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>
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
import math
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from typing import List, Optional
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from tqdm import tqdm
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from surya.common.predictor import BasePredictor
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from surya.ocr_error.loader import OCRErrorModelLoader
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from surya.ocr_error.model.config import ID2LABEL
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from surya.ocr_error.schema import OCRErrorDetectionResult
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from surya.settings import settings
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class OCRErrorPredictor(BasePredictor):
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model_loader_cls = OCRErrorModelLoader
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batch_size = settings.OCR_ERROR_BATCH_SIZE
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default_batch_sizes = {"cpu": 8, "mps": 8, "cuda": 64}
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def __call__(self, texts: List[str], batch_size: Optional[int] = None):
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return self.batch_ocr_error_detection(texts, batch_size)
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def batch_ocr_error_detection(
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self, texts: List[str], batch_size: Optional[int] = None
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):
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if batch_size is None:
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batch_size = self.get_batch_size()
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num_batches = math.ceil(len(texts) / batch_size)
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texts_processed = self.processor(
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texts, padding="longest", truncation=True, return_tensors="pt"
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)
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predictions = []
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for batch_idx in tqdm(
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range(num_batches),
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desc="Running OCR Error Detection",
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disable=self.disable_tqdm,
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):
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start_idx, end_idx = batch_idx * batch_size, (batch_idx + 1) * batch_size
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batch_input_ids = texts_processed.input_ids[start_idx:end_idx].to(
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self.model.device
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)
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batch_attention_mask = texts_processed.attention_mask[start_idx:end_idx].to(
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self.model.device
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)
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with settings.INFERENCE_MODE():
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pred = self.model(batch_input_ids, attention_mask=batch_attention_mask)
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logits = pred.logits.argmax(dim=1).cpu().tolist()
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predictions.extend(logits)
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return OCRErrorDetectionResult(
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texts=texts, labels=[ID2LABEL[p] for p in predictions]
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)
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