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511 lines
23 KiB
Python
511 lines
23 KiB
Python
3 weeks ago
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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"""
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PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5.
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Usage:
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import torch
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model
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model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch
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model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model
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model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo
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"""
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import torch
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def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
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"""
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Creates or loads a YOLOv5 model, with options for pretrained weights and model customization.
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Args:
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name (str): Model name (e.g., 'yolov5s') or path to the model checkpoint (e.g., 'path/to/best.pt').
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pretrained (bool, optional): If True, loads pretrained weights into the model. Defaults to True.
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channels (int, optional): Number of input channels the model expects. Defaults to 3.
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classes (int, optional): Number of classes the model is expected to detect. Defaults to 80.
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autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper for various input formats. Defaults to True.
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verbose (bool, optional): If True, prints detailed information during the model creation/loading process. Defaults to True.
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device (str | torch.device | None, optional): Device to use for model parameters (e.g., 'cpu', 'cuda'). If None, selects
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the best available device. Defaults to None.
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Returns:
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(DetectMultiBackend | AutoShape): The loaded YOLOv5 model, potentially wrapped with AutoShape if specified.
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Examples:
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```python
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import torch
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from ultralytics import _create
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# Load an official YOLOv5s model with pretrained weights
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model = _create('yolov5s')
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# Load a custom model from a local checkpoint
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model = _create('path/to/custom_model.pt', pretrained=False)
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# Load a model with specific input channels and classes
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model = _create('yolov5s', channels=1, classes=10)
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```
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Notes:
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For more information on model loading and customization, visit the
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[YOLOv5 PyTorch Hub Documentation](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading).
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"""
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from pathlib import Path
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from models.common import AutoShape, DetectMultiBackend
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from models.experimental import attempt_load
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from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
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from utils.downloads import attempt_download
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from utils.general import LOGGER, ROOT, check_requirements, intersect_dicts, logging
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from utils.torch_utils import select_device
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if not verbose:
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LOGGER.setLevel(logging.WARNING)
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check_requirements(ROOT / "requirements.txt", exclude=("opencv-python", "tensorboard", "thop"))
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name = Path(name)
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path = name.with_suffix(".pt") if name.suffix == "" and not name.is_dir() else name # checkpoint path
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try:
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device = select_device(device)
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if pretrained and channels == 3 and classes == 80:
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try:
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model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model
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if autoshape:
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if model.pt and isinstance(model.model, ClassificationModel):
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LOGGER.warning(
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"WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. "
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"You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)."
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)
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elif model.pt and isinstance(model.model, SegmentationModel):
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LOGGER.warning(
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"WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. "
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"You will not be able to run inference with this model."
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)
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else:
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model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS
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except Exception:
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model = attempt_load(path, device=device, fuse=False) # arbitrary model
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else:
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cfg = list((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))[0] # model.yaml path
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model = DetectionModel(cfg, channels, classes) # create model
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if pretrained:
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ckpt = torch.load(attempt_download(path), map_location=device) # load
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csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32
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csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"]) # intersect
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model.load_state_dict(csd, strict=False) # load
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if len(ckpt["model"].names) == classes:
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model.names = ckpt["model"].names # set class names attribute
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if not verbose:
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LOGGER.setLevel(logging.INFO) # reset to default
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return model.to(device)
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except Exception as e:
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help_url = "https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading"
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s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help."
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raise Exception(s) from e
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def custom(path="path/to/model.pt", autoshape=True, _verbose=True, device=None):
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"""
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Loads a custom or local YOLOv5 model from a given path with optional autoshaping and device specification.
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Args:
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path (str): Path to the custom model file (e.g., 'path/to/model.pt').
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autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model if True, enabling compatibility with various input
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types (default is True).
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_verbose (bool): If True, prints all informational messages to the screen; otherwise, operates silently
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(default is True).
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device (str | torch.device | None): Device to load the model on, e.g., 'cpu', 'cuda', torch.device('cuda:0'), etc.
