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Engine Model and Results Docs improvements (#14564)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Felipe Parodi <fparodi@pennmedicine.upenn.edu>
This commit is contained in:
Glenn Jocher 2024-07-21 20:14:49 +02:00 • committed by GitHub
parent e59376b55f
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123 changed files with 3789 additions and 1368 deletions
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235
ultralytics/cfg/__init__.py
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@ -187,11 +187,11 @@ CFG_BOOL_KEYS = { # boolean-only arguments
def cfg2dict(cfg):
"""
Convert a configuration object to a dictionary, whether it is a file path, a string, or a SimpleNamespace object.
Converts a configuration object to a dictionary.
Args:
cfg (str | Path | Dict | SimpleNamespace): Configuration object to be converted to a dictionary. This may be a
path to a configuration file, a dictionary, or a SimpleNamespace object.
cfg (str | Path | Dict | SimpleNamespace): Configuration object to be converted. Can be a file path,
a string, a dictionary, or a SimpleNamespace object.
Returns:
(Dict): Configuration object in dictionary format.
@ -209,8 +209,9 @@ def cfg2dict(cfg):
>>> config_dict = cfg2dict({'param1': 'value1', 'param2': 'value2'})
Notes:
- If `cfg` is a path or a string, it will be loaded as YAML and converted to a dictionary.
- If `cfg` is a SimpleNamespace object, it will be converted to a dictionary using `vars()`.
- If cfg is a path or string, it's loaded as YAML and converted to a dictionary.
- If cfg is a SimpleNamespace object, it's converted to a dictionary using vars().
- If cfg is already a dictionary, it's returned unchanged.
"""
if isinstance(cfg, (str, Path)):
cfg = yaml_load(cfg) # load dict
@ -224,24 +225,23 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, ove
Load and merge configuration data from a file or dictionary, with optional overrides.
Args:
cfg (str | Path | Dict | SimpleNamespace): Configuration data source.
cfg (str | Path | Dict | SimpleNamespace): Configuration data source. Can be a file path, dictionary, or
SimpleNamespace object.
overrides (Dict | None): Dictionary containing key-value pairs to override the base configuration.
Returns:
(SimpleNamespace): Namespace containing the merged training arguments.
(SimpleNamespace): Namespace containing the merged configuration arguments.
Examples:
>>> from ultralytics import get_cfg
>>> config = get_cfg() # Load default configuration
>>> config = get_cfg('path/to/config.yaml', overrides={'epochs': 50, 'batch_size': 16})
Notes:
- If both `cfg` and `overrides` are provided, the values in `overrides` will take precedence.
- Special handling ensures alignment and correctness of the configuration, such as converting numeric `project`
and `name` to strings and validating configuration keys and values.
Examples:
Load default configuration:
>>> from ultralytics import get_cfg
>>> config = get_cfg()
Load from a custom file with overrides:
>>> config = get_cfg('path/to/config.yaml', overrides={'epochs': 50, 'batch_size': 16})
- Special handling ensures alignment and correctness of the configuration, such as converting numeric
`project` and `name` to strings and validating configuration keys and values.
- The function performs type and value checks on the configuration data.
"""
cfg = cfg2dict(cfg)
@ -270,24 +270,31 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, ove
def check_cfg(cfg, hard=True):
"""
Checks configuration argument types and values for the Ultralytics library, ensuring correctness and converting them
if necessary.
Checks configuration argument types and values for the Ultralytics library.
This function validates the types and values of configuration arguments, ensuring correctness and converting
them if necessary. It checks for specific key types defined in global variables such as CFG_FLOAT_KEYS,
CFG_FRACTION_KEYS, CFG_INT_KEYS, and CFG_BOOL_KEYS.
Args:
cfg (Dict): Configuration dictionary to validate.
hard (bool): If True, raises exceptions for invalid types and values; if False, attempts to convert them.
Examples:
Validate a configuration with a mix of valid and invalid values:
>>> config = {
... 'epochs': 50, # valid integer
... 'lr0': 0.01, # valid float
... 'momentum': 1.2, # invalid float (out of 0.0-1.0 range)
... 'save': 'true', # invalid bool
... 'epochs': 50, # valid integer
... 'lr0': 0.01, # valid float
... 'momentum': 1.2, # invalid float (out of 0.0-1.0 range)
... 'save': 'true', # invalid bool
... }
>>> check_cfg(config, hard=False)
>>> print(config)
{'epochs': 50, 'lr0': 0.01, 'momentum': 1.2, 'save': False} # corrected 'save' key and retained other values
{'epochs': 50, 'lr0': 0.01, 'momentum': 1.2, 'save': False} # corrected 'save' key
Notes:
- The function modifies the input dictionary in-place.
- None values are ignored as they may be from optional arguments.
- Fraction keys are checked to be within the range [0.0, 1.0].
"""
for k, v in cfg.items():
if v is not None: # None values may be from optional args
@ -328,16 +335,15 @@ def get_save_dir(args, name=None):
Returns the directory path for saving outputs, derived from arguments or default settings.
Args:
args (SimpleNamespace): Namespace object containing configurations such as 'project', 'name', 'task', 'mode', and
'save_dir'.
name (str | None): Optional name for the output directory. If not provided, it defaults to 'args.name' or the
'args.mode'.
args (SimpleNamespace): Namespace object containing configurations such as 'project', 'name', 'task',
'mode', and 'save_dir'.
name (str | None): Optional name for the output directory. If not provided, it defaults to 'args.name'
or the 'args.mode'.
Returns:
(Path): Directory path where outputs should be saved.
Examples:
Generate a save directory using provided arguments
>>> from types import SimpleNamespace
>>> args = SimpleNamespace(project='my_project', task='detect', mode='train', exist_ok=True)
>>> save_dir = get_save_dir(args)
@ -369,6 +375,11 @@ def _handle_deprecation(custom):
>>> _handle_deprecation(custom_config)
>>> print(custom_config)
{'show_boxes': True, 'show_labels': True, 'line_width': 2}
Notes:
This function modifies the input dictionary in-place, replacing deprecated keys with their current
equivalents. It also handles value conversions where necessary, such as inverting boolean values for
'hide_labels' and 'hide_conf'.
"""
for key in custom.copy().keys():
@ -390,32 +401,29 @@ def _handle_deprecation(custom):
def check_dict_alignment(base: Dict, custom: Dict, e=None):
"""
Check for key alignment between custom and base configuration dictionaries, handling deprecated keys and providing
informative error messages for mismatched keys.
Checks alignment between custom and base configuration dictionaries, handling deprecated keys and providing error
messages for mismatched keys.
Args:
base (Dict): The base configuration dictionary containing valid keys.
custom (Dict): The custom configuration dictionary to be checked for alignment.
e (Exception | None): Optional error instance passed by the calling function. Default is None.
e (Exception | None): Optional error instance passed by the calling function.
