196 lines
8.6 KiB
Python
196 lines
8.6 KiB
Python
# YOLOv5 🚀 by Ultralytics, 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.
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Arguments:
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name (str): model name 'yolov5s' or path 'path/to/best.pt'
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pretrained (bool): load pretrained weights into the model
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channels (int): number of input channels
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classes (int): number of model classes
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autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
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verbose (bool): print all information to screen
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device (str, torch.device, None): device to use for model parameters
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Returns:
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YOLOv5 model
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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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"""Loads a custom or local YOLOv5 model from a given path with optional autoshaping and device specification."""
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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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"""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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"""
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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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"""Creates YOLOv5-small model with options for pretraining, input channels, class count, autoshaping, verbosity, and
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device.
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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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"""Instantiates the YOLOv5-medium model with customizable pretraining, channel count, class count, autoshaping,
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verbosity, and device.
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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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"""Creates YOLOv5-large model with options for pretraining, channels, classes, autoshaping, verbosity, and device
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selection.
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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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"""Instantiates the YOLOv5-xlarge model with customizable pretraining, channel count, class count, autoshaping,
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verbosity, and device.
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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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"""Creates YOLOv5-nano-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and
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device.
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"""
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return _create("yolov5n6", pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""Instantiate YOLOv5-small-P6 model with options for pretraining, input channels, number of classes, autoshaping,
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verbosity, and device selection.
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"""
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return _create("yolov5s6", pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""Creates YOLOv5-medium-P6 model with options for pretraining, channel count, class count, autoshaping, verbosity,
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and device.
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"""
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return _create("yolov5m6", pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""Instantiates the YOLOv5-large-P6 model with customizable pretraining, channel and class counts, autoshaping,
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verbosity, and device selection.
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"""
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return _create("yolov5l6", pretrained, channels, classes, autoshape, _verbose, device)
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def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
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"""Creates YOLOv5-xlarge-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and
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device.
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"""
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return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device)
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if __name__ == "__main__":
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import argparse
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from pathlib import Path
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import numpy as np
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from PIL import Image
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from utils.general import cv2, print_args
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# Argparser
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", type=str, default="yolov5s", help="model name")
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opt = parser.parse_args()
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print_args(vars(opt))
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# Model
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model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
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# model = custom(path='path/to/model.pt') # custom
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# Images
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imgs = [
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"data/images/zidane.jpg", # filename
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Path("data/images/zidane.jpg"), # Path
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"https://ultralytics.com/images/zidane.jpg", # URI
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cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV
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Image.open("data/images/bus.jpg"), # PIL
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np.zeros((320, 640, 3)),
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] # numpy
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# Inference
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results = model(imgs, size=320) # batched inference
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# Results
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results.print()
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results.save()
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