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from typing import Dict, List, Any |
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from PIL import Image |
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import requests |
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import torch |
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from blip import blip_decoder |
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from torchvision import transforms |
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from torchvision.transforms.functional import InterpolationMode |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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class PreTrainedPipeline(): |
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def __init__(self, path=""): |
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self.model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth' |
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self.model = blip_decoder(pretrained=self.model_url, image_size=384, vit='large') |
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self.model.eval() |
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self.model = model.to(device) |
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image_size = 384 |
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self.transform = transforms.Compose([ |
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transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC), |
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transforms.ToTensor(), |
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transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) |
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]) |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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""" |
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Args: |
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data (:obj:): |
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includes the input data and the parameters for the inference. |
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Return: |
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A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing : |
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- "label": A string representing what the label/class is. There can be multiple labels. |
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- "score": A score between 0 and 1 describing how confident the model is for this label/class. |
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""" |
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image = data.pop("inputs", data) |
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parameters = data.pop("parameters", None) |
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image = transform(image).unsqueeze(0).to(device) |
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with torch.no_grad(): |
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caption = self.model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5) |
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return caption |
