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New Residual Model and Fix relative import

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* Adding Residual MLP
* Adding test Residual MLP
* Modified relative import Continuous Conv
This commit is contained in:
Dario Coscia
2023-09-13 12:45:22 +02:00
committed by Nicola Demo
parent ba7371f350
commit 17464ceca9
7 changed files with 211 additions and 9 deletions
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133
pina/model/feed_forward.py
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@@ -1,6 +1,8 @@
"""Module for FeedForward model"""
import torch
import torch.nn as nn
from ..utils import check_consistency
from .layers.residual import EnhancedLinear
class FeedForward(torch.nn.Module):
@@ -8,8 +10,8 @@ class FeedForward(torch.nn.Module):
The PINA implementation of feedforward network, also refered as multilayer
perceptron.
:param int input_dimensons: The number of input components of the model.
Expected tensor shape of the form (*, input_dimensons), where *
:param int input_dimensions: The number of input components of the model.
Expected tensor shape of the form (*, input_dimensions), where *
means any number of dimensions including none.
:param int output_dimensions: The number of output components of the model.
Expected tensor shape of the form (*, output_dimensions), where *
@@ -80,3 +82,130 @@ class FeedForward(torch.nn.Module):
:rtype: LabelTensor
"""
return self.model(x)
class ResidualFeedForward(torch.nn.Module):
"""
The PINA implementation of feedforward network, also with skipped connection
and transformer network, as presented in **Understanding and mitigating gradient
pathologies in physics-informed neural networks**
.. seealso::
**Original reference**: Wang, Sifan, Yujun Teng, and Paris Perdikaris.
"Understanding and mitigating gradient flow pathologies in physics-informed
neural networks." SIAM Journal on Scientific Computing 43.5 (2021): A3055-A3081.
DOI: `10.1137/20M1318043
<https://epubs.siam.org/doi/abs/10.1137/20M1318043>`_
:param int input_dimensions: The number of input components of the model.
Expected tensor shape of the form (*, input_dimensions), where *
means any number of dimensions including none.
:param int output_dimensions: The number of output components of the model.
Expected tensor shape of the form (*, output_dimensions), where *
means any number of dimensions including none.
:param int inner_size: number of neurons in the hidden layer(s). Default is
20.
:param int n_layers: number of hidden layers. Default is 2.
:param func: the activation function to use. If a single
:class:`torch.nn.Module` is passed, this is used as activation function
after any layers, except the last one. If a list of Modules is passed,
they are used as activation functions at any layers, in order.
:param bool bias: If `True` the MLP will consider some bias.
:param list | tuple transformer_nets: a list or tuple containing the two
torch.nn.Module which act as transformer network. The input dimension
of the network must be the same as ``input_dimensions``, and the output
dimension must be the same as ``inner_size``.
"""
def __init__(self, input_dimensions, output_dimensions, inner_size=20,
n_layers=2, func=nn.Tanh, bias=True, transformer_nets=None):
"""
"""
super().__init__()
# check type consistency
check_consistency(input_dimensions, int)
check_consistency(output_dimensions, int)
check_consistency(inner_size, int)
check_consistency(n_layers, int)
check_consistency(func, torch.nn.Module, subclass=True)
check_consistency(bias, bool)
# check transformer nets
if transformer_nets is None:
transformer_nets = [
EnhancedLinear(nn.Linear(in_features=input_dimensions, out_features=inner_size),
nn.Tanh()),
EnhancedLinear(nn.Linear(in_features=input_dimensions, out_features=inner_size),
nn.Tanh())
]
elif isinstance(transformer_nets, (list, tuple)):
if len(transformer_nets) != 2:
raise ValueError('transformer_nets needs to be a list of len two.')
for net in transformer_nets:
if not isinstance(net, nn.Module):
raise ValueError('transformer_nets needs to be a list of torch.nn.Module.')
x = torch.rand(10, input_dimensions)
try:
out = net(x)
except RuntimeError:
raise ValueError('transformer network input incompatible with input_dimensions.')
if out.shape[-1] != inner_size:
raise ValueError('transformer network output incompatible with inner_size.')
else:
RuntimeError('Runtime error for transformer nets, check official documentation.')
# assign variables
self.input_dimension = input_dimensions
self.output_dimension = output_dimensions
self.transformer_nets = nn.ModuleList(transformer_nets)
# build layers
layers = [inner_size] * n_layers
tmp_layers = layers.copy()
tmp_layers.insert(0, self.input_dimension)
self.layers = []
for i in range(len(tmp_layers) - 1):
self.layers.append(
nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias)
)
self.last_layer = nn.Linear(tmp_layers[len(tmp_layers) - 1], output_dimensions, bias=bias)
if isinstance(func, list):
self.functions = func()
else:
self.functions = [func() for _ in range(len(self.layers))]
if len(self.layers) != len(self.functions):
raise RuntimeError('uncosistent number of layers and functions')
unique_list = []
for layer, func in zip(self.layers, self.functions):
unique_list.append(EnhancedLinear(layer=layer,
activation=func))
self.inner_layers = torch.nn.Sequential(*unique_list)
def forward(self, x):
"""
Defines the computation performed at every call.
:param x: .
:type x: :class:`pina.LabelTensor`
:return: the output computed by the model.
:rtype: LabelTensor
"""
# enhance the input with transformer
input_ = []
for nets in self.transformer_nets:
input_.append(nets(x))
# skip connections pass
for layer in self.inner_layers.children():
x = layer(x)
x = (1. - x) * input_[0] + x * input_[1]
# last layer
return self.last_layer(x)