/
common_net.py
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/
common_net.py
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"""
Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import cv2
import numpy as np
import torch.nn.init as init
import numpy as np
def gaussian_weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1 and classname.find('Conv') == 0:
# print m.__class__.__name__
m.weight.data.normal_(0.0, 0.02)
def xavier_weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_uniform(m.weight, gain=np.sqrt(2))
init.constant(m.bias, 0.1)
class GaussianSmoother(nn.Module):
def __init__(self, kernel_size=5):
super(GaussianSmoother, self).__init__()
self.sigma = 0.3*((kernel_size-1)*0.5-1)+0.8
kernel = cv2.getGaussianKernel(kernel_size, -1)
kernel2d = np.dot(kernel.reshape(kernel_size,1),kernel.reshape(1,kernel_size))
data = torch.Tensor(3, 1, kernel_size, kernel_size)
self.pad = (kernel_size-1)/2
for i in range(0,3):
data[i,0,:,:] = torch.from_numpy(kernel2d)
self.blur_kernel = Variable(data, requires_grad=False)
def forward(self, x):
out = nn.functional.pad(x, [self.pad, self.pad, self.pad, self.pad], mode ='replicate')
out = nn.functional.conv2d(out, self.blur_kernel, groups=3)
return out
def cuda(self, gpu):
self.blur_kernel = self.blur_kernel.cuda(gpu)
class GaussianNoiseLayer(nn.Module):
def __init__(self,):
super(GaussianNoiseLayer, self).__init__()
def forward(self, x):
if self.training == False:
return x
noise = Variable(torch.randn(x.size()).cuda(x.data.get_device()))
return x + noise
class GaussianVAE2D(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding=0):
super(GaussianVAE2D, self).__init__()
self.en_mu = nn.Conv2d(n_in, n_out, kernel_size, stride, padding)
self.en_sigma = nn.Conv2d(n_in, n_out, kernel_size, stride, padding)
self.softplus = nn.Softplus()
self.reset_parameters()
def reset_parameters(self):
self.en_mu.weight.data.normal_(0, 0.002)
self.en_mu.bias.data.normal_(0, 0.002)
self.en_sigma.weight.data.normal_(0, 0.002)
self.en_sigma.bias.data.normal_(0, 0.002)
def forward(self, x):
mu = self.en_mu(x)
sd = self.softplus(self.en_sigma(x))
return mu, sd
def sample(self, x):
mu = self.en_mu(x)
sd = self.softplus(self.en_sigma(x))
noise = Variable(torch.randn(mu.size(0), mu.size(1), mu.size(2), mu.size(3))).cuda(x.data.get_device())
return mu + sd.mul(noise), mu, sd
class Bias2d(nn.Module):
def __init__(self, channels):
super(Bias2d, self).__init__()
self.bias = nn.Parameter(torch.Tensor(channels))
self.reset_parameters()
def reset_parameters(self):
self.bias.data.normal_(0, 0.002)
def forward(self, x):
n, c, h, w = x.size()
return x + self.bias.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand(n, c, h, w)
##################################################################################
# Residual Blocks
##################################################################################
class INSResBlock(nn.Module):
def conv3x3(self, inplanes, out_planes, stride=1):
return nn.Conv2d(inplanes, out_planes, kernel_size=3, stride=stride, padding=1)
def __init__(self, inplanes, planes, stride=1, dropout=0.0):
super(INSResBlock, self).__init__()
model = []
model += [self.conv3x3(inplanes, planes, stride)]
model += [nn.InstanceNorm2d(planes)]
model += [nn.ReLU(inplace=True)]
model += [self.conv3x3(planes, planes)]
model += [nn.InstanceNorm2d(planes)]
if dropout > 0:
model += [nn.Dropout(p=dropout)]
self.model = nn.Sequential(*model)
self.model.apply(gaussian_weights_init)
def forward(self, x):
residual = x
out = self.model(x)
out += residual
return out
##################################################################################
# Leaky ReLU-based conv layers
##################################################################################
