<reponame>dapatil211/Jacinle<gh_stars>100-1000
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File   : softmax.py
# Author : <NAME>
# Email  : <EMAIL>
# Date   : 04/01/2018
#
# This file is part of Jacinle.
# Distributed under terms of the MIT license.

import torch.nn as nn

from .functional import gumbel_softmax, greedy_softmax, general_softmax, SoftmaxImplmentation

__all__ = ['SoftmaxImplmentation', 'GumbelSoftmax', 'GeneralSoftmax']


class GumbelSoftmax(nn.Module):
    def __init__(self, dim=-1, tau=1.0, hard=False, eps=1e-10):
        super().__init__()
        self.dim = dim
        self.tau = tau
        self.hard = hard
        self.eps = eps

    def forward(self, logits, mask=None):
        if self.training:
            return gumbel_softmax(logits, dim=self.dim, tau=self.tau, hard=self.hard, mask=mask, eps=self.eps)
        else:
            return greedy_softmax(logits, dim=self.dim, mask=mask)


class GeneralSoftmax(nn.Module):
    def __init__(self, dim=-1, tau=1.0, impl='standard'):
        super().__init__()
        self.dim = dim
        self.tau = tau
        self.impl = SoftmaxImplmentation.from_string(impl)

    def forward(self, logits, mask=None):
        return general_softmax(logits, dim=self.dim, tau=self.tau, impl=self.impl, mask=mask, training=self.training)


#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File   : softmax.py
# Author : <NAME>
# Email  : <EMAIL>
# Date   : 04/01/2018
#
# This file is part of Jacinle.
# Distributed under terms of the MIT license.

import torch.nn as nn

from .functional import gumbel_softmax, greedy_softmax, general_softmax, SoftmaxImplmentation

__all__ = ['SoftmaxImplmentation', 'GumbelSoftmax', 'GeneralSoftmax']


class GumbelSoftmax(nn.Module):
    def __init__(self, dim=-1, tau=1.0, hard=False, eps=1e-10):
        super().__init__()
        self.dim = dim
        self.tau = tau
        self.hard = hard
        self.eps = eps

    def forward(self, logits, mask=None):
        if self.training:
            return gumbel_softmax(logits, dim=self.dim, tau=self.tau, hard=self.hard, mask=mask, eps=self.eps)
        else:
            return greedy_softmax(logits, dim=self.dim, mask=mask)


class GeneralSoftmax(nn.Module):
    def __init__(self, dim=-1, tau=1.0, impl='standard'):
        super().__init__()
        self.dim = dim
        self.tau = tau
        self.impl = SoftmaxImplmentation.from_string(impl)

    def forward(self, logits, mask=None):
        return general_softmax(logits, dim=self.dim, tau=self.tau, impl=self.impl, mask=mask, training=self.training)
#! /usr/bin/env python3 # -*- coding: utf-8 -*- # File   : softmax.py # Author : <NAME> # Email  : <EMAIL> # Date   : 04/01/2018 # # This file is part of Jacinle. # Distributed under terms of the MIT license.