wehub-resource-sync
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
from typing import Optional
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import torch
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from surya.common.load import ModelLoader
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from surya.settings import settings
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class BasePredictor:
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model_loader_cls = ModelLoader
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batch_size: Optional[int] = None
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default_batch_sizes = {"cpu": 1, "mps": 1, "cuda": 1}
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torch_dtype = settings.MODEL_DTYPE
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@property
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def disable_tqdm(self) -> bool:
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return self._disable_tqdm
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@disable_tqdm.setter
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def disable_tqdm(self, value: bool) -> None:
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self._disable_tqdm = bool(value)
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def __init__(
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self,
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checkpoint: Optional[str] = None,
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device: torch.device | str | None = settings.TORCH_DEVICE_MODEL,
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dtype: Optional[torch.dtype | str] = None,
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attention_implementation: Optional[str] = None,
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):
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if dtype is None:
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dtype = self.torch_dtype
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loader = self.model_loader_cls(checkpoint)
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self.model = loader.model(device, dtype, attention_implementation)
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self.processor = loader.processor()
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self._disable_tqdm = settings.DISABLE_TQDM
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def to(self, device_dtype: torch.device | str | None = None):
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if hasattr(self, "model") and self.model:
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self.model.to(device_dtype)
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return
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# Predictors that don't own a torch model (e.g. VLM-backed predictors that
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# rely on an external server) treat .to() as a no-op.
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if hasattr(self, "manager") and self.manager is not None:
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return
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raise ValueError("Model not loaded")
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def get_batch_size(self):
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batch_size = self.batch_size
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if batch_size is None:
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batch_size = self.default_batch_sizes["cpu"]
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if settings.TORCH_DEVICE_MODEL in self.default_batch_sizes:
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batch_size = self.default_batch_sizes[settings.TORCH_DEVICE_MODEL]
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return batch_size
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def __call__(self, *args, **kwargs):
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raise NotImplementedError()
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