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ultralytics 8.2.80 add PIL type hints to model() (#15719)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Josh Jarabek 2024-08-22 07:08:32 -05:00 • committed by GitHub
parent 5f93df6fca
commit 0c6e1e16bd
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2 changed files with 5 additions and 4 deletions
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2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license # Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = "8.2.79" __version__ = "8.2.80"
import os import os

7
ultralytics/engine/model.py
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@ -6,6 +6,7 @@ from typing import List, Union
import numpy as np import numpy as np
import torch import torch
from PIL import Image
from ultralytics.cfg import TASK2DATA, get_cfg, get_save_dir from ultralytics.cfg import TASK2DATA, get_cfg, get_save_dir
from ultralytics.engine.results import Results from ultralytics.engine.results import Results
@ -143,7 +144,7 @@ class Model(nn.Module):
def __call__( def __call__(
self, self,
source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None, source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
stream: bool = False, stream: bool = False,
**kwargs, **kwargs,
) -> list: ) -> list:
@ -504,7 +505,7 @@ class Model(nn.Module):
def predict( def predict(
self, self,
source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None, source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
stream: bool = False, stream: bool = False,
predictor=None, predictor=None,
**kwargs, **kwargs,
@ -517,7 +518,7 @@ class Model(nn.Module):
types of image sources and can operate in a streaming mode. types of image sources and can operate in a streaming mode.
Args: Args:
source (str | Path | int | List[str] | List[Path] | List[int] | np.ndarray | torch.Tensor): The source source (str | Path | int | PIL.Image | np.ndarray | torch.Tensor | List | Tuple): The source
of the image(s) to make predictions on. Accepts various types including file paths, URLs, PIL of the image(s) to make predictions on. Accepts various types including file paths, URLs, PIL
images, numpy arrays, and torch tensors. images, numpy arrays, and torch tensors.
stream (bool): If True, treats the input source as a continuous stream for predictions. stream (bool): If True, treats the input source as a continuous stream for predictions.