# python: 3.6
# encoding: utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
[docs]class AvgPool(nn.Module):
"""1-d average pooling module."""
def __init__(self, stride=None, padding=0):
super(AvgPool, self).__init__()
self.stride = stride
self.padding = padding
[docs] def forward(self, x):
# [N,C,L] -> [N,C]
kernel_size = x.size(2)
x = F.max_pool1d(
input=x,
kernel_size=kernel_size,
stride=self.stride,
padding=self.padding)
return x.squeeze(dim=-1)
[docs]class MeanPoolWithMask(nn.Module):
def __init__(self):
super(MeanPoolWithMask, self).__init__()
self.inf = 10e12
[docs] def forward(self, tensor, mask, dim=0):
masks = mask.view(mask.size(0), mask.size(1), -1).float()
return torch.sum(tensor * masks, dim=dim) / torch.sum(masks, dim=1)