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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 | |