Describe what you were trying to get done.
Tell us what happened, what went wrong, and what you expected to happen.
What I Did
Hi, I'm pretty new to torch. I'm using my own neuromorphic dataset with labels . I am not using Tonic as my dataset has been converted into the suitable format of an Event representation format I have them as Event Frames. I loaded them and converted them to tensors for torch and I used TensorDataset and then used the DataLoader. So when I check them after tensordataset the shape is [1446,1,260,346] which is correct and then when I try to check the shape by iterating over trainloader as shown on the tutorial :
event_tensor, target = next(iter(trainloader))
print(event_tensor.shape)
torch.Size([311, 128, 2, 34, 34])
I don't get 5 dimensions for my event tensor rather it is coming as [32,1,260,346] where 32 is the batch size. I should be getting [1446,32,1,260,346] as 1446 is the sequence length which should be my number of timesteps. So when I train I get error with the sequential model.
RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x5146 and 164672x14) and it points to my forward pass function. I am using the exact one shown on the tutorial. The only thing I had to change were these for the model and here is the model:
net = nn.Sequential(nn.Conv2d(1, 12, 5),
nn.MaxPool2d(2),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
nn.Conv2d(12, 32, 5),
nn.MaxPool2d(2),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
nn.Flatten(),
nn.Linear(326283, 14),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True)
).to(device)
So how can we use neuromorphic datasets without Tonic for snnTorch. As what I can understand Tonic is used mainly for the conversion to any suitable event representation and pass it to the CSNN model.
Paste the command(s) you ran and the output.
If there was a crash, please include the traceback here.
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[25], line 21
19 # forward pass
20 net.train()
---> 21 spk_rec = forward_pass(net, data)
23 # initialize the loss & sum over time
24 loss_val = loss_fn(spk_rec, tr_label)
Cell In[24], line 7
4 utils.reset(net) # resets hidden states for all LIF neurons in net
6 for step in range(data.size(0)): # data.size(0) = number of time steps
----> 7 spk_out, mem_out = net(data[step])
8 spk_rec.append(spk_out)
10 return torch.stack(spk_rec)
File /data/m/event/lib/python3.8/site-packages/torch/nn/modules/module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)
File /data/m/event/lib/python3.8/site-packages/torch/nn/modules/module.py:1541, in Module._call_impl(self, *args, **kwargs)
1536 # If we don't have any hooks, we want to skip the rest of the logic in
1537 # this function, and just call forward.
1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1543 try:
1544 result = None
File /data/m/event/lib/python3.8/site-packages/torch/nn/modules/container.py:217, in Sequential.forward(self, input)
215 def forward(self, input):
216 for module in self:
--> 217 input = module(input)
218 return input
File /data/m/event/lib/python3.8/site-packages/torch/nn/modules/module.py:1
532, in Module._wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)
File /data/m/event/lib/python3.8/site-packages/torch/nn/modules/module.py:1541, in Module._call_impl(self, *args, **kwargs)
1536 # If we don't have any hooks, we want to skip the rest of the logic in
1537 # this function, and just call forward.
1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1543 try:
1544 result = None
File /data/m/event/lib/python3.8/site-packages/torch/nn/modules/linear.py:116, in Linear.forward(self, input)
115 def forward(self, input: Tensor) -> Tensor:
--> 116 return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x5146 and 164672x14)
Description
Describe what you were trying to get done. Tell us what happened, what went wrong, and what you expected to happen.
What I Did
Hi, I'm pretty new to torch. I'm using my own neuromorphic dataset with labels . I am not using Tonic as my dataset has been converted into the suitable format of an Event representation format I have them as Event Frames. I loaded them and converted them to tensors for torch and I used TensorDataset and then used the DataLoader. So when I check them after tensordataset the shape is [1446,1,260,346] which is correct and then when I try to check the shape by iterating over trainloader as shown on the tutorial : event_tensor, target = next(iter(trainloader)) print(event_tensor.shape) torch.Size([311, 128, 2, 34, 34])
I don't get 5 dimensions for my event tensor rather it is coming as [32,1,260,346] where 32 is the batch size. I should be getting [1446,32,1,260,346] as 1446 is the sequence length which should be my number of timesteps. So when I train I get error with the sequential model. RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x5146 and 164672x14) and it points to my forward pass function. I am using the exact one shown on the tutorial. The only thing I had to change were these for the model and here is the model: net = nn.Sequential(nn.Conv2d(1, 12, 5), nn.MaxPool2d(2), snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), nn.Conv2d(12, 32, 5), nn.MaxPool2d(2), snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), nn.Flatten(), nn.Linear(326283, 14), snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True) ).to(device)
So how can we use neuromorphic datasets without Tonic for snnTorch. As what I can understand Tonic is used mainly for the conversion to any suitable event representation and pass it to the CSNN model.