Comments (2)
Sorry for the slow response.
The easiest way to do this would be to create a normal resnet34 (without masks), and then copy across the weights.
You'll want to do something like:
net = NormalResNet34()
masked_net = MaskedResNet34()
for normal, masked in zip([net.layer1, net.layer2, net.layer3, net.layer4], [masked_net.layer1, masked_net.layer2, masked_net.layer3, masked_net.layer4]):
normal.conv1.weight = masked.conv1.weight
normal.bn1.weight = masked.bn1.weight
normal.conv2.weight = masked.conv2.weight
# etc.
Note that you're probably going to have to copy some biases as well (you can just do normal.bn1.bias
).
It would be cool to have this as a feature in the code. If you figure it out, please feel free to open a pull request 😊
If not I'll get to implementing it soon.
from deep-compression.
Sorry for the slow response.
The easiest way to do this would be to create a normal resnet34 (without masks), and then copy across the weights.
You'll want to do something like:
net = NormalResNet34() masked_net = MaskedResNet34() for normal, masked in zip([net.layer1, net.layer2, net.layer3, net.layer4], [masked_net.layer1, masked_net.layer2, masked_net.layer3, masked_net.layer4]): normal.conv1.weight = masked.conv1.weight normal.bn1.weight = masked.bn1.weight normal.conv2.weight = masked.conv2.weight # etc.Note that you're probably going to have to copy some biases as well (you can just do
normal.bn1.bias
).It would be cool to have this as a feature in the code. If you figure it out, please feel free to open a pull request
If not I'll get to implementing it soon.
Good idea, thank you. @jack-willturner
Using mask for pruning training is a very common way in pruning algorithm. I didn't want to extract effective parameters after mask training before. Your description gives me a specific implementation path (Although using this scheme needs to reimplement a model definition that can customize the number of parameters of each layer)
from deep-compression.
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from deep-compression.