_top1, cnn_top5 = train_shared_cnn(train_loader, shared_cnn, controller, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, logger)
     logger.log('[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, cnn_loss, cnn_top1, cnn_top5))
@@ -264,7 +266,7 @@ def main(xargs):
     logger.log('[{:}] controller : loss={:.2f}, accuracy={:.2f}%, baseline={:.2f}, reward={:.2f}, current-baseline={:.4f}'.format(epoch_str, ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline))
     best_arch, _ = get_best_arch(controller, shared_cnn, valid_loader)
     shared_cnn.module.update_arch(best_arch)
-    best_valid_acc = valid_func(valid_loader, shared_cnn, criterion)
+    _, best_valid_acc, _ = valid_func(valid_loader, shared_cnn, criterion)
 
     genotypes[epoch] = best_arch
     # check the best accuracy
@@ -301,6 +303,14 @@ def main(xargs):
     start_time = time.time()
 
   logger.log('\n' + '-'*100)
+  logger.log('During searching, the best architecture is {:}'.format(genotypes['best']))
+  logger.log('Its accuracy is {:.2f}%'.format(valid_accuracies['best']))
+  logger.log('Randomly select {:} architectures and select the best.'.format(xargs.controller_num_samples))
+  final_arch, _ = get_best_arch(controller, shared_cnn, valid_loader, xargs.controller_num_samples)
+  shared_cnn.module.update_arch(final_arch)
+  final_loss, final_top1, final_top5 = valid_func(valid_loader, shared_cnn, criterion)
+  logger.log('The Selected Final Architecture : {:}'.format(final_arch))
+  logger.log('Loss={:.3f}, Accuracy@1={:.2f}%, Accuracy@5={:.2f}%'.format(final_loss, final_top1, final_top5))
   # check the performance from the architecture dataset
   #if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset):
   #  logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset))