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model.py
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model.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Feb 18 09:28:51 2021
@author: Administrator
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.model_zoo as model_zoo
import numpy as np
import math
#基础conv
class BasicConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, bias=False):
super(BasicConv, self).__init__()
if dilation != 1:
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, dilation, dilation=dilation, bias=bias)
else:
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, kernel_size//2, dilation=dilation, bias=bias)
self.activation = nn.LeakyReLU()
def forward(self, x):
x = self.conv(x)
x = self.activation(x)
return x
class Resblock(nn.Module):
def __init__(self, channel):
super(Resblock, self).__init__()
self.conv = nn.Sequential(
BasicConv(channel, channel,3),
BasicConv(channel, channel,3)
)
def forward(self, x):
return self.conv(x)+x
#特征金字塔模块
class SpatialPyramidPooling(nn.Module):
def __init__(self,channel, kernel_sizes,stride=1):
super(SpatialPyramidPooling, self).__init__()
self.conv = nn.ModuleList([nn.Sequential(nn.Conv2d(channel, channel, kernel_size, stride, kernel_size//2,bias=True))\
for kernel_size in kernel_sizes])
def forward(self, x):
features = [convi(x) for convi in self.conv[::-1]]
features = torch.cat(features + [x], dim=1)
return features
class GNet(nn.Module):
def __init__(self):
super(GNet,self).__init__()
self.a = T_Attention()
self.t = TIDFE()
self.f = FDFE()
self.r = FI()
def forward(self,x):
A_map = self.a(x)
x = x+A_map
out1 = self.t(x)
out2 = self.f(x)
out = self.r(x,out1,out2)
return A_map, out1, out2, out
class T_Attention(nn.Module):
def __init__(self,channel_in=3,channel=32):
super(T_Attention,self).__init__()
self.conv_in = nn.Sequential(
nn.MaxPool2d(kernel_size=3,stride=2,padding=1),
BasicConv(channel_in, channel),
SpatialPyramidPooling(channel,[5,7,9]),
BasicConv(channel*4, channel,1),
BasicConv(channel, channel),
nn.Conv2d(channel,channel_in,kernel_size=3,stride=1,padding=1),nn.Sigmoid()
)
def _upsample(self,x,y):
_,_,H,W = y.size()
return F.upsample(x,size=(H,W),mode='bilinear')
def forward(self,x_in):
A_map = self._upsample(self.conv_in(x_in),x_in)*x_in
return A_map
class TIDFE(nn.Module):
def __init__(self, channel_in = 3, channel_out = 3, channel_1 = 32, channel_2 = 64, channel_3 = 128):
super(TIDFE, self).__init__()
self.conv1_1 = nn.Sequential(
BasicConv(channel_in, channel_1),
BasicConv(channel_1, channel_1))
self.conv2_1 = nn.Sequential(
nn.MaxPool2d(kernel_size=3,stride=2,padding=1),
BasicConv(channel_1, channel_2),
BasicConv(channel_2, channel_2),
BasicConv(channel_2, channel_2),
BasicConv(channel_2, channel_2))
self.conv3_1 = nn.Sequential(
nn.MaxPool2d(kernel_size=3,stride=2,padding=1),
BasicConv(channel_2, channel_3),
BasicConv(channel_3, channel_3),
BasicConv(channel_3, channel_3),
BasicConv(channel_3, channel_3))
self.conv4_0 = BasicConv(channel_3, channel_2)
self.conv4_1 = nn.Sequential(
BasicConv(channel_2, channel_2),
BasicConv(channel_2, channel_2),
BasicConv(channel_2, channel_2))
self.conv5_0 = BasicConv(channel_2, channel_1)
self.conv5_1 = nn.Sequential(
BasicConv(channel_1, channel_1),
BasicConv(channel_1, channel_1),
BasicConv(channel_1, channel_1))
self.conv5_2 = nn.Conv2d(channel_1,channel_out,kernel_size=3,stride=1,padding=1)
def _upsample(self,x,y):
_,_,H,W = y.size()
return F.upsample(x,size=(H,W),mode='bilinear')
def forward(self, x):
#encoder
x1_1 = self.conv1_1(x)
x2_1 = self.conv2_1(x1_1)
x3_1 = self.conv3_1(x2_1)
#attention
#x3_3 = self.densenet(x3_1)
#decoder
x4_1 = self.conv4_0(self._upsample(x3_1,x2_1))+x2_1
x4_1 = self.conv4_1(x4_1)
x5_1 = self.conv5_0(self._upsample(x4_1,x1_1))+x1_1
x5_1 = self.conv5_1(x5_1)
out = self.conv5_2(x5_1)
return out
class FDFE(nn.Module):
def __init__(self, channel = 32, channel_in = 3, channel_out = 3):
super(FDFE,self).__init__()
self.conv1 = BasicConv(channel_in, channel)
self.conv2 = BasicConv(channel, channel)
self.conv3 = BasicConv(channel*2, channel)
self.conv4 = BasicConv(channel*3, channel)
self.conv5 = BasicConv(channel*4, channel_out)
def forward(self,x):
x1_1 = self.conv1(x)
x1_2 = self.conv2(x1_1)
x1_3 = self.conv3(torch.cat((x1_2,x1_1),1))
x1_4 = self.conv4(torch.cat((x1_3,x1_2,x1_1),1))
x_out = self.conv5(torch.cat((x1_4,x1_3,x1_2,x1_1),1))
return x_out
class FI(nn.Module):
def __init__(self,channel = 32,channel_in = 9, channel_out = 3):
super(FI,self).__init__()
self.conv = nn.Sequential(
BasicConv(channel_in, channel),
Resblock(channel),
Resblock(channel),
Resblock(channel),
Resblock(channel),
Resblock(channel),
nn.Conv2d(channel, channel_out,kernel_size=3,stride=1,padding=1,bias=False)
)
def forward(self,x,y,z):
out = torch.cat((x,y,z),dim=1)
out = self.conv(out)
return out