From 9268747cad293ee2bb3070e88a941600edcddfa3 Mon Sep 17 00:00:00 2001
From: Martin Kozlovsky <martin.kozlovsky@luxonis.com>
Date: Fri, 8 Nov 2024 17:45:11 +0100
Subject: [PATCH 1/2] fixed warnings

---
 luxonis_ml/data/__init__.py                   |   2 +-
 .../data/augmentations/batch_compose.py       |   8 +-
 .../augmentations/custom/letterbox_resize.py  | 100 +++++------
 luxonis_ml/data/augmentations/custom/mixup.py |  66 ++++----
 .../data/augmentations/custom/mosaic.py       | 158 +++++++++---------
 luxonis_ml/data/augmentations/utils.py        |   2 -
 luxonis_ml/data/datasets/annotation.py        |   6 +-
 luxonis_ml/data/datasets/base_dataset.py      |  15 +-
 luxonis_ml/data/datasets/luxonis_dataset.py   |  12 +-
 luxonis_ml/data/datasets/source.py            |   2 +-
 luxonis_ml/data/loaders/base_loader.py        |  14 +-
 luxonis_ml/data/loaders/luxonis_loader.py     |   4 +-
 luxonis_ml/data/parsers/base_parser.py        |   7 +-
 13 files changed, 201 insertions(+), 195 deletions(-)

diff --git a/luxonis_ml/data/__init__.py b/luxonis_ml/data/__init__.py
index 76d91449..684fdb96 100644
--- a/luxonis_ml/data/__init__.py
+++ b/luxonis_ml/data/__init__.py
@@ -1,6 +1,6 @@
 import pkg_resources
 
-from ..guard_extras import guard_missing_extra
+from luxonis_ml.guard_extras import guard_missing_extra
 
 with guard_missing_extra("data"):
     from .augmentations import Augmentations
diff --git a/luxonis_ml/data/augmentations/batch_compose.py b/luxonis_ml/data/augmentations/batch_compose.py
index fbea3111..5969ee0d 100644
--- a/luxonis_ml/data/augmentations/batch_compose.py
+++ b/luxonis_ml/data/augmentations/batch_compose.py
@@ -42,8 +42,8 @@ def __init__(
 
         @param transforms: List of transformations to compose
         @type transforms: TransformsSeqType
-        @param bboxparams: Parameters for bounding boxes transforms. Defaults to None.
-        @type bboxparams: Optional[Union[dict, BboxParams]]
+        @param bbox_params: Parameters for bounding boxes transforms. Defaults to None.
+        @type bbox_params: Optional[Union[dict, BboxParams]]
         @param keypoint_params: Parameters for keypoint transforms. Defaults to None.
         @type keypoint_params: Optional[Union[dict, KeypointParams]]
         @param additional_targets: Dict with keys - new target name, values - old target
@@ -283,8 +283,8 @@ def __init__(
 
         @param transforms: List of transformations to compose
         @type transforms: TransformsSeqType
-        @param bboxparams: Parameters for bounding boxes transforms. Defaults to None.
-        @type bboxparams: Optional[Union[dict, BboxParams]]
+        @param bbox_params: Parameters for bounding boxes transforms. Defaults to None.
+        @type bbox_params: Optional[Union[dict, BboxParams]]
         @param keypoint_params: Parameters for keypoint transforms. Defaults to None.
         @type keypoint_params: Optional[Union[dict, KeypointParams]]
         @param additional_targets: Dict with keys - new target name, values - old target
diff --git a/luxonis_ml/data/augmentations/custom/letterbox_resize.py b/luxonis_ml/data/augmentations/custom/letterbox_resize.py
index 95884bc8..073e5c57 100644
--- a/luxonis_ml/data/augmentations/custom/letterbox_resize.py
+++ b/luxonis_ml/data/augmentations/custom/letterbox_resize.py
@@ -23,23 +23,23 @@ def __init__(
         """Augmentation to apply letterbox resizing to images. Also
         transforms masks, bboxes and keypoints to correct shape.
 
-        @param height: Desired height of the output.
         @type height: int
-        @param width: Desired width of the output.
+        @param height: Desired height of the output.
         @type width: int
-        @param interpolation: Cv2 flag to specify interpolation used
-            when resizing. Defaults to cv2.INTER_LINEAR.
+        @param width: Desired width of the output.
         @type interpolation: int, optional
-        @param border_value: Padding value for images. Defaults to 0.
+        @param interpolation: cv2 flag to specify interpolation used
+            when resizing. Defaults to C{cv2.INTER_LINEAR}.
         @type border_value: int, optional
-        @param mask_value: Padding value for masks. Defaults to 0.
+        @param border_value: Padding value for images. Defaults to C{0}.
         @type mask_value: int, optional
-        @param always_apply: Whether to always apply the transform.
-            Defaults to False.
+        @param mask_value: Padding value for masks. Defaults to C{0}.
         @type always_apply: bool, optional
-        @param p: Probability of applying the transform. Defaults to
-            1.0.
+        @param always_apply: Whether to always apply the transform.
+            Defaults to C{False}.
         @type p: float, optional
+        @param p: Probability of applying the transform. Defaults to
+            C{1.0}.
         """
 
         super().__init__(always_apply, p)
@@ -108,20 +108,20 @@ def apply(
     ) -> np.ndarray:
         """Applies the letterbox augmentation to an image.
 
-        @param img: Input image to which resize is applied.
         @type img: np.ndarray
-        @param pad_top: Number of pixels to pad at the top.
+        @param img: Input image to which resize is applied.
         @type pad_top: int
-        @param pad_bottom: Number of pixels to pad at the bottom.
+        @param pad_top: Number of pixels to pad at the top.
         @type pad_bottom: int
-        @param pad_left: Number of pixels to pad on the left.
+        @param pad_bottom: Number of pixels to pad at the bottom.
         @type pad_left: int
-        @param pad_right: Number of pixels to pad on the right.
+        @param pad_left: Number of pixels to pad on the left.
         @type pad_right: int
-        @param params: Additional parameters for the padding operation.
-        @type params: Any
-        @return: Image with applied letterbox resize.
+        @param pad_right: Number of pixels to pad on the right.
+        @type kwargs: Any
+        @param kwargs: Additional parameters for the padding operation.
         @rtype: np.ndarray
+        @return: Image with applied letterbox resize.
         """
 
         resized_img = cv2.resize(
@@ -155,20 +155,20 @@ def apply_to_mask(
     ) -> np.ndarray:
         """Applies letterbox augmentation to the input mask.
 
