Fu DaiandClaude Opus 4.8
1a585693be
FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
54 lines
1.7 KiB
Python
54 lines
1.7 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.detection.processor import SegformerImageProcessor
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from surya.detection.model.config import EfficientViTConfig
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from surya.detection.model.encoderdecoder import EfficientViTForSemanticSegmentation
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from surya.logging import get_logger
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from surya.settings import settings
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logger = get_logger()
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class DetectionModelLoader(ModelLoader):
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def __init__(self, checkpoint: Optional[str] = None):
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super().__init__(checkpoint)
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if self.checkpoint is None:
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self.checkpoint = settings.DETECTOR_MODEL_CHECKPOINT
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def model(
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self,
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device: Optional[torch.device | str] = None,
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dtype: Optional[torch.dtype | str] = None,
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attention_implementation: Optional[str] = None,
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) -> EfficientViTForSemanticSegmentation:
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if device is None:
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device = settings.TORCH_DEVICE_MODEL
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if dtype is None:
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dtype = settings.MODEL_DTYPE
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config = EfficientViTConfig.from_pretrained(self.checkpoint)
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model = EfficientViTForSemanticSegmentation.from_pretrained(
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self.checkpoint,
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dtype=dtype,
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config=config,
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)
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model = model.to(device)
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model = model.eval()
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logger.debug(
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f"Loaded detection model {self.checkpoint} from {EfficientViTForSemanticSegmentation.get_local_path(self.checkpoint)} onto device {device} with dtype {dtype}"
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)
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return model
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def processor(
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self,
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device: Optional[torch.device | str] = None,
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dtype: Optional[torch.dtype | str] = None,
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) -> SegformerImageProcessor:
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return SegformerImageProcessor.from_pretrained(self.checkpoint)
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