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import torch
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, size), Image.LANCZOS)
elif scale is not None:
img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.LANCZOS)
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 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
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