You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
222 lines
8.2 KiB
222 lines
8.2 KiB
from pathlib import Path
|
|
|
|
from ultralytics import yolo # noqa
|
|
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_weights
|
|
from ultralytics.yolo.configs import get_config
|
|
from ultralytics.yolo.engine.exporter import Exporter
|
|
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, yaml_load
|
|
from ultralytics.yolo.utils.checks import check_imgsz, check_yaml
|
|
from ultralytics.yolo.utils.torch_utils import guess_task_from_head, smart_inference_mode
|
|
|
|
# Map head to model, trainer, validator, and predictor classes
|
|
MODEL_MAP = {
|
|
"classify": [
|
|
ClassificationModel, 'yolo.TYPE.classify.ClassificationTrainer', 'yolo.TYPE.classify.ClassificationValidator',
|
|
'yolo.TYPE.classify.ClassificationPredictor'],
|
|
"detect": [
|
|
DetectionModel, 'yolo.TYPE.detect.DetectionTrainer', 'yolo.TYPE.detect.DetectionValidator',
|
|
'yolo.TYPE.detect.DetectionPredictor'],
|
|
"segment": [
|
|
SegmentationModel, 'yolo.TYPE.segment.SegmentationTrainer', 'yolo.TYPE.segment.SegmentationValidator',
|
|
'yolo.TYPE.segment.SegmentationPredictor']}
|
|
|
|
|
|
class YOLO:
|
|
"""
|
|
YOLO
|
|
|
|
A python interface which emulates a model-like behaviour by wrapping trainers.
|
|
"""
|
|
|
|
def __init__(self, model='yolov8n.yaml', type="v8") -> None:
|
|
"""
|
|
Initializes the YOLO object.
|
|
|
|
Args:
|
|
model (str, Path): model to load or create
|
|
type (str): Type/version of models to use. Defaults to "v8".
|
|
"""
|
|
self.type = type
|
|
self.ModelClass = None # model class
|
|
self.TrainerClass = None # trainer class
|
|
self.ValidatorClass = None # validator class
|
|
self.PredictorClass = None # predictor class
|
|
self.model = None # model object
|
|
self.trainer = None # trainer object
|
|
self.task = None # task type
|
|
self.ckpt = None # if loaded from *.pt
|
|
self.ckpt_path = None
|
|
self.cfg = None # if loaded from *.yaml
|
|
self.overrides = {} # overrides for trainer object
|
|
self.init_disabled = False # disable model initialization
|
|
|
|
# Load or create new YOLO model
|
|
{'.pt': self._load, '.yaml': self._new}[Path(model).suffix](model)
|
|
|
|
def __call__(self, source):
|
|
return self.predict(source)
|
|
|
|
def _new(self, cfg: str, verbose=True):
|
|
"""
|
|
Initializes a new model and infers the task type from the model definitions.
|
|
|
|
Args:
|
|
cfg (str): model configuration file
|
|
verbose (bool): display model info on load
|
|
"""
|
|
cfg = check_yaml(cfg) # check YAML
|
|
cfg_dict = yaml_load(cfg, append_filename=True) # model dict
|
|
self.task = guess_task_from_head(cfg_dict["head"][-1][-2])
|
|
self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = \
|
|
self._guess_ops_from_task(self.task)
|
|
self.model = self.ModelClass(cfg_dict, verbose=verbose) # initialize
|
|
self.cfg = cfg
|
|
|
|
def _load(self, weights: str):
|
|
"""
|
|
Initializes a new model and infers the task type from the model head
|
|
|
|
Args:
|
|
weights (str): model checkpoint to be loaded
|
|
"""
|
|
self.model = attempt_load_weights(weights)
|
|
self.ckpt_path = weights
|
|
self.task = self.model.args["task"]
|
|
self.overrides = self.model.args
|
|
self._reset_ckpt_args(self.overrides)
|
|
self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = \
|
|
self._guess_ops_from_task(self.task)
|
|
|
|
def reset(self):
|
|
"""
|
|
Resets the model modules .
|
|
"""
|
|
for m in self.model.modules():
|
|
if hasattr(m, 'reset_parameters'):
|
|
m.reset_parameters()
|
|
for p in self.model.parameters():
|
|
p.requires_grad = True
|
|
|
|
def info(self, verbose=False):
|
|
"""
|
|
Logs model info
|
|
|
|
Args:
|
|
verbose (bool): Controls verbosity.
|
|
"""
|
|
if not self.model:
|
|
LOGGER.info("model not initialized!")
