# -*- coding: utf-8 -*-
"""
Created on Tue Aug 10 17:18:49 2021
@author: angelou
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.conv_layer import Conv, BNPReLU
import math
class CFPModule(nn.Module):
def __init__(self, nIn, d=1, KSize=3,dkSize=3):
super().__init__()
self.bn_relu_1 = BNPReLU(nIn)
self.bn_relu_2 = BNPReLU(nIn)
self.conv1x1_1 = Conv(nIn, nIn // 4, KSize, 1, padding=1, bn_acti=True)
self.dconv_4_1 = Conv(nIn //4, nIn //16, (dkSize,dkSize),1,padding = (1*d+1,1*d+1),
dilation=(d+1,d+1), groups = nIn //16, bn_acti=True)
self.dconv_4_2 = Conv(nIn //16, nIn //16, (dkSize,dkSize),1,padding = (1*d+1,1*d+1),
dilation=(d+1,d+1), groups = nIn //16, bn_acti=True)
self.dconv_4_3 = Conv(nIn //16, nIn //8, (dkSize,dkSize),1,padding = (1*d+1,1*d+1),
dilation=(d+1,d+1), groups = nIn //16, bn_acti=True)
self.dconv_1_1 = Conv(nIn //4, nIn //16, (dkSize,dkSize),1,padding = (1,1),
dilation=(1,1), groups = nIn //16, bn_acti=True)
self.dconv_1_2 = Conv(nIn //16, nIn //16, (dkSize,dkSize),1,padding = (1,1),
dilation=(1,1), groups = nIn //16, bn_acti=True)
self.dconv_1_3 = Conv(nIn //16, nIn //8, (dkSize,dkSize),1,padding = (1,1),
dilation=(1,1), groups = nIn //16, bn_acti=True)
self.dconv_2_1 = Conv(nIn //4, nIn //16, (dkSize,dkSize),1,padding = (int(d/4+1),int(d/4+1)),
dilation=(int(d/4+1),int(d/4+1)), groups = nIn //16, bn_acti=True)
self.dconv_2_2 = Conv(nIn //16, nIn //16, (dkSize,dkSize),1,padding = (int(d/4+1),int(d/4+1)),
dilation=(int(d/4+1),int(d/4+1)), groups = nIn //16, bn_acti=True)
self.dconv_2_3 = Conv(nIn //16, nIn //8, (dkSize,dkSize),1,padding = (int(d/4+1),int(d/4+1)),
dilation=(int(d/4+1),int(d/4+1)), groups = nIn //16, bn_acti=True)
self.dconv_3_1 = Conv(nIn //4, nIn //16, (dkSize,dkSize),1,padding = (int(d/2+1),int(d/2+1)),
dilation=(int(d/2+1),int(d/2+1)), groups = nIn //16, bn_acti=True)
self.dconv_3_2 = Conv(nIn //16, nIn //16, (dkSize,dkSize),1,padding = (int(d/2+1),int(d/2+1)),
dilation=(int(d/2+1),int(d/2+1)), groups = nIn //16, bn_acti=True)
self.dconv_3_3 = Conv(nIn //16, nIn //8, (dkSize,dkSize),1,padding = (int(d/2+1),int(d/2+1)),
dilation=(int(d/2+1),int(d/2+1)), groups = nIn //16, bn_acti=True)
self.conv1x1 = Conv(nIn, nIn, 1, 1, padding=0,bn_acti=False)
def forward(self, input):
inp = self.bn_relu_1(input)
inp = self.conv1x1_1(inp)
o1_1 = self.dconv_1_1(inp)
o1_2 = self.dconv_1_2(o1_1)
o1_3 = self.dconv_1_3(o1_2)
o2_1 = self.dconv_2_1(inp)
o2_2 = self.dconv_2_2(o2_1)
o2_3 = self.dconv_2_3(o2_2)
o3_1 = self.dconv_3_1(inp)
o3_2 = self.dconv_3_2(o3_1)
o3_3 = self.dconv_3_3(o3_2)
o4_1 = self.dconv_4_1(inp)
o4_2 = self.dconv_4_2(o4_1)
o4_3 = self.dconv_4_3(o4_2)
output_1 = torch.cat([o1_1,o1_2,o1_3], 1)
output_2 = torch.cat([o2_1,o2_2,o2_3], 1)
output_3 = torch.cat([o3_1,o3_2,o3_3], 1)
output_4 = torch.cat([o4_1,o4_2,o4_3], 1)
ad1 = output_1
ad2 = ad1 + output_2
ad3 = ad2 + output_3
ad4 = ad3 + output_4
output = torch.cat([ad1,ad2,ad3,ad4],1)
output = self.bn_relu_2(output)
output = self.conv1x1(output)
return output+input
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