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# Densenet decoder encoder with intermediate fully connected layers and dropout
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import functools
from torch.autograd import gradcheck
from torch.autograd import Function
from torch.autograd import Variable
from torch.autograd import gradcheck
from torch.autograd import Function
import numpy as np
def add_coordConv_channels(t):
n,c,h,w=t.size()
xx_channel=np.ones((h, w))
xx_range=np.array(range(h))
xx_range=np.expand_dims(xx_range,-1)
xx_coord=xx_channel*xx_range
yy_coord=xx_coord.transpose()
xx_coord=xx_coord/(h-1)
yy_coord=yy_coord/(h-1)
xx_coord=xx_coord*2 - 1
yy_coord=yy_coord*2 - 1
xx_coord=torch.from_numpy(xx_coord).float()
yy_coord=torch.from_numpy(yy_coord).float()
if t.is_cuda:
xx_coord=xx_coord.cuda()
yy_coord=yy_coord.cuda()
xx_coord=xx_coord.unsqueeze(0).unsqueeze(0).repeat(n,1,1,1)
yy_coord=yy_coord.unsqueeze(0).unsqueeze(0).repeat(n,1,1,1)
t_cc=torch.cat((t,xx_coord,yy_coord),dim=1)
return t_cc
class DenseBlockEncoder(nn.Module):
def __init__(self, n_channels, n_convs, activation=nn.ReLU, args=[False]):
super(DenseBlockEncoder, self).__init__()
assert(n_convs > 0)
self.n_channels = n_channels
self.n_convs = n_convs
self.layers = nn.ModuleList()
for i in range(n_convs):
self.layers.append(nn.Sequential(
nn.BatchNorm2d(n_channels),
activation(*args),
nn.Conv2d(n_channels, n_channels, 3, stride=1, padding=1, bias=False),))
def forward(self, inputs):
outputs = []
for i, layer in enumerate(self.layers):
if i > 0:
next_output = 0
for no in outputs:
next_output = next_output + no
outputs.append(next_output)
else:
outputs.append(layer(inputs))
return outputs[-1]
# Dense block in encoder.
class DenseBlockDecoder(nn.Module):
def __init__(self, n_channels, n_convs, activation=nn.ReLU, args=[False]):
super(DenseBlockDecoder, self).__init__()
assert(n_convs > 0)
self.n_channels = n_channels
self.n_convs = n_convs
self.layers = nn.ModuleList()
for i in range(n_convs):
self.layers.append(nn.Sequential(
nn.BatchNorm2d(n_channels),
activation(*args),
nn.ConvTranspose2d(n_channels, n_channels, 3, stride=1, padding=1, bias=False),))
def forward(self, inputs):
outputs = []
for i, layer in enumerate(self.layers):
if i > 0:
next_output = 0
for no in outputs:
next_output = next_output + no
outputs.append(next_output)
else:
outputs.append(layer(inputs))
return outputs[-1]
class DenseTransitionBlockEncoder(nn.Module):
def __init__(self, n_channels_in, n_channels_out, mp, activation=nn.ReLU, args=[False]):
super(DenseTransitionBlockEncoder, self).__init__()
self.n_channels_in = n_channels_in
self.n_channels_out = n_channels_out
self.mp = mp
self.main = nn.Sequential(
nn.BatchNorm2d(n_channels_in),
activation(*args),
nn.Conv2d(n_channels_in, n_channels_out, 1, stride=1, padding=0, bias=False),
nn.MaxPool2d(mp),
)
def forward(self, inputs):
# print(inputs.shape,'222222222222222',self.main(inputs).shape)
return self.main(inputs)
class DenseTransitionBlockDecoder(nn.Module):
def __init__(self, n_channels_in, n_channels_out, activation=nn.ReLU, args=[False]):
super(DenseTransitionBlockDecoder, self).__init__()
self.n_channels_in = n_channels_in
self.n_channels_out = n_channels_out
self.main = nn.Sequential(
nn.BatchNorm2d(n_channels_in),
activation(*args),
nn.ConvTranspose2d(n_channels_in, n_channels_out, 4, stride=2, padding=1, bias=False),
)
def forward(self, inputs):
# print(inputs.shape,'333333333333',self.main(inputs).shape)
return self.main(inputs)
## Dense encoders and decoders for image of size 128 128
class waspDenseEncoder128(nn.Module):
