<filename>src/nlp/classification/tf1/traditional_cls/focalloss.py<gh_stars>10-100
# !/user/bin/python
# -*- coding:utf-8 -*-
"""
date：          2019-04-12
Description :
auther : wcy
"""
# import modules
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn as nn
import torch
import os
import sys

curr_path = os.path.dirname(__file__)
sys.path.append(curr_path)


# define class
class FocalLoss(nn.Module):
    def __init__(self, gamma=0, alpha=None, size_average=True):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        # if isinstance(alpha, (float, int, long)):
        """
        python 3 中没有long 类型
                                        by wcy
        """
        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)
        logpt = logpt.gather(1, target)
        logpt = logpt.view(-1)
        pt = Variable(logpt.data.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 * Variable(at)

        loss = -1 * (1 - pt)**self.gamma * logpt
        if self.size_average:
            return loss.mean()
        else:
            return loss.sum()


# main
if __name__ == "__main__":
    fl = FocalLoss()
    pass

<filename>src/nlp/classification/tf1/traditional_cls/focalloss.py<gh_stars>10-100
# !/user/bin/python
# -*- coding:utf-8 -*-
"""
date：          2019-04-12
Description :
auther : wcy
"""
# import modules
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn as nn
import torch
import os
import sys

curr_path = os.path.dirname(__file__)
sys.path.append(curr_path)


# define class
class FocalLoss(nn.Module):
    def __init__(self, gamma=0, alpha=None, size_average=True):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        # if isinstance(alpha, (float, int, long)):
        """
        python 3 中没有long 类型
                                        by wcy
        """
        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)
        logpt = logpt.gather(1, target)
        logpt = logpt.view(-1)
        pt = Variable(logpt.data.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 * Variable(at)

        loss = -1 * (1 - pt)**self.gamma * logpt
        if self.size_average:
            return loss.mean()
        else:
            return loss.sum()


# main
if __name__ == "__main__":
    fl = FocalLoss()
    pass

# !/user/bin/python # -*- coding:utf-8 -*- date：          2019-04-12 Description : auther : wcy # import modules # define class # if isinstance(alpha, (float, int, long)): python 3 中没有long 类型                                         by wcy # N,C,H,W => N,C,H*W # N,C,H*W => N,H*W,C # N,H*W,C => N*H*W,C # main