REVA-QCAV/utils/dice_score.py
milesial 5cd2a3b0b7 Restructure
Former-commit-id: beadb49b75ea79a3c0f95df589f64a8274419c5b
2021-08-16 06:08:09 +02:00

41 lines
1.6 KiB
Python

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