def __init__(self, nc=1, ndf = 32, ndim = 128, activation=nn.LeakyReLU, args=[0.2, False], f_activation=nn.Tanh, f_args=[]):
super(waspDenseEncoder128, self).__init__()
self.ndim = ndim
self.main = nn.Sequential(
# input is (nc) x 128 x 128
nn.BatchNorm2d(nc),
nn.ReLU(True),
nn.Conv2d(nc, ndf, 4, stride=2, padding=1),
# state size. (ndf) x 64 x 64
DenseBlockEncoder(ndf, 6),
DenseTransitionBlockEncoder(ndf, ndf*2, 2, activation=activation, args=args),
# state size. (ndf*2) x 32 x 32
DenseBlockEncoder(ndf*2, 12),
DenseTransitionBlockEncoder(ndf*2, ndf*4, 2, activation=activation, args=args),
# state size. (ndf*4) x 16 x 16
DenseBlockEncoder(ndf*4, 16),
DenseTransitionBlockEncoder(ndf*4, ndf*8, 2, activation=activation, args=args),
# state size. (ndf*4) x 8 x 8
DenseBlockEncoder(ndf*8, 16),
DenseTransitionBlockEncoder(ndf*8, ndf*8, 2, activation=activation, args=args),
# state size. (ndf*8) x 4 x 4
DenseBlockEncoder(ndf*8, 16),
DenseTransitionBlockEncoder(ndf*8, ndim, 4, activation=activation, args=args),
f_activation(*f_args),
)
def forward(self, input):
input=add_coordConv_channels(input)
output = self.main(input).view(-1,self.ndim)
#print(output.size())
return output
class waspDenseDecoder128(nn.Module):
def __init__(self, nz=128, nc=1, ngf=32, lb=0, ub=1, activation=nn.ReLU, args=[False], f_activation=nn.Hardtanh, f_args=[]):
super(waspDenseDecoder128, self).__init__()
self.main = nn.Sequential(
# input is Z, going into convolution
nn.BatchNorm2d(nz),
activation(*args),
nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
# state size. (ngf*8) x 4 x 4
DenseBlockDecoder(ngf*8, 16),
DenseTransitionBlockDecoder(ngf*8, ngf*8),
# state size. (ngf*4) x 8 x 8
DenseBlockDecoder(ngf*8, 16),
DenseTransitionBlockDecoder(ngf*8, ngf*4),
# state size. (ngf*2) x 16 x 16
DenseBlockDecoder(ngf*4, 12),
DenseTransitionBlockDecoder(ngf*4, ngf*2),
# state size. (ngf) x 32 x 32
DenseBlockDecoder(ngf*2, 6),
DenseTransitionBlockDecoder(ngf*2, ngf),
# state size. (ngf) x 64 x 64
DenseBlockDecoder(ngf, 6),
DenseTransitionBlockDecoder(ngf, ngf),
# state size (ngf) x 128 x 128
nn.BatchNorm2d(ngf),
activation(*args),
nn.ConvTranspose2d(ngf, nc, 3, stride=1, padding=1, bias=False),
f_activation(*f_args),
)
# self.smooth=nn.Sequential(
# nn.Conv2d(nc, nc, 1, stride=1, padding=0, bias=False),
# f_activation(*f_args),
# )
def forward(self, inputs):
# return self.smooth(self.main(inputs))
return self.main(inputs)
## Dense encoders and decoders for image of size 512 512
class waspDenseEncoder512(nn.Module):
def __init__(self, nc=1, ndf = 32, ndim = 128, activation=nn.LeakyReLU, args=[0.2, False], f_activation=nn.Tanh, f_args=[]):
super(waspDenseEncoder512, self).__init__()
self.ndim = ndim
self.main = nn.Sequential(
# input is (nc) x 128 x 128 > *4
nn.BatchNorm2d(nc),
nn.ReLU(True),
nn.Conv2d(nc, ndf, 4, stride=2, padding=1),
# state size. (ndf) x 64 x 64 > *4
DenseBlockEncoder(ndf, 6),
DenseTransitionBlockEncoder(ndf, ndf*2, 2, activation=activation, args=args),
# state size. (ndf*2) x 32 x 32 > *4
DenseBlockEncoder(ndf*2, 12),
DenseTransitionBlockEncoder(ndf*2, ndf*4, 2, activation=activation, args=args),
# state size. (ndf*4) x 16 x 16 > *4
DenseBlockEncoder(ndf*4, 16),
DenseTransitionBlockEncoder(ndf*4, ndf*8, 2, activation=activation, args=args),
# state size. (ndf*8) x 8 x 8 *4
DenseBlockEncoder(ndf*8, 16),
DenseTransitionBlockEncoder(ndf*8, ndf*8, 2, activation=activation, args=args),
# state size. (ndf*8) x 4 x 4 > *4
DenseBlockEncoder(ndf*8, 16),
DenseTransitionBlockEncoder(ndf*8, ndf*8, 4, activation=activation, args=args),
f_activation(*f_args),
# state size. (ndf*8) x 2 x 2 > *4
DenseBlockEncoder(ndf*8, 16),
DenseTransitionBlockEncoder(ndf*8, ndim, 4, activation=activation, args=args),
