updates

test.py

              opt.augment)
 
     elif opt.task == 'study':  # run over a range of settings and save/plot
-        for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolovl.p5', 'yolov5x.pt', 'yolov3-spp.pt']:
+        for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolovl.pt', 'yolov5x.pt', 'yolov3-spp.pt']:
             f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem)  # filename to save to
             x = list(range(256, 1024, 32))  # x axis
             y = []  # y axis

train.py

     google_utils.attempt_download(weights)
     start_epoch, best_fitness = 0, 0.0
     if weights.endswith('.pt'):  # pytorch format
-        chkpt = torch.load(weights, map_location=device)
+        ckpt = torch.load(weights, map_location=device)  # load checkpoint
 
         # load model
         try:
-            chkpt['model'] = \
-                {k: v for k, v in chkpt['model'].state_dict().items() if model.state_dict()[k].numel() == v.numel()}
-            model.load_state_dict(chkpt['model'], strict=False)
+            ckpt['model'] = \
+                {k: v for k, v in ckpt['model'].state_dict().items() if model.state_dict()[k].numel() == v.numel()}
+            model.load_state_dict(ckpt['model'], strict=False)
         except KeyError as e:
             s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s." \
                 % (opt.weights, opt.cfg, opt.weights)
             raise KeyError(s) from e
 
         # load optimizer
-        if chkpt['optimizer'] is not None:
-            optimizer.load_state_dict(chkpt['optimizer'])
-            best_fitness = chkpt['best_fitness']
+        if ckpt['optimizer'] is not None:
+            optimizer.load_state_dict(ckpt['optimizer'])
+            best_fitness = ckpt['best_fitness']
 
         # load results
-        if chkpt.get('training_results') is not None:
+        if ckpt.get('training_results') is not None:
             with open(results_file, 'w') as file:
-                file.write(chkpt['training_results'])  # write results.txt
+                file.write(ckpt['training_results'])  # write results.txt
 
-        start_epoch = chkpt['epoch'] + 1
-        del chkpt
+        start_epoch = ckpt['epoch'] + 1
+        del ckpt
 
     # Mixed precision training https://github.com/NVIDIA/apex
     if mixed_precision:
         save = (not opt.nosave) or (final_epoch and not opt.evolve)
         if save:
             with open(results_file, 'r') as f:  # create checkpoint
-                chkpt = {'epoch': epoch,
+                ckpt = {'epoch': epoch,
                          'best_fitness': best_fitness,
                          'training_results': f.read(),
                          'model': ema.ema.module if hasattr(model, 'module') else ema.ema,
                          'optimizer': None if final_epoch else optimizer.state_dict()}
 
             # Save last, best and delete
-            torch.save(chkpt, last)
+            torch.save(ckpt, last)
             if (best_fitness == fi) and not final_epoch:
-                torch.save(chkpt, best)
-            del chkpt
+                torch.save(ckpt, best)
+            del ckpt
 
         # end epoch ----------------------------------------------------------------------------------------------------
     # end training