Avoid FP64 ops for MPS support in train.py (#8511)

Avoid FP64 ops for MPS support

Resolves #7878 (comment)

utils/general.py

@@ -644,7 +644,7 @@ def labels_to_class_weights(labels, nc=80):
         return torch.Tensor()
 
     labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
-    classes = labels[:, 0].astype(np.int)  # labels = [class xywh]
+    classes = labels[:, 0].astype(int)  # labels = [class xywh]
     weights = np.bincount(classes, minlength=nc)  # occurrences per class
 
     # Prepend gridpoint count (for uCE training)
@@ -654,13 +654,13 @@ def labels_to_class_weights(labels, nc=80):
     weights[weights == 0] = 1  # replace empty bins with 1
     weights = 1 / weights  # number of targets per class
     weights /= weights.sum()  # normalize
-    return torch.from_numpy(weights)
+    return torch.from_numpy(weights).float()
 
 
 def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
     # Produces image weights based on class_weights and image contents
     # Usage: index = random.choices(range(n), weights=image_weights, k=1)  # weighted image sample
-    class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
+    class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels])
     return (class_weights.reshape(1, nc) * class_counts).sum(1)