inference.py

import torch
from u2net import U2NET
from torchvision import transforms
import numpy as np
from PIL import Image
import torch.nn.functional as F
import data_transforms

# Load the model
def load_model():
    model = U2NET(3, 1)
    model.load_state_dict(torch.load("u2net.pth", map_location="cpu"))
    model.eval()
    return model

# Preprocessing function (same as you defined locally)
def preprocess(image):
    transform = transforms.Compose([data_transforms.RescaleT(320), data_transforms.ToTensorLab(flag=0)])
    label_3 = np.zeros(image.shape)
    label = np.zeros(label_3.shape[0:2])
    sample = transform({"imidx": np.array([0]), "image": image, "label": label})
    return sample

# Inference function
def infer(model, image):
    input_size = [1024, 1024]
    im_shp = image.shape[0:2]
    im_tensor = torch.tensor(image, dtype=torch.float32).permute(2, 0, 1)
    im_tensor = F.upsample(torch.unsqueeze(im_tensor, 0), input_size, mode="bilinear").type(torch.uint8)
    image = torch.divide(im_tensor, 255.0)
    result = model(image)
    result = torch.squeeze(F.upsample(result[0][0], im_shp, mode='bilinear'), 0)
    result = (result - result.min()) / (result.max() - result.min())
    return result.numpy()