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Use Any type-hints for args and kwargs (#18372)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-12-24 20:32:59 +01:00 • committed by GitHub
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3 changed files with 26 additions and 25 deletions
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2
docs/en/guides/streamlit-live-inference.md
View file

@ -45,7 +45,7 @@ Streamlit makes it simple to build and deploy interactive web applications. Comb
```bash
yolo solutions inference
yolo solutions inference model="path/to/model/file.pt"
yolo solutions inference model="path/to/model.pt"
```
=== "Python"

42
ultralytics/engine/model.py
View file

@ -2,7 +2,7 @@
import inspect
from pathlib import Path
from typing import Dict, List, Union
from typing import Any, Dict, List, Union
import numpy as np
import torch
@ -152,7 +152,7 @@ class Model(nn.Module):
self,
source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
stream: bool = False,
**kwargs,
**kwargs: Any,
) -> list:
"""
Alias for the predict method, enabling the model instance to be callable for predictions.
@ -165,7 +165,7 @@ class Model(nn.Module):
the image(s) to make predictions on. Can be a file path, URL, PIL image, numpy array, PyTorch
tensor, or a list/tuple of these.
stream (bool): If True, treat the input source as a continuous stream for predictions.
**kwargs (Any): Additional keyword arguments to configure the prediction process.
**kwargs: Additional keyword arguments to configure the prediction process.
Returns:
(List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a
@ -466,7 +466,7 @@ class Model(nn.Module):
self,
source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
stream: bool = False,
**kwargs,
**kwargs: Any,
) -> list:
"""
Generates image embeddings based on the provided source.
@ -478,7 +478,7 @@ class Model(nn.Module):
source (str | Path | int | List | Tuple | np.ndarray | torch.Tensor): The source of the image for
generating embeddings. Can be a file path, URL, PIL image, numpy array, etc.
stream (bool): If True, predictions are streamed.
**kwargs (Any): Additional keyword arguments for configuring the embedding process.
**kwargs: Additional keyword arguments for configuring the embedding process.
Returns:
(List[torch.Tensor]): A list containing the image embeddings.
@ -501,7 +501,7 @@ class Model(nn.Module):
source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
stream: bool = False,
predictor=None,
**kwargs,
**kwargs: Any,
) -> List[Results]:
"""
Performs predictions on the given image source using the YOLO model.
@ -517,7 +517,7 @@ class Model(nn.Module):
stream (bool): If True, treats the input source as a continuous stream for predictions.
predictor (BasePredictor | None): An instance of a custom predictor class for making predictions.
If None, the method uses a default predictor.
**kwargs (Any): Additional keyword arguments for configuring the prediction process.
**kwargs: Additional keyword arguments for configuring the prediction process.
Returns:
(List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a
@ -562,7 +562,7 @@ class Model(nn.Module):
source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
stream: bool = False,
persist: bool = False,
**kwargs,
**kwargs: Any,
) -> List[Results]:
"""
Conducts object tracking on the specified input source using the registered trackers.
@ -576,7 +576,7 @@ class Model(nn.Module):
tracking. Can be a file path, URL, or video stream.
stream (bool): If True, treats the input source as a continuous video stream. Defaults to False.
persist (bool): If True, persists trackers between different calls to this method. Defaults to False.
**kwargs (Any): Additional keyword arguments for configuring the tracking process.
**kwargs: Additional keyword arguments for configuring the tracking process.
Returns:
(List[ultralytics.engine.results.Results]): A list of tracking results, each a Results object.
@ -607,7 +607,7 @@ class Model(nn.Module):
def val(
self,
validator=None,
**kwargs,
**kwargs: Any,
):
"""
Validates the model using a specified dataset and validation configuration.
