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class MyEncoder(nn.Module): | |
def __init__(self, enc_sizes): | |
super().__init__() | |
self.conv_blokcs = nn.Sequential(*[conv_block(in_f, out_f, kernel_size=3, padding=1) | |
for in_f, out_f in zip(enc_sizes, enc_sizes[1:])]) | |
def forward(self, x): | |
return self.conv_blokcs(x) | |
class MyDecoder(nn.Module): | |
def __init__(self, dec_sizes, n_classes): | |
super().__init__() | |
self.dec_blocks = nn.Sequential(*[dec_block(in_f, out_f) | |
for in_f, out_f in zip(dec_sizes, dec_sizes[1:])]) | |
self.last = nn.Linear(dec_sizes[-1], n_classes) | |
def forward(self, x): | |
return self.dec_blocks() | |
class MyCNNClassifier(nn.Module): | |
def __init__(self, in_c, enc_sizes, dec_sizes, n_classes): | |
super().__init__() | |
self.enc_sizes = [in_c, *enc_sizes] | |
self.dec_sizes = [32 * 28 * 28, *dec_sizes] | |
self.encoder = MyEncoder(self.enc_sizes) | |
self.decoder = MyDecoder(dec_sizes, n_classes) | |
def forward(self, x): | |
x = self.encoder(x) | |
x = x.flatten(1) # flat | |
x = self.decoder(x) | |
return x |
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