我试图将一个5D张量(1,8,32,32,32)输入到我写的一个VAE中:
self.encoder = nn.Sequential(
nn.Conv3d( 8, 16, 4, 2, 1 ), # 32 -> 16
nn.BatchNorm3d( 16 ),
nn.LeakyReLU( 0.2 ),
nn.Conv3d( 16, 32, 4, 2, 1 ), # 16 -> 8
nn.BatchNorm3d( 32 ),
nn.LeakyReLU( 0.2 ),
nn.Conv3d( 32, 48, 4, 2, 1 ), # 16 -> 4
nn.BatchNorm3d( 48 ),
nn.LeakyReLU( 0.2 ),
)
self.fc_mu = nn.Linear( 3072, 100 ) # 48*4*4*4 = 3072
self.fc_logvar = nn.Linear( 3072, 100 )
self.decoder = nn.Sequential(
nn.Linear( 100, 3072 ),
nn.Unflatten( 1, ( 48, 4, 4 )),
nn.ConvTranspose3d( 48, 32, 4, 2, 1 ), # 4 -> 8
nn.BatchNorm3d( 32 ),
nn.Tanh(),
nn.ConvTranspose3d( 32, 16, 4, 2, 1 ), # 8 -> 16
nn.BatchNorm3d( 16 ),
nn.Tanh(),
nn.ConvTranspose3d( 16, 8, 4, 2, 1 ), # 16 -> 32
nn.BatchNorm3d( 8 ),
nn.Tanh(),
)
def reparametrize( self, mu, logvar ):
std = torch.exp( 0.5 * logvar )
eps = torch.randn_like( std )
return mu + eps * std
def encode( self, x ) :
x = self.encoder( x )
x = x.view( -1, x.size( 1 ))
mu = self.fc_mu( x )
logvar = self.fc_logvar( x )
return self.reparametrize( mu, logvar ), mu, logvar
def decode( self, x ):
return self.decoder( x )
def forward( self, data ):
z, mu, logvar = self.encode( data )
return self.decode( z ), mu, logvar
我得到的错误是:RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x48 and 3072x100)
。我以为我已经正确地计算了每一层的输出尺寸,但我一定是弄错了,但我不确定在哪里
这条线
意味着您将第二维度(通道)保持原样,并将其他所有内容放在第一维度(批次)
由于
self.encoder
的输出是(1, 48, 4, 4, 4)
,这样做意味着你会得到(64, 48)
,但从它的外观上看,我认为你想要(1, 3072)
所以这应该解决这个特殊的问题
然后您将遇到
RuntimeError: unflatten: Provided sizes [48, 4, 4] don't multiply up to the size of dim 1 (3072) in the input tensor
原因是这里不平坦
必须改为
(48, 4, 4, 4)
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