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choosing architectures #3

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sezan92 opened this issue Oct 9, 2020 · 4 comments · May be fixed by #4
Open

choosing architectures #3

sezan92 opened this issue Oct 9, 2020 · 4 comments · May be fixed by #4

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@sezan92
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sezan92 commented Oct 9, 2020

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@sezan92 sezan92 linked a pull request Oct 9, 2020 that will close this issue
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@avilash
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avilash commented Oct 11, 2020

Looks good, will test each one and merge. The only doubt I have is the number modules of each architecture. I believe they are same for all the architectures and hence shouldn't be a problem

@sezan92
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sezan92 commented Oct 12, 2020

can you explain by "number of modules for each architectures"? i see the training worked after changing the archs to resnet 18 and 34 at least

@avilash
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avilash commented Oct 12, 2020

self.features = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1, resnet.layer2, resnet.layer3, resnet.layer4, resnet.avgpool)
https://github.com/avilash/pytorch-siamese-triplet/blob/master/model/embedding.py#L12
These are the modules.
Should be same across architectures

@sezan92
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sezan92 commented Oct 13, 2020

oh yes. they are same

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Issue#3 sezan92/pytorch-siamese-triplet
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@avilash @sezan92