Warn `save` disabled for torch inference (#3361)

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -294,10 +294,11 @@ class LoadPilAndNumpy:
 
 class LoadTensor:
 
-    def __init__(self, imgs) -> None:
+    def __init__(self, im0) -> None:
-        self.im0 = imgs
+        self.im0 = im0
-        self.bs = imgs.shape[0]
+        self.bs = im0.shape[0]
         self.mode = 'image'
+        self.paths = [getattr(im, 'filename', f'image{i}.jpg') for i, im in enumerate(im0)]
 
     def __iter__(self):
         """Returns an iterator object."""
@@ -309,7 +310,7 @@ class LoadTensor:
         if self.count == 1:
             raise StopIteration
         self.count += 1
-        return None, self.im0, None, ''  # self.paths, im, self.im0, None, ''
+        return self.paths, self.im0, None, ''
 
     def __len__(self):
         """Returns the batch size."""

ultralytics/yolo/engine/predictor.py

@@ -147,7 +147,6 @@ class BasePredictor:
         log_string = ''
         if len(im.shape) == 3:
             im = im[None]  # expand for batch dim
-        self.seen += 1
         if self.source_type.webcam or self.source_type.from_img:  # batch_size >= 1
             log_string += f'{idx}: '
             frame = self.dataset.count
@@ -251,11 +250,14 @@ class BasePredictor:
             # Visualize, save, write results
             n = len(im0s)
             for i in range(n):
+                self.seen += 1
                 self.results[i].speed = {
                     'preprocess': profilers[0].dt * 1E3 / n,
                     'inference': profilers[1].dt * 1E3 / n,
                     'postprocess': profilers[2].dt * 1E3 / n}
                 if self.source_type.tensor:  # skip write, show and plot operations if input is raw tensor
+                    if self.args.save or self.args.save_txt or self.args.show:
+                        LOGGER.warning('WARNING ⚠️ save, save_txt and show argument not enabled for tensor inference.')
                     continue
                 p, im0 = path[i], im0s[i].copy()
                 p = Path(p)