fix pytorch yolo docstrings

Signed-off-by: Farhan Ahmed <[email protected]>

Author: f4str

art/estimators/object_detection/pytorch_yolo.py

@@ -153,7 +153,7 @@ def __init__(
 
                       - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                         0 <= y1 < y2 <= H.
-                      - labels [N]: the labels for each image
+                      - labels [N]: the labels for each image.
                       - scores [N]: the scores of each prediction.
         :param input_shape: The shape of one input sample.
         :param optimizer: The optimizer for training the classifier.
@@ -275,8 +275,7 @@ def _preprocess_and_convert_inputs(
                   The fields of the Dict are as follows:
 
                   - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
-                  - labels [N]: the labels for each image
-                  - scores [N]: the scores of each prediction.
+                  - labels [N]: the labels for each image.
         :param fit: `True` if the function is call before fit/training and `False` if the function is called before a
                     predict operation.
         :param no_grad: `True` if no gradients required.
@@ -365,8 +364,7 @@ def _get_losses(
                   The fields of the Dict are as follows:
 
                   - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
-                  - labels [N]: the labels for each image
-                  - scores [N]: the scores of each prediction.
+                  - labels [N]: the labels for each image.
         :return: Loss gradients of the same shape as `x`.
         """
         self._model.train()
@@ -401,8 +399,7 @@ def loss_gradient(  # pylint: disable=W0613
                   The fields of the Dict are as follows:
 
                   - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
-                  - labels [N]: the labels for each image
-                  - scores [N]: the scores of each prediction.
+                  - labels [N]: the labels for each image.
         :return: Loss gradients of the same shape as `x`.
         """
         import torch
@@ -457,7 +454,7 @@ def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> List[Dict[s
                  are as follows:
 
                   - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
-                  - labels [N]: the labels for each image
+                  - labels [N]: the labels for each image.
                   - scores [N]: the scores of each prediction.
         """
         import torch
@@ -521,8 +518,7 @@ def fit(  # pylint: disable=W0221
                   The fields of the Dict are as follows:
 
                   - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
-                  - labels [N]: the labels for each image
-                  - scores [N]: the scores of each prediction.
+                  - labels [N]: the labels for each image.
         :param batch_size: Size of batches.
         :param nb_epochs: Number of epochs to use for training.
         :param drop_last: Set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by
@@ -604,8 +600,7 @@ def compute_losses(
                   The fields of the Dict are as follows:
 
                   - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
-                  - labels [N]: the labels for each image
-                  - scores [N]: the scores of each prediction.
+                  - labels [N]: the labels for each image.
         :return: Dictionary of loss components.
         """
         loss_components, _ = self._get_losses(x=x, y=y)
@@ -625,8 +620,7 @@ def compute_loss(  # type: ignore
                   The fields of the Dict are as follows:
 
                   - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
-                  - labels [N]: the labels for each image
-                  - scores [N]: the scores of each prediction.
+                  - labels [N]: the labels for each image.
         :return: Loss.
         """
         import torch