Hey all,
I’m quite new to PyTorch and am currently trying to implement a CNN-based classifier for some multivariate (9 dimensions/axes) timeseries data. I intend to use 1D convolutions and Max pools in the network.
My Dataset class returns each sample (which reflects 125 timesteps) as a 9 x 125 tensor. My (toy) CNN is constructed as described below:
self.conv1 = nn.Conv1d(9, 18, kernel_size=3) #9 input channels, 18 output channels
self.conv2 = nn.Conv1d(18, 36, kernel_size=3) #18 input channels from previous Conv. layer, 36 out
self.conv2_drop = nn.Dropout2d() #dropout
self.fc1 = nn.Linear(36, 72) #Fully-connected classifier layer
self.fc2 = nn.Linear(72, 19) #Fully-connected classifier layer

And the forward method of the CNN is described as follows:
 x = F.relu(F.max_pool1d(self.conv1(x), 2))
x = F.relu(F.max_pool1d(self.conv2_drop(self.conv2(x)),2))
#point A
x = x.view(-1, 36)
#point B
x = self.fc1(x)
x = F.relu(x)
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)

The dataloader uses a batch size of 64. At point A in the code above, the shape of x is [64,36,29] - as I understand it, 64 samples (in the batch), each with 36 channels and a length of 29 timesteps. I’d now like to reshape this tensor appropriately for use by the Fully-Connected layer(s) - the code above is how the sample code I’m following (the MNIST example) did it. However, it seems that x.view() is not maintaining the batches, instead folding the tensor into this shape: [1856,36] which is definitely not what I want - or what I think I should be getting. Naturally, I get some error:

ValueError: Expected input batch_size (1856) to match target batch_size (64)

But I do not expect the input batch_size to be anything other than 64 - I believe this is happening because I’m using x.view() wrongly.
Any help with this would be highly appreciated. I’m quite new to PyTorch and don’t know what to do about it.
Thanks in advance