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import os
import sys
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
from argparse import Namespace
from e4e.models.psp import pSp
from util import *
@ torch.no_grad()
def projection(img, name, device='cuda'):
model_path = 'e4e.pt'
ckpt = torch.load(model_path, map_location='cpu')
opts = ckpt['opts']
opts['checkpoint_path'] = model_path
opts= Namespace(**opts)
net = pSp(opts, device).eval().to(device)
transform = transforms.Compose(
[
transforms.Resize(256),
transforms.CenterCrop(256),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
]
)
img = transform(img).unsqueeze(0).to(device)
images, w_plus = net(img, randomize_noise=False, return_latents=True)
result_file = {}
result_file['latent'] = w_plus[0]
torch.save(result_file, name)
return w_plus[0]
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