-        @param img: Input mask to which resize is applied.
         @type img: np.ndarray
-        @param pad_top: Number of pixels to pad at the top.
+        @param img: Input mask to which resize is applied.
         @type pad_top: int
-        @param pad_bottom: Number of pixels to pad at the bottom.
+        @param pad_top: Number of pixels to pad at the top.
         @type pad_bottom: int
-        @param pad_left: Number of pixels to pad on the left.
+        @param pad_bottom: Number of pixels to pad at the bottom.
         @type pad_left: int
-        @param pad_right: Number of pixels to pad on the right.
+        @param pad_left: Number of pixels to pad on the left.
         @type pad_right: int
-        @param params: Additional parameters for the padding operation.
+        @param pad_right: Number of pixels to pad on the right.
         @type params: Any
-        @return: Mask with applied letterbox resize.
+        @param params: Additional parameters for the padding operation.
         @rtype: np.ndarray
+        @return: Mask with applied letterbox resize.
         """
 
         resized_img = cv2.resize(
@@ -202,20 +202,20 @@ def apply_to_bbox(
     ) -> BoxType:
         """Applies letterbox augmentation to the bounding box.
 
-        @param img: Bounding box to which resize is applied.
-        @type img: BoxType
-        @param pad_top: Number of pixels to pad at the top.
+        @type bbox: BoxType
+        @param bbox: Bounding box to which resize is applied.
         @type pad_top: int
-        @param pad_bottom: Number of pixels to pad at the bottom.
+        @param pad_top: Number of pixels to pad at the top.
         @type pad_bottom: int
-        @param pad_left: Number of pixels to pad on the left.
+        @param pad_bottom: Number of pixels to pad at the bottom.
         @type pad_left: int
-        @param pad_right: Number of pixels to pad on the right.
+        @param pad_left: Number of pixels to pad on the left.
         @type pad_right: int
-        @param params: Additional parameters for the padding operation.
+        @param pad_right: Number of pixels to pad on the right.
         @type params: Any
-        @return: Bounding box with applied letterbox resize.
+        @param params: Additional parameters for the padding operation.
         @rtype: BoxType
+        @return: Bounding box with applied letterbox resize.
         """
 
         x_min, y_min, x_max, y_max = denormalize_bbox(
@@ -245,29 +245,29 @@ def apply_to_keypoint(
         pad_bottom: int,
         pad_left: int,
         pad_right: int,
-        **params,
+        **kwargs,
     ) -> KeypointType:
         """Applies letterbox augmentation to the keypoint.
 
-        @param img: Keypoint to which resize is applied.
-        @type img: KeypointType
-        @param pad_top: Number of pixels to pad at the top.
+        @type keypoint: KeypointType
+        @param keypoint: Keypoint to which resize is applied.
         @type pad_top: int
-        @param pad_bottom: Number of pixels to pad at the bottom.
+        @param pad_top: Number of pixels to pad at the top.
         @type pad_bottom: int
-        @param pad_left: Number of pixels to pad on the left.
+        @param pad_bottom: Number of pixels to pad at the bottom.
         @type pad_left: int
-        @param pad_right: Number of pixels to pad on the right.
+        @param pad_left: Number of pixels to pad on the left.
         @type pad_right: int
-        @param params: Additional parameters for the padding operation.
-        @type params: Any
-        @return: Keypoint with applied letterbox resize.
+        @param pad_right: Number of pixels to pad on the right.
+        @type kwargs: Any
+        @param kwargs: Additional parameters for the padding operation.
         @rtype: KeypointType
+        @return: Keypoint with applied letterbox resize.
         """
 
         x, y, angle, scale = keypoint[:4]
-        scale_x = (self.width - pad_left - pad_right) / params["cols"]
-        scale_y = (self.height - pad_top - pad_bottom) / params["rows"]
+        scale_x = (self.width - pad_left - pad_right) / kwargs["cols"]
+        scale_y = (self.height - pad_top - pad_bottom) / kwargs["rows"]
         new_x = (x * scale_x) + pad_left
         new_y = (y * scale_y) + pad_top
         # if keypoint is in the padding then set coordinates to -1
@@ -286,8 +286,8 @@ def apply_to_keypoint(
     def get_transform_init_args_names(self) -> Tuple[str, ...]:
         """Gets the default arguments for the letterbox augmentation.
 
-        @return: The string keywords of the arguments.
         @rtype: Tuple[str, ...]
+        @return: The string keywords of the arguments.
         """
 
         return (
@@ -303,14 +303,14 @@ def _out_of_bounds(
     ) -> bool:
         """ "Check if the given value is outside the specified limits.
 
-        @param value: The value to be checked.
         @type value: float
-        @param min_limit: Minimum limit.
+        @param value: The value to be checked.
         @type min_limit: float
-        @param max_limit: Maximum limit.
+        @param min_limit: Minimum limit.
         @type max_limit: float
+        @param max_limit: Maximum limit.
+        @rtype: bool
         @return: True if the value is outside the specified limits,
             False otherwise.
-        @rtype: bool
         """
         return value < min_limit or value > max_limit
diff --git a/luxonis_ml/data/augmentations/custom/mixup.py b/luxonis_ml/data/augmentations/custom/mixup.py
index 17dce2b7..866f28a9 100644
--- a/luxonis_ml/data/augmentations/custom/mixup.py
+++ b/luxonis_ml/data/augmentations/custom/mixup.py
@@ -23,18 +23,18 @@ def __init__(
         annotations into one. If images are not of same size then second
         one is first resized to match the first one.
 
