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