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This commit is contained in:
ndem0
2024-02-09 11:25:00 +00:00
committed by Nicola Demo
parent 591aeeb02b
commit cbb43a5392
64 changed files with 1323 additions and 955 deletions
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92
pina/model/feed_forward.py
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@@ -1,4 +1,5 @@
"""Module for FeedForward model"""
import torch
import torch.nn as nn
from ..utils import check_consistency
@@ -29,24 +30,25 @@ class FeedForward(torch.nn.Module):
:param bool bias: If ``True`` the MLP will consider some bias.
"""
def __init__(self,
input_dimensions,
output_dimensions,
inner_size=20,
n_layers=2,
func=nn.Tanh,
layers=None,
bias=True):
"""
"""
def __init__(
self,
input_dimensions,
output_dimensions,
inner_size=20,
n_layers=2,
func=nn.Tanh,
layers=None,
bias=True,
):
""" """
super().__init__()
if not isinstance(input_dimensions, int):
raise ValueError('input_dimensions expected to be int.')
raise ValueError("input_dimensions expected to be int.")
self.input_dimension = input_dimensions
if not isinstance(output_dimensions, int):
raise ValueError('output_dimensions expected to be int.')
raise ValueError("output_dimensions expected to be int.")
self.output_dimension = output_dimensions
if layers is None:
layers = [inner_size] * n_layers
@@ -58,7 +60,8 @@ class FeedForward(torch.nn.Module):
self.layers = []
for i in range(len(tmp_layers) - 1):
self.layers.append(
nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias))
nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias)
)
if isinstance(func, list):
self.functions = func
@@ -66,7 +69,7 @@ class FeedForward(torch.nn.Module):
self.functions = [func for _ in range(len(self.layers) - 1)]
if len(self.layers) != len(self.functions) + 1:
raise RuntimeError('uncosistent number of layers and functions')
raise RuntimeError("uncosistent number of layers and functions")
unique_list = []
for layer, func in zip(self.layers[:-1], self.functions):
@@ -97,7 +100,7 @@ class ResidualFeedForward(torch.nn.Module):
.. seealso::
**Original reference**: Wang, Sifan, Yujun Teng, and Paris Perdikaris.
**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
@@ -124,16 +127,17 @@ class ResidualFeedForward(torch.nn.Module):
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):
"""
"""
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
@@ -148,35 +152,42 @@ class ResidualFeedForward(torch.nn.Module):
if transformer_nets is None:
transformer_nets = [
EnhancedLinear(
nn.Linear(in_features=input_dimensions,
out_features=inner_size), nn.Tanh()),
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())
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.')
"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.'
"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.'
"transformer network input incompatible with input_dimensions."
)
if out.shape[-1] != inner_size:
raise ValueError(
'transformer network output incompatible with inner_size.'
"transformer network output incompatible with inner_size."
)
else:
RuntimeError(
'Runtime error for transformer nets, check official documentation.'
"Runtime error for transformer nets, check official documentation."
)
# assign variables
@@ -193,10 +204,11 @@ class ResidualFeedForward(torch.nn.Module):
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)
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()
@@ -204,7 +216,7 @@ class ResidualFeedForward(torch.nn.Module):
self.functions = [func() for _ in range(len(self.layers))]
if len(self.layers) != len(self.functions):
raise RuntimeError('uncosistent number of layers and functions')
raise RuntimeError("uncosistent number of layers and functions")
unique_list = []
for layer, func in zip(self.layers, self.functions):
@@ -228,7 +240,7 @@ class ResidualFeedForward(torch.nn.Module):
# skip connections pass
for layer in self.inner_layers.children():
x = layer(x)
x = (1. - x) * input_[0] + x * input_[1]
x = (1.0 - x) * input_[0] + x * input_[1]
# last layer
return self.last_layer(x)