Created
October 19, 2018 14:55
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import torch | |
from torch import nn | |
class autoencoder(nn.Module): | |
def __init__(self,downsizing_factor=None,in_channels=1): | |
self.downsize = downsizing_factor | |
self.in_channels = in_channels | |
super(autoencoder,self).__init__() | |
conv_modules=[] | |
self.in_channels = self.in_channels | |
self.out_channels = 2 * self.in_channels | |
self.block = [nn.Conv2d(self.in_channels,self.out_channels,3,stride=2,padding=1), | |
nn.ReLU(True)] | |
conv_modules.extend(self.block) | |
for i in range(1,self.downsize): | |
print(f"in channels , out channel {self.in_channels,self.out_channels}") | |
self.in_channels=int((self.out_channels)) | |
self.out_channels = int(2 * self.in_channels) | |
self.block = [nn.Conv2d(self.in_channels,self.out_channels,3,stride=2,padding=1), | |
nn.ReLU(True)] | |
conv_modules.extend(self.block) | |
self.conv = nn.Sequential(*conv_modules) | |
## Doconv part | |
self.deconv_in_channels = int((self.out_channels)) | |
deconv_modules=[] | |
for i in range(self.downsize): | |
print(f"in channels , out channel {self.deconv_in_channels,int(self.deconv_in_channels/2)}") | |
self.deconv_block = [nn.ConvTranspose2d(self.deconv_in_channels,int(self.deconv_in_channels/2),2,stride=2), | |
nn.ReLU(True)] | |
deconv_modules.extend(self.deconv_block) | |
self.deconv_in_channels=int(self.deconv_in_channels/2) | |
self.deconv = nn.Sequential(*deconv_modules) | |
def forward(self,x): | |
x = self.conv(x) | |
print(f"shape of input after encoder part {np.shape(x)}") | |
return self.deconv(x) |
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a = autoencoder(downsizing_factor=3,in_channels=1)
print(a)
b = a(torch.unsqueeze(torch.randn(1,512,512),0))
print(np.shape(b))