Raises:
SystemExit: Terminates the program execution if mismatched keys are found.
Notes:
- The function suggests corrections for mismatched keys based on similarity to valid keys.
- Deprecated keys in the custom configuration are automatically replaced with their updated equivalents.
- Detailed error messages are printed for each mismatched key to help users identify and correct their custom
configurations.
SystemExit: If mismatched keys are found between the custom and base dictionaries.
Examples:
>>> base_cfg = {'epochs': 50, 'lr0': 0.01, 'batch_size': 16}
>>> custom_cfg = {'epoch': 100, 'lr': 0.02, 'batch_size': 32}
>>> try:
... check_dict_alignment(base_cfg, custom_cfg)
... except SystemExit:
... # Handle the error or correct the configuration
... pass
... print("Mismatched keys found")
Notes:
- Suggests corrections for mismatched keys based on similarity to valid keys.
- Automatically replaces deprecated keys in the custom configuration with updated equivalents.
- Prints detailed error messages for each mismatched key to help users correct their configurations.
"""
custom = _handle_deprecation(custom)
base_keys, custom_keys = (set(x.keys()) for x in (base, custom))
@ -434,7 +442,10 @@ def check_dict_alignment(base: Dict, custom: Dict, e=None):
def merge_equals_args(args: List[str]) -> List[str]:
"""
Merges arguments around isolated '=' in a list of strings.
Merges arguments around isolated '=' in a list of strings, handling three cases:
1. ['arg', '=', 'val'] becomes ['arg=val'],
2. ['arg=', 'val'] becomes ['arg=val'],
3. ['arg', '=val'] becomes ['arg=val'].
Args:
args (List[str]): A list of strings where each element represents an argument.
@ -443,20 +454,9 @@ def merge_equals_args(args: List[str]) -> List[str]:
(List[str]): A list of strings where the arguments around isolated '=' are merged.
Examples:
Merge arguments where equals sign is separated:
>>> args = ["arg1", "=", "value"]
>>> args = ["arg1", "=", "value", "arg2=", "value2", "arg3", "=value3"]
>>> merge_equals_args(args)
["arg1=value"]
Merge arguments where equals sign is at the end of the first argument:
>>> args = ["arg1=", "value"]
>>> merge_equals_args(args)
["arg1=value"]
Merge arguments where equals sign is at the beginning of the second argument:
>>> args = ["arg1", "=value"]
>>> merge_equals_args(args)
["arg1=value"]
['arg1=value', 'arg2=value2', 'arg3=value3']
"""
new_args = []
for i, arg in enumerate(args):
@ -475,18 +475,24 @@ def merge_equals_args(args: List[str]) -> List[str]:
def handle_yolo_hub(args: List[str]) -> None:
"""
Handle Ultralytics HUB command-line interface (CLI) commands.
Handles Ultralytics HUB command-line interface (CLI) commands for authentication.
This function processes Ultralytics HUB CLI commands such as login and logout. It should be called when executing a
script with arguments related to HUB authentication.
Args:
args (List[str]): A list of command line arguments.
args (List[str]): A list of command line arguments. The first argument should be either 'login'
or 'logout'. For 'login', an optional second argument can be the API key.
Examples:
```bash
yolo hub login YOUR_API_KEY
```
Notes:
- The function imports the 'hub' module from ultralytics to perform login and logout operations.
- For the 'login' command, if no API key is provided, an empty string is passed to the login function.
- The 'logout' command does not require any additional arguments.
"""
from ultralytics import hub
@ -501,21 +507,26 @@ def handle_yolo_hub(args: List[str]) -> None:
def handle_yolo_settings(args: List[str]) -> None:
"""
Handle YOLO settings command-line interface (CLI) commands.
Handles YOLO settings command-line interface (CLI) commands.
This function processes YOLO settings CLI commands such as reset. It should be called when executing a script with
arguments related to YOLO settings management.
This function processes YOLO settings CLI commands such as reset and updating individual settings. It should be
called when executing a script with arguments related to YOLO settings management.
Args:
args (List[str]): A list of command line arguments for YOLO settings management.
Examples:
Reset YOLO settings:
>>> yolo settings reset
>>> handle_yolo_settings(["reset"]) # Reset YOLO settings
>>> handle_yolo_settings(["default_cfg_path=yolov8n.yaml"]) # Update a specific setting
Notes:
For more information on handling YOLO settings, visit:
https://docs.ultralytics.com/quickstart/#ultralytics-settings
- If no arguments are provided, the function will display the current settings.
- The 'reset' command will delete the existing settings file and create new default settings.
- Other arguments are treated as key-value pairs to update specific settings.
- The function will check for alignment between the provided settings and the existing ones.
- After processing, the updated settings will be displayed.
- For more information on handling YOLO settings, visit:
https://docs.ultralytics.com/quickstart/#ultralytics-settings
"""
url = "https://docs.ultralytics.com/quickstart/#ultralytics-settings" # help URL
try:
@ -539,12 +550,17 @@ def handle_explorer():
"""
Open the Ultralytics Explorer GUI for dataset exploration and analysis.
This function launches a graphical user interface that provides tools for interacting with and analyzing datasets
using the Ultralytics Explorer API.
This function launches a graphical user interface that provides tools for interacting with and analyzing
datasets using the Ultralytics Explorer API. It checks for the required 'streamlit' package and informs
the user that the Explorer dashboard is loading.
Examples:
Start the Ultralytics Explorer:
>>> handle_explorer()
Notes:
- Requires 'streamlit' package version 1.29.0 or higher.
- The function does not take any arguments or return any values.
- It is typically called from the command line interface using the 'yolo explorer' command.
"""
checks.check_requirements("streamlit>=1.29.0")
LOGGER.info("💡 Loading Explorer dashboard...")
@ -553,18 +569,18 @@ def handle_explorer():
def handle_streamlit_inference():
"""
Open the Ultralytics Live Inference streamlit app for real-time object detection.
Open the Ultralytics Live Inference Streamlit app for real-time object detection.
This function initializes and runs a Streamlit application designed for performing live object detection using
Ultralytics models.
References:
- Streamlit documentation: https://docs.streamlit.io/
- Ultralytics: https://docs.ultralytics.com
Ultralytics models. It checks for the required Streamlit package and launches the app.
Examples:
To run the live inference Streamlit app, execute:
>>> handle_streamlit_inference()
Notes:
- Requires Streamlit version 1.29.0 or higher.
- The app is launched using the 'streamlit run' command.
- The Streamlit app file is located in the Ultralytics package directory.
"""
checks.check_requirements("streamlit>=1.29.0")
LOGGER.info("💡 Loading Ultralytics Live Inference app...")
@ -573,20 +589,32 @@ def handle_streamlit_inference():
def parse_key_value_pair(pair):
"""
Parse a 'key=value' pair and return the key and value.
Parses a key-value pair string into separate key and value components.
