@@ -0,0 +1,22 @@ | |||||
# python: 3.6 | |||||
# encoding: utf-8 | |||||
import torch.nn as nn | |||||
# import torch.nn.functional as F | |||||
class AvgPool1d(nn.Module): | |||||
"""1-d average pooling module.""" | |||||
def __init__(self, kernel_size, stride=None, padding=0, | |||||
ceil_mode=False, count_include_pad=True): | |||||
super(AvgPool1d, self).__init__() | |||||
self.pool = nn.AvgPool1d( | |||||
kernel_size=kernel_size, | |||||
stride=stride, | |||||
padding=padding, | |||||
ceil_mode=ceil_mode, | |||||
count_include_pad=count_include_pad) | |||||
def forward(self, x): | |||||
return self.pool(x) |
@@ -0,0 +1,28 @@ | |||||
# python: 3.6 | |||||
# encoding: utf-8 | |||||
import torch.nn as nn | |||||
# import torch.nn.functional as F | |||||
class Conv1d(nn.Module): | |||||
""" | |||||
Basic 1-d convolution module. | |||||
""" | |||||
def __init__(self, in_channels, out_channels, kernel_size, | |||||
stride=1, padding=0, dilation=1, | |||||
groups=1, bias=True): | |||||
super(Conv1d, self).__init__() | |||||
self.conv = nn.Conv1d( | |||||
in_channels=in_channels, | |||||
out_channels=out_channels, | |||||
kernel_size=kernel_size, | |||||
stride=stride, | |||||
padding=padding, | |||||
dilation=dilation, | |||||
groups=groups, | |||||
bias=bias) | |||||
def forward(self, x): | |||||
return self.conv(x) |
@@ -0,0 +1,23 @@ | |||||
# python: 3.6 | |||||
# encoding: utf-8 | |||||
import torch.nn as nn | |||||
# import torch.nn.functional as F | |||||
class MaxPool1d(nn.Module): | |||||
"""1-d max-pooling module.""" | |||||
def __init__(self, kernel_size, stride=None, padding=0, | |||||
dilation=1, return_indices=False, ceil_mode=False): | |||||
super(MaxPool1d, self).__init__() | |||||
self.maxpool = nn.MaxPool1d( | |||||
kernel_size=kernel_size, | |||||
stride=stride, | |||||
padding=padding, | |||||
dilation=dilation, | |||||
return_indices=return_indices, | |||||
ceil_mode=ceil_mode) | |||||
def forward(self, x): | |||||
return self.maxpool(x) |