259 lines
9.4 KiB
Python
259 lines
9.4 KiB
Python
"""Module for FeedForward model"""
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import torch
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from torch import nn
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from ..utils import check_consistency
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from .block.residual import EnhancedLinear
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class FeedForward(torch.nn.Module):
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"""
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The PINA implementation of feedforward network, also refered as multilayer
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perceptron.
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:param int input_dimensions: The number of input components of the model.
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Expected tensor shape of the form :math:`(*, d)`, where *
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means any number of dimensions including none, and :math:`d` the
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``input_dimensions``.
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:param int output_dimensions: The number of output components of the model.
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Expected tensor shape of the form :math:`(*, d)`, where *
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means any number of dimensions including none, and :math:`d` the
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``output_dimensions``.
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:param int inner_size: number of neurons in the hidden layer(s). Default is
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20.
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:param int n_layers: number of hidden layers. Default is 2.
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:param torch.nn.Module func: the activation function to use. If a single
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:class:`torch.nn.Module` is passed, this is used as activation function
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after any layers, except the last one. If a list of Modules is passed,
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they are used as activation functions at any layers, in order.
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:param list(int) | tuple(int) layers: a list containing the number of
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neurons for any hidden layers. If specified, the parameters ``n_layers``
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and ``inner_size`` are not considered.
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:param bool bias: If ``True`` the MLP will consider some bias.
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"""
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def __init__(
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self,
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input_dimensions,
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output_dimensions,
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inner_size=20,
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n_layers=2,
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func=nn.Tanh,
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layers=None,
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bias=True,
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):
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""" """
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super().__init__()
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if not isinstance(input_dimensions, int):
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raise ValueError("input_dimensions expected to be int.")
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self.input_dimension = input_dimensions
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if not isinstance(output_dimensions, int):
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raise ValueError("output_dimensions expected to be int.")
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self.output_dimension = output_dimensions
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if layers is None:
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layers = [inner_size] * n_layers
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tmp_layers = layers.copy()
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tmp_layers.insert(0, self.input_dimension)
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tmp_layers.append(self.output_dimension)
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self.layers = []
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for i in range(len(tmp_layers) - 1):
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self.layers.append(
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nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias)
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)
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if isinstance(func, list):
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self.functions = func
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else:
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self.functions = [func for _ in range(len(self.layers) - 1)]
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if len(self.layers) != len(self.functions) + 1:
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raise RuntimeError("uncosistent number of layers and functions")
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unique_list = []
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for layer, func_ in zip(self.layers[:-1], self.functions):
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unique_list.append(layer)
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if func_ is not None:
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unique_list.append(func_())
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unique_list.append(self.layers[-1])
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self.model = nn.Sequential(*unique_list)
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def forward(self, x):
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"""
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Defines the computation performed at every call.
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:param x: The tensor to apply the forward pass.
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:type x: torch.Tensor
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:return: the output computed by the model.
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:rtype: torch.Tensor
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"""
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return self.model(x)
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class ResidualFeedForward(torch.nn.Module):
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"""
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The PINA implementation of feedforward network, also with skipped connection
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and transformer network, as presented in **Understanding and mitigating
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gradient pathologies in physics-informed neural networks**
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.. seealso::
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**Original reference**: Wang, Sifan, Yujun Teng, and Paris Perdikaris.
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*Understanding and mitigating gradient flow pathologies in
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physics-informed neural networks*. SIAM Journal on Scientific Computing
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43.5 (2021): A3055-A3081.
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DOI: `10.1137/20M1318043
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<https://epubs.siam.org/doi/abs/10.1137/20M1318043>`_
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:param int input_dimensions: The number of input components of the model.
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Expected tensor shape of the form :math:`(*, d)`, where *
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means any number of dimensions including none, and :math:`d` the
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``input_dimensions``.
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:param int output_dimensions: The number of output components of the model.
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Expected tensor shape of the form :math:`(*, d)`, where *
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means any number of dimensions including none, and :math:`d` the
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``output_dimensions``.
