|
from PIL import Image |
|
|
|
import torch |
|
import torchvision.transforms as transforms |
|
from safetensors.torch import load_file |
|
|
|
def preprocess_img(img, img_size, normalize=False): |
|
if type(img) == str: img = Image.open(img) |
|
original_size = img.size |
|
|
|
if normalize: |
|
transform = transforms.Compose([ |
|
transforms.Resize((img_size, img_size)), |
|
transforms.ToTensor(), |
|
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
|
]) |
|
else: |
|
transform = transforms.Compose([ |
|
transforms.Resize((img_size, img_size)), |
|
transforms.ToTensor() |
|
]) |
|
img = transform(img).unsqueeze(0) |
|
return img, original_size |
|
|
|
def postprocess_img(img, original_size, normalize=False): |
|
img = img.detach().cpu().squeeze(0) |
|
|
|
|
|
if normalize: |
|
mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1) |
|
std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1) |
|
img = img * std + mean |
|
img = torch.clamp(img, 0, 1) |
|
|
|
img = transforms.ToPILImage()(img) |
|
img = img.resize(original_size, Image.Resampling.LANCZOS) |
|
return img |
|
|
|
def load_model_without_module(model, model_path, device): |
|
state_dict = { |
|
k[7:] if k.startswith('module.') else k: v |
|
for k, v in load_file(model_path, device=device).items() |
|
} |
|
model.load_state_dict(state_dict) |