|
|
self.model.info(verbose=verbose)
|
|
|
|
def fuse(self):
|
|
if not self.model:
|
|
LOGGER.info("model not initialized!")
|
|
self.model.fuse()
|
|
|
|
@smart_inference_mode()
|
|
def predict(self, source, **kwargs):
|
|
"""
|
|
Visualize prediction.
|
|
|
|
Args:
|
|
source (str): Accepts all source types accepted by yolo
|
|
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
|
|
"""
|
|
overrides = self.overrides.copy()
|
|
overrides.update(kwargs)
|
|
overrides["mode"] = "predict"
|
|
predictor = self.PredictorClass(overrides=overrides)
|
|
|
|
predictor.args.imgsz = check_imgsz(predictor.args.imgsz, min_dim=2) # check image size
|
|
predictor.setup(model=self.model, source=source)
|
|
predictor()
|
|
|
|
@smart_inference_mode()
|
|
def val(self, data=None, **kwargs):
|
|
"""
|
|
Validate a model on a given dataset
|
|
|
|
Args:
|
|
data (str): The dataset to validate on. Accepts all formats accepted by yolo
|
|
**kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
|
|
"""
|
|
if not self.model:
|
|
raise ModuleNotFoundError("model not initialized!")
|
|
|
|
overrides = self.overrides.copy()
|
|
overrides.update(kwargs)
|
|
overrides["mode"] = "val"
|
|
args = get_config(config=DEFAULT_CONFIG, overrides=overrides)
|
|
args.data = data or args.data
|
|
args.task = self.task
|
|
|
|
validator = self.ValidatorClass(args=args)
|
|
validator(model=self.model)
|
|
|
|
@smart_inference_mode()
|
|
def export(self, **kwargs):
|
|
"""
|
|
Export model.
|
|
|
|
Args:
|
|
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in docs
|
|
"""
|
|
|
|
overrides = self.overrides.copy()
|
|
overrides.update(kwargs)
|
|
args = get_config(config=DEFAULT_CONFIG, overrides=overrides)
|
|
args.task = self.task
|
|
|
|
exporter = Exporter(overrides=args)
|
|
exporter(model=self.model)
|
|
|
|
def train(self, **kwargs):
|
|
"""
|
|
Trains the model on given dataset.
|
|
|
|
Args:
|
|
**kwargs (Any): Any number of arguments representing the training configuration. List of all args can be found in 'config' section.
|
|
You can pass all arguments as a yaml file in `cfg`. Other args are ignored if `cfg` file is passed
|
|
"""
|
|
if not self.model:
|
|
raise AttributeError("model not initialized. Use .new() or .load()")
|
|
overrides = self.overrides.copy()
|
|
overrides.update(kwargs)
|
|
if kwargs.get("cfg"):
|
|
LOGGER.info(f"cfg file passed. Overriding default params with {kwargs['cfg']}.")
|
|
overrides = yaml_load(check_yaml(kwargs["cfg"]), append_filename=True)
|
|
overrides["task"] = self.task
|
|
overrides["mode"] = "train"
|
|
if not overrides.get("data"):
|
|
raise AttributeError("dataset not provided! Please define `data` in config.yaml or pass as an argument.")
|
|
|
|
if overrides.get("resume"):
|
|
overrides["resume"] = self.ckpt_path
|
|
self.trainer = self.TrainerClass(overrides=overrides)
|
|
if not overrides.get("resume"):
|
|
self.trainer.model = self.trainer.load_model(weights=self.model,
|
|
model_cfg=self.model.yaml if self.task != "classify" else None)
|
|
self.model = self.trainer.model # override here to save memory
|
|
|
|
self.trainer.train()
|
|
|
|
def to(self, device):
|
|
self.model.to(device)
|
|
|
|
def _guess_ops_from_task(self, task):
|
|
model_class, train_lit, val_lit, pred_lit = MODEL_MAP[task]
|
|
# warning: eval is unsafe. Use with caution
|
|
trainer_class = eval(train_lit.replace("TYPE", f"{self.type}"))
|
|
validator_class = eval(val_lit.replace("TYPE", f"{self.type}"))
|
|
predictor_class = eval(pred_lit.replace("TYPE", f"{self.type}"))
|
|
|
|
return model_class, trainer_class, validator_class, predictor_class
|
|
|
|
@staticmethod
|
|
def _reset_ckpt_args(args):
|
|
args.pop("device", None)
|
|
args.pop("project", None)
|
|
args.pop("name", None)
|
|
args.pop("batch", None)
|
|
args.pop("epochs", None)
|