34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
from typing import List
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import torch
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import torch.nn.functional as F
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def masked_l2_loss(pred, target, mask, weight_known, weight_missing):
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per_pixel_l2 = F.mse_loss(pred, target, reduction='none')
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pixel_weights = mask * weight_missing + (1 - mask) * weight_known
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return (pixel_weights * per_pixel_l2).mean()
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def masked_l1_loss(pred, target, mask, weight_known, weight_missing):
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per_pixel_l1 = F.l1_loss(pred, target, reduction='none')
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pixel_weights = mask * weight_missing + (1 - mask) * weight_known
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return (pixel_weights * per_pixel_l1).mean()
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def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None):
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if mask is None:
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res = torch.stack([F.mse_loss(fake_feat, target_feat)
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for fake_feat, target_feat in zip(fake_features, target_features)]).mean()
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else:
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res = 0
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norm = 0
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for fake_feat, target_feat in zip(fake_features, target_features):
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cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False)
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error_weights = 1 - cur_mask
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cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean()
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res = res + cur_val
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norm += 1
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res = res / norm
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return res
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