+        @type alpha: Union[float, Tuple[float, float]]
         @param alpha: Mixing coefficient, either a single float or a
-            tuple representing the range. Defaults to 0.5.
-        @type alpha: Union[float, Tuple[float, float]], optional
+            tuple representing the range. Defaults to C{0.5}.
+        @type out_batch_size: int
         @param out_batch_size: Number of output images in the batch.
-            Defaults to 1.
-        @type out_batch_size: int, optional
+            Defaults to C{1}.
+        @type always_apply: bool
         @param always_apply: Whether to always apply the transform.
-            Defaults to False.
-        @type always_apply: bool, optional
-        @param p: Probability of applying the transform. Defaults to
-            0.5.
+            Defaults to C{False}.
         @type p: float, optional
+        @param p: Probability of applying the transform. Defaults to
+            C{0.5}.
         """
         super().__init__(batch_size=2, always_apply=always_apply, p=p)
 
@@ -44,8 +44,8 @@ def __init__(
     def get_transform_init_args_names(self) -> Tuple[str, ...]:
         """Gets the default arguments for the mixup augmentation.
 
-        @return: The string keywords of the arguments.
         @rtype: Tuple[str, ...]
+        @return: The string keywords of the arguments.
         """
         return ("alpha", "out_batch_size")
 
@@ -53,8 +53,8 @@ def get_transform_init_args_names(self) -> Tuple[str, ...]:
     def targets_as_params(self) -> List[str]:
         """List of augmentation targets.
 
-        @return: Output list of augmentation targets.
         @rtype: List[str]
+        @return: Output list of augmentation targets.
         """
         return ["image_batch"]
 
@@ -66,15 +66,15 @@ def apply_to_image_batch(
     ) -> List[np.ndarray]:
         """Applies the transformation to a batch of images.
 
+        @type image_batch: List[np.ndarray]
         @param image_batch: Batch of input images to which the
             transformation is applied.
-        @type image_batch: List[np.ndarray]
-        @param image_shapes: Shapes of the input images in the batch.
         @type image_shapes: List[Tuple[int, int]]
-        @param params: Additional parameters for the transformation.
+        @param image_shapes: Shapes of the input images in the batch.
         @type params: Any
-        @return: List of transformed images.
+        @param params: Additional parameters for the transformation.
         @rtype: List[np.ndarray]
+        @return: List of transformed images.
         """
         image1 = image_batch[0]
         # resize second image to size of the first one
@@ -101,15 +101,15 @@ def apply_to_mask_batch(
     ) -> List[np.ndarray]:
         """Applies the transformation to a batch of masks.
 
-        @param image_batch: Batch of input masks to which the
+        @type mask_batch: List[np.ndarray]
+        @param mask_batch: Batch of input masks to which the
             transformation is applied.
-        @type image_batch: List[np.ndarray]
-        @param image_shapes: Shapes of the input images in the batch.
         @type image_shapes: List[Tuple[int, int]]
-        @param params: Additional parameters for the transformation.
+        @param image_shapes: Shapes of the input images in the batch.
         @type params: Any
-        @return: List of transformed masks.
+        @param params: Additional parameters for the transformation.
         @rtype: List[np.ndarray]
+        @return: List of transformed masks.
         """
         mask1 = mask_batch[0]
         mask2 = cv2.resize(
@@ -127,19 +127,19 @@ def apply_to_bboxes_batch(
         self,
         bboxes_batch: List[BoxType],
         image_shapes: List[Tuple[int, int]],
-        **params,
+        **kwargs,
     ) -> List[BoxType]:
         """Applies the transformation to a batch of bboxes.
 
-        @param image_batch: Batch of input bboxes to which the
+        @type bboxes_batch: List[BoxType]
+        @param bboxes_batch: Batch of input bboxes to which the
             transformation is applied.
-        @type image_batch: List[BoxType]
-        @param image_shapes: Shapes of the input images in the batch.
         @type image_shapes: List[Tuple[int, int]]
-        @param params: Additional parameters for the transformation.
-        @type params: Any
-        @return: List of transformed bboxes.
+        @param image_shapes: Shapes of the input images in the batch.
+        @type kwargs: Any
+        @param kwargs: Additional parameters for the transformation.
         @rtype: List[BoxType]
+        @return: List of transformed bboxes.
         """
         return [bboxes_batch[0] + bboxes_batch[1]]
 
@@ -147,19 +147,19 @@ def apply_to_keypoints_batch(
         self,
         keypoints_batch: List[KeypointType],
         image_shapes: List[Tuple[int, int]],
-        **params,
+        **kwargs,
     ) -> List[KeypointType]:
         """Applies the transformation to a batch of keypoints.
 
-        @param image_batch: Batch of input keypoints to which the
+        @type keypoints_batch: List[BoxType]
+        @param keypoints_batch: Batch of input keypoints to which the
             transformation is applied.
-        @type image_batch: List[BoxType]
-        @param image_shapes: Shapes of the input images in the batch.
         @type image_shapes: List[Tuple[int, int]]
-        @param params: Additional parameters for the transformation.
-        @type params: Any
-        @return: List of transformed keypoints.
+        @param image_shapes: Shapes of the input images in the batch.
+        @type kwargs: Any
+        @param kwargs: Additional parameters for the transformation.
         @rtype: List[BoxType]
+        @return: List of transformed keypoints.
         """
         scaled_kpts2 = []
         scale_x = image_shapes[0][1] / image_shapes[1][1]
diff --git a/luxonis_ml/data/augmentations/custom/mosaic.py b/luxonis_ml/data/augmentations/custom/mosaic.py
index a0c912fc..9e16538a 100644
--- a/luxonis_ml/data/augmentations/custom/mosaic.py
+++ b/luxonis_ml/data/augmentations/custom/mosaic.py
@@ -35,30 +35,32 @@ def __init__(
         have different widths and heights. The output is cropped around
         the intersection point of the four images with the size
         (out_with x out_height). If the mosaic image is smaller than
-        width x height, the gap is filled by the fill_value.
+        width x height, the gap is filled by the C{fill_value}.
 
