✏️ [Fix] Process <- Proccess typo

yolo/__init__.py

@@ -10,7 +10,7 @@ from yolo.utils.logging_utils import (
     YOLORichModelSummary,
     YOLORichProgressBar,
 )
-from yolo.utils.model_utils import PostProccess
+from yolo.utils.model_utils import PostProcess
 
 all = [
     "create_model",
@@ -29,5 +29,5 @@ all = [
     "create_dataloader",
     "FastModelLoader",
     "TrainModel",
-    "PostProccess",
+    "PostProcess",
 ]

yolo/tools/data_loader.py

@@ -170,7 +170,7 @@ def collate_fn(batch: List[Tuple[Tensor, Tensor]]) -> Tuple[Tensor, List[Tensor]
     """
     batch_size = len(batch)
     target_sizes = [item[1].size(0) for item in batch]
-    # TODO: Improve readability of these proccess
+    # TODO: Improve readability of these process
     # TODO: remove maxBbox or reduce loss function memory usage
     batch_targets = torch.zeros(batch_size, min(max(target_sizes), 100), 5)
     batch_targets[:, :, 0] = -1

yolo/tools/solver.py

@@ -6,7 +6,7 @@ from yolo.model.yolo import create_model
 from yolo.tools.data_loader import create_dataloader
 from yolo.tools.loss_functions import create_loss_function
 from yolo.utils.bounding_box_utils import create_converter, to_metrics_format
-from yolo.utils.model_utils import PostProccess, create_optimizer, create_scheduler
+from yolo.utils.model_utils import PostProcess, create_optimizer, create_scheduler
 
 
 class BaseModel(LightningModule):
@@ -34,14 +34,14 @@ class ValidateModel(BaseModel):
         self.vec2box = create_converter(
             self.cfg.model.name, self.model, self.cfg.model.anchor, self.cfg.image_size, self.device
         )
-        self.post_proccess = PostProccess(self.vec2box, self.validation_cfg.nms)
+        self.post_process = PostProcess(self.vec2box, self.validation_cfg.nms)
 
     def val_dataloader(self):
         return self.val_loader
 
     def validation_step(self, batch, batch_idx):
         batch_size, images, targets, rev_tensor, img_paths = batch
-        predicts = self.post_proccess(self(images))
+        predicts = self.post_process(self(images))
         batch_metrics = self.metric(
             [to_metrics_format(predict) for predict in predicts], [to_metrics_format(target) for target in targets]
         )

yolo/utils/model_utils.py

@@ -124,7 +124,7 @@ def get_device(device_spec: Union[str, int, List[int]]) -> torch.device:
     return device, ddp_flag
 
 
-class PostProccess:
+class PostProcess:
     """
     TODO: function document
     scale back the prediction and do nms for pred_bbox