def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
else:
self.up = nn.ConvTranpose2d(in_channels // 2, in_channels // 2,
kernel_size=2, stride=2)
self.conv = double_conv(in_channels, out_channels)
def __init__(self, in_channels, out_channels, bilinear=True):
def double_conv(in_channels, out_channels):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels,
kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
super(up,self).__init__()
if bilinear:
self.up = nn.Upsample(
scale_factor=2, mode='bilinear', align_corners=True)
else:
self.up = nn.ConvTranpose2d(in_channels // 2, in_channels // 2,
kernel_size=2, stride=2)
self.conv = double_conv(in_channels, out_channels)
def __init__(self,nfeats,nchannels):
super (Generator,self).__init__()
#Feeding Latent vector which will be the dot product of two latent vectors of size 1X1X100
self.convt1 = nn.ConvTranspose2d(in_channels=100,out_channels=nfeats*2,kernel_size=4,stride=1,padding=0,bias=False,padding_mode='zeros')
#Output has the shape 4X4 with nfeats*2 channels which will be tentatively 128 channels if nfeats = 64
self.convt2 = nn.ConvTranspose2d(in_channels=nfeats*2,out_channels=nfeats*8,kernel_size=4,stride=2,padding=1,bias=False,padding_mode='zeros')
self.bn1 = nn.BatchNorm2d(nfeats*8)
#Output has the shape 8X8 with nfeats*8 channels which will be tentatively 512 channels if nfeats = 64
self.convt3 = nn.ConvTranspose2d(in_channels=nfeats*8,out_channels=nfeats*8,kernel_size=4,stride=2,padding=1,bias=False,padding_mode='zeros')
self.bn2 = nn.BatchNorm2d(nfeats*8)
#Output has shape 16X16 with nfeats*8
self.convt4 = nn.ConvTranspose2d(in_channels=nfeats*8,out_channels=nfeats*8,kernel_size=4,stride=2,padding=1,bias=False,padding_mode='zeros')
self.bn3 = nn.BatchNorm2d(nfeats*8)
#Output has shape 32X32 with nfeats*8
self.convt5 = nn.ConvTranspose2d(in_channels=nfeats*8,out_channels=nfeats*4,kernel_size=4,stride=2,padding=1,bias=False,padding_mode='zeros')
self.bn4 = nn.BatchNorm2d(nfeats*4)
#Output has shape 64X64 with nfeats *4 (256)
self.convt6 = nn.Conv2d(in_channels=nfeats*4,out_channels=nchannels,kernel_size=1,stride=1,padding=0,bias=False,padding_mode='zeros')
#Output has shape 64X64 with 3 channels -- A RGB Image
#----------------------------------------------------------------------------------------
#Defining the components for the residual blocks :)
#This is for 4x4 to 8x8
self.resconv1 = nn.ConvTranpose2d(in_channels=nfeats*2,out_channels=nfeats*8,kernel_size=4,stride=2,padding=1,padding_mode='zeros',bias=False)
self.resbatch1 = nn.BatchNorm2d(nfeats*8)
#This is for 4x4 to 16x16 via 8x8 weights
self.resconv2 = nn.ConvTranspose2d(in_channels=nfeats*8,out_channels=nfeats*8,kernel_size=4,stride=2,padding=1,padding_mode='zeros',bias=False)
self.resbatch2 = nn.BatchNorm2d(nfeats*8)
#This is for 4x4 to 32x32 va 16x16 weights
self.resconv3 = nn.ConvTranspose2d(in_channels=nfeats*8,out_channels=nfeats*8,kernel_size=4,stride=2,padding=1,padding_mode='zeros',bias=False)
self.resbatch3 = nn.BatchNorm2d(nfeats*8)
#------------------------------Now designing the Resblocks from 8x8 to 32x32,64x64
self.resconv4 = nn.ConvTranspose2d(in_channels=nfeats*8,out_channels=nfeats*8,kernel_size=4,stride=2,padding=1,padding_mode='zeros',bias=False)
self.resbatch4 = nn.BatchNorm2d(nfeats*8)
self.resconv5 = nn.ConvTranspose2d(in_channels=nfeats*8,out_channels=nfeats*8,kernel_size=4,stride=2,padding=1,padding_mode='zeros',bias=False)
self.resbatch5 = nn.BatchNorm2d(nfeats*8)