Moving common layers to ops#4504
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Highlighting the interesting parts of the implementation
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Can we move stochastic depth from |
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@oke-aditya thanks for the comment. Note that the StochasticDepth layer lives in its own module at The historical convention for where to include things seems to be that layers with functional and class components end up on their own modules while those that are simple and use standard building convolutional blocks end up in |
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Summary: * Moving _make_divisible to utils. * Replace the old ConvBNReLU and ConvBNActivation layers * Fix minor bug. * Moving SE layer to ops. * Adding deprecation warnings on old layers. * Apply changes to regnets. Reviewed By: prabhat00155, NicolasHug Differential Revision: D31309549 fbshipit-source-id: 2780783ddfeb58974829607ac90f122b915f7366
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* Moving _make_divisible to utils. * Replace the old ConvBNReLU and ConvBNActivation layers * Fix minor bug. * Moving SE layer to ops. * Adding deprecation warnings on old layers. * Apply changes to regnets.
cyyever
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* Moving _make_divisible to utils. * Replace the old ConvBNReLU and ConvBNActivation layers * Fix minor bug. * Moving SE layer to ops. * Adding deprecation warnings on old layers. * Apply changes to regnets.
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Partially resolves #4333
In this PR:
_make_divisible()from MobileNetV2 tomodels._utils.ConvBNReLUandConvBNActivationclasses in favour of a newConvNormActivationadded inops.misc.SqueezeExcitationtoops.miscand slightly modified the class to use only nn.Modules instead of functional.