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(default is None, which automatically selects the best available device).
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Returns:
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torch.nn.Module: A YOLOv5 model loaded with the specified parameters.
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Notes:
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For more details on loading models from PyTorch Hub:
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https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading
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Examples:
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```python
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# Load model from a given path with autoshape enabled on the best available device
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model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
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# Load model from a local path without autoshape on the CPU device
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model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local', autoshape=False, device='cpu')
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```
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"""
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return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
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def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""
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Instantiates the YOLOv5-nano model with options for pretraining, input channels, class count, autoshaping,
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verbosity, and device.
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Args:
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pretrained (bool): If True, loads pretrained weights into the model. Defaults to True.
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channels (int): Number of input channels for the model. Defaults to 3.
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classes (int): Number of classes for object detection. Defaults to 80.
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autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper to the model for various formats (file/URI/PIL/
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cv2/np) and non-maximum suppression (NMS) during inference. Defaults to True.
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_verbose (bool): If True, prints detailed information to the screen. Defaults to True.
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device (str | torch.device | None): Specifies the device to use for model computation. If None, uses the best device
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available (i.e., GPU if available, otherwise CPU). Defaults to None.
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Returns:
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DetectionModel | ClassificationModel | SegmentationModel: The instantiated YOLOv5-nano model, potentially with
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pretrained weights and autoshaping applied.
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Notes:
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For further details on loading models from PyTorch Hub, refer to [PyTorch Hub models](https://pytorch.org/hub/
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ultralytics_yolov5).
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Examples:
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```python
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import torch
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from ultralytics import yolov5n
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# Load the YOLOv5-nano model with defaults
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model = yolov5n()
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# Load the YOLOv5-nano model with a specific device
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model = yolov5n(device='cuda')
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```
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"""
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return _create("yolov5n", pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""
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Create a YOLOv5-small (yolov5s) model with options for pretraining, input channels, class count, autoshaping,
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verbosity, and device configuration.
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Args:
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pretrained (bool, optional): Flag to load pretrained weights into the model. Defaults to True.
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channels (int, optional): Number of input channels. Defaults to 3.
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classes (int, optional): Number of model classes. Defaults to 80.
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autoshape (bool, optional): Whether to wrap the model with YOLOv5's .autoshape() for handling various input formats.
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Defaults to True.
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_verbose (bool, optional): Flag to print detailed information regarding model loading. Defaults to True.
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device (str | torch.device | None, optional): Device to use for model computation, can be 'cpu', 'cuda', or
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torch.device instances. If None, automatically selects the best available device. Defaults to None.
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Returns:
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torch.nn.Module: The YOLOv5-small model configured and loaded according to the specified parameters.
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Example:
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```python
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import torch
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# Load the official YOLOv5-small model with pretrained weights
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
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# Load the YOLOv5-small model from a specific branch
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model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s')
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# Load a custom YOLOv5-small model from a local checkpoint
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model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
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# Load a local YOLOv5-small model specifying source as local repository
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model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local')
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```
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Notes:
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For more details on model loading and customization, visit
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the [YOLOv5 PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5).
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"""
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return _create("yolov5s", pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""
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Instantiates the YOLOv5-medium model with customizable pretraining, channel count, class count, autoshaping,
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verbosity, and device.
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Args:
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pretrained (bool, optional): Whether to load pretrained weights into the model. Default is True.
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channels (int, optional): Number of input channels. Default is 3.
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classes (int, optional): Number of model classes. Default is 80.
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autoshape (bool, optional): Apply YOLOv5 .autoshape() wrapper to the model for handling various input formats.
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Default is True.
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_verbose (bool, optional): Whether to print detailed information to the screen. Default is True.
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device (str | torch.device | None, optional): Device specification to use for model parameters (e.g., 'cpu', 'cuda').
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Default is None.
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Returns:
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torch.nn.Module: The instantiated YOLOv5-medium model.