@ -619,7 +619,7 @@ class Model(nn.Module):
Args:
validator (ultralytics.engine.validator.BaseValidator | None): An instance of a custom validator class for
validating the model.
**kwargs (Any): Arbitrary keyword arguments for customizing the validation process.
**kwargs: Arbitrary keyword arguments for customizing the validation process.
Returns:
(ultralytics.utils.metrics.DetMetrics): Validation metrics obtained from the validation process.
@ -642,7 +642,7 @@ class Model(nn.Module):
def benchmark(
self,
**kwargs,
**kwargs: Any,
):
"""
Benchmarks the model across various export formats to evaluate performance.
@ -653,7 +653,7 @@ class Model(nn.Module):
defaults, and any additional user-provided keyword arguments.
Args:
**kwargs (Any): Arbitrary keyword arguments to customize the benchmarking process. These are combined with
**kwargs: Arbitrary keyword arguments to customize the benchmarking process. These are combined with
default configurations, model-specific arguments, and method defaults. Common options include:
- data (str): Path to the dataset for benchmarking.
- imgsz (int | List[int]): Image size for benchmarking.
@ -691,7 +691,7 @@ class Model(nn.Module):
def export(
self,
**kwargs,
**kwargs: Any,
) -> str:
"""
Exports the model to a different format suitable for deployment.
@ -701,7 +701,7 @@ class Model(nn.Module):
defaults, and any additional arguments provided.
Args:
**kwargs (Dict): Arbitrary keyword arguments to customize the export process. These are combined with
**kwargs: Arbitrary keyword arguments to customize the export process. These are combined with
the model's overrides and method defaults. Common arguments include:
format (str): Export format (e.g., 'onnx', 'engine', 'coreml').
half (bool): Export model in half-precision.
@ -740,7 +740,7 @@ class Model(nn.Module):
def train(
self,
trainer=None,
**kwargs,
**kwargs: Any,
):
"""
Trains the model using the specified dataset and training configuration.
@ -755,7 +755,7 @@ class Model(nn.Module):
Args:
trainer (BaseTrainer | None): Custom trainer instance for model training. If None, uses default.
**kwargs (Any): Arbitrary keyword arguments for training configuration. Common options include:
**kwargs: Arbitrary keyword arguments for training configuration. Common options include:
data (str): Path to dataset configuration file.
epochs (int): Number of training epochs.
batch_size (int): Batch size for training.
@ -816,8 +816,8 @@ class Model(nn.Module):
self,
use_ray=False,
iterations=10,
*args,
**kwargs,
*args: Any,
**kwargs: Any,
):
"""
Conducts hyperparameter tuning for the model, with an option to use Ray Tune.
@ -830,8 +830,8 @@ class Model(nn.Module):
Args:
use_ray (bool): If True, uses Ray Tune for hyperparameter tuning. Defaults to False.
iterations (int): The number of tuning iterations to perform. Defaults to 10.
*args (List): Variable length argument list for additional arguments.
**kwargs (Dict): Arbitrary keyword arguments. These are combined with the model's overrides and defaults.
*args: Variable length argument list for additional arguments.
**kwargs: Arbitrary keyword arguments. These are combined with the model's overrides and defaults.
Returns:
(Dict): A dictionary containing the results of the hyperparameter search.

7
ultralytics/solutions/streamlit_inference.py
View file

@ -2,6 +2,7 @@
import io
import time
from typing import Any
import cv2
@ -37,16 +38,16 @@ class Inference:
inference: Performs real-time object detection inference.
Examples:
>>> inf = solutions.Inference(model="path/to/model/file.pt") # Model is not necessary argument.
>>> inf = solutions.Inference(model="path/to/model.pt") # Model is not necessary argument.
>>> inf.inference()
"""
def __init__(self, **kwargs):
def __init__(self, **kwargs: Any):
"""
Initializes the Inference class, checking Streamlit requirements and setting up the model path.
Args:
**kwargs (Dict): Additional keyword arguments for model configuration.
**kwargs (Any): Additional keyword arguments for model configuration.
"""
check_requirements("streamlit>=1.29.0") # scope imports for faster ultralytics package load speeds
import streamlit as st