Morning all,
I am hoping that someone will be able to help me as I am at the end of my tether…
I am trying to write the autoencoder for my network. having hit a problem i started experiementing with a know working decoder (shown below):
class Decoder(nn.Module):
def __init__(self, z_dim):
super(Decoder, self).__init__()
self.fc1 = nn.Linear(z_dim, 672)
self.conv1 = nn.ConvTranspose1d(32, 32, 8, 2, padding=3)
self.conv2 = nn.ConvTranspose1d(32, 32, 8, 2, padding=3)
self.conv3 = nn.ConvTranspose1d(32, 16, 8, 2, padding=3)
self.conv4 = nn.ConvTranspose1d(16, 16, 8, 2, padding=3)
self.conv5 = nn.ConvTranspose1d(16, 1, 7, 1, padding=3)
self.bn1 = nn.BatchNorm1d(32)
self.bn2 = nn.BatchNorm1d(32)
self.bn3 = nn.BatchNorm1d(16)
self.bn4 = nn.BatchNorm1d(16)
self.relu = nn.ReLU()
def forward(self, z):
z = self.relu(self.fc1(z))
print(z)
print(z.shape)
#z = F.dropout(z, 0.3)
z = z.view(-1, 32, 21)
z = self.relu(self.conv1(z))
z = self.bn1(z)
#z = F.dropout(z, 0.3)
z = self.relu(self.conv2(z))
z = self.bn2(z)
#z = F.dropout(z, 0.3)
z = self.relu(self.conv3(z))
z = self.bn3(z)
#z = F.dropout(z, 0.3)
z = self.relu(self.conv4(z))
z = self.bn4(z)
#z = F.dropout(z, 0.3)
z = self.conv5(z)
recon = torch.sigmoid(z)
return recon
summary(vae.decoder, (1, 2))
this gives the following results:
**tensor([[[0.0000, 0.3415, 0.6484, ..., 0.2840, 0.0000, 0.3873]],**
** [[0.0000, 0.4126, 0.6034, ..., 0.2224, 0.0000, 0.3813]]],**
** device='cuda:0', grad_fn=<ReluBackward0>)**
**torch.Size([2, 1, 672])**
**----------------------------------------------------------------**
** Layer (type) Output Shape Param #**
**================================================================**
** Linear-1 [-1, 1, 672] 2,016**
** ReLU-2 [-1, 1, 672] 0**
** ConvTranspose1d-3 [-1, 32, 42] 8,224**
** ReLU-4 [-1, 32, 42] 0**
** BatchNorm1d-5 [-1, 32, 42] 64**
** ConvTranspose1d-6 [-1, 32, 84] 8,224**
** ReLU-7 [-1, 32, 84] 0**
** BatchNorm1d-8 [-1, 32, 84] 64**
** ConvTranspose1d-9 [-1, 16, 168] 4,112**
** ReLU-10 [-1, 16, 168] 0**
** BatchNorm1d-11 [-1, 16, 168] 32**
** ConvTranspose1d-12 [-1, 16, 336] 2,064**
** ReLU-13 [-1, 16, 336] 0**
** BatchNorm1d-14 [-1, 16, 336] 32**
** ConvTranspose1d-15 [-1, 1, 336] 113**
**================================================================**
**Total params: 24,945**
**Trainable params: 24,945**
**Non-trainable params: 0**
**----------------------------------------------------------------**
**Input size (MB): 0.00**
**Forward/backward pass size (MB): 0.29**
**Params size (MB): 0.10**
**Estimated Total Size (MB): 0.38**
**----------------------------------------------------------------**
**which is expected.**
however changing the decoder to be included in a nn.sequential module the summary no longer works (see below).
class Decoder(nn.Module):
def __init__(self, z_dim):
super(Decoder, self).__init__()
self.decode = nn.Sequential(OrderedDict([
('fc1',nn.Linear(z_dim, 672)),
('conv1',nn.ConvTranspose1d(32, 32, 8, 2, padding=3)),
('conv2',nn.ConvTranspose1d(32, 32, 8, 2, padding=3)),
('conv3',nn.ConvTranspose1d(32, 16, 8, 2, padding=3)),
('conv4',nn.ConvTranspose1d(16, 16, 8, 2, padding=3)),
('conv5',nn.ConvTranspose1d(16, 1, 7, 1, padding=3)),
('bn1',nn.BatchNorm1d(32)),
('bn2',nn.BatchNorm1d(32)),
('bn3',nn.BatchNorm1d(16)),
('bn4',nn.BatchNorm1d(16)),
('relu',nn.ReLU())
]))
def forward(self, z):
z = self.decode.relu(self.decode.fc1(z))
print(z)
print(z.shape)
z = z.view(-1, 32, 31)
z = self.decode(z)
#z = self.relu(self.fc1(z))
#z = F.dropout(z, 0.3)
recon = torch.sigmoid(z)
return recon
summary(vae.decoder, (1, 2))
This gives the following error:
tensor([[[0.6222, 0.0000, 0.0000, ..., 0.0180, 0.3997, 0.3615]],
[[0.7606, 0.0000, 0.0000, ..., 0.0000, 0.2021, 0.2654]]],
device='cuda:0', grad_fn=<ReluBackward0>)
torch.Size([2, 1, 672])
Traceback (most recent call last):
File "G:/decoder-test.py", line 30, in forward
z = z.view(-1, 32, 31)
RuntimeError: shape '[-1, 32, 31]' is invalid for input of size 1344
I know I have done something silly, but I cannot for the life of me see what (removing the z = self.decode.relu(self.decode.fc1(z)) line just changes the size from 1344 to 4)…
I’m hopng that someone can point me in the correct direction…
Chaslie