Args:
pair (str): The 'key=value' string to be parsed.
pair (str): A string containing a key-value pair in the format "key=value".
Returns:
(tuple[str, str]): A tuple containing the key and value as separate strings.
(tuple): A tuple containing two elements:
- key (str): The parsed key.
- value (str): The parsed value.
Raises:
AssertionError: If the value is missing or empty.
Examples:
>>> key, value = parse_key_value_pair("model=yolov8n.pt")
>>> key
'model'
>>> value
'yolov8n.pt
>>> print(f"Key: {key}, Value: {value}")
Key: model, Value: yolov8n.pt
>>> key, value = parse_key_value_pair("epochs=100")
>>> print(f"Key: {key}, Value: {value}")
Key: epochs, Value: 100
Notes:
- The function splits the input string on the first '=' character.
- Leading and trailing whitespace is removed from both key and value.
- An assertion error is raised if the value is empty after stripping.
"""
k, v = pair.split("=", 1) # split on first '=' sign
k, v = k.strip(), v.strip() # remove spaces
@ -596,17 +624,19 @@ def parse_key_value_pair(pair):
def smart_value(v):
"""
Convert a string representation of a value into its appropriate Python type (int, float, bool, None, etc.).
Converts a string representation of a value to its appropriate Python type.
This function attempts to convert a given string into a Python object of the most appropriate type. It handles
conversions to None, bool, int, float, and other types that can be evaluated safely.
Args:
v (str): String representation of the value to be converted.
v (str): The string representation of the value to be converted.
Returns:
(Any): The converted value, which can be of type int, float, bool, None, or the original string if no conversion
(Any): The converted value. The type can be None, bool, int, float, or the original string if no conversion
is applicable.
Examples:
Convert a string to various types:
>>> smart_value("42")
42
>>> smart_value("3.14")
@ -617,6 +647,11 @@ def smart_value(v):
None
>>> smart_value("some_string")
'some_string'
Notes:
- The function uses a case-insensitive comparison for boolean and None values.
- For other types, it attempts to use Python's eval() function, which can be unsafe if used with untrusted input.
- If no conversion is possible, the original string is returned.
"""
v_lower = v.lower()
if v_lower == "none":
@ -639,7 +674,7 @@ def entrypoint(debug=""):
executing the corresponding tasks such as training, validation, prediction, exporting models, and more.
Args:
debug (str, optional): Space-separated string of command-line arguments for debugging purposes.
debug (str): Space-separated string of command-line arguments for debugging purposes.
Examples:
Train a detection model for 10 epochs with an initial learning_rate of 0.01:
@ -652,9 +687,9 @@ def entrypoint(debug=""):
>>> entrypoint("val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640")
Notes:
- For a list of all available commands and their arguments, see the provided help messages and the Ultralytics
documentation at https://docs.ultralytics.com.
- If no arguments are passed, the function will display the usage help message.
- For a list of all available commands and their arguments, see the provided help messages and the
Ultralytics documentation at https://docs.ultralytics.com.
"""
args = (debug.split(" ") if debug else ARGV)[1:]
if not args: # no arguments passed
@ -793,16 +828,24 @@ def entrypoint(debug=""):
# Special modes --------------------------------------------------------------------------------------------------------
def copy_default_cfg():
"""
Copy and create a new default configuration file with '_copy' appended to its name, providing a usage example.
Copies the default configuration file and creates a new one with '_copy' appended to its name.
This function duplicates the existing default configuration file and appends '_copy' to its name in the current
working directory.
This function duplicates the existing default configuration file (DEFAULT_CFG_PATH) and saves it
with '_copy' appended to its name in the current working directory. It provides a convenient way
to create a custom configuration file based on the default settings.
Examples:
Copy the default configuration file and use it in a YOLO command:
>>> copy_default_cfg()
>>> # Example YOLO command with this new custom cfg:
>>> # yolo cfg='default_copy.yaml' imgsz=320 batch=8
# Output: default.yaml copied to /path/to/current/directory/default_copy.yaml
# Example YOLO command with this new custom cfg:
# yolo cfg='/path/to/current/directory/default_copy.yaml' imgsz=320 batch=8
Notes:
- The new configuration file is created in the current working directory.
- After copying, the function prints a message with the new file's location and an example
YOLO command demonstrating how to use the new configuration file.
- This function is useful for users who want to modify the default configuration without
altering the original file.
"""
new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace(".yaml", "_copy.yaml")
shutil.copy2(DEFAULT_CFG_PATH, new_file)

1669
ultralytics/data/augment.py
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653
ultralytics/engine/model.py
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@ -30,26 +30,18 @@ class Model(nn.Module):
This class provides a common interface for various operations related to YOLO models, such as training,
validation, prediction, exporting, and benchmarking. It handles different types of models, including those
loaded from local files, Ultralytics HUB, or Triton Server. The class is designed to be flexible and
extendable for different tasks and model configurations.
Args:
model (Union[str, Path], optional): Path or name of the model to load or create. This can be a local file
path, a model name from Ultralytics HUB, or a Triton Server model. Defaults to 'yolov8n.pt'.
task (Any, optional): The task type associated with the YOLO model. This can be used to specify the model's
application domain, such as object detection, segmentation, etc. Defaults to None.
verbose (bool, optional): If True, enables verbose output during the model's operations. Defaults to False.
loaded from local files, Ultralytics HUB, or Triton Server.
Attributes:
callbacks (dict): A dictionary of callback functions for various events during model operations.
callbacks (Dict): A dictionary of callback functions for various events during model operations.
predictor (BasePredictor): The predictor object used for making predictions.
model (nn.Module): The underlying PyTorch model.
trainer (BaseTrainer): The trainer object used for training the model.
ckpt (dict): The checkpoint data if the model is loaded from a *.pt file.
ckpt (Dict): The checkpoint data if the model is loaded from a *.pt file.
cfg (str): The configuration of the model if loaded from a *.yaml file.
ckpt_path (str): The path to the checkpoint file.
overrides (dict): A dictionary of overrides for model configuration.
metrics (dict): The latest training/validation metrics.
overrides (Dict): A dictionary of overrides for model configuration.
metrics (Dict): The latest training/validation metrics.
session (HUBTrainingSession): The Ultralytics HUB session, if applicable.
task (str): The type of task the model is intended for.
model_name (str): The name of the model.
@ -75,19 +67,14 @@ class Model(nn.Module):
add_callback: Adds a callback function for an event.
clear_callback: Clears all callbacks for an event.
reset_callbacks: Resets all callbacks to their default functions.
is_triton_model: Checks if a model is a Triton Server model.
is_hub_model: Checks if a model is an Ultralytics HUB model.
_reset_ckpt_args: Resets checkpoint arguments when loading a PyTorch model.
_smart_load: Loads the appropriate module based on the model task.
task_map: Provides a mapping from model tasks to corresponding classes.
