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January 25, 2022 12:49
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import torch.nn.functional as F | |
import torch | |
import numpy as np | |
from torch.autograd import Function | |
import torch.nn as nn | |
from pdb import set_trace as breakpoint | |
import sys | |
import math | |
from torch.nn.parameter import Parameter | |
from torch.nn import init | |
import torch.nn as nn | |
from torch.autograd import Variable | |
class FocalLoss(nn.Module): | |
def __init__(self, gamma=2, alpha=0.25, size_average=True): | |
super(FocalLoss, self).__init__() | |
self.gamma = gamma | |
self.alpha = alpha | |
if isinstance(alpha, (float, int)): self.alpha = torch.Tensor([alpha, 1 - alpha]) | |
if isinstance(alpha, list): self.alpha = torch.Tensor(alpha) | |
self.size_average = size_average | |
def forward(self, input, target): | |
if input.dim()>2: | |
input = input.view(input.size(0), input.size(1), -1) # N,C,H,W => N,C,H*W | |
input = input.transpose(1, 2) # N,C,H*W => N,H*W,C | |
input = input.contiguous().view(-1, input.size(2)) # N,H*W,C => N*H*W,C | |
target = target.view(-1, 1) | |
logpt = F.log_softmax(input, dim=1) | |
logpt = logpt.gather(1,target) | |
logpt = logpt.view(-1) | |
pt = logpt.exp() | |
if self.alpha is not None: | |
if self.alpha.type() != input.data.type(): | |
self.alpha = self.alpha.type_as(input.data) | |
at = self.alpha.gather(0, target.data.view(-1)) | |
logpt = logpt * at | |
loss = -1 * (1 - pt)**self.gamma * logpt | |
if self.size_average: return loss.mean() | |
else: return loss.sum() |
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