Bug fix: value of best_top1 stored in the checkpoint may be wrong (#77)

* Bug fix: value of best_top1 stored in the checkpoint may be wrong

If you invoke compress_clasifier.py with --num-best-scores=n
with n>1, then the value of best_top1 stored in checkpoints is wrong.

examples/classifier_compression/compress_classifier.py

@@ -399,19 +399,18 @@ def main():
         if compression_scheduler:
             compression_scheduler.on_epoch_end(epoch, optimizer)
 
-        # remember best top1 and save checkpoint
-        #sparsity = distiller.model_sparsity(model)
-        is_best = top1 > best_epochs[0].top1
-        if is_best:
+        # Update the list of top scores achieved so far, and save the checkpoint
+        is_best = top1 > best_epochs[-1].top1
+        if top1 > best_epochs[0].top1:
             best_epochs[0].epoch = epoch
             best_epochs[0].top1 = top1
-            #best_epoch.sparsity = sparsity
+            # Keep best_epochs sorted such that best_epochs[0] is the lowest top1 in the best_epochs list
             best_epochs = sorted(best_epochs, key=lambda score: score.top1)
         for score in reversed(best_epochs):
             if score.top1 > 0:
                 msglogger.info('==> Best Top1: %.3f on Epoch: %d', score.top1, score.epoch)
         apputils.save_checkpoint(epoch, args.arch, model, optimizer, compression_scheduler,
-                                 best_epochs[0].top1, is_best, args.name, msglogger.logdir)
+                                 best_epochs[-1].top1, is_best, args.name, msglogger.logdir)
 
     # Finally run results on the test set
     test(test_loader, model, criterion, [pylogger], activations_collectors, args=args)