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#-*- coding: utf-8 -*-
import torch
import torch.nn as nn
BN_MOMENTUM = 0.1
def point_wise_block(inplanes, outplanes):
return nn.Sequential(
nn.Conv2d(in_channels=inplanes, out_channels=outplanes, kernel_size=1, padding=0, stride=1, bias=False),
nn.BatchNorm2d(outplanes, momentum=BN_MOMENTUM),
nn.ReLU(inplace=True),
)
def conv_block(inplanes, outplanes, kernel_size, stride=1, padding=0):
return nn.Sequential(
nn.Conv2d(in_channels=inplanes, out_channels=outplanes, kernel_size=kernel_size, padding=padding, stride=stride, bias=False),
nn.BatchNorm2d(outplanes, momentum=BN_MOMENTUM),
nn.ReLU(inplace=True)
)
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class InceptionBlock(nn.Module):
def __init__(self, inplanes, outplanes, stride=1, pool_size=3):
self.inplanes = inplanes
self.outplanes = outplanes
self.stride = stride
self.pool_size = pool_size
super(InceptionBlock, self).__init__()
self.pw_block = point_wise_block(self.inplanes, self.outplanes//4)
self.mp_layer = nn.MaxPool2d(kernel_size=self.pool_size, stride=stride, padding=1)
self.conv3_block = conv_block(self.outplanes//4, self.outplanes//4, kernel_size=3, stride=1, padding=1)
self.conv5_block = conv_block(self.outplanes//4, self.outplanes//4, kernel_size=5, stride=1, padding=2)
def forward(self, x):
x1 = self.pw_block(x)
x2 = self.pw_block(x)
x2 = self.conv3_block(x2)
x3 = self.pw_block(x)
x3 = self.conv5_block(x3)
x4 = self.mp_layer(x)
x4 = self.pw_block(x4)
x = torch.cat((x1, x2, x3, x4), dim=1)
return x
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
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