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https://github.com/Laurent2916/REVA-QCAV.git
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41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
import torch
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from torch import Tensor
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def dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon=1e-6):
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# Average of Dice coefficient for all batches, or for a single mask
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assert input.size() == target.size()
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if input.dim() == 2 and reduce_batch_first:
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raise ValueError(f'Dice: asked to reduce batch but got tensor without batch dimension (shape {input.shape})')
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if input.dim() == 2 or reduce_batch_first:
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inter = torch.dot(input.reshape(-1), target.reshape(-1))
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sets_sum = torch.sum(input) + torch.sum(target)
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if sets_sum.item() == 0:
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sets_sum = 2 * inter
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return (2 * inter + epsilon) / (sets_sum + epsilon)
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else:
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# compute and average metric for each batch element
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dice = 0
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for i in range(input.shape[0]):
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dice += dice_coeff(input[i, ...], target[i, ...])
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return dice / input.shape[0]
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def multiclass_dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon=1e-6):
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# Average of Dice coefficient for all classes
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assert input.size() == target.size()
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dice = 0
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for channel in range(input.shape[1]):
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dice += dice_coeff(input[:, channel, ...], target[:, channel, ...], reduce_batch_first, epsilon)
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return dice / input.shape[1]
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def dice_loss(input: Tensor, target: Tensor, multiclass: bool = False):
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# Dice loss (objective to minimize) between 0 and 1
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assert input.size() == target.size()
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fn = multiclass_dice_coeff if multiclass else dice_coeff
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return 1 - fn(input, target, reduce_batch_first=True)
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