File size: 1,960 Bytes
1069391 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
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 midjourney5M import Model5M
class PreTrainedPipeline():
def __init__(self, path=""):
self.model = Model5M()
ckpt = torch.load(os.path.join(path, "midjourney5M.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
|