-        @param out_height: Output image height. The mosaic image is cropped by this height around the mosaic center.
-        If the size of the mosaic image is smaller than this value the gap is filled by the `value`.
         @type out_height: int
-
-        @param out_width: Output image width. The mosaic image is cropped by this height around the mosaic center.
-        If the size of the mosaic image is smaller than this value the gap is filled by the `value`.
+        @param out_height: Output image height. The mosaic image is
+            cropped by this height around the mosaic center. If the size
+            of the mosaic image is smaller than this value the gap is
+            filled by the C{value}.
         @type out_width: int
-
-        @param value: Padding value. Defaults to None.
-        @type value: Optional[Union[int, float, List[int], List[float]]], optional
-
-        @param out_batch_size: Number of output images in the batch. Defaults to 1.
-        @type out_batch_size: int, optional
-
-        @param mask_value: Padding value for masks. Defaults to None.
-        @type mask_value: Optional[Union[int, float, List[int], List[float]]], optional
-
-        @param always_apply: Whether to always apply the transform. Defaults to False.
-        @type always_apply: bool, optional
-
-        @param p: Probability of applying the transform. Defaults to 0.5.
-        @type p: float, optional
+        @param out_width: Output image width. The mosaic image is
+            cropped by this height around the mosaic center. If the size
+            of the mosaic image is smaller than this value the gap is
+            filled by the C{value}.
+        @type value: Optional[Union[int, float, List[int], List[float]]]
+        @param value: Padding value. Defaults to C{None}.
+        @type out_batch_size: int
+        @param out_batch_size: Number of output images in the batch.
+            Defaults to C{1}.
+        @type mask_value: Optional[Union[int, float, List[int],
+            List[float]]]
+        @param mask_value: Padding value for masks. Defaults to C{None}.
+        @type always_apply: bool
+        @param always_apply: Whether to always apply the transform.
+            Defaults to C{False}.
+        @type p: float
+        @param p: Probability of applying the transform. Defaults to
+            C{0.5}.
         """
 
         super().__init__(batch_size=4, always_apply=always_apply, p=p)
@@ -86,8 +88,8 @@ def __init__(
     def get_transform_init_args_names(self) -> Tuple[str, ...]:
         """Gets the default arguments for the mixup augmentation.
 
-        @return: The string keywords of the arguments.
         @rtype: Tuple[str, ...]
+        @return: The string keywords of the arguments.
         """
         return (
             "out_height",
@@ -109,8 +111,8 @@ def _generate_random_crop_center(self) -> Tuple[int, int]:
     def targets_as_params(self):
         """List of augmentation targets.
 
-        @return: Output list of augmentation targets.
         @rtype: List[str]
+        @return: Output list of augmentation targets.
         """
         return ["image_batch"]
 
@@ -124,19 +126,19 @@ def apply_to_image_batch(
     ) -> List[np.ndarray]:
         """Applies the transformation to a batch of images.
 
+        @type image_batch: List[np.ndarray]
         @param image_batch: Batch of input images to which the
             transformation is applied.
-        @type image_batch: List[np.ndarray]
-        @param indices: Indices of images in the batch.
         @type indices: List[Tuple[int, int]]
-        @param params: Additional parameters for the transformation.
+        @param indices: Indices of images in the batch.
         @type params: Any
-        @param x_crop: x-coordinate of the croping start point
+        @param params: Additional parameters for the transformation.
         @type x_crop: int
-        @param y_crop: y-coordinate of the croping start point
+        @param x_crop: x-coordinate of the croping start point
         @type y_crop: int
-        @return: List of transformed images.
+        @param y_crop: y-coordinate of the croping start point
         @rtype: List[np.ndarray]
+        @return: List of transformed images.
         """
         output_batch = []
         for i_batch in range(self.out_batch_size):
@@ -165,19 +167,19 @@ def apply_to_mask_batch(
     ) -> List[np.ndarray]:
         """Applies the transformation to a batch of masks.
 
+        @type mask_batch: List[np.ndarray]
         @param mask_batch: Batch of input masks to which the
             transformation is applied.
-        @type mask_batch: List[np.ndarray]
-        @param indices: Indices of images in the batch.
         @type indices: List[Tuple[int, int]]
-        @param params: Additional parameters for the transformation.
+        @param indices: Indices of images in the batch.
         @type params: Any
-        @param x_crop: x-coordinate of the croping start point
+        @param params: Additional parameters for the transformation.
         @type x_crop: int
-        @param y_crop: y-coordinate of the croping start point
+        @param x_crop: x-coordinate of the croping start point
         @type y_crop: int
-        @return: List of transformed masks.
+        @param y_crop: y-coordinate of the croping start point
         @rtype: List[np.ndarray]
+        @return: List of transformed masks.
         """
         output_batch = []
         for i_batch in range(self.out_batch_size):
@@ -207,21 +209,21 @@ def apply_to_bboxes_batch(
     ) -> List[BoxType]:
         """Applies the transformation to a batch of bboxes.
 
+        @type bboxes_batch: List[BboxType]
         @param bboxes_batch: Batch of input bboxes to which the
             transformation is applied.
-        @type bboxes_batch: List[BboxType]
-        @param indices: Indices of images in the batch.
         @type indices: List[Tuple[int, int]]
-        @param image_shapes: Shapes of the input images in the batch.
+        @param indices: Indices of images in the batch.
         @type image_shapes: List[Tuple[int, int]]
-        @param params: Additional parameters for the transformation.
+        @param image_shapes: Shapes of the input images in the batch.
         @type params: Any
-        @param x_crop: x-coordinate of the croping start point
+        @param params: Additional parameters for the transformation.
         @type x_crop: int
-        @param y_crop: y-coordinate of the croping start point
+        @param x_crop: x-coordinate of the croping start point
         @type y_crop: int
-        @return: List of transformed bboxes.
+        @param y_crop: y-coordinate of the croping start point
         @rtype: List[BoxType]
+        @return: List of transformed bboxes.
         """
         output_batch = []
         for i_batch in range(self.out_batch_size):
@@ -251,7 +253,7 @@ def apply_to_bboxes_batch(
 
     def apply_to_keypoints_batch(
         self,
-        keyboints_batch: List[KeypointType],
+        keypoints_batch: List[KeypointType],
         indices: List[int],
         image_shapes: List[Tuple[int, int]],
         x_crop: int,
@@ -260,28 +262,28 @@ def apply_to_keypoints_batch(
     ) -> List[KeypointType]:
         """Applies the transformation to a batch of keypoints.
 
