utils.py

from PIL import Image


def load_image(filename, size=None, scale=None):
    img = Image.open(filename).convert('RGB')

    if size is not None:
        img = img.resize((size[0], size[1]), Image.ANTIALIAS)

    if scale is not None:
        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)

    return img


def save_image(filename, data):
    img = data.clone().clamp(0, 255).numpy()
    img = img.transpose(1, 2, 0).astype("uint8")
    img = Image.fromarray(img)
    img.save(filename)


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 tv_loss(img):
    # Calculate total variation loss
    tv_x = (img[:, :, :, :-1] - img[:, :, :, 1:]).mean()
    tv_y = (img[:, :, :-1, :] - img[:, :, 1:, :]).mean()
    return 1/2 * (tv_x**2 + tv_y**2)

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