Miscellaneous

This contains a set of experiments.

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InputWrapper

def InputWrapper(
    arch, c_in, c_out, seq_len, new_c_in:NoneType=None, new_seq_len:NoneType=None, kwargs:VAR_KEYWORD
):

Same as nn.Module, but no need for subclasses to call super().__init__

from tsai.models.TST import *

xb = torch.randn(16, 1, 1000)
model = InputWrapper(TST, 1, 4, 1000, 10, 224)
test_eq(model.to(xb.device)(xb).shape, (16,4))

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ResidualWrapper

def ResidualWrapper(
    model
):

Same as nn.Module, but no need for subclasses to call super().__init__

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RecursiveWrapper

def RecursiveWrapper(
    model, n_steps, anchored:bool=False
):

Same as nn.Module, but no need for subclasses to call super().__init__

xb = torch.randn(16, 1, 20)
model = RecursiveWrapper(TST(1, 1, 20), 5)
test_eq(model.to(xb.device)(xb).shape, (16, 5))