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Net.py
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172 lines (142 loc) · 5.92 KB
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import torch
from torch import nn
from torch import autograd
from torch.autograd import Variable
from torch.nn import functional as F
from torch.nn.parameter import Parameter
class parameterNet(nn.Module):
def __init__(self, in_channel, out_channel):
super(parameterNet, self).__init__()
# derain parameters
self.residual_conv = nn.Sequential(
nn.Conv2d(in_channel, out_channel, kernel_size=1, padding=0),
nn.PReLU()
)
self.background_conv = nn.Sequential(
nn.Conv2d(in_channel, out_channel, kernel_size=1, padding=0),
nn.PReLU()
)
self.gt_conv = nn.Sequential(
nn.Conv2d(out_channel*2, out_channel, kernel_size=1, padding=0),
nn.PReLU()
)
def forward(self, n, bilater, res):
residual = self.residual_conv(res)
residual = n - residual
background = self.background_conv(bilater)
x = torch.cat([residual, background], dim=1)
gt = self.gt_conv(x)
return residual, background, gt
class parameterNet_conv3(nn.Module):
def __init__(self, in_channel, out_channel):
super(parameterNet_conv3, self).__init__()
# derain parameters
self.residual_conv = nn.Sequential(
nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1),
# nn.PReLU()
)
self.background_conv = nn.Sequential(
nn.Conv2d(in_channel, out_channel, kernel_size=3, padding=1),
# nn.PReLU()
)
self.gt_conv = nn.Sequential(
nn.Conv2d(out_channel*2, out_channel, kernel_size=3, padding=1),
# nn.PReLU()
)
def forward(self, n, bilater, res):
residual = self.residual_conv(res)
residual = n - residual
background = self.background_conv(bilater)
x = torch.cat([residual, background], dim=1)
gt = self.gt_conv(x)
return residual, background, gt
class parameterNet_pure(nn.Module):
def __init__(self, in_channel, out_channel):
super(parameterNet_pure, self).__init__()
# derain parameters
self.background_conv = nn.Sequential(
nn.Conv2d(in_channel, out_channel, kernel_size=1, padding=0),
nn.PReLU()
)
def forward(self, n, bilater, res):
background = self.background_conv(bilater)
return background
class parameterNet_linearpure(nn.Module):
def __init__(self, in_channel, out_channel):
super(parameterNet_linearpure, self).__init__()
# derain parameters
self.background_conv = nn.Conv2d(in_channel, out_channel, kernel_size=1, padding=0)
def forward(self, n, bilater, res):
background = self.background_conv(bilater)
return background
class parameterNet_linear(nn.Module):
def __init__(self, in_channel, out_channel):
super(parameterNet_linear, self).__init__()
# derain parameters
self.residual_conv = nn.Conv2d(in_channel, out_channel, kernel_size=1, padding=0)
self.background_conv = nn.Conv2d(in_channel, out_channel, kernel_size=1, padding=0)
self.gt_conv = nn.Conv2d(out_channel*2, out_channel, kernel_size=1, padding=0)
def forward(self, n, bilater, res):
residual = self.residual_conv(res)
residual = n - residual
background = self.background_conv(bilater)
x = torch.cat([residual, background], dim=1)
gt = self.gt_conv(x)
return residual, background, gt
class parameterNet_mlp(nn.Module):
def __init__(self, in_channel, out_channel):
super(parameterNet_mlp, self).__init__()
# derain parameters
self.residual_conv = nn.Sequential(
nn.Conv2d(in_channel, 64, kernel_size=1, padding=0),
nn.PReLU(),
nn.Conv2d(64, 64, kernel_size=1, padding=0),
nn.PReLU(),
nn.Conv2d(64, 64, kernel_size=1, padding=0),
nn.PReLU(),
nn.Conv2d(64, 64, kernel_size=1, padding=0),
nn.PReLU(),
nn.Conv2d(64, out_channel, kernel_size=1, padding=0),
)
self.background_conv = nn.Sequential(
nn.Conv2d(in_channel, 64, kernel_size=1, padding=0),
nn.PReLU(),
nn.Conv2d(64, 64, kernel_size=1, padding=0),
nn.PReLU(),
nn.Conv2d(64, 64, kernel_size=1, padding=0),
nn.PReLU(),
nn.Conv2d(64, 64, kernel_size=1, padding=0),
nn.PReLU(),
nn.Conv2d(64, out_channel, kernel_size=1, padding=0),
)
self.gt_conv = nn.Sequential(
nn.Conv2d(out_channel*2, out_channel, kernel_size=1, padding=0),
nn.PReLU()
)
def forward(self, n, bilater, res):
residual = self.residual_conv(res)
residual = n - residual
background = self.background_conv(bilater)
x = torch.cat([residual, background], dim=1)
gt = self.gt_conv(x)
return residual, background, gt
# class residual_block(nn.Module):
# def __init__(self, feature_dim):
# super(residual_block, self).__init__()
# self.double_conv = nn.Sequential(
# nn.Conv2d(feature_dim, feature_dim, kernel_size=3, padding=1),
# nn.BatchNorm2d(feature_dim),
# nn.PReLU(),
# nn.Conv2d(feature_dim, feature_dim, kernel_size=3, padding=1),
# nn.BatchNorm2d(feature_dim),
# nn.PReLU()
# )
#
# def forward(self, x):
# return self.double_conv(x)
if __name__ == '__main__':
net = parameterNet_linear(3*9, 3)
print('Total number of network parameters is {}'.format(sum(x.numel() for x in net.parameters())))
for name, param in net.named_parameters():
# if "derain" in name:
print(sum(param.size()))