| import torch |
| from torch import Tensor |
| from transformers import AutoTokenizer, AutoModel |
| from typing import List |
| import os |
|
|
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
|
|
|
|
| class PreTrainedPipeline(): |
| def __init__(self, path=""): |
| |
| self.tokenizer = AutoTokenizer.from_pretrained(path) |
| self.model = AutoModel.from_pretrained(path) |
| self.model.eval() |
| self.model = self.model.to(device) |
|
|
| def __call__(self, inputs: str) -> List[float]: |
| """ |
| Args: |
| data (:obj:): |
| includes the input data and the parameters for the inference. |
| Return: |
| A :obj:`dict`:. The object returned should be a dict like {"feature_vector": [0.6331314444541931,0.8802216053009033,...,-0.7866355180740356,]} containing : |
| - "feature_vector": A list of floats corresponding to the image embedding. |
| """ |
|
|
| batch_dict = self.tokenizer([inputs], max_length=512, |
| padding=True, truncation=True, return_tensors='pt') |
| with torch.no_grad(): |
| outputs = self.model(**batch_dict) |
| embeddings = self.average_pool(outputs.last_hidden_state, |
| batch_dict['attention_mask']) |
| return embeddings.cpu().numpy().tolist() |
|
|
| def average_pool(self, last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: |
| last_hidden = last_hidden_states.masked_fill( |
| ~attention_mask[..., None].bool(), 0.0) |
| return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
|
