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63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
"""
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The module baseclass.
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"""
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from abc import abstractmethod
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from torch import nn
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from torchtyping import TensorType
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class Module(nn.Module):
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"""_summary_
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Args:
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nn (_type_): _description_
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"""
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def __init__(self) -> None:
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"""Default initialization of module"""
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super().__init__()
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self.in_dim = 0
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self.out_dim = 0
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def build_nn_modules(self) -> None:
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"""Function instantiates any torch.nn members within the module.
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If none exist, do nothing."""
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def set_in_dim(self, in_dim: int) -> None:
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"""Sets input dimension of encoding
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Args:
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in_dim (int): input dimension
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"""
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if in_dim <= 0:
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raise ValueError("Input dimension should be greater than zero")
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self.in_dim = in_dim
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def get_out_dim(self) -> int:
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"""Calculates output dimension of encoding.
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Returns:
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int: output dimension
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"""
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if not hasattr(self, "out_dim"):
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raise ValueError("Output dimension has not been set")
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if self.out_dim <= 0:
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raise ValueError("Output dimension should be greater than zero")
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return self.out_dim
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@abstractmethod
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def forward(self, in_tensor: TensorType[..., "input_dim"]) -> TensorType[..., "output_dim"]:
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"""_summary_
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Args:
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x (TensorType[..., "input_dim"]): Input tensor to be encoded
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Raises:
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NotImplementedError: _description_
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Returns:
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TensorType[..., "output_dim"]: A encoded input tensor
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"""
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raise NotImplementedError
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