Raises:
FileNotFoundError: If the specified model file does not exist or is inaccessible.
ValueError: If the model file or configuration is invalid or unsupported.
ImportError: If required dependencies for specific model types (like HUB SDK) are not installed.
TypeError: If the model is not a PyTorch model when required.
AttributeError: If required attributes or methods are not implemented or available.
NotImplementedError: If a specific model task or mode is not supported.
Examples:
>>> from ultralytics import YOLO
>>> model = YOLO('yolov8n.pt')
>>> results = model.predict('image.jpg')
>>> model.train(data='coco128.yaml', epochs=3)
>>> metrics = model.val()
>>> model.export(format='onnx')
"""
def __init__(
@ -99,22 +86,27 @@ class Model(nn.Module):
"""
Initializes a new instance of the YOLO model class.
This constructor sets up the model based on the provided model path or name. It handles various types of model
sources, including local files, Ultralytics HUB models, and Triton Server models. The method initializes several
important attributes of the model and prepares it for operations like training, prediction, or export.
This constructor sets up the model based on the provided model path or name. It handles various types of
model sources, including local files, Ultralytics HUB models, and Triton Server models. The method
initializes several important attributes of the model and prepares it for operations like training,
prediction, or export.
Args:
model (Union[str, Path], optional): The path or model file to load or create. This can be a local
file path, a model name from Ultralytics HUB, or a Triton Server model. Defaults to 'yolov8n.pt'.
task (Any, optional): The task type associated with the YOLO model, specifying its application domain.
Defaults to None.
verbose (bool, optional): If True, enables verbose output during the model's initialization and subsequent
operations. Defaults to False.
model (Union[str, Path]): Path or name of the model to load or create. Can be a local file path, a
model name from Ultralytics HUB, or a Triton Server model.
task (str | None): The task type associated with the YOLO model, specifying its application domain.
verbose (bool): If True, enables verbose output during the model's initialization and subsequent
operations.
Raises:
FileNotFoundError: If the specified model file does not exist or is inaccessible.
ValueError: If the model file or configuration is invalid or unsupported.
ImportError: If required dependencies for specific model types (like HUB SDK) are not installed.
Examples:
>>> model = Model("yolov8n.pt")
>>> model = Model("path/to/model.yaml", task="detect")
>>> model = Model("hub_model", verbose=True)
"""
super().__init__()
self.callbacks = callbacks.get_default_callbacks()
@ -155,27 +147,50 @@ class Model(nn.Module):
**kwargs,
) -> list:
"""
An alias for the predict method, enabling the model instance to be callable.
Alias for the predict method, enabling the model instance to be callable for predictions.
This method simplifies the process of making predictions by allowing the model instance to be called directly
with the required arguments for prediction.
This method simplifies the process of making predictions by allowing the model instance to be called
directly with the required arguments.
Args:
source (str | Path | int | PIL.Image | np.ndarray, optional): The source of the image for making
predictions. Accepts various types, including file paths, URLs, PIL images, and numpy arrays.
Defaults to None.
stream (bool, optional): If True, treats the input source as a continuous stream for predictions.
Defaults to False.
**kwargs (any): Additional keyword arguments for configuring the prediction process.
source (str | Path | int | PIL.Image | np.ndarray | torch.Tensor | List | Tuple): The source of
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.
Returns:
(List[ultralytics.engine.results.Results]): A list of prediction results, encapsulated in the Results class.
(List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a
Results object.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model('https://ultralytics.com/images/bus.jpg')
>>> for r in results:
... print(f"Detected {len(r)} objects in image")
"""
return self.predict(source, stream, **kwargs)
@staticmethod
def is_triton_model(model: str) -> bool:
"""Is model a Triton Server URL string, i.e. <scheme>://<netloc>/<endpoint>/<task_name>"""
"""
Checks if the given model string is a Triton Server URL.
This static method determines whether the provided model string represents a valid Triton Server URL by
parsing its components using urllib.parse.urlsplit().
Args:
model (str): The model string to be checked.
Returns:
(bool): True if the model string is a valid Triton Server URL, False otherwise.
Examples:
>>> Model.is_triton_model('http://localhost:8000/v2/models/yolov8n')
True
>>> Model.is_triton_model('yolov8n.pt')
False
"""
from urllib.parse import urlsplit
url = urlsplit(model)
@ -183,7 +198,30 @@ class Model(nn.Module):
@staticmethod
def is_hub_model(model: str) -> bool:
"""Check if the provided model is a HUB model."""
"""
Check if the provided model is an Ultralytics HUB model.
This static method determines whether the given model string represents a valid Ultralytics HUB model
identifier. It checks for three possible formats: a full HUB URL, an API key and model ID combination,
or a standalone model ID.
Args:
model (str): The model identifier to check. This can be a URL, an API key and model ID
combination, or a standalone model ID.
Returns:
(bool): True if the model is a valid Ultralytics HUB model, False otherwise.
Examples:
>>> Model.is_hub_model("https://hub.ultralytics.com/models/example_model")
True
>>> Model.is_hub_model("api_key_example_model_id")
True
>>> Model.is_hub_model("example_model_id")
True
>>> Model.is_hub_model("not_a_hub_model.pt")
False
"""
return any(
(
model.startswith(f"{HUB_WEB_ROOT}/models/"), # i.e. https://hub.ultralytics.com/models/MODEL_ID
@ -196,11 +234,24 @@ class Model(nn.Module):
"""
Initializes a new model and infers the task type from the model definitions.
This method creates a new model instance based on the provided configuration file. It loads the model
configuration, infers the task type if not specified, and initializes the model using the appropriate
class from the task map.
Args:
cfg (str): model configuration file
task (str | None): model task
model (BaseModel): Customized model.
verbose (bool): display model info on load
cfg (str): Path to the model configuration file in YAML format.
task (str | None): The specific task for the model. If None, it will be inferred from the config.
model (torch.nn.Module | None): A custom model instance. If provided, it will be used instead of creating
a new one.
verbose (bool): If True, displays model information during loading.
Raises:
ValueError: If the configuration file is invalid or the task cannot be inferred.
ImportError: If the required dependencies for the specified task are not installed.
Examples:
>>> model = Model()
>>> model._new('yolov8n.yaml', task='detect', verbose=True)
"""
cfg_dict = yaml_model_load(cfg)
self.cfg = cfg
@ -216,11 +267,23 @@ class Model(nn.Module):
def _load(self, weights: str, task=None) -> None:
"""
Initializes a new model and infers the task type from the model head.
Loads a model from a checkpoint file or initializes it from a weights file.
This method handles loading models from either .pt checkpoint files or other weight file formats. It sets
up the model, task, and related attributes based on the loaded weights.
Args:
weights (str): model checkpoint to be loaded
task (str | None): model task
weights (str): Path to the model weights file to be loaded.
task (str | None): The task associated with the model. If None, it will be inferred from the model.
