File size: 1,166 Bytes
fe8202e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
import torch
from torch import nn, Tensor
import numpy as np
class RobustCrossEntropyLoss(nn.CrossEntropyLoss):
"""
this is just a compatibility layer because my target tensor is float and has an extra dimension
input must be logits, not probabilities!
"""
def forward(self, input: Tensor, target: Tensor) -> Tensor:
if len(target.shape) == len(input.shape):
assert target.shape[1] == 1
target = target[:, 0]
return super().forward(input, target.long())
class TopKLoss(RobustCrossEntropyLoss):
"""
input must be logits, not probabilities!
"""
def __init__(self, weight=None, ignore_index: int = -100, k: float = 10, label_smoothing: float = 0):
self.k = k
super(TopKLoss, self).__init__(weight, False, ignore_index, reduce=False, label_smoothing=label_smoothing)
def forward(self, inp, target):
target = target[:, 0].long()
res = super(TopKLoss, self).forward(inp, target)
num_voxels = np.prod(res.shape, dtype=np.int64)
res, _ = torch.topk(res.view((-1, )), int(num_voxels * self.k / 100), sorted=False)
return res.mean()
|