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PyCharm Docs Inspect fixes (#18432)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Muhammad Rizwan Munawar 2024-12-29 23:19:05 +05:00 • committed by GitHub
parent ef16c56c99
commit f7b9009c91
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15 changed files with 52 additions and 50 deletions
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14
ultralytics/models/sam/predict.py
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@ -91,9 +91,9 @@ class Predictor(BasePredictor):
_callbacks (Dict | None): Dictionary of callback functions to customize behavior.
Examples:
>>> predictor = Predictor(cfg=DEFAULT_CFG)
>>> predictor = Predictor(overrides={"imgsz": 640})
>>> predictor = Predictor(_callbacks={"on_predict_start": custom_callback})
>>> predictor_example = Predictor(cfg=DEFAULT_CFG)
>>> predictor_example_with_imgsz = Predictor(overrides={"imgsz": 640})
>>> predictor_example_with_callback = Predictor(_callbacks={"on_predict_start": custom_callback})
"""
if overrides is None:
overrides = {}
@ -215,7 +215,7 @@ class Predictor(BasePredictor):
im (torch.Tensor): Preprocessed input image tensor with shape (N, C, H, W).
bboxes (np.ndarray | List | None): Bounding boxes in XYXY format with shape (N, 4).
points (np.ndarray | List | None): Points indicating object locations with shape (N, 2) or (N, num_points, 2), in pixels.
labels (np.ndarray | List | None): Point prompt labels with shape (N,) or (N, num_points). 1 for foreground, 0 for background.
labels (np.ndarray | List | None): Point prompt labels with shape (N) or (N, num_points). 1 for foreground, 0 for background.
masks (np.ndarray | None): Low-res masks from previous predictions with shape (N, H, W). For SAM, H=W=256.
multimask_output (bool): Flag to return multiple masks for ambiguous prompts.
@ -260,7 +260,7 @@ class Predictor(BasePredictor):
dst_shape (tuple): The target shape (height, width) for the prompts.
bboxes (np.ndarray | List | None): Bounding boxes in XYXY format with shape (N, 4).
points (np.ndarray | List | None): Points indicating object locations with shape (N, 2) or (N, num_points, 2), in pixels.
labels (np.ndarray | List | None): Point prompt labels with shape (N,) or (N, num_points). 1 for foreground, 0 for background.
labels (np.ndarray | List | None): Point prompt labels with shape (N) or (N, num_points). 1 for foreground, 0 for background.
masks (List | np.ndarray, Optional): Masks for the objects, where each mask is a 2D array.
Raises:
@ -853,8 +853,8 @@ class SAM2VideoPredictor(SAM2Predictor):
Examples:
>>> predictor = SAM2VideoPredictor(cfg=DEFAULT_CFG)
>>> predictor = SAM2VideoPredictor(overrides={"imgsz": 640})
>>> predictor = SAM2VideoPredictor(_callbacks={"on_predict_start": custom_callback})
>>> predictor_example_with_imgsz = SAM2VideoPredictor(overrides={"imgsz": 640})
>>> predictor_example_with_callback = SAM2VideoPredictor(_callbacks={"on_predict_start": custom_callback})
"""
super().__init__(cfg, overrides, _callbacks)
self.inference_state = {}

2
ultralytics/utils/plotting.py
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@ -1269,7 +1269,7 @@ def plt_color_scatter(v, f, bins=20, cmap="viridis", alpha=0.8, edgecolors="none
def plot_tune_results(csv_file="tune_results.csv"):
"""
Plot the evolution results stored in an 'tune_results.csv' file. The function generates a scatter plot for each key
Plot the evolution results stored in a 'tune_results.csv' file. The function generates a scatter plot for each key
in the CSV, color-coded based on fitness scores. The best-performing configurations are highlighted on the plots.
Args: