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fnn update, pinn torch models, tests update. (#88)

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* fnn update, remove labeltensors
* allow custom torch models
* updating tests

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
Co-authored-by: Dario Coscia <dariocoscia@dhcp-031.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-05-02 15:19:48 +02:00
committed by Nicola Demo
parent c8fb7715c4
commit be11110bb2
11 changed files with 149 additions and 177 deletions
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59
pina/model/feed_forward.py
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@@ -10,10 +10,12 @@ class FeedForward(torch.nn.Module):
The PINA implementation of feedforward network, also refered as multilayer
perceptron.
:param list(str) input_variables: the list containing the labels
corresponding to the input components of the model.
:param list(str) output_variables: the list containing the labels
corresponding to the components of the output computed by the model.
:param int input_variables: The number of input components of the model.
Expected tensor shape of the form (*, input_variables), where *
means any number of dimensions including none.
:param int output_variables: The number of output components of the model.
Expected tensor shape of the form (*, output_variables), 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.
@@ -24,46 +26,31 @@ class FeedForward(torch.nn.Module):
:param iterable(int) layers: a list containing the number of neurons for
any hidden layers. If specified, the parameters `n_layers` e
`inner_size` are not considered.
:param iterable(torch.nn.Module) extra_features: the additional input
features to use ad augmented input.
:param bool bias: If `True` the MLP will consider some bias.
"""
def __init__(self, input_variables, output_variables, inner_size=20,
n_layers=2, func=nn.Tanh, layers=None, extra_features=None,
bias=True):
n_layers=2, func=nn.Tanh, layers=None, bias=True):
"""
"""
super().__init__()
if extra_features is None:
extra_features = []
self.extra_features = nn.Sequential(*extra_features)
if isinstance(input_variables, int):
self.input_variables = None
self.input_dimension = input_variables
elif isinstance(input_variables, (tuple, list)):
self.input_variables = input_variables
self.input_dimension = len(input_variables)
if isinstance(output_variables, int):
self.output_variables = None
self.output_dimension = output_variables
elif isinstance(output_variables, (tuple, list)):
self.output_variables = output_variables
self.output_dimension = len(output_variables)
n_features = len(extra_features)
if not isinstance(input_variables, int):
raise ValueError('input_variables expected to be int.')
self.input_dimension = input_variables
if not isinstance(output_variables, int):
raise ValueError('output_variables expected to be int.')
self.output_dimension = output_variables
if layers is None:
layers = [inner_size] * n_layers
tmp_layers = layers.copy()
tmp_layers.insert(0, self.input_dimension+n_features)
tmp_layers.insert(0, self.input_dimension)
tmp_layers.append(self.output_dimension)
self.layers = []
for i in range(len(tmp_layers)-1):
for i in range(len(tmp_layers) - 1):
self.layers.append(
nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias)
)
@@ -71,7 +58,7 @@ class FeedForward(torch.nn.Module):
if isinstance(func, list):
self.functions = func
else:
self.functions = [func for _ in range(len(self.layers)-1)]
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')
@@ -94,16 +81,4 @@ class FeedForward(torch.nn.Module):
:return: the output computed by the model.
:rtype: LabelTensor
"""
if self.input_variables:
x = x.extract(self.input_variables)
for feature in self.extra_features:
x = x.append(feature(x))
output = self.model(x).as_subclass(LabelTensor)
if self.output_variables:
output.labels = self.output_variables
return output
return self.model(x)