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"""
author: Min Seok Lee and Wooseok Shin
"""
import torch.nn as nn
class BasicConv2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=(1, 1), padding=(0, 0), dilation=(1, 1)):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channel, out_channel, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, bias=False)
self.bn = nn.BatchNorm2d(out_channel)
self.selu = nn.SELU()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.selu(x)
return x
class DWConv(nn.Module):
def __init__(self, in_channel, out_channel, kernel, dilation, padding):
super(DWConv, self).__init__()
self.out_channel = out_channel
self.DWConv = nn.Conv2d(in_channel, out_channel, kernel_size=kernel, padding=padding, groups=in_channel,
dilation=dilation, bias=False)
self.bn = nn.BatchNorm2d(out_channel)
self.selu = nn.SELU()
def forward(self, x):
x = self.DWConv(x)
out = self.selu(self.bn(x))
return out
class DWSConv(nn.Module):
def __init__(self, in_channel, out_channel, kernel, padding, kernels_per_layer):
super(DWSConv, self).__init__()
self.out_channel = out_channel
self.DWConv = nn.Conv2d(in_channel, in_channel * kernels_per_layer, kernel_size=kernel, padding=padding,
groups=in_channel, bias=False)
self.bn = nn.BatchNorm2d(in_channel * kernels_per_layer)
self.selu = nn.SELU()
self.PWConv = nn.Conv2d(in_channel * kernels_per_layer, out_channel, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channel)
def forward(self, x):
x = self.DWConv(x)
x = self.selu(self.bn(x))
out = self.PWConv(x)
out = self.selu(self.bn2(out))
return out |