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Documentation for v0.1 version (#199)

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* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-11-08 14:39:00 +01:00
committed by Nicola Demo
parent 3f9305d475
commit 8b7b61b3bd
144 changed files with 2741 additions and 1766 deletions
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162
pina/model/deeponet.py
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@@ -17,15 +17,16 @@ class MIONet(torch.nn.Module):
.. seealso::
**Original reference**: Jin, Pengzhan, Shuai Meng, and Lu Lu.
"MIONet: Learning multiple-input operators via tensor product."
*MIONet: Learning multiple-input operators via tensor product.*
SIAM Journal on Scientific Computing 44.6 (2022): A3490-A351
DOI: `10.1137/22M1477751
<https://doi.org/10.1137/22M1477751>`_
"""
def __init__(self,
networks,
aggregator="*",
aggregator="*",
reduction="+",
scale=True,
translation=True):
@@ -35,28 +36,27 @@ class MIONet(torch.nn.Module):
as value the list of indeces to extract from the input variable
in the forward pass of the neural network. If a list of ``int`` is passed,
the corresponding columns of the inner most entries are extracted.
If a list of ``str`` is passed the variables of the corresponding :class:`LabelTensor`
If a list of ``str`` is passed the variables of the corresponding :py:obj:`pina.label_tensor.LabelTensor`
are extracted. The ``torch.nn.Module`` model has to take as input a
:class:`LabelTensor` or :class:`torch.Tensor`. Default implementation consist of different
branch nets and one trunk net.
:param str | callable aggregator: Aggregator to be used to aggregate
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`.
Default implementation consist of different branch nets and one trunk nets.
:param str or Callable aggregator: Aggregator to be used to aggregate
partial results from the modules in `nets`. Partial results are
aggregated component-wise. See
:func:`pina.model.deeponet.MIONet._symbol_functions` for the
available default aggregators.
:param str | callable reduction: Reduction to be used to reduce
aggregated component-wise. Available aggregators include
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``.
:param str or Callable reduction: Reduction to be used to reduce
the aggregated result of the modules in `nets` to the desired output
dimension. See :py:obj:`pina.model.deeponet.MIONet._symbol_functions`
for the available default reductions.
:param bool | callable scale: Scaling the final output before returning the
forward pass, default True.
:param bool | callable translation: Translating the final output before
returning the forward pass, default True.
dimension. Available reductions include
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``.
:param bool or Callable scale: Scaling the final output before returning the
forward pass, default ``True``.
:param bool or Callable translation: Translating the final output before
returning the forward pass, default ``True``.
.. warning::
In the forward pass we do not check if the input is instance of
:class:`LabelTensor` or :class:`torch.Tensor`. A general rule is
that for a :class:`LabelTensor` input both list of integers and
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`. A general rule is
that for a :py:obj:`pina.label_tensor.LabelTensor` input both list of integers and
list of strings can be passed for ``input_indeces_branch_net``
and ``input_indeces_trunk_net``. Differently, for a :class:`torch.Tensor`
only a list of integers can be passed for ``input_indeces_branch_net``
@@ -133,8 +133,10 @@ class MIONet(torch.nn.Module):
self._init_reduction(reduction=reduction)
# scale and translation
self._scale = torch.nn.Parameter(torch.tensor([1.0])) if scale else torch.tensor([1.0])
self._trasl = torch.nn.Parameter(torch.tensor([1.0])) if translation else torch.tensor([1.0])
self._scale = torch.nn.Parameter(torch.tensor(
[1.0])) if scale else torch.tensor([1.0])
self._trasl = torch.nn.Parameter(torch.tensor(
[1.0])) if translation else torch.tensor([1.0])
@staticmethod
def _symbol_functions(**kwargs):
@@ -149,7 +151,7 @@ class MIONet(torch.nn.Module):
"min": lambda x: torch.min(x, **kwargs).values,
"max": lambda x: torch.max(x, **kwargs).values,
}
def _init_aggregator(self, aggregator):
aggregator_funcs = DeepONet._symbol_functions(dim=2)
if aggregator in aggregator_funcs:
@@ -161,7 +163,6 @@ class MIONet(torch.nn.Module):
self._aggregator = aggregator_func
def _init_reduction(self, reduction):
reduction_funcs = DeepONet._symbol_functions(dim=-1)
if reduction in reduction_funcs:
@@ -178,27 +179,32 @@ class MIONet(torch.nn.Module):
try:
return x.extract(indeces)
except AttributeError:
raise RuntimeError('Not possible to extract input variables from tensor.'
' Ensure that the passed tensor is a LabelTensor or'
' pass list of integers to extract variables. For'
' more information refer to warning in the documentation.')
raise RuntimeError(
'Not possible to extract input variables from tensor.'
' Ensure that the passed tensor is a LabelTensor or'
' pass list of integers to extract variables. For'
' more information refer to warning in the documentation.')
elif isinstance(indeces[0], int):
return x[..., indeces]
else:
raise RuntimeError('Not able to extract right indeces for tensor.'
' For more information refer to warning in the documentation.')
raise RuntimeError(
'Not able to extract right indeces for tensor.'
' For more information refer to warning in the documentation.')
def forward(self, x):
"""
Defines the computation performed at every call.
:param LabelTensor | torch.Tensor x: The input tensor for the forward call.
:param LabelTensor or torch.Tensor x: The input tensor for the forward call.
:return: The output computed by the DeepONet model.