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Usage Example:
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```python
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import torch
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model = torch.hub.load('ultralytics/yolov5', 'yolov5m') # Load YOLOv5-medium from Ultralytics repository
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model = torch.hub.load('ultralytics/yolov5:master', 'yolov5m') # Load from the master branch
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model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m.pt') # Load a custom/local YOLOv5-medium model
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model = torch.hub.load('.', 'custom', 'yolov5m.pt', source='local') # Load from a local repository
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```
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For more information, visit https://pytorch.org/hub/ultralytics_yolov5.
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"""
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return _create("yolov5m", pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""
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Creates YOLOv5-large model with options for pretraining, channels, classes, autoshaping, verbosity, and device
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selection.
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Args:
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pretrained (bool): Load pretrained weights into the model. Default is True.
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channels (int): Number of input channels. Default is 3.
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classes (int): Number of model classes. Default is 80.
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autoshape (bool): Apply YOLOv5 .autoshape() wrapper to model. Default is True.
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_verbose (bool): Print all information to screen. Default is True.
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device (str | torch.device | None): Device to use for model parameters, e.g., 'cpu', 'cuda', or a torch.device instance.
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Default is None.
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Returns:
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YOLOv5 model (torch.nn.Module): The YOLOv5-large model instantiated with specified configurations and possibly
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pretrained weights.
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Examples:
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```python
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import torch
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model = torch.hub.load('ultralytics/yolov5', 'yolov5l')
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```
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Notes:
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For additional details, refer to the PyTorch Hub models documentation:
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https://pytorch.org/hub/ultralytics_yolov5
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"""
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return _create("yolov5l", pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""
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Perform object detection using the YOLOv5-xlarge model with options for pretraining, input channels, class count,
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autoshaping, verbosity, and device specification.
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Args:
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pretrained (bool): If True, loads pretrained weights into the model. Defaults to True.
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channels (int): Number of input channels for the model. Defaults to 3.
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classes (int): Number of model classes for object detection. Defaults to 80.
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autoshape (bool): If True, applies the YOLOv5 .autoshape() wrapper for handling different input formats. Defaults to
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True.
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_verbose (bool): If True, prints detailed information during model loading. Defaults to True.
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device (str | torch.device | None): Device specification for computing the model, e.g., 'cpu', 'cuda:0', torch.device('cuda').
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Defaults to None.
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Returns:
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torch.nn.Module: The YOLOv5-xlarge model loaded with the specified parameters, optionally with pretrained weights and
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autoshaping applied.
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Example:
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```python
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import torch
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model = torch.hub.load('ultralytics/yolov5', 'yolov5x')
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```
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For additional details, refer to the official YOLOv5 PyTorch Hub models documentation:
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https://pytorch.org/hub/ultralytics_yolov5
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"""
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return _create("yolov5x", pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""
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Creates YOLOv5-nano-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and device.
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Args:
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pretrained (bool, optional): If True, loads pretrained weights into the model. Default is True.
|
||
|
|
channels (int, optional): Number of input channels. Default is 3.
|
||
|
|
classes (int, optional): Number of model classes. Default is 80.
|
||
|
|
autoshape (bool, optional): If True, applies the YOLOv5 .autoshape() wrapper to the model. Default is True.
|
||
|
|
_verbose (bool, optional): If True, prints all information to screen. Default is True.
|
||
|
|
device (str | torch.device | None, optional): Device to use for model parameters. Can be 'cpu', 'cuda', or None.
|
||
|
|
Default is None.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
torch.nn.Module: YOLOv5-nano-P6 model loaded with the specified configurations.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import torch
|
||
|
|
model = yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device='cuda')
|
||
|
|
```
|
||
|
|
|
||
|
|
Notes:
|
||
|
|
For more information on PyTorch Hub models, visit: https://pytorch.org/hub/ultralytics_yolov5
|
||
|
|
"""
|
||
|
|
return _create("yolov5n6", pretrained, channels, classes, autoshape, _verbose, device)
|
||
|
|
|
||
|
|
|
||
|
|
def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||
|
|
"""
|
||
|
|
Instantiate the YOLOv5-small-P6 model with options for pretraining, input channels, number of classes, autoshaping,
|
||
|
|
verbosity, and device selection.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pretrained (bool): If True, loads pretrained weights. Default is True.
