Hugging face model #2
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Signed-off-by: Bepitic <[email protected]>
Signed-off-by: Bepitic <[email protected]>
Signed-off-by: Bepitic <[email protected]>
samet-akcay
reviewed
Aug 20, 2024
src/anomalib/models/image/hugging_face_wrapper/lightning_model.py
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| self.max_new_tokens = max_new_tokens | ||
| self.pre_images: list[str] = [] | ||
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| self.model = LlavaForConditionalGeneration.from_pretrained( |
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Does this mean that models would only be Llava-based ?
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| @staticmethod | ||
| def configure_optimizers() -> None: | ||
| """WinCLIP doesn't require optimization, therefore returns no optimizers.""" |
| return | ||
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| def validation_step(self, batch: dict[str, str | torch.Tensor], *args, **kwargs) -> dict: | ||
| """Validation Step of WinCLIP.""" |
| Returns: | ||
| ref_images (Tensor): A tensor containing the reference images. | ||
| """ | ||
| ref_images: list[str] = [] |
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Just for the consistency with the name of the method
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| ref_images: list[str] = [] | |
| reference_images: list[str] = [] |
| def _load_image(self, image_file: str) -> Image.Image: | ||
| return Image.open(image_file).convert("RGB") | ||
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| def _api_call_zero_shot(self, image_path: str) -> str: |
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Would it be possible for a user to configure the prompt if needed? Or does it have to be hardcoded?
Co-authored-by: Samet Akcay <[email protected]>
Co-authored-by: Samet Akcay <[email protected]>
| __all__ = ["HuggingFaceWrapper"] | ||
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| class HuggingFaceWrapper(AnomalyModule): |
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