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@ -1,32 +1,44 @@ |
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""" |
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Top-level YOLO model interface. First principle usage example - https://github.com/ultralytics/ultralytics/issues/13 |
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""" |
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import torch |
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import yaml |
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import ultralytics.yolo as yolo |
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from ultralytics.yolo.utils import LOGGER |
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from ultralytics.yolo.utils.checks import check_yaml |
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from ultralytics.yolo.utils.modeling import get_model |
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from ultralytics.yolo.utils.modeling.tasks import ClassificationModel, DetectionModel, SegmentationModel |
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# map head: [model, trainer] |
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MODEL_MAP = { |
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"Classify": [ClassificationModel, 'yolo.VERSION.classify.train.ClassificationTrainer'], |
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"Detect": [ClassificationModel, 'yolo.VERSION.classify.train.ClassificationTrainer'], # temp |
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"Segment": []} |
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"classify": [ClassificationModel, 'yolo.VERSION.classify.train.ClassificationTrainer'], |
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"detect": [ClassificationModel, 'yolo.VERSION.classify.train.ClassificationTrainer'], # temp |
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"segment": []} |
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class YOLO: |
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def __init__(self, version=8) -> None: |
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def __init__(self, task=None, version=8) -> None: |
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self.version = version |
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self.ModelClass = None |
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self.TrainerClass = None |
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self.model = None |
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self.trainer = None |
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self.pretrained_weights = None |
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if task: |
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if task.lower() not in MODEL_MAP: |
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raise Exception(f"Unsupported task {task}. The supported tasks are: \n {MODEL_MAP.keys()}") |
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self.ModelClass, self.TrainerClass = MODEL_MAP[task] |
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self.TrainerClass = eval(self.trainer.replace("VERSION", f"v{self.version}")) |
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def new(self, cfg: str): |
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cfg = check_yaml(cfg) # check YAML |
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self.model, self.trainer = self._get_model_and_trainer(cfg) |
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if self.model: |
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self.model = self.model(cfg) |
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else: |
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with open(cfg, encoding='ascii', errors='ignore') as f: |
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cfg = yaml.safe_load(f) # model dict |
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self.ModelClass, self.TrainerClass = self._get_model_and_trainer(cfg["head"]) |
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self.model = self.ModelClass(cfg) # initialize |
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def load(self, weights, autodownload=True): |
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if not isinstance(self.pretrained_weights, type(None)): |
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@ -36,28 +48,45 @@ class YOLO: |
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self.model.load(weights) |
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LOGGER.info("Checkpoint loaded successfully") |
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else: |
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# TODO: infer model and trainer |
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pass |
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self.model = get_model(weights) |
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self.ModelClass, self.TrainerClass = self._guess_model_and_trainer(list(self.model.named_children())) |
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self.pretrained_weights = weights |
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def reset(self): |
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pass |
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for m in self.model.modules(): |
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if hasattr(m, 'reset_parameters'): |
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m.reset_parameters() |
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for p in self.model.parameters(): |
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p.requires_grad = True |
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def train(self, **kwargs): |
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if 'data' not in kwargs: |
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raise Exception("data is required to train") |
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if not self.model: |
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raise Exception("model not initialized. Use .new() or .load()") |
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kwargs["model"] = self.model |
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trainer = self.trainer(overrides=kwargs) |
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# kwargs["model"] = self.model |
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trainer = self.TrainerClass(overrides=kwargs) |
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trainer.model = self.model |
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trainer.train() |
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def _get_model_and_trainer(self, cfg): |
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with open(cfg, encoding='ascii', errors='ignore') as f: |
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cfg = yaml.safe_load(f) # model dict |
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model, trainer = MODEL_MAP[cfg["head"][-1][-2]] |
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def _guess_model_and_trainer(self, cfg): |
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# TODO: warn |
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head = cfg[-1][-2] |
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if head.lower() in ["classify", "classifier", "cls", "fc"]: |
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task = "classify" |
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if head.lower() in ["detect"]: |
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task = "detect" |
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if head.lower() in ["segment"]: |
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task = "segment" |
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model_class, trainer_class = MODEL_MAP[task] |
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# warning: eval is unsafe. Use with caution |
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trainer = eval(trainer.replace("VERSION", f"v{self.version}")) |
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trainer_class = eval(trainer_class.replace("VERSION", f"v{self.version}")) |
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return model_class, trainer_class |
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return model(cfg), trainer |
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if __name__ == "__main__": |
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model = YOLO() |
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# model.new("assets/dummy_model.yaml") |
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model.load("yolov5n-cls.pt") |
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model.train(data="imagenette160", epochs=1, lr0=0.01) |
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