Use pathlib in DOTA ops (#7552)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/utils/benchmarks.py

@@ -84,12 +84,8 @@ def benchmark(
         emoji, filename = "❌", None  # export defaults
         try:
             assert i != 9 or LINUX, "Edge TPU export only supported on Linux"
-            if i == 5:
-                assert MACOS or LINUX, "CoreML export only supported on macOS and Linux"
-            elif i == 10:
-                assert MACOS or LINUX, "TF.js export only supported on macOS and Linux"
-            # elif i == 11:
-            #     assert sys.version_info < (3, 11), "PaddlePaddle export only supported on Python<=3.10"
+            if i in {5, 10}:  # CoreML and TF.js
+                assert MACOS or LINUX, "export only supported on macOS and Linux"
             if "cpu" in device.type:
                 assert cpu, "inference not supported on CPU"
             if "cuda" in device.type:

ultralytics/utils/callbacks/comet.py

@@ -105,12 +105,7 @@ def _fetch_trainer_metadata(trainer):
     save_interval = curr_epoch % save_period == 0
     save_assets = save and save_period > 0 and save_interval and not final_epoch
 
-    return dict(
-        curr_epoch=curr_epoch,
-        curr_step=curr_step,
-        save_assets=save_assets,
-        final_epoch=final_epoch,
-    )
+    return dict(curr_epoch=curr_epoch, curr_step=curr_step, save_assets=save_assets, final_epoch=final_epoch)
 
 
 def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad):
@@ -218,11 +213,7 @@ def _log_confusion_matrix(experiment, trainer, curr_step, curr_epoch):
     conf_mat = trainer.validator.confusion_matrix.matrix
     names = list(trainer.data["names"].values()) + ["background"]
     experiment.log_confusion_matrix(
-        matrix=conf_mat,
-        labels=names,
-        max_categories=len(names),
-        epoch=curr_epoch,
-        step=curr_step,
+        matrix=conf_mat, labels=names, max_categories=len(names), epoch=curr_epoch, step=curr_step
     )
 
 
@@ -294,12 +285,7 @@ def _log_plots(experiment, trainer):
 def _log_model(experiment, trainer):
     """Log the best-trained model to Comet.ml."""
     model_name = _get_comet_model_name()
-    experiment.log_model(
-        model_name,
-        file_or_folder=str(trainer.best),
-        file_name="best.pt",
-        overwrite=True,
-    )
+    experiment.log_model(model_name, file_or_folder=str(trainer.best), file_name="best.pt", overwrite=True)
 
 
 def on_pretrain_routine_start(trainer):
@@ -320,11 +306,7 @@ def on_train_epoch_end(trainer):
     curr_epoch = metadata["curr_epoch"]
     curr_step = metadata["curr_step"]
 
-    experiment.log_metrics(
-        trainer.label_loss_items(trainer.tloss, prefix="train"),
-        step=curr_step,
-        epoch=curr_epoch,
-    )
+    experiment.log_metrics(trainer.label_loss_items(trainer.tloss, prefix="train"), step=curr_step, epoch=curr_epoch)
 
     if curr_epoch == 1:
         _log_images(experiment, trainer.save_dir.glob("train_batch*.jpg"), curr_step)

ultralytics/utils/loss.py

@@ -38,9 +38,7 @@ class VarifocalLoss(nn.Module):
 class FocalLoss(nn.Module):
     """Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
 
-    def __init__(
-        self,
-    ):
+    def __init__(self):
         """Initializer for FocalLoss class with no parameters."""
         super().__init__()