class LeakyReLUConv2d(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding=0):
super(LeakyReLUConv2d, self).__init__()
model = []
model += [nn.Conv2d(n_in, n_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=True)]
model += [nn.LeakyReLU(inplace=True)]
self.model = nn.Sequential(*model)
self.model.apply(gaussian_weights_init)
def forward(self, x):
return self.model(x)
class LeakyReLUConvTranspose2d(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding=0, output_padding=0):
super(LeakyReLUConvTranspose2d, self).__init__()
model = []
model += [nn.ConvTranspose2d(n_in, n_out, kernel_size=kernel_size, stride=stride, padding=padding, output_padding=output_padding, bias=True)]
model += [nn.LeakyReLU(inplace=True)]
self.model = nn.Sequential(*model)
self.model.apply(gaussian_weights_init)
def forward(self, x):
return self.model(x)
class LeakyReLUBNConv2d(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding=0):
super(LeakyReLUBNConv2d, self).__init__()
model = []
model += [nn.Conv2d(n_in, n_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)]
model += [nn.BatchNorm2d(n_out)]
model += [nn.LeakyReLU(inplace=True)]
self.model = nn.Sequential(*model)
self.model.apply(gaussian_weights_init)
def forward(self, x):
return self.model(x)
class LeakyReLUBNConvTranspose2d(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding=0, output_padding=0):
super(LeakyReLUBNConvTranspose2d, self).__init__()
model = []
model += [nn.ConvTranspose2d(n_in, n_out, kernel_size=kernel_size, stride=stride, padding=padding, output_padding=output_padding, bias=False)]
model += [nn.BatchNorm2d(n_out)]
model += [nn.LeakyReLU(inplace=True)]
self.model = nn.Sequential(*model)
self.model.apply(gaussian_weights_init)
def forward(self, x):
return self.model(x)
class LeakyReLUBNNSConv2d(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding=0):
super(LeakyReLUBNNSConv2d, self).__init__()
model = []
model += [nn.Conv2d(n_in, n_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=True)]
model += [nn.BatchNorm2d(n_out, affine=False)]
model += [Bias2d(n_out)]
model += [nn.LeakyReLU(inplace=True)]
self.model = nn.Sequential(*model)
self.model.apply(gaussian_weights_init)
def forward(self, x):
return self.model(x)
class LeakyReLUBNNSConvTranspose2d(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding=0):
super(LeakyReLUBNNSConvTranspose2d, self).__init__()
model = []
model += [nn.ConvTranspose2d(n_in, n_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=True)]
model += [nn.BatchNorm2d(n_out, affine=False)]
model += [Bias2d(n_out)]
model += [nn.LeakyReLU(inplace=True)]
self.model = nn.Sequential(*model)
self.model.apply(gaussian_weights_init)
def forward(self, x):
return self.model(x)
##################################################################################
# ReLU-based conv layers
##################################################################################
class ReLUINSConv2d(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding=0):
super(ReLUINSConv2d, self).__init__()
model = []
model += [nn.Conv2d(n_in, n_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=True)]
model += [nn.InstanceNorm2d(n_out, affine=False)]
model += [nn.ReLU(inplace=True)]
self.model = nn.Sequential(*model)
self.model.apply(gaussian_weights_init)
def forward(self, x):
return self.model(x)
class ReLUINSConvTranspose2d(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding, output_padding):
super(ReLUINSConvTranspose2d, self).__init__()
model = []
model += [nn.ConvTranspose2d(n_in, n_out, kernel_size=kernel_size, stride=stride, padding=padding,
output_padding=output_padding, bias=True)]
model += [nn.InstanceNorm2d(n_out, affine=False)]
model += [nn.ReLU(inplace=True)]
self.model = nn.Sequential(*model)
self.model.apply(gaussian_weights_init)
def forward(self, x):
return self.model(x)