|
||
|
|
channels (int): Number of input channels. Default is 3.
|
||
|
|
classes (int): Number of object detection classes. Default is 80.
|
||
|
|
autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model, allowing for varied input formats.
|
||
|
|
Default is True.
|
||
|
|
_verbose (bool): If True, prints detailed information during model loading. Default is True.
|
||
|
|
device (str | torch.device | None): Device specification for model parameters (e.g., 'cpu', 'cuda', or torch.device).
|
||
|
|
Default is None, which selects an available device automatically.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
torch.nn.Module: The YOLOv5-small-P6 model instance.
|
||
|
|
|
||
|
|
Usage:
|
||
|
|
```python
|
||
|
|
import torch
|
||
|
|
|
||
|
|
model = torch.hub.load('ultralytics/yolov5', 'yolov5s6')
|
||
|
|
model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s6') # load from a specific branch
|
||
|
|
model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5s6.pt') # custom/local model
|
||
|
|
model = torch.hub.load('.', 'custom', 'path/to/yolov5s6.pt', source='local') # local repo model
|
||
|
|
```
|
||
|
|
|
||
|
|
Notes:
|
||
|
|
- For more information, refer to the PyTorch Hub models documentation at https://pytorch.org/hub/ultralytics_yolov5
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
Exception: If there is an error during model creation or loading, with a suggestion to visit the YOLOv5
|
||
|
|
tutorials for help.
|
||
|
|
"""
|
||
|
|
return _create("yolov5s6", pretrained, channels, classes, autoshape, _verbose, device)
|
||
|
|
|
||
|
|
|
||
|
|
def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||
|
|
"""
|
||
|
|
Create YOLOv5-medium-P6 model with options for pretraining, channel count, class count, autoshaping, verbosity, and
|
||
|
|
device.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pretrained (bool): If True, loads pretrained weights. Default is True.
|
||
|
|
channels (int): Number of input channels. Default is 3.
|
||
|
|
classes (int): Number of model classes. Default is 80.
|
||
|
|
autoshape (bool): Apply YOLOv5 .autoshape() wrapper to the model for file/URI/PIL/cv2/np inputs and NMS.
|
||
|
|
Default is True.
|
||
|
|
_verbose (bool): If True, prints detailed information to the screen. Default is True.
|
||
|
|
device (str | torch.device | None): Device to use for model parameters. Default is None, which uses the
|
||
|
|
best available device.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
torch.nn.Module: The YOLOv5-medium-P6 model.
|
||
|
|
|
||
|
|
Refer to the PyTorch Hub models documentation: https://pytorch.org/hub/ultralytics_yolov5 for additional details.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import torch
|
||
|
|
|
||
|
|
# Load YOLOv5-medium-P6 model
|
||
|
|
model = torch.hub.load('ultralytics/yolov5', 'yolov5m6')
|
||
|
|
```
|
||
|
|
|
||
|
|
Notes:
|
||
|
|
- The model can be loaded with pre-trained weights for better performance on specific tasks.
|
||
|
|
- The autoshape feature simplifies input handling by allowing various popular data formats.
|
||
|
|
"""
|
||
|
|
return _create("yolov5m6", pretrained, channels, classes, autoshape, _verbose, device)
|
||
|
|
|
||
|
|
|
||
|
|
def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||
|
|
"""
|
||
|
|
Instantiate the YOLOv5-large-P6 model with options for pretraining, channel and class counts, autoshaping,
|
||
|
|
verbosity, and device selection.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pretrained (bool, optional): If True, load pretrained weights into the model. Default is True.
|
||
|
|
channels (int, optional): Number of input channels. Default is 3.
|
||
|
|
classes (int, optional): Number of model classes. Default is 80.