+        @type keypoints_batch: List[KeypointType]
         @param keypoints_batch: Batch of input keypoints to which the
             transformation is applied.
-        @type keypoints_batch: List[KeypointType]
-        @param indices: Indices of images in the batch.
         @type indices: List[Tuple[int, int]]
-        @param image_shapes: Shapes of the input images in the batch.
+        @param indices: Indices of images in the batch.
         @type image_shapes: List[Tuple[int, int]]
-        @param params: Additional parameters for the transformation.
+        @param image_shapes: Shapes of the input images in the batch.
         @type params: Any
-        @param x_crop: x-coordinate of the croping start point
+        @param params: Additional parameters for the transformation.
         @type x_crop: int
-        @param y_crop: y-coordinate of the croping start point
+        @param x_crop: x-coordinate of the croping start point
         @type y_crop: int
-        @return: List of transformed keypoints.
+        @param y_crop: y-coordinate of the croping start point
         @rtype: List[KeypointType]
+        @return: List of transformed keypoints.
         """
         output_batch = []
         for i_batch in range(self.out_batch_size):
             idx_chunk = indices[
                 self.n_tiles * i_batch : self.n_tiles * (i_batch + 1)
             ]
-            keypoints_chunk = [keyboints_batch[i] for i in idx_chunk]
+            keypoints_chunk = [keypoints_batch[i] for i in idx_chunk]
             shape_chunk = [image_shapes[i] for i in idx_chunk]
             new_keypoints = []
             for i in range(self.n_tiles):
@@ -307,11 +309,11 @@ def get_params_dependent_on_targets(
     ) -> Dict[str, Any]:
         """Get parameters dependent on the targets.
 
-        @param params: Dictionary containing parameters.
         @type params: Dict[str, Any]
+        @param params: Dictionary containing parameters.
+        @rtype: Dict[str, Any]
         @return: Dictionary containing parameters dependent on the
             targets.
-        @rtype: Dict[str, Any]
         """
         image_batch = params["image_batch"]
         n = len(image_batch)
@@ -343,21 +345,21 @@ def mosaic4(
     have the same number of channels but can have different widths and
     heights. The gaps are filled by the value.
 
+    @type image_batch: List[np.ndarray]
     @param image_batch: Image list. The length should be four. Each
         image can has different size.
-    @type image_batch: List[np.ndarray]
-    @param height: Height of output mosaic image
     @type height: int
-    @param width: Width of output mosaic image
+    @param height: Height of output mosaic image
     @type width: int
-    @param value: Padding value
+    @param width: Width of output mosaic image
     @type value: Optional[int]
-    @param x_crop: x-coordinate of the croping start point
+    @param value: Padding value
     @type x_crop: int
-    @param y_crop: y-coordinate of the croping start point
+    @param x_crop: x-coordinate of the croping start point
     @type y_crop: int
-    @return: Final output image
+    @param y_crop: y-coordinate of the croping start point
     @rtype: np.ndarray
+    @return: Final output image
     """
     N_TILES = 4
     if len(image_batch) != N_TILES:
@@ -455,25 +457,25 @@ def bbox_mosaic4(
     in a 2x2 grid mosaic, shifting their coordinates based on the tile's
     relative position within the mosaic.
 
-    @param bbox: Bounding box coordinates to be transformed.
     @type bbox: BoxInternalType
-    @param rows: Height of the original image.
+    @param bbox: Bounding box coordinates to be transformed.
     @type rows: int
-    @param cols: Width of the original image.
+    @param rows: Height of the original image.
     @type cols: int
+    @param cols: Width of the original image.
+    @type position_index: int
     @param position_index: Position of the image in the 2x2 grid. (0 =
         top-left, 1 = top-right, 2 = bottom-left, 3 = bottom-right).
-    @type position_index: int
-    @param height: Height of the final output mosaic image.
     @type height: int
-    @param width: Width of the final output mosaic image.
+    @param height: Height of the final output mosaic image.
     @type width: int
-    @param x_crop: x-coordinate of the croping start point
+    @param width: Width of the final output mosaic image.
     @type x_crop: int
-    @param y_crop: y-coordinate of the croping start point
+    @param x_crop: x-coordinate of the croping start point
     @type y_crop: int
-    @return: Transformed bounding box coordinates.
+    @param y_crop: y-coordinate of the croping start point
     @rtype: BoxInternalType
+    @return: Transformed bounding box coordinates.
     """
 
     bbox = denormalize_bbox(bbox, rows, cols)
@@ -519,26 +521,26 @@ def keypoint_mosaic4(
     one of the 2x2 mosaic grid cells, with shifts relative to the mosaic
     center.
 
+    @type keypoint: KeypointInternalType
     @param keypoint: Keypoint coordinates and attributes (x, y, angle,
         scale).
-    @type keypoint: KeypointInternalType
-    @param rows: Height of the original image.
     @type rows: int
-    @param cols: Width of the original image.
+    @param rows: Height of the original image.
     @type cols: int
+    @param cols: Width of the original image.
+    @type position_index: int
     @param position_index: Position of the image in the 2x2 grid. (0 =
         top-left, 1 = top-right, 2 = bottom-left, 3 = bottom-right).
-    @type position_index: int
-    @param height: Height of the final output mosaic image.
     @type height: int
-    @param width: Width of the final output mosaic image.
+    @param height: Height of the final output mosaic image.
     @type width: int
-    @param x_crop: x-coordinate of the croping start point
+    @param width: Width of the final output mosaic image.
     @type x_crop: int
-    @param y_crop: y-coordinate of the croping start point
+    @param x_crop: x-coordinate of the croping start point
     @type y_crop: int
-    @return: Adjusted keypoint coordinates.
+    @param y_crop: y-coordinate of the croping start point
     @rtype: KeypointInternalType
+    @return: Adjusted keypoint coordinates.
     """
     x, y, angle, scale = keypoint
 
diff --git a/luxonis_ml/data/augmentations/utils.py b/luxonis_ml/data/augmentations/utils.py
index cc616997..0a6b58f6 100644
--- a/luxonis_ml/data/augmentations/utils.py
+++ b/luxonis_ml/data/augmentations/utils.py
@@ -213,8 +213,6 @@ def __call__(
         @type data: List[Tuple[np.ndarray, Dict[LabelType, np.ndarray]]]
         @param data: Data with list of input images and their
             annotations
-        @type nc: int
-        @param nc: Number of classes
         @type ns: int
         @param ns: Number of segmentation classes
         @type nk: int
diff --git a/luxonis_ml/data/datasets/annotation.py b/luxonis_ml/data/datasets/annotation.py
index 9a11bc76..61b1d5d4 100644
--- a/luxonis_ml/data/datasets/annotation.py
+++ b/luxonis_ml/data/datasets/annotation.py
@@ -76,7 +76,7 @@ class Annotation(ABC, BaseModelExtraForbid):
     @type instance_id: int
     @ivar instance_id: The instance id of the annotation. This
         determines the order in which individual instances are loaded in
-        L{LuxonisLoader}.
+        L{luxonis_ml.data.LuxonisLoader}.
     @type _label_type: ClassVar[L{LabelType}]
     @ivar _label_type: The label type of the annotation.
     """
@@ -482,7 +482,7 @@ class ArrayAnnotation(Annotation):
 
     All instances of this annotation must have the same shape.
 
