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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 |
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class PreTrainedPipeline(): |
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def __init__(self, path=""): |
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model_dir = os.path.join(path, "ckpt_epoch_3_step_6900") |
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self.model = FlaxVisionEncoderDecoderModel.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} |
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@jax.jit |
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def _generate(pixel_values): |
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output_ids = self.model.generate(pixel_values, **self.gen_kwargs).sequences |
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return output_ids |
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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="np").pixel_values |
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output_ids = 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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return preds |
