utils.py

import torch
from PIL import Image
import os


from torchvision import transforms


def load_image(filename, size=None, scale=None):
    img = Image.open(filename)
    if size is not None:
        img = img.resize((size, size), Image.ANTIALIAS)
    elif scale is not None:
        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
    return img


def gram_matrix(y):
    (b, ch, h, w) = y.size()
    features = y.view(b, ch, w * h)
    features_t = features.transpose(1, 2)
    gram = features.bmm(features_t) / (ch * h * w)
    return gram

def normalize_batch(batch):
    # normalize using imagenet mean and std
    mean = batch.new_tensor([0.485, 0.456, 0.406]).view(-1, 1, 1)
    std = batch.new_tensor([0.229, 0.224, 0.225]).view(-1, 1, 1)
    batch = batch.div_(255.0)
    return (batch - mean) / std


unloader = transforms.ToPILImage()


def save_image_epoch(tensor, path, num):
    """Save a single image."""
    image = tensor.cpu().clone()
    image = image.squeeze(0)
    image = unloader(image)
    image.save(os.path.join(path, "out_" + str(num) + '.jpg'))


def normalize(img):
    # normalize using imagenet mean and std
    mean = torch.tensor([0.485, 0.456, 0.406]).view(-1, 1, 1).cuda()
    std = torch.tensor([0.229, 0.224, 0.225]).view(-1, 1, 1).cuda()
    return (img - mean) / std