from typing import Dict, List, Any from PIL import Image import os import json import torch import torchvision from torch.nn import functional as F from diffusions5M import Model5M class PreTrainedPipeline(): def __init__(self, path=""): self.model = Model5M() ckpt = torch.load(os.path.join(path, "diffusions5M.pt"), map_location=torch.device('cpu')) self.model.load_state_dict(ckpt) self.model.eval() with open(os.path.join(path, "config.json")) as config: config = json.load(config) self.id2label = config["id2label"] self.tfm = torchvision.transforms.Compose([ torchvision.transforms.Resize((224, 224)), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) def __call__(self, inputs: "Image.Image") -> List[Dict[str, Any]]: """ Args: inputs (:obj:`PIL.Image`): The raw image representation as PIL. No transformation made whatsoever from the input. Make all necessary transformations here. Return: A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82} It is preferred if the returned list is in decreasing `score` order """ img = self.tfm(inputs) return self.predict_from_model(img) def predict_from_model(self, img): y = self.model.forward(img[None, ...]) y_1 = F.softmax(y, dim=1)[:, 1].cpu().detach().numpy() y_2 = F.softmax(y, dim=1)[:, 0].cpu().detach().numpy() labels = [ {"label": str(self.id2label["0"]), "score": y_1.tolist()[0]}, {"label": str(self.id2label["1"]), "score": y_2.tolist()[0]}, ] return labels