I am using pytorch 0.3.0. I'm trying to selectively copy a neuron and it's weights within the same layer, then replace the original neuron with an another set of weights. Here's my attempt at that:
reshaped_data2 = data2.unsqueeze(0)
new_layer_data = torch.cat([new_layer.data, reshaped_data2], dim=0)
new_layer_data[i] = data1
new_layer.data.copy_(new_layer_data)
First I unsqueezed data2
to make it a 1*X
tensor instead of 0*X
. Then I concatenate my layer's tensor with the reshaped data2
along dimension 0. I then replace the original data2
located at index i
with data1
. Finally, I copy all of that into my layer.
The error I get is:
RuntimeError: inconsistent tensor size, expected tensor [10 x 128] and src [11 x 128] to have the same number of elements, but got 1280 and 1408 elements respectively at /Users/soumith/code/builder/wheel/pytorch-src/torch/lib/TH/generic/THTensorCopy.c:86
If I do a simple assignment instead of copy I get
RuntimeError: The expanded size of the tensor (11) must match the existing size (10) at non-singleton dimension 1. at /Users/soumith/code/builder/wheel/pytorch-src/torch/lib/TH/generic/THTensor.c:309
I understand the error, but what is the right way to go about this?
You're trying to replace a 10x128
tensor with a 11x128
tensor, which the model doesn't allow. Is new_layer
initialised with the size (11, 128)
? If not, try creating your new layer with your desired size (11, 128)
and then copy/assign your new_layer_data
.
The solution here is to create a new model with the correct size and pass in weights as default values. No dynamic expansion solution was found.
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