Rename img_size
to imgsz
(#86)
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@ -55,11 +55,11 @@ class ClassificationPredictor(BasePredictor):
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@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
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def predict(cfg):
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cfg.model = cfg.model or "squeezenet1_0"
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sz = cfg.img_size
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sz = cfg.imgsz
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if type(sz) != int: # recieved listConfig
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cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]] # expand
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cfg.imgsz = [sz[0], sz[0]] if len(cfg.imgsz) == 1 else [sz[0], sz[1]] # expand
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else:
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cfg.img_size = [sz, sz]
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cfg.imgsz = [sz, sz]
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predictor = ClassificationPredictor(cfg)
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predictor()
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@ -36,7 +36,7 @@ class ClassificationTrainer(BaseTrainer):
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def get_dataloader(self, dataset_path, batch_size, rank=0, mode="train"):
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return build_classification_dataloader(path=dataset_path,
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imgsz=self.args.img_size,
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imgsz=self.args.imgsz,
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batch_size=batch_size,
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rank=rank)
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@ -70,7 +70,7 @@ def train(cfg):
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if __name__ == "__main__":
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"""
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CLI usage:
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python ultralytics/yolo/v8/classify/train.py model=resnet18 data=imagenette160 epochs=1 img_size=224
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python ultralytics/yolo/v8/classify/train.py model=resnet18 data=imagenette160 epochs=1 imgsz=224
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TODO:
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Direct cli support, i.e, yolov8 classify_train args.epochs 10
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@ -28,7 +28,7 @@ class ClassificationValidator(BaseValidator):
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return {"top1": top1, "top5": top5, "fitness": top5}
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def get_dataloader(self, dataset_path, batch_size):
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return build_classification_dataloader(path=dataset_path, imgsz=self.args.img_size, batch_size=batch_size)
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return build_classification_dataloader(path=dataset_path, imgsz=self.args.imgsz, batch_size=batch_size)
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@property
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def metric_keys(self):
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