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import torch |
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from PIL import Image |
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from PIL import ImageFilter |
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def load_image(filename, size=None, scale=None): |
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img = Image.open(filename).convert('RGB') |
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if size is not None: |
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img = img.resize((size, size), Image.Resampling.LANCZOS) |
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elif scale is not None: |
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img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.Resampling.LANCZOS) |
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return img |
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def save_image(filename, data): |
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img = data.clone().clamp(0, 255).numpy() |
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img = img.transpose(1, 2, 0).astype("uint8") |
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img = Image.fromarray(img) |
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img.save(filename) |
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def gram_matrix(y): |
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(b, ch, h, w) = y.size() |
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features = y.view(b, ch, w * h) |
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features_t = features.transpose(1, 2) |
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gram = features.bmm(features_t) / (ch * h * w) |
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return gram |
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def normalize_batch(batch): |
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mean = batch.new_tensor([0.485, 0.456, 0.406]).view(-1, 1, 1) |
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std = batch.new_tensor([0.229, 0.224, 0.225]).view(-1, 1, 1) |
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batch = batch.div_(255.0) |
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return (batch - mean) / std |
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