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import os |
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from typing import Dict, List, Any |
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from PIL import Image |
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import jax |
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from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel, VisionEncoderDecoderModel |
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import torch |
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class PreTrainedPipeline(): |
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def __init__(self, path=""): |
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model_dir = path |
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self.model = VisionEncoderDecoderModel.from_pretrained(model_dir) |
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self.feature_extractor = ViTFeatureExtractor.from_pretrained(model_dir) |
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self.tokenizer = AutoTokenizer.from_pretrained(model_dir) |
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max_length = 16 |
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num_beams = 4 |
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self.gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "return_dict_in_generate": True, "output_scores": True} |
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self.model.to("cpu") |
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self.model.eval() |
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def _generate(pixel_values): |
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with torch.no_grad(): |
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outputs = self.model.generate(pixel_values, **self.gen_kwargs) |
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output_ids = outputs.sequences |
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sequences_scores = outputs.sequences_scores |
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return output_ids, sequences_scores |
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self.generate = _generate |
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image_path = os.path.join(path, 'val_000000039769.jpg') |
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image = Image.open(image_path) |
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self(image) |
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image.close() |
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def __call__(self, inputs: "Image.Image") -> List[str]: |
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""" |
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Args: |
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Return: |
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""" |
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pixel_values = self.feature_extractor(images=inputs, return_tensors="pt").pixel_values |
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output_ids, sequences_scores = self.generate(pixel_values) |
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preds = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
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preds = [pred.strip() for pred in preds] |
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preds = [{"label": preds[0], "score": float(sequences_scores[0])}] |
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return preds |
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