-    @type path: L{FilePath}
+    @type path: FilePath
     @ivar path: The path to the numpy array saved as a C{.npy} file.
     """
 
@@ -546,7 +546,7 @@ def combine_to_numpy(
 class DatasetRecord(BaseModelExtraForbid):
     """A record of an image and its annotation.
 
-    @type file: L{FilePath}
+    @type file: FilePath
     @ivar file: A path to the image.
     @type annotation: Optional[Annotation]
     @ivar annotation: The annotation for the image.
diff --git a/luxonis_ml/data/datasets/base_dataset.py b/luxonis_ml/data/datasets/base_dataset.py
index 57f6a832..5a36e08e 100644
--- a/luxonis_ml/data/datasets/base_dataset.py
+++ b/luxonis_ml/data/datasets/base_dataset.py
@@ -3,12 +3,11 @@
 
 from typing_extensions import TypeAlias
 
+from luxonis_ml.data.datasets.annotation import DatasetRecord
+from luxonis_ml.data.datasets.source import LuxonisSource
 from luxonis_ml.utils import AutoRegisterMeta, Registry
 from luxonis_ml.utils.filesystem import PathType
 
-from .annotation import DatasetRecord
-from .source import LuxonisSource
-
 DATASETS_REGISTRY: Registry[Type["BaseDataset"]] = Registry(name="datasets")
 
 
@@ -24,7 +23,10 @@ class BaseDataset(
     @property
     @abstractmethod
     def identifier(self) -> str:
-        """The unique identifier for the dataset."""
+        """The unique identifier for the dataset.
+
+        @type: str
+        """
         pass
 
     @abstractmethod
@@ -66,9 +68,6 @@ def get_classes(self) -> Tuple[List[str], Dict[str, List[str]]]:
         """Gets overall classes in the dataset and classes according to
         computer vision task.
 
-        @type sync_mode: bool
-        @param sync_mode: If C{True}, reads classes from remote storage.
-            If C{False}, classes are read locally.
         @rtype: Tuple[List[str], Dict]
         @return: A combined list of classes for all tasks and a
             dictionary mapping tasks to the classes used in each task.
@@ -121,7 +120,7 @@ def add(
     ) -> None:
         """Write annotations to parquet files.
 
-        @type generator: L{DatasetGenerator}
+        @type generator: L{DatasetIterator}
         @param generator: A Python iterator that yields either instances
             of L{DatasetRecord} or a dictionary that can be converted to
             L{DatasetRecord}.
diff --git a/luxonis_ml/data/datasets/luxonis_dataset.py b/luxonis_ml/data/datasets/luxonis_dataset.py
index ee7c8c28..4afbefee 100644
--- a/luxonis_ml/data/datasets/luxonis_dataset.py
+++ b/luxonis_ml/data/datasets/luxonis_dataset.py
@@ -818,9 +818,15 @@ def exists(
 
         @type dataset_name: str
         @param dataset_name: Name of the dataset to check
-        @type remote: bool
-        @param remote: Whether to check if the dataset exists in the
-            cloud
+        @type team_id: Optional[str]
+        @param team_id: Optional team identifier
+        @type bucket_storage: BucketStorage
+        @param bucket_storage: Underlying bucket storage from C{local},
+            C{S3}, or C{GCS}. Default is C{local}.
+        @type bucket: Optional[str]
+        @param bucket: Name of the bucket. Default is C{None}.
+        @rtype: bool
+        @return: Whether the dataset exists
         """
         return dataset_name in LuxonisDataset.list_datasets(
             team_id, bucket_storage, bucket
diff --git a/luxonis_ml/data/datasets/source.py b/luxonis_ml/data/datasets/source.py
index 57174abe..41dc792b 100644
--- a/luxonis_ml/data/datasets/source.py
+++ b/luxonis_ml/data/datasets/source.py
@@ -71,7 +71,7 @@ def __init__(
 
         For example, with an U{OAK-D<https://docs.luxonis.com/projects/hardware
         /en/latest/pages/BW1098OAK/>}, you can have a source with 4 image
-        components: rgb (color), left (mono), right (mono), and depth.
+        components: C{rgb} (color), C{left} (mono), C{right} (mono), and C{depth}.
 
         @type name: str
         @param name: A recognizable name for the source. Defaults to "default".
diff --git a/luxonis_ml/data/loaders/base_loader.py b/luxonis_ml/data/loaders/base_loader.py
index ea33b355..71f72983 100644
--- a/luxonis_ml/data/loaders/base_loader.py
+++ b/luxonis_ml/data/loaders/base_loader.py
@@ -9,14 +9,14 @@
 from ..utils.enums import LabelType
 
 Labels: TypeAlias = Dict[str, Tuple[np.ndarray, LabelType]]
-"""C{Labels} is a dictionary mappping task names to their L{LabelType}
-and annotations as L{numpy arrays<np.ndarray>}."""
+"""C{Labels} is a dictionary mappping task names to a tuple composed of
+the annotation as C{np.ndarray} and its corresponding C{LabelType}"""
 
 
 LuxonisLoaderOutput: TypeAlias = Tuple[np.ndarray, Labels]
-"""C{LuxonisLoaderOutput} is a tuple of an image as a L{numpy
-array<np.ndarray>} and a dictionary of task group names and their
-annotations as L{Annotations}."""
+"""C{LuxonisLoaderOutput} is a tuple of an image as a C{np.ndarray>} and
+a dictionary of task group names and their annotations as
+L{Annotations}."""
 