f_activation(*f_args),
)
def forward(self, input):
input=add_coordConv_channels(input)
output = self.main(input).view(-1,self.ndim)
# output = self.main(input).view(8,-1)
# print(input.shape,'---------------------')
#print(output.size())
return output
class waspDenseDecoder512(nn.Module):
def __init__(self, nz=128, nc=1, ngf=32, lb=0, ub=1, activation=nn.ReLU, args=[False], f_activation=nn.Tanh, f_args=[]):
super(waspDenseDecoder512, self).__init__()
self.main = nn.Sequential(
# input is Z, going into convolution
nn.BatchNorm2d(nz),
activation(*args),
nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
# state size. (ngf*8) x 4 x 4
DenseBlockDecoder(ngf*8, 16),
DenseTransitionBlockDecoder(ngf*8, ngf*8),
# state size. (ngf*8) x 8 x 8
DenseBlockDecoder(ngf*8, 16),
DenseTransitionBlockDecoder(ngf*8, ngf*8),
# state size. (ngf*4) x 16 x 16
DenseBlockDecoder(ngf*8, 16),
DenseTransitionBlockDecoder(ngf*8, ngf*4),
# state size. (ngf*2) x 32 x 32
DenseBlockDecoder(ngf*4, 12),
DenseTransitionBlockDecoder(ngf*4, ngf*2),
# state size. (ngf) x 64 x 64
DenseBlockDecoder(ngf*2, 6),
DenseTransitionBlockDecoder(ngf*2, ngf),
# state size. (ngf) x 128 x 128
DenseBlockDecoder(ngf, 6),
DenseTransitionBlockDecoder(ngf, ngf),
# state size. (ngf) x 256 x 256
DenseBlockDecoder(ngf, 6),
DenseTransitionBlockDecoder(ngf, ngf),
# state size (ngf) x 512 x 512
nn.BatchNorm2d(ngf),
activation(*args),
nn.ConvTranspose2d(ngf, nc, 3, stride=1, padding=1, bias=False),
f_activation(*f_args),
)
# self.smooth=nn.Sequential(
# nn.Conv2d(nc, nc, 1, stride=1, padding=0, bias=False),
# f_activation(*f_args),
# )
def forward(self, inputs):
# return self.smooth(self.main(inputs))
return self.main(inputs)
class dnetccnl(nn.Module):
#in_channels -> nc | encoder first layer
#filters -> ndf | encoder first layer
#img_size(h,w) -> ndim
#out_channels -> optical flow (x,y)
def __init__(self, img_size=448, in_channels=3, out_channels=2, filters=32,fc_units=100):
super(dnetccnl, self).__init__()
self.nc=in_channels
self.nf=filters
self.ndim=img_size
self.oc=out_channels
self.fcu=fc_units
self.encoder=waspDenseEncoder128(nc=self.nc+2,ndf=self.nf,ndim=self.ndim)
self.decoder=waspDenseDecoder128(nz=self.ndim,nc=self.oc,ngf=self.nf)
# self.fc_layers= nn.Sequential(nn.Linear(self.ndim, self.fcu),
# nn.ReLU(True),
# nn.Dropout(0.25),
# nn.Linear(self.fcu,self.ndim),
# nn.ReLU(True),
# nn.Dropout(0.25),
# )
def forward(self, inputs):
encoded=self.encoder(inputs)
encoded=encoded.unsqueeze(-1).unsqueeze(-1)
decoded=self.decoder(encoded)
# print torch.max(decoded)
# print torch.min(decoded)
# print(decoded.shape,'11111111111111111',encoded.shape)
return decoded
class dnetccnl512(nn.Module):
#in_channels -> nc | encoder first layer
#filters -> ndf | encoder first layer
#img_size(h,w) -> ndim
#out_channels -> optical flow (x,y)
def __init__(self, img_size=448, in_channels=3, out_channels=2, filters=32,fc_units=100):
super(dnetccnl512, self).__init__()
self.nc=in_channels
self.nf=filters
self.ndim=img_size
self.oc=out_channels
self.fcu=fc_units
self.encoder=waspDenseEncoder512(nc=self.nc+2,ndf=self.nf,ndim=self.ndim)
self.decoder=waspDenseDecoder512(nz=self.ndim,nc=self.oc,ngf=self.nf)
# self.fc_layers= nn.Sequential(nn.Linear(self.ndim, self.fcu),
# nn.ReLU(True),
# nn.Dropout(0.25),
# nn.Linear(self.fcu,self.ndim),
# nn.ReLU(True),
# nn.Dropout(0.25),
# )
def forward(self, inputs):
encoded=self.encoder(inputs)
encoded=encoded.unsqueeze(-1).unsqueeze(-1)
decoded=self.decoder(encoded)
# print torch.max(decoded)
# print torch.min(decoded)
# print(decoded.shape,'11111111111111111',encoded.shape)
return decoded