Raises:
FileNotFoundError: If the specified weights file does not exist or is inaccessible.
ValueError: If the weights file format is unsupported or invalid.
Examples:
>>> model = Model()
>>> model._load('yolov8n.pt')
>>> model._load('path/to/weights.pth', task='detect')
"""
if weights.lower().startswith(("https://", "http://", "rtsp://", "rtmp://", "tcp://")):
weights = checks.check_file(weights) # automatically download and return local filename
@ -241,7 +304,22 @@ class Model(nn.Module):
self.model_name = weights
def _check_is_pytorch_model(self) -> None:
"""Raises TypeError is model is not a PyTorch model."""
"""
Checks if the model is a PyTorch model and raises a TypeError if it's not.
This method verifies that the model is either a PyTorch module or a .pt file. It's used to ensure that
certain operations that require a PyTorch model are only performed on compatible model types.
Raises:
TypeError: If the model is not a PyTorch module or a .pt file. The error message provides detailed
information about supported model formats and operations.
Examples:
>>> model = Model("yolov8n.pt")
>>> model._check_is_pytorch_model() # No error raised
>>> model = Model("yolov8n.onnx")
>>> model._check_is_pytorch_model() # Raises TypeError
"""
pt_str = isinstance(self.model, (str, Path)) and Path(self.model).suffix == ".pt"
pt_module = isinstance(self.model, nn.Module)
if not (pt_module or pt_str):
@ -255,17 +333,21 @@ class Model(nn.Module):
def reset_weights(self) -> "Model":
"""
Resets the model parameters to randomly initialized values, effectively discarding all training information.
Resets the model's weights to their initial state.
This method iterates through all modules in the model and resets their parameters if they have a
'reset_parameters' method. It also ensures that all parameters have 'requires_grad' set to True, enabling them
to be updated during training.
'reset_parameters' method. It also ensures that all parameters have 'requires_grad' set to True,
enabling them to be updated during training.
Returns:
self (ultralytics.engine.model.Model): The instance of the class with reset weights.
(Model): The instance of the class with reset weights.
Raises:
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = Model('yolov8n.pt')
>>> model.reset_weights()
"""
self._check_is_pytorch_model()
for m in self.model.modules():
@ -283,13 +365,18 @@ class Model(nn.Module):
name and shape and transfers them to the model.
Args:
weights (str | Path): Path to the weights file or a weights object. Defaults to 'yolov8n.pt'.
weights (Union[str, Path]): Path to the weights file or a weights object.
Returns:
self (ultralytics.engine.model.Model): The instance of the class with loaded weights.
(Model): The instance of the class with loaded weights.
Raises:
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = Model()
>>> model.load('yolov8n.pt')
>>> model.load(Path('path/to/weights.pt'))
"""
self._check_is_pytorch_model()
if isinstance(weights, (str, Path)):
@ -301,14 +388,19 @@ class Model(nn.Module):
"""
Saves the current model state to a file.
This method exports the model's checkpoint (ckpt) to the specified filename.
This method exports the model's checkpoint (ckpt) to the specified filename. It includes metadata such as
the date, Ultralytics version, license information, and a link to the documentation.
Args:
filename (str | Path): The name of the file to save the model to. Defaults to 'saved_model.pt'.
use_dill (bool): Whether to try using dill for serialization if available. Defaults to True.
filename (Union[str, Path]): The name of the file to save the model to.
use_dill (bool): Whether to try using dill for serialization if available.
Raises:
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = Model('yolov8n.pt')
>>> model.save('my_model.pt')
"""
self._check_is_pytorch_model()
from copy import deepcopy
@ -329,30 +421,47 @@ class Model(nn.Module):
"""
Logs or returns model information.
This method provides an overview or detailed information about the model, depending on the arguments passed.
It can control the verbosity of the output.
This method provides an overview or detailed information about the model, depending on the arguments
passed. It can control the verbosity of the output and return the information as a list.
Args:
detailed (bool): If True, shows detailed information about the model. Defaults to False.
verbose (bool): If True, prints the information. If False, returns the information. Defaults to True.
detailed (bool): If True, shows detailed information about the model layers and parameters.
verbose (bool): If True, prints the information. If False, returns the information as a list.
Returns:
(list): Various types of information about the model, depending on the 'detailed' and 'verbose' parameters.
(List[str]): A list of strings containing various types of information about the model, including
model summary, layer details, and parameter counts. Empty if verbose is True.
Raises:
AssertionError: If the model is not a PyTorch model.
TypeError: If the model is not a PyTorch model.
Examples:
>>> model = Model('yolov8n.pt')
>>> model.info() # Prints model summary
>>> info_list = model.info(detailed=True, verbose=False) # Returns detailed info as a list
"""
self._check_is_pytorch_model()
return self.model.info(detailed=detailed, verbose=verbose)
def fuse(self):
"""
Fuses Conv2d and BatchNorm2d layers in the model.
Fuses Conv2d and BatchNorm2d layers in the model for optimized inference.
This method optimizes the model by fusing Conv2d and BatchNorm2d layers, which can improve inference speed.
This method iterates through the model's modules and fuses consecutive Conv2d and BatchNorm2d layers
into a single layer. This fusion can significantly improve inference speed by reducing the number of
operations and memory accesses required during forward passes.
The fusion process typically involves folding the BatchNorm2d parameters (mean, variance, weight, and
bias) into the preceding Conv2d layer's weights and biases. This results in a single Conv2d layer that
performs both convolution and normalization in one step.
Raises:
AssertionError: If the model is not a PyTorch model.
TypeError: If the model is not a PyTorch nn.Module.
Examples:
>>> model = Model("yolov8n.pt")
>>> model.fuse()
>>> # Model is now fused and ready for optimized inference
"""
self._check_is_pytorch_model()
self.model.fuse()
@ -366,20 +475,26 @@ class Model(nn.Module):
"""
Generates image embeddings based on the provided source.
This method is a wrapper around the 'predict()' method, focusing on generating embeddings from an image source.
It allows customization of the embedding process through various keyword arguments.
This method is a wrapper around the 'predict()' method, focusing on generating embeddings from an image
source. It allows customization of the embedding process through various keyword arguments.
Args:
source (str | int | PIL.Image | np.ndarray): The source of the image for generating embeddings.
The source can be a file path, URL, PIL image, numpy array, etc. Defaults to None.
stream (bool): If True, predictions are streamed. Defaults to False.
**kwargs (any): Additional keyword arguments for configuring the embedding process.
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.
Returns:
(List[torch.Tensor]): A list containing the image embeddings.
Raises:
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> image = 'https://ultralytics.com/images/bus.jpg'
>>> embeddings = model.embed(image)
>>> print(embeddings[0].shape)
"""
if not kwargs.get("embed"):
kwargs["embed"] = [len(self.model.model) - 2] # embed second-to-last layer if no indices passed
@ -397,28 +512,31 @@ class Model(nn.Module):
This method facilitates the prediction process, allowing various configurations through keyword arguments.