 LOADERS_REGISTRY: Registry[Type["BaseLoader"]] = Registry(name="loaders")
 
@@ -44,7 +44,7 @@ def __getitem__(self, idx: int) -> LuxonisLoaderOutput:
 
         @type idx: int
         @param idx: Index of the sample to load.
-        @rtype: LuxonisLoaderOutput
+        @rtype: L{LuxonisLoaderOutput}
         @return: Sample's data in L{LuxonisLoaderOutput} format.
         """
         pass
@@ -52,7 +52,7 @@ def __getitem__(self, idx: int) -> LuxonisLoaderOutput:
     def __iter__(self) -> Iterator[LuxonisLoaderOutput]:
         """Iterates over the dataset.
 
-        @rtype: Iterator
+        @rtype: Iterator[L{LuxonisLoaderOutput}]
         @return: Iterator over the dataset.
         """
         for i in range(len(self)):
diff --git a/luxonis_ml/data/loaders/luxonis_loader.py b/luxonis_ml/data/loaders/luxonis_loader.py
index 4c66c39c..c6245aaf 100644
--- a/luxonis_ml/data/loaders/luxonis_loader.py
+++ b/luxonis_ml/data/loaders/luxonis_loader.py
@@ -259,8 +259,8 @@ def _load_image_with_annotations(
 
         @type idx: int
         @param idx: Index of the image
-        @rtype: Tuple[L{np.ndarray}, dict]
-        @return: Image as L{np.ndarray} in RGB format and a dictionary
+        @rtype: Tuple[np.ndarray, Labels]
+        @return: Image as C{np.ndarray} in RGB format and a dictionary
             with all the present annotations
         """
 
diff --git a/luxonis_ml/data/parsers/base_parser.py b/luxonis_ml/data/parsers/base_parser.py
index 0d5c2593..84207155 100644
--- a/luxonis_ml/data/parsers/base_parser.py
+++ b/luxonis_ml/data/parsers/base_parser.py
@@ -55,11 +55,12 @@ def from_dir(
 
         @type dataset_dir: str
         @param dataset_dir: Path to source dataset directory.
-        @type parser_kwargs: Dict[str, Any]
-        @param parser_kwargs: Additional kwargs for specific parser
+        @type kwargs: Any
+        @param kwargs: Additional arguments for a specific parser
             implementation.
         @rtype: Tuple[List[str], List[str], List[str]]
-        @return: Tuple with added images for train, val and test splits.
+        @return: Tuple with added images for C{train}, C{val} and
+            C{test} splits.
         """
         pass
 

From 78ba32022e82b73b27d907753bf5243b3de89c3f Mon Sep 17 00:00:00 2001
From: Martin Kozlovsky <martin.kozlovsky@luxonis.com>
Date: Fri, 8 Nov 2024 17:53:47 +0100
Subject: [PATCH 2/2] formatting

---
 luxonis_ml/data/datasets/annotation.py      | 30 ++++++++++++++-------
 luxonis_ml/data/datasets/base_dataset.py    |  6 ++---
 luxonis_ml/data/datasets/luxonis_dataset.py | 21 ++++++++++-----
 luxonis_ml/data/loaders/base_loader.py      |  2 +-
 luxonis_ml/data/loaders/luxonis_loader.py   | 11 ++++----
 5 files changed, 43 insertions(+), 27 deletions(-)

diff --git a/luxonis_ml/data/datasets/annotation.py b/luxonis_ml/data/datasets/annotation.py
index 61b1d5d4..9037d0d6 100644
--- a/luxonis_ml/data/datasets/annotation.py
+++ b/luxonis_ml/data/datasets/annotation.py
@@ -77,7 +77,7 @@ class Annotation(ABC, BaseModelExtraForbid):
     @ivar instance_id: The instance id of the annotation. This
         determines the order in which individual instances are loaded in
         L{luxonis_ml.data.LuxonisLoader}.
-    @type _label_type: ClassVar[L{LabelType}]
+    @type _label_type: ClassVar[LabelType]
     @ivar _label_type: The label type of the annotation.
     """
 
@@ -110,7 +110,17 @@ def combine_to_numpy(
         width: int,
     ) -> np.ndarray:
         """Combines multiple instance annotations into a single numpy
-        array."""
+        array.
+
+        @type annotations: List[Annotation]
+        @param annotations: List of annotations to combine.
+        @type class_mapping: Dict[str, int]
+        @param class_mapping: Mapping of class names to class indices.
+        @type height: int
+        @param height: The height of the image.
+        @type width: int
+        @param width: The width of the image.
+        """
         pass
 
 
@@ -138,14 +148,14 @@ class BBoxAnnotation(Annotation):
 
     @type x: float
     @ivar x: The top-left x coordinate of the bounding box. Normalized
-        to [0, 1].
+        to M{[0, 1]}.
     @type y: float
     @ivar y: The top-left y coordinate of the bounding box. Normalized
-        to [0, 1].
+        to M{[0, 1]}.
     @type w: float
-    @ivar w: The width of the bounding box. Normalized to [0, 1].
+    @ivar w: The width of the bounding box. Normalized to M{[0, 1]}.
     @type h: float
-    @ivar h: The height of the bounding box. Normalized to [0, 1].
+    @ivar h: The height of the bounding box. Normalized to M{[0, 1]}.
     """
 
     type_: Literal["boundingbox"] = Field("boundingbox", alias="type")
@@ -211,11 +221,11 @@ def combine_to_numpy(
 class KeypointAnnotation(Annotation):
     """Keypoint annotation.
 
-    Values are normalized to [0, 1] based on the image size.
+    Values are normalized to M{[0, 1]} based on the image size.
 
