Spaces:
Sleeping
Sleeping
File size: 2,986 Bytes
f039b6d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import torch.nn as nn
import torch
class Generator(torch.nn.Module):
def __init__(self, nc_input=1, nc_output=1, ndf=128, nz=128, ngf=128, dropout_rate = 0.5 ):
super(Generator, self).__init__()
self.encoder = nn.Sequential(
nn.Dropout(0.05),
# input is (nc) x 64 x 64
nn.Conv2d(nc_input, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf) x 32 x 32
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*2) x 16 x 16
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*4) x 8 x 8
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*8) x 4 x 4
nn.Conv2d(ndf * 8, 1, 1, 1, 0, bias=False),
##nn.Conv2d(1, 1, 5, 1, 0, bias=False),
##nn.Sigmoid()
)
self.linearEncoder = nn.Sequential(
nn.Linear(64, 128)
)
self.decoder = nn.Sequential(
# input is Z, going into a convolution
nn.Dropout(0.05),
nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
nn.Dropout(dropout_rate),
# state size. (ngf*8) x 4 x 4 == 1024 x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
nn.Dropout(dropout_rate), # state size. (ngf*4) x 8 x 8 == 512 x 4 x 4
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
nn.Dropout(dropout_rate), # state size. (ngf*2) x 16 x 16 == 256 x 4 x 4
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
nn.Dropout(dropout_rate), # state size. (ngf) x 32 x 32 == 128 x 4 x 4
nn.ConvTranspose2d(ngf, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
nn.ConvTranspose2d( ngf, nc_output, 4, 2, 1, bias=False),
nn.Sigmoid()
# state size. (nc) x 64 x 64
)
def forward(self, x):
encoded = self.forward_encoder(x)
decoded = self.forward_decoder(encoded)
return decoded
def forward_encoder(self, x):
encoded = self.encoder(x).reshape(-1, 64)
return self.linearEncoder(encoded).unsqueeze(2).unsqueeze(2)
def forward_decoder(self, encoded):
decoded = self.decoder(encoded)
return decoded
|