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:param int inner_size: number of neurons in the hidden layer(s). Default is
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20.
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:param int n_layers: number of hidden layers. Default is 2.
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:param torch.nn.Module func: the activation function to use. If a single
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:class:`torch.nn.Module` is passed, this is used as activation function
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after any layers, except the last one. If a list of Modules is passed,
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they are used as activation functions at any layers, in order.
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:param bool bias: If ``True`` the MLP will consider some bias.
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:param list | tuple transformer_nets: a list or tuple containing the two
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torch.nn.Module which act as transformer network. The input dimension
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of the network must be the same as ``input_dimensions``, and the output
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dimension must be the same as ``inner_size``.
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"""
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def __init__(
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self,
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input_dimensions,
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output_dimensions,
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inner_size=20,
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n_layers=2,
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func=nn.Tanh,
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bias=True,
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transformer_nets=None,
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):
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""" """
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super().__init__()
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# check type consistency
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check_consistency(input_dimensions, int)
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check_consistency(output_dimensions, int)
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check_consistency(inner_size, int)
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check_consistency(n_layers, int)
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check_consistency(func, torch.nn.Module, subclass=True)
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check_consistency(bias, bool)
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transformer_nets = self._check_transformer_nets(
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transformer_nets, input_dimensions, inner_size
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)
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# assign variables
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self.transformer_nets = nn.ModuleList(transformer_nets)
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# build layers
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layers = [inner_size] * n_layers
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layers = layers.copy()
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layers.insert(0, input_dimensions)
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self.layers = []
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for i in range(len(layers) - 1):
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self.layers.append(nn.Linear(layers[i], layers[i + 1], bias=bias))
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self.last_layer = nn.Linear(
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layers[len(layers) - 1], output_dimensions, bias=bias
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)
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if isinstance(func, list):
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self.functions = func()
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else:
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self.functions = [func() for _ in range(len(self.layers))]
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if len(self.layers) != len(self.functions):
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raise RuntimeError("uncosistent number of layers and functions")
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unique_list = []
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for layer, func_ in zip(self.layers, self.functions):
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unique_list.append(EnhancedLinear(layer=layer, activation=func_))
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self.inner_layers = torch.nn.Sequential(*unique_list)
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def forward(self, x):
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"""
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Defines the computation performed at every call.
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:param x: The tensor to apply the forward pass.
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:type x: torch.Tensor
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:return: the output computed by the model.
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:rtype: torch.Tensor
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"""
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# enhance the input with transformer
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input_ = []
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for nets in self.transformer_nets:
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input_.append(nets(x))
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# skip connections pass
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for layer in self.inner_layers.children():
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x = layer(x)
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x = (1.0 - x) * input_[0] + x * input_[1]
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# last layer
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return self.last_layer(x)
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@staticmethod
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def _check_transformer_nets(transformer_nets, input_dimensions, inner_size):
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# check transformer nets
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if transformer_nets is None:
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transformer_nets = [
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EnhancedLinear(
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nn.Linear(
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in_features=input_dimensions, out_features=inner_size
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),
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nn.Tanh(),
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),
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EnhancedLinear(
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nn.Linear(
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in_features=input_dimensions, out_features=inner_size
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),
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nn.Tanh(),
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),
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]
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elif isinstance(transformer_nets, (list, tuple)):
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if len(transformer_nets) != 2:
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raise ValueError(
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"transformer_nets needs to be a list of len two."
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)
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for net in transformer_nets:
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if not isinstance(net, nn.Module):
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raise ValueError(
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"transformer_nets needs to be a list of "
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"torch.nn.Module."
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)
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x = torch.rand(10, input_dimensions)
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try:
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out = net(x)
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except RuntimeError as e:
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raise ValueError(
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"transformer network input incompatible with "
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"input_dimensions."
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) from e
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if out.shape[-1] != inner_size:
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raise ValueError(
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"transformer network output incompatible with "
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"inner_size."
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)
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else:
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raise RuntimeError(
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"Runtime error for transformer nets, check official "
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"documentation."
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)
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return transformer_nets
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