# python: 3.6
# encoding: utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
from fastNLP.modules.utils import initial_parameter
[docs]class ConvMaxpool(nn.Module):
"""
Convolution and max-pooling module with multiple kernel sizes.
"""
def __init__(self, in_channels, out_channels, kernel_sizes,
stride=1, padding=0, dilation=1,
groups=1, bias=True, activation='relu',initial_method = None ):
super(ConvMaxpool, self).__init__()
# convolution
if isinstance(kernel_sizes, (list, tuple, int)):
if isinstance(kernel_sizes, int):
out_channels = [out_channels]
kernel_sizes = [kernel_sizes]
self.convs = nn.ModuleList([nn.Conv1d(
in_channels=in_channels,
out_channels=oc,
kernel_size=ks,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias)
for oc, ks in zip(out_channels, kernel_sizes)])
else:
raise Exception(
'Incorrect kernel sizes: should be list, tuple or int')
# activation function
if activation == 'relu':
self.activation = F.relu
else:
raise Exception(
"Undefined activation function: choose from: relu")
initial_parameter(self, initial_method)
[docs] def forward(self, x):
# [N,L,C] -> [N,C,L]
x = torch.transpose(x, 1, 2)
# convolution
xs = [self.activation(conv(x)) for conv in self.convs] # [[N,C,L]]
# max-pooling
xs = [F.max_pool1d(input=i, kernel_size=i.size(2)).squeeze(2)
for i in xs] # [[N, C]]
return torch.cat(xs, dim=-1) # [N,C]