It supports predictions with custom predictors or the default predictor method. The method handles different
types of image sources and can operate in a streaming mode. It also provides support for SAM-type models
through 'prompts'.
The method sets up a new predictor if not already present and updates its arguments with each call.
It also issues a warning and uses default assets if the 'source' is not provided. The method determines if it
is being called from the command line interface and adjusts its behavior accordingly, including setting defaults
for confidence threshold and saving behavior.
types of image sources and can operate in a streaming mode.
Args:
source (str | int | PIL.Image | np.ndarray, optional): The source of the image for making predictions.
Accepts various types, including file paths, URLs, PIL images, and numpy arrays. Defaults to ASSETS.
stream (bool, optional): Treats the input source as a continuous stream for predictions. Defaults to False.
predictor (BasePredictor, optional): An instance of a custom predictor class for making predictions.
If None, the method uses a default predictor. Defaults to None.
**kwargs (any): Additional keyword arguments for configuring the prediction process. These arguments allow
for further customization of the prediction behavior.
source (str | Path | int | List[str] | List[Path] | List[int] | np.ndarray | torch.Tensor): The source
of the image(s) to make predictions on. Accepts various types including file paths, URLs, PIL
images, numpy arrays, and torch tensors.
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.
Returns:
(List[ultralytics.engine.results.Results]): A list of prediction results, encapsulated in the Results class.
(List[ultralytics.engine.results.Results]): A list of prediction results, each encapsulated in a
Results object.
Raises:
AttributeError: If the predictor is not properly set up.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.predict(source='path/to/image.jpg', conf=0.25)
>>> for r in results:
... print(r.boxes.data) # print detection bounding boxes
Notes:
- If 'source' is not provided, it defaults to the ASSETS constant with a warning.
- The method sets up a new predictor if not already present and updates its arguments with each call.
- For SAM-type models, 'prompts' can be passed as a keyword argument.
"""
if source is None:
source = ASSETS
@ -453,26 +571,33 @@ class Model(nn.Module):
"""
Conducts object tracking on the specified input source using the registered trackers.
This method performs object tracking using the model's predictors and optionally registered trackers. It is
capable of handling different types of input sources such as file paths or video streams. The method supports
customization of the tracking process through various keyword arguments. It registers trackers if they are not
already present and optionally persists them based on the 'persist' flag.
The method sets a default confidence threshold specifically for ByteTrack-based tracking, which requires low
confidence predictions as input. The tracking mode is explicitly set in the keyword arguments.
This method performs object tracking using the model's predictors and optionally registered trackers. It handles
various input sources such as file paths or video streams, and supports customization through keyword arguments.
The method registers trackers if not already present and can persist them between calls.
Args:
source (str, optional): The input source for object tracking. It can be a file path, URL, or video stream.
stream (bool, optional): Treats the input source as a continuous video stream. Defaults to False.
persist (bool, optional): Persists the trackers between different calls to this method. Defaults to False.
**kwargs (any): Additional keyword arguments for configuring the tracking process. These arguments allow
for further customization of the tracking behavior.
source (Union[str, Path, int, List, Tuple, np.ndarray, torch.Tensor], optional): Input source for object
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.
Returns:
(List[ultralytics.engine.results.Results]): A list of tracking results, encapsulated in the Results class.
(List[ultralytics.engine.results.Results]): A list of tracking results, each encapsulated in a Results object.
Raises:
AttributeError: If the predictor does not have registered trackers.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.track(source='path/to/video.mp4', show=True)
>>> for r in results:
... print(r.boxes.id) # print tracking IDs
Notes:
- This method sets a default confidence threshold of 0.1 for ByteTrack-based tracking.
- The tracking mode is explicitly set in the keyword arguments.
- Batch size is set to 1 for tracking in videos.
"""
if not hasattr(self.predictor, "trackers"):
from ultralytics.trackers import register_tracker
@ -491,26 +616,25 @@ class Model(nn.Module):
"""
Validates the model using a specified dataset and validation configuration.
This method facilitates the model validation process, allowing for a range of customization through various
settings and configurations. It supports validation with a custom validator or the default validation approach.
The method combines default configurations, method-specific defaults, and user-provided arguments to configure
the validation process. After validation, it updates the model's metrics with the results obtained from the
validator.
The method supports various arguments that allow customization of the validation process. For a comprehensive
list of all configurable options, users should refer to the 'configuration' section in the documentation.
This method facilitates the model validation process, allowing for customization through various settings. It
supports validation with a custom validator or the default validation approach. The method combines default
configurations, method-specific defaults, and user-provided arguments to configure the validation process.
Args:
validator (BaseValidator, optional): An instance of a custom validator class for validating the model. If
None, the method uses a default validator. Defaults to None.
**kwargs (any): Arbitrary keyword arguments representing the validation configuration. These arguments are
used to customize various aspects of the validation process.
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.
Returns:
(ultralytics.utils.metrics.DetMetrics): Validation metrics obtained from the validation process.
Raises:
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.val(data='coco128.yaml', imgsz=640)
>>> print(results.box.map) # Print mAP50-95
"""
custom = {"rect": True} # method defaults
args = {**self.overrides, **custom, **kwargs, "mode": "val"} # highest priority args on the right
@ -528,23 +652,31 @@ class Model(nn.Module):
Benchmarks the model across various export formats to evaluate performance.
This method assesses the model's performance in different export formats, such as ONNX, TorchScript, etc.
It uses the 'benchmark' function from the ultralytics.utils.benchmarks module. The benchmarking is configured
using a combination of default configuration values, model-specific arguments, method-specific defaults, and
any additional user-provided keyword arguments.
The method supports various arguments that allow customization of the benchmarking process, such as dataset
choice, image size, precision modes, device selection, and verbosity. For a comprehensive list of all
configurable options, users should refer to the 'configuration' section in the documentation.
It uses the 'benchmark' function from the ultralytics.utils.benchmarks module. The benchmarking is
configured using a combination of default configuration values, model-specific arguments, method-specific
defaults, and any additional user-provided keyword arguments.
Args:
**kwargs (any): Arbitrary keyword arguments to customize the benchmarking process. These are combined with
default configurations, model-specific arguments, and method defaults.
**kwargs (Any): 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.
- half (bool): Whether to use half-precision (FP16) mode.
- int8 (bool): Whether to use int8 precision mode.
- device (str): Device to run the benchmark on (e.g., 'cpu', 'cuda').
- verbose (bool): Whether to print detailed benchmark information.
Returns:
(dict): A dictionary containing the results of the benchmarking process.
(Dict): A dictionary containing the results of the benchmarking process, including metrics for
different export formats.
