Cleanup docstrings

ultralytics/models/sam/modules/sam.py

@@ -30,11 +30,10 @@ class Sam(nn.Module):
         SAM predicts object masks from an image and input prompts.
 
         Args:
-          image_encoder (ImageEncoderViT): The backbone used to encode the
-            image into image embeddings that allow for efficient mask prediction.
+          image_encoder (ImageEncoderViT): The backbone used to encode the image into image embeddings that allow for
+            efficient mask prediction.
           prompt_encoder (PromptEncoder): Encodes various types of input prompts.
-          mask_decoder (MaskDecoder): Predicts masks from the image embeddings
-            and encoded prompts.
+          mask_decoder (MaskDecoder): Predicts masks from the image embeddings and encoded prompts.
           pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
           pixel_std (list(float)): Std values for normalizing pixels in the input image.
         """
@@ -65,34 +64,25 @@ class Sam(nn.Module):
 
         Args:
           batched_input (list(dict)): A list over input images, each a dictionary with the following keys. A prompt
-          key can be excluded if it is not present.
-              'image': The image as a torch tensor in 3xHxW format,
-                already transformed for input to the model.
-              'original_size': (tuple(int, int)) The original size of
-                the image before transformation, as (H, W).
-              'point_coords': (torch.Tensor) Batched point prompts for
-                this image, with shape BxNx2. Already transformed to the
-                input frame of the model.
-              'point_labels': (torch.Tensor) Batched labels for point prompts,
-                with shape BxN.
-              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
-                Already transformed to the input frame of the model.
-              'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
-                in the form Bx1xHxW.
+              key can be excluded if it is not present.
+              'image': The image as a torch tensor in 3xHxW format, already transformed for input to the model.
+              'original_size': (tuple(int, int)) The original size of the image before transformation, as (H, W).
+              'point_coords': (torch.Tensor) Batched point prompts for this image, with shape BxNx2. Already
+                transformed to the input frame of the model.
+              'point_labels': (torch.Tensor) Batched labels for point prompts, with shape BxN.
+              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4. Already transformed to the input frame of
+                the model.
+              'mask_inputs': (torch.Tensor) Batched mask inputs to the model, in the form Bx1xHxW.
           multimask_output (bool): Whether the model should predict multiple disambiguating masks, or return a single
             mask.
 
         Returns:
           (list(dict)): A list over input images, where each element is as dictionary with the following keys.
-              'masks': (torch.Tensor) Batched binary mask predictions,
-                with shape BxCxHxW, where B is the number of input prompts,
-                C is determined by multimask_output, and (H, W) is the
-                original size of the image.
-              'iou_predictions': (torch.Tensor) The model's predictions
-                of mask quality, in shape BxC.
-              'low_res_logits': (torch.Tensor) Low resolution logits with
-                shape BxCxHxW, where H=W=256. Can be passed as mask input
-                to subsequent iterations of prediction.
+              'masks': (torch.Tensor) Batched binary mask predictions, with shape BxCxHxW, where B is the number of
+                input prompts, C is determined by multimask_output, and (H, W) is the original size of the image.
+              'iou_predictions': (torch.Tensor) The model's predictions of mask quality, in shape BxC.
+              'low_res_logits': (torch.Tensor) Low resolution logits with shape BxCxHxW, where H=W=256. Can be passed
+                as mask input to subsequent iterations of prediction.
         """
         input_images = torch.stack([self.preprocess(x['image']) for x in batched_input], dim=0)
         image_embeddings = self.image_encoder(input_images)
@@ -137,16 +127,12 @@ class Sam(nn.Module):
         Remove padding and upscale masks to the original image size.
 
         Args:
-          masks (torch.Tensor): Batched masks from the mask_decoder,
-            in BxCxHxW format.
-          input_size (tuple(int, int)): The size of the image input to the
-            model, in (H, W) format. Used to remove padding.
-          original_size (tuple(int, int)): The original size of the image
-            before resizing for input to the model, in (H, W) format.
+          masks (torch.Tensor): Batched masks from the mask_decoder, in BxCxHxW format.
+          input_size (tuple(int, int)): The size of the model input image, in (H, W) format. Used to remove padding.
+          original_size (tuple(int, int)): The original image size before resizing for input to the model, in (H, W).
 
         Returns:
-          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
-            is given by original_size.
+          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W) is given by original_size.
         """
         masks = F.interpolate(
             masks,