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https://github.com/NVIDIA/TensorRT-LLM.git
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16d2467ea8
* Update TensorRT-LLM --------- Co-authored-by: Denis Kayshev <topenkoff@gmail.com> Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Patrick Reiter Horn <patrick.horn@gmail.com> Update
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
# Adapt from
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# https://github.com/vllm-project/vllm/blob/2e33fe419186c65a18da6668972d61d7bbc31564/vllm/multimodal/image.py
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from typing import Any, cast
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from transformers import AutoImageProcessor
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from transformers.image_processing_utils import BaseImageProcessor
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def get_hf_image_processor(
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processor_name: str,
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*args: Any,
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trust_remote_code: bool = False,
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**kwargs: Any,
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):
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"""Load an image processor for the given model name via HuggingFace."""
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try:
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processor = AutoImageProcessor.from_pretrained(
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processor_name,
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*args,
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trust_remote_code=trust_remote_code,
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**kwargs)
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except ValueError as e:
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# If the error pertains to the processor class not existing or not
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# currently being imported, suggest using the --trust-remote-code flag.
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# Unlike AutoTokenizer, AutoImageProcessor does not separate such errors
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if not trust_remote_code:
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err_msg = (
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"Failed to load the image processor. If the image processor is "
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"a custom processor not yet available in the HuggingFace "
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"transformers library, consider setting "
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"`trust_remote_code=True` in LLM or using the "
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"`--trust-remote-code` flag in the CLI.")
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raise RuntimeError(err_msg) from e
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else:
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raise e
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return cast(BaseImageProcessor, processor)
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