Simplify argument names (#141)

ultralytics/yolo/engine/exporter.py

@@ -164,14 +164,14 @@ class Exporter:
             assert not self.args.dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic'
 
         # Checks
-        # if self.args.batch_size == model.args['batch_size']:  # user has not modified training batch_size
-        self.args.batch_size = 1
+        # if self.args.batch == model.args['batch_size']:  # user has not modified training batch_size
+        self.args.batch = 1
         self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2)  # check image size
         if self.args.optimize:
             assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
 
         # Input
-        im = torch.zeros(self.args.batch_size, 3, *self.imgsz).to(self.device)
+        im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device)
         file = Path(getattr(model, 'pt_path', None) or model.yaml['yaml_file'])
         if file.suffix == '.yaml':
             file = Path(file.name)

ultralytics/yolo/engine/trainer.py

@@ -102,7 +102,7 @@ class BaseTrainer:
             yaml_save(self.save_dir / 'args.yaml', OmegaConf.to_container(self.args, resolve=True))  # save run args
         self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt'  # checkpoint paths
 
-        self.batch_size = self.args.batch_size
+        self.batch_size = self.args.batch
         self.epochs = self.args.epochs
         self.start_epoch = 0
         if RANK == -1:

ultralytics/yolo/engine/validator.py

@@ -87,18 +87,18 @@ class BaseValidator:
             callbacks.add_integration_callbacks(self)
             self.run_callbacks('on_val_start')
             assert model is not None, "Either trainer or model is needed for validation"
-            self.device = select_device(self.args.device, self.args.batch_size)
+            self.device = select_device(self.args.device, self.args.batch)
             self.args.half &= self.device.type != 'cpu'
             model = AutoBackend(model, device=self.device, dnn=self.args.dnn, fp16=self.args.half)
             self.model = model
             stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
             imgsz = check_imgsz(self.args.imgsz, stride=stride)
             if engine:
-                self.args.batch_size = model.batch_size
+                self.args.batch = model.batch_size
             else:
                 self.device = model.device
                 if not pt and not jit:
-                    self.args.batch_size = 1  # export.py models default to batch-size 1
+                    self.args.batch = 1  # export.py models default to batch-size 1
                     self.logger.info(
                         f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
 
@@ -110,7 +110,7 @@ class BaseValidator:
             if self.device.type == 'cpu':
                 self.args.workers = 0  # faster CPU val as time dominated by inference, not dataloading
             self.dataloader = self.dataloader or \
-                              self.get_dataloader(data.get("val") or data.set("test"), self.args.batch_size)
+                              self.get_dataloader(data.get("val") or data.set("test"), self.args.batch)
             self.data = data
 
         model.eval()