|
||
|
|
autoshape (bool, optional): If True, apply YOLOv5 .autoshape() wrapper to the model for input flexibility. Default is True.
|
||
|
|
_verbose (bool, optional): If True, print all information to the screen. Default is True.
|
||
|
|
device (str | torch.device | None, optional): Device to use for model parameters, e.g., 'cpu', 'cuda', or torch.device.
|
||
|
|
If None, automatically selects the best available device. Default is None.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
torch.nn.Module: The instantiated YOLOv5-large-P6 model.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import torch
|
||
|
|
model = torch.hub.load('ultralytics/yolov5', 'yolov5l6') # official model
|
||
|
|
model = torch.hub.load('ultralytics/yolov5:master', 'yolov5l6') # from specific branch
|
||
|
|
model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/yolov5l6.pt') # custom/local model
|
||
|
|
model = torch.hub.load('.', 'custom', 'path/to/yolov5l6.pt', source='local') # local repository
|
||
|
|
```
|
||
|
|
|
||
|
|
Note:
|
||
|
|
Refer to [PyTorch Hub Documentation](https://pytorch.org/hub/ultralytics_yolov5) for additional usage instructions.
|
||
|
|
"""
|
||
|
|
return _create("yolov5l6", pretrained, channels, classes, autoshape, _verbose, device)
|
||
|
|
|
||
|
|
|
||
|
|
def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||
|
|
"""
|
||
|
|
Creates the YOLOv5-xlarge-P6 model with options for pretraining, number of input channels, class count, autoshaping,
|
||
|
|
verbosity, and device selection.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pretrained (bool): If True, loads pretrained weights into the model. Default is True.
|
||
|
|
channels (int): Number of input channels. Default is 3.
|
||
|
|
classes (int): Number of model classes. Default is 80.
|
||
|
|
autoshape (bool): If True, applies YOLOv5 .autoshape() wrapper to the model. Default is True.
|
||
|
|
_verbose (bool): If True, prints all information to the screen. Default is True.
|
||
|
|
device (str | torch.device | None): Device to use for model parameters, can be a string, torch.device object, or
|
||
|
|
None for default device selection. Default is None.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
torch.nn.Module: The instantiated YOLOv5-xlarge-P6 model.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import torch
|
||
|
|
model = torch.hub.load('ultralytics/yolov5', 'yolov5x6') # load the YOLOv5-xlarge-P6 model
|
||
|
|
```
|
||
|
|
|
||
|
|
Note:
|
||
|
|
For more information on YOLOv5 models, visit the official documentation:
|
||
|
|
https://docs.ultralytics.com/yolov5
|
||
|
|
"""
|
||
|
|
return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
import argparse
|
||
|
|
from pathlib import Path
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from PIL import Image
|
||
|
|
|
||
|
|
from utils.general import cv2, print_args
|
||
|
|
|
||
|
|
# Argparser
|
||
|
|
parser = argparse.ArgumentParser()
|
||
|
|
parser.add_argument("--model", type=str, default="yolov5s", help="model name")
|
||
|
|
opt = parser.parse_args()
|
||
|
|
print_args(vars(opt))
|
||
|
|
|
||
|
|
# Model
|
||
|
|
model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
|
||
|
|
# model = custom(path='path/to/model.pt') # custom
|
||
|
|
|
||
|
|
# Images
|
||
|
|
imgs = [
|
||
|
|
"data/images/zidane.jpg", # filename
|
||
|
|
Path("data/images/zidane.jpg"), # Path
|
||
|
|
"https://ultralytics.com/images/zidane.jpg", # URI
|
||
|
|
cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV
|
||
|
|
Image.open("data/images/bus.jpg"), # PIL
|
||
|
|
np.zeros((320, 640, 3)),
|
||
|
|
] # numpy
|
||
|
|
|
||
|
|
# Inference
|
||
|
|
results = model(imgs, size=320) # batched inference
|
||
|
|
|
||
|
|
# Results
|
||
|
|
results.print()
|
||
|
|
results.save()
|