| 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 diffusions200M import Model200M | |
| class PreTrainedPipeline(): | |
| def __init__(self, path=""): | |
| self.model = Model200M() | |
| ckpt = torch.load(os.path.join(path, "diffusions200M.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((640, 640)), | |
| 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 | |