     @type keypoints: List[Tuple[float, float, L{KeypointVisibility}]]
     @ivar keypoints: List of keypoints. Each keypoint is a tuple of (x, y, visibility).
-        x and y are normalized to [0, 1]. visibility is one of {0, 1, 2} where:
+        x and y are normalized to M{[0, 1]}. visibility is one of M{0}, M{1}, or M{2} where:
             - 0: Not visible / not labeled
             - 1: Occluded
             - 2: Visible
@@ -430,8 +440,8 @@ class PolylineSegmentationAnnotation(SegmentationAnnotation):
 
     @type points: List[Tuple[float, float]]
     @ivar points: List of points that define the polyline. Each point is
-        a tuple of (x, y). x and y are normalized to [0, 1] based on the
-        image size.
+        a tuple of (x, y). x and y are normalized to M{[0, 1]} based on
+        the image size.
     """
 
     type_: Literal["polyline"] = Field("polyline", alias="type")
diff --git a/luxonis_ml/data/datasets/base_dataset.py b/luxonis_ml/data/datasets/base_dataset.py
index 5a36e08e..066faab9 100644
--- a/luxonis_ml/data/datasets/base_dataset.py
+++ b/luxonis_ml/data/datasets/base_dataset.py
@@ -44,7 +44,7 @@ def update_source(self, source: LuxonisSource) -> None:
         LuxonisSource.
 
         @type source: L{LuxonisSource}
-        @param source: The new L{LuxonisSource} to replace the old one.
+        @param source: The new C{LuxonisSource} to replace the old one.
         """
         pass
 
@@ -122,8 +122,8 @@ def add(
 
         @type generator: L{DatasetIterator}
         @param generator: A Python iterator that yields either instances
-            of L{DatasetRecord} or a dictionary that can be converted to
-            L{DatasetRecord}.
+            of C{DatasetRecord} or a dictionary that can be converted to
+            C{DatasetRecord}.
         @type batch_size: int
         @param batch_size: The number of annotations generated before
             processing. This can be set to a lower value to reduce
diff --git a/luxonis_ml/data/datasets/luxonis_dataset.py b/luxonis_ml/data/datasets/luxonis_dataset.py
index 4afbefee..c79f19b8 100644
--- a/luxonis_ml/data/datasets/luxonis_dataset.py
+++ b/luxonis_ml/data/datasets/luxonis_dataset.py
@@ -64,8 +64,8 @@ def __init__(
         @type bucket_type: BucketType
         @param bucket_type: Whether to use external cloud buckets
         @type bucket_storage: BucketStorage
-        @param bucket_storage: Underlying bucket storage from local, S3,
-            or GCS
+        @param bucket_storage: Underlying bucket storage. Can be one of
+            C{local}, C{S3}, or C{GCS}.
         @type delete_existing: bool
         @param delete_existing: Whether to delete a dataset with the
             same name if it exists
@@ -283,8 +283,8 @@ def update_source(self, source: LuxonisSource) -> None:
         """Updates underlying source of the dataset with a new
         L{LuxonisSource}.
 
-        @type source: L{LuxonisSource}
-        @param source: The new L{LuxonisSource} to replace the old one.
+        @type source: LuxonisSource
+        @param source: The new C{LuxonisSource} to replace the old one.
         """
 
         self.metadata["source"] = source.to_document()
@@ -826,7 +826,7 @@ def exists(
         @type bucket: Optional[str]
         @param bucket: Name of the bucket. Default is C{None}.
         @rtype: bool
-        @return: Whether the dataset exists
+        @return: Whether the dataset exists.
         """
         return dataset_name in LuxonisDataset.list_datasets(
             team_id, bucket_storage, bucket
@@ -840,8 +840,15 @@ def list_datasets(
     ) -> List[str]:
         """Returns a dictionary of all datasets.
 
-        @rtype: Dict
-        @return: Dictionary of all datasets
+        @type team_id: Optional[str]
+        @param team_id: Optional team identifier
+        @type bucket_storage: BucketStorage
+        @param bucket_storage: Underlying bucket storage from C{local},
+            C{S3}, or C{GCS}. Default is C{local}.
+        @type bucket: Optional[str]
+        @param bucket: Name of the bucket. Default is C{None}.
+        @rtype: List[str]
+        @return: List of all dataset names.
         """
         base_path = environ.LUXONISML_BASE_PATH
 
diff --git a/luxonis_ml/data/loaders/base_loader.py b/luxonis_ml/data/loaders/base_loader.py
index 71f72983..8d7256f6 100644
--- a/luxonis_ml/data/loaders/base_loader.py
+++ b/luxonis_ml/data/loaders/base_loader.py
@@ -45,7 +45,7 @@ def __getitem__(self, idx: int) -> LuxonisLoaderOutput:
         @type idx: int
         @param idx: Index of the sample to load.
         @rtype: L{LuxonisLoaderOutput}
-        @return: Sample's data in L{LuxonisLoaderOutput} format.
+        @return: Sample's data in C{LuxonisLoaderOutput} format.
         """
         pass
 
diff --git a/luxonis_ml/data/loaders/luxonis_loader.py b/luxonis_ml/data/loaders/luxonis_loader.py
index c6245aaf..c448abf0 100644
--- a/luxonis_ml/data/loaders/luxonis_loader.py
+++ b/luxonis_ml/data/loaders/luxonis_loader.py
@@ -30,10 +30,10 @@ def __init__(
         """A loader class used for loading data from L{LuxonisDataset}.
 
         @type dataset: LuxonisDataset
-        @param dataset: LuxonisDataset to use
+        @param dataset: Instance of C{LuxonisDataset} to use.
         @type view: Union[str, List[str]]
         @param view: What splits to use. Can be either a single split or
-            a list of splits. Defaults to "train".
+            a list of splits. Defaults to C{"train"}.
         @type stream: bool
         @param stream: Flag for data streaming. Defaults to C{False}.
         @type augmentations: Optional[luxonis_ml.loader.Augmentations]
@@ -141,7 +141,7 @@ def __len__(self) -> int:
         """Returns length of the dataset.
 
         @rtype: int
-        @return: Length of dataset.
+        @return: Length of the loader.
         """
         return len(self.instances)
 
@@ -150,9 +150,8 @@ def __getitem__(self, idx: int) -> LuxonisLoaderOutput:
         annotations.
 
         @type idx: int
-        @param idx: The (often random) integer index to retrieve a
-            sample from the dataset.
-        @rtype: LuxonisLoaderOutput
+        @param idx: The integer index of the sample to retrieve.
+        @rtype: L{LuxonisLoaderOutput}
         @return: The loader ouput consisting of the image and a
             dictionary defining its annotations.
         """