Raises:
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.benchmark(data='coco8.yaml', imgsz=640, half=True)
>>> print(results)
"""
self._check_is_pytorch_model()
from ultralytics.utils.benchmarks import benchmark
@ -570,20 +702,31 @@ class Model(nn.Module):
This method facilitates the export of the model to various formats (e.g., ONNX, TorchScript) for deployment
purposes. It uses the 'Exporter' class for the export process, combining model-specific overrides, method
defaults, and any additional arguments provided. The combined arguments are used to configure export settings.
The method supports a wide range of arguments to customize the export process. For a comprehensive list of all
possible arguments, refer to the 'configuration' section in the documentation.
defaults, and any additional arguments provided.
Args:
**kwargs (any): Arbitrary keyword arguments to customize the export process. These are combined with the
model's overrides and method defaults.
**kwargs (Dict): 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.
int8 (bool): Export model in int8 precision.
device (str): Device to run the export on.
workspace (int): Maximum memory workspace size for TensorRT engines.
nms (bool): Add Non-Maximum Suppression (NMS) module to model.
simplify (bool): Simplify ONNX model.
Returns:
(str): The exported model filename in the specified format, or an object related to the export process.
(str): The path to the exported model file.
Raises:
AssertionError: If the model is not a PyTorch model.
ValueError: If an unsupported export format is specified.
RuntimeError: If the export process fails due to errors.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> model.export(format='onnx', dynamic=True, simplify=True)
'path/to/exported/model.onnx'
"""
self._check_is_pytorch_model()
from .exporter import Exporter
@ -606,29 +749,38 @@ class Model(nn.Module):
"""
Trains the model using the specified dataset and training configuration.
This method facilitates model training with a range of customizable settings and configurations. It supports
training with a custom trainer or the default training approach defined in the method. The method handles
different scenarios, such as resuming training from a checkpoint, integrating with Ultralytics HUB, and
updating model and configuration after training.
This method facilitates model training with a range of customizable settings. It supports training with a
custom trainer or the default training approach. The method handles scenarios such as resuming training
from a checkpoint, integrating with Ultralytics HUB, and updating model and configuration after training.
When using Ultralytics HUB, if the session already has a loaded model, the method prioritizes HUB training
arguments and issues a warning if local arguments are provided. It checks for pip updates and combines default
configurations, method-specific defaults, and user-provided arguments to configure the training process. After
training, it updates the model and its configurations, and optionally attaches metrics.
When using Ultralytics HUB, if the session has a loaded model, the method prioritizes HUB training
arguments and warns if local arguments are provided. It checks for pip updates and combines default
configurations, method-specific defaults, and user-provided arguments to configure the training process.
Args:
trainer (BaseTrainer, optional): An instance of a custom trainer class for training the model. If None, the
method uses a default trainer. Defaults to None.
**kwargs (any): Arbitrary keyword arguments representing the training configuration. These arguments are
used to customize various aspects of the training process.
trainer (BaseTrainer | None): Custom trainer instance for model training. If None, uses default.
**kwargs (Any): 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.
imgsz (int): Input image size.
device (str): Device to run training on (e.g., 'cuda', 'cpu').
workers (int): Number of worker threads for data loading.
optimizer (str): Optimizer to use for training.
lr0 (float): Initial learning rate.
patience (int): Epochs to wait for no observable improvement for early stopping of training.
Returns:
(dict | None): Training metrics if available and training is successful; otherwise, None.
(Dict | None): Training metrics if available and training is successful; otherwise, None.
Raises:
AssertionError: If the model is not a PyTorch model.
PermissionError: If there is a permission issue with the HUB session.
ModuleNotFoundError: If the HUB SDK is not installed.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.train(data='coco128.yaml', epochs=3)
"""
self._check_is_pytorch_model()
if hasattr(self.session, "model") and self.session.model.id: # Ultralytics HUB session with loaded model
@ -682,14 +834,19 @@ 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 (any): Arbitrary keyword arguments. These are combined with the model's overrides and defaults.
*args (List): Variable length argument list for additional arguments.
**kwargs (Dict): Arbitrary keyword arguments. These are combined with the model's overrides and defaults.
Returns:
(dict): A dictionary containing the results of the hyperparameter search.
(Dict): A dictionary containing the results of the hyperparameter search.
Raises:
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> results = model.tune(use_ray=True, iterations=20)
>>> print(results)
"""
self._check_is_pytorch_model()
if use_ray:
@ -704,7 +861,27 @@ class Model(nn.Module):
return Tuner(args=args, _callbacks=self.callbacks)(model=self, iterations=iterations)
def _apply(self, fn) -> "Model":
"""Apply to(), cpu(), cuda(), half(), float() to model tensors that are not parameters or registered buffers."""
"""
Applies a function to model tensors that are not parameters or registered buffers.
This method extends the functionality of the parent class's _apply method by additionally resetting the
predictor and updating the device in the model's overrides. It's typically used for operations like
moving the model to a different device or changing its precision.
Args:
fn (Callable): A function to be applied to the model's tensors. This is typically a method like
to(), cpu(), cuda(), half(), or float().
Returns:
(Model): The model instance with the function applied and updated attributes.
Raises:
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = Model("yolov8n.pt")
>>> model = model._apply(lambda t: t.cuda()) # Move model to GPU
"""
self._check_is_pytorch_model()
self = super()._apply(fn) # noqa
self.predictor = None # reset predictor as device may have changed
@ -717,10 +894,19 @@ class Model(nn.Module):
Retrieves the class names associated with the loaded model.
This property returns the class names if they are defined in the model. It checks the class names for validity
using the 'check_class_names' function from the ultralytics.nn.autobackend module.
using the 'check_class_names' function from the ultralytics.nn.autobackend module. If the predictor is not
initialized, it sets it up before retrieving the names.
Returns:
(list | None): The class names of the model if available, otherwise None.
(List[str]): A list of class names associated with the model.
Raises:
AttributeError: If the model or predictor does not have a 'names' attribute.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> print(model.names)
['person', 'bicycle', 'car', ...]
"""
from ultralytics.nn.autobackend import check_class_names
@ -736,11 +922,22 @@ class Model(nn.Module):
"""
Retrieves the device on which the model's parameters are allocated.
This property is used to determine whether the model's parameters are on CPU or GPU. It only applies to models
that are instances of nn.Module.
This property determines the device (CPU or GPU) where the model's parameters are currently stored. It is
applicable only to models that are instances of nn.Module.
Returns:
(torch.device | None): The device (CPU/GPU) of the model if it is a PyTorch model, otherwise None.
(torch.device): The device (CPU/GPU) of the model.
Raises:
AttributeError: If the model is not a PyTorch nn.Module instance.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> print(model.device)
device(type='cuda', index=0) # if CUDA is available
>>> model = model.to("cpu")
>>> print(model.device)
device(type='cpu')
"""
return next(self.model.parameters()).device if isinstance(self.model, nn.Module) else None
@ -749,10 +946,20 @@ class Model(nn.Module):
"""
Retrieves the transformations applied to the input data of the loaded model.
This property returns the transformations if they are defined in the model.
This property returns the transformations if they are defined in the model. The transforms
typically include preprocessing steps like resizing, normalization, and data augmentation
that are applied to input data before it is fed into the model.
Returns:
(object | None): The transform object of the model if available, otherwise None.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> transforms = model.transforms
>>> if transforms:
... print(f"Model transforms: {transforms}")
... else:
... print("No transforms defined for this model.")
"""
return self.model.transforms if hasattr(self.model, "transforms") else None
@ -760,15 +967,25 @@ class Model(nn.Module):
"""
Adds a callback function for a specified event.
This method allows the user to register a custom callback function that is triggered on a specific event during
model training or inference.
This method allows registering custom callback functions that are triggered on specific events during
model operations such as training or inference. Callbacks provide a way to extend and customize the
behavior of the model at various stages of its lifecycle.
Args:
event (str): The name of the event to attach the callback to.
func (callable): The callback function to be registered.
event (str): The name of the event to attach the callback to. Must be a valid event name recognized
by the Ultralytics framework.
func (Callable): The callback function to be registered. This function will be called when the
specified event occurs.
Raises:
ValueError: If the event name is not recognized.
ValueError: If the event name is not recognized or is invalid.
Examples:
>>> def on_train_start(trainer):
... print("Training is starting!")
>>> model = YOLO('yolov8n.pt')
>>> model.add_callback("on_train_start", on_train_start)
>>> model.train(data='coco128.yaml', epochs=1)
"""
self.callbacks[event].append(func)
@ -777,12 +994,26 @@ class Model(nn.Module):
Clears all callback functions registered for a specified event.
This method removes all custom and default callback functions associated with the given event.
It resets the callback list for the specified event to an empty list, effectively removing all
registered callbacks for that event.
Args:
event (str): The name of the event for which to clear the callbacks.
event (str): The name of the event for which to clear the callbacks. This should be a valid event name
recognized by the Ultralytics callback system.
Raises:
ValueError: If the event name is not recognized.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> model.add_callback('on_train_start', lambda: print('Training started'))
>>> model.clear_callback('on_train_start')
>>> # All callbacks for 'on_train_start' are now removed
Notes:
- This method affects both custom callbacks added by the user and default callbacks
provided by the Ultralytics framework.
- After calling this method, no callbacks will be executed for the specified event
until new ones are added.
- Use with caution as it removes all callbacks, including essential ones that might
be required for proper functioning of certain operations.
"""
self.callbacks[event] = []
@ -791,14 +1022,45 @@ class Model(nn.Module):
Resets all callbacks to their default functions.
This method reinstates the default callback functions for all events, removing any custom callbacks that were
added previously.
previously added. It iterates through all default callback events and replaces the current callbacks with the
default ones.
The default callbacks are defined in the 'callbacks.default_callbacks' dictionary, which contains predefined
functions for various events in the model's lifecycle, such as on_train_start, on_epoch_end, etc.
This method is useful when you want to revert to the original set of callbacks after making custom modifications,
ensuring consistent behavior across different runs or experiments.
Examples:
>>> model = YOLO('yolov8n.pt')
>>> model.add_callback('on_train_start', custom_function)
>>> model.reset_callbacks()
# All callbacks are now reset to their default functions
"""
for event in callbacks.default_callbacks.keys():
self.callbacks[event] = [callbacks.default_callbacks[event][0]]
@staticmethod
def _reset_ckpt_args(args: dict) -> dict:
"""Reset arguments when loading a PyTorch model."""
"""
Resets specific arguments when loading a PyTorch model checkpoint.
This static method filters the input arguments dictionary to retain only a specific set of keys that are
considered important for model loading. It's used to ensure that only relevant arguments are preserved
when loading a model from a checkpoint, discarding any unnecessary or potentially conflicting settings.
Args:
args (dict): A dictionary containing various model arguments and settings.
Returns:
(dict): A new dictionary containing only the specified include keys from the input arguments.
Examples:
>>> original_args = {'imgsz': 640, 'data': 'coco.yaml', 'task': 'detect', 'batch': 16, 'epochs': 100}
>>> reset_args = Model._reset_ckpt_args(original_args)
>>> print(reset_args)
{'imgsz': 640, 'data': 'coco.yaml', 'task': 'detect'}
"""
include = {"imgsz", "data", "task", "single_cls"} # only remember these arguments when loading a PyTorch model
return {k: v for k, v in args.items() if k in include}
@ -808,7 +1070,31 @@ class Model(nn.Module):
# raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
def _smart_load(self, key: str):
"""Load model/trainer/validator/predictor."""
"""
Loads the appropriate module based on the model task.
This method dynamically selects and returns the correct module (model, trainer, validator, or predictor)
based on the current task of the model and the provided key. It uses the task_map attribute to determine
the correct module to load.
Args:
key (str): The type of module to load. Must be one of 'model', 'trainer', 'validator', or 'predictor'.
Returns:
(object): The loaded module corresponding to the specified key and current task.
Raises:
NotImplementedError: If the specified key is not supported for the current task.
Examples:
>>> model = Model(task='detect')
>>> predictor = model._smart_load('predictor')
>>> trainer = model._smart_load('trainer')
Notes:
- This method is typically used internally by other methods of the Model class.
- The task_map attribute should be properly initialized with the correct mappings for each task.
"""
try:
return self.task_map[self.task][key]
except Exception as e:
@ -821,9 +1107,30 @@ class Model(nn.Module):
@property
def task_map(self) -> dict:
"""
Map head to model, trainer, validator, and predictor classes.
Provides a mapping from model tasks to corresponding classes for different modes.
This property method returns a dictionary that maps each supported task (e.g., detect, segment, classify)
to a nested dictionary. The nested dictionary contains mappings for different operational modes
(model, trainer, validator, predictor) to their respective class implementations.
The mapping allows for dynamic loading of appropriate classes based on the model's task and the
desired operational mode. This facilitates a flexible and extensible architecture for handling
various tasks and modes within the Ultralytics framework.
Returns:
task_map (dict): The map of model task to mode classes.
(Dict[str, Dict[str, Any]]): A dictionary where keys are task names (str) and values are
nested dictionaries. Each nested dictionary has keys 'model', 'trainer', 'validator', and
'predictor', mapping to their respective class implementations.
Example:
>>> model = Model()
>>> task_map = model.task_map
>>> detect_class_map = task_map['detect']
>>> segment_class_map = task_map['segment']
Note:
The actual implementation of this method may vary depending on the specific tasks and
classes supported by the Ultralytics framework. The docstring provides a general
description of the expected behavior and structure.
"""
raise NotImplementedError("Please provide task map for your model!")

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ultralytics/engine/results.py
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