:rtype: LabelTensor | torch.Tensor
:rtype: LabelTensor or torch.Tensor
"""
# forward pass
output_ = [model(self._get_vars(x, indeces)) for model, indeces in zip(self.models, self._indeces)]
output_ = [
model(self._get_vars(x, indeces))
for model, indeces in zip(self.models, self._indeces)
]
# aggregation
aggregated = self._aggregator(torch.dstack(output_))
@@ -206,7 +212,7 @@ class MIONet(torch.nn.Module):
# reduce
output_ = self._reduction(aggregated).reshape(-1, 1)
# scale and translate
# scale and translate
output_ *= self._scale
output_ += self._trasl
@@ -218,7 +224,7 @@ class MIONet(torch.nn.Module):
The aggregator function.
"""
return self._aggregator
@property
def reduction(self):
"""
@@ -232,28 +238,28 @@ class MIONet(torch.nn.Module):
The scale factor.
"""
return self._scale
@property
def translation(self):
"""
The translation factor for MIONet.
"""
return self._trasl
@property
def indeces_variables_extracted(self):
"""
The input indeces for each model in form of list.
"""
return self._indeces
@property
def model(self):
"""
The models in form of list.
"""
return self._indeces
class DeepONet(MIONet):
"""
@@ -273,52 +279,52 @@ class DeepONet(MIONet):
<https://doi.org/10.1038/s42256-021-00302-5>`_
"""
def __init__(self,
branch_net,
trunk_net,
input_indeces_branch_net,
input_indeces_trunk_net,
aggregator="*",
aggregator="*",
reduction="+",
scale=True,
translation=True):
"""
:param torch.nn.Module branch_net: The neural network to use as branch
model. It has to take as input a :class:`LabelTensor`
model. It has to take as input a :py:obj:`pina.label_tensor.LabelTensor`
or :class:`torch.Tensor`. The number of dimensions of the output has
to be the same of the ``trunk_net``.
:param torch.nn.Module trunk_net: The neural network to use as trunk
model. It has to take as input a :class:`LabelTensor`
model. It has to take as input a :py:obj:`pina.label_tensor.LabelTensor`
or :class:`torch.Tensor`. The number of dimensions of the output
has to be the same of the ``branch_net``.
:param list(int) | list(str) input_indeces_branch_net: List of indeces
:param list(int) or list(str) input_indeces_branch_net: List of indeces
to extract from the input variable in the forward pass for the
branch net. If a list of ``int`` is passed, the corresponding columns
of the inner most entries are extracted. If a list of ``str`` is passed
the variables of the corresponding :class:`LabelTensor` are extracted.
:param list(int) | list(str) input_indeces_trunk_net: List of indeces
the variables of the corresponding :py:obj:`pina.label_tensor.LabelTensor` are extracted.
:param list(int) or list(str) input_indeces_trunk_net: List of indeces
to extract from the input variable in the forward pass for the
trunk net. If a list of ``int`` is passed, the corresponding columns
of the inner most entries are extracted. If a list of ``str`` is passed
the variables of the corresponding :class:`LabelTensor` are extracted.
:param str | callable aggregator: Aggregator to be used to aggregate
the variables of the corresponding :py:obj:`pina.label_tensor.LabelTensor` are extracted.
:param str or Callable aggregator: Aggregator to be used to aggregate
partial results from the modules in `nets`. Partial results are
aggregated component-wise. See
:func:`pina.model.deeponet.MIONet._symbol_functions` for the
available default aggregators.
:param str | callable reduction: Reduction to be used to reduce
aggregated component-wise. Available aggregators include
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``.
:param str or Callable reduction: Reduction to be used to reduce
the aggregated result of the modules in `nets` to the desired output
dimension. See :py:obj:`pina.model.deeponet.MIONet._symbol_functions` for the available default
reductions.
:param bool | callable scale: Scaling the final output before returning the
dimension. Available reductions include
sum: ``+``, product: ``*``, mean: ``mean``, min: ``min``, max: ``max``.
:param bool or Callable scale: Scaling the final output before returning the
forward pass, default True.
:param bool | callable translation: Translating the final output before
:param bool or Callable translation: Translating the final output before
returning the forward pass, default True.
.. warning::
In the forward pass we do not check if the input is instance of
:class:`LabelTensor` or :class:`torch.Tensor`. A general rule is
that for a :class:`LabelTensor` input both list of integers and
:py:obj:`pina.label_tensor.LabelTensor` or :class:`torch.Tensor`. A general rule is
that for a :py:obj:`pina.label_tensor.LabelTensor` input both list of integers and
list of strings can be passed for ``input_indeces_branch_net``
and ``input_indeces_trunk_net``. Differently, for a :class:`torch.Tensor`
only a list of integers can be passed for ``input_indeces_branch_net``
@@ -355,24 +361,38 @@ class DeepONet(MIONet):
)
)
"""
networks = {branch_net : input_indeces_branch_net,
trunk_net : input_indeces_trunk_net}
networks = {
branch_net: input_indeces_branch_net,
trunk_net: input_indeces_trunk_net
}
super().__init__(networks=networks,
aggregator=aggregator,
reduction=reduction,
scale=scale,
translation=translation)
@property
def branch_net(self):
"""
The branch net for DeepONet.
"""
return self.models[0]
@property
def trunk_net(self):
"""
The trunk net for DeepONet.
"""
return self.models[1]
def forward(self, x):
"""
Defines the computation performed at every call.
:param LabelTensor or torch.Tensor x: The input tensor for the forward call.
:return: The output computed by the DeepONet model.
:rtype: LabelTensor or torch.Tensor
"""
return super().forward(x)
@property
def branch_net(self):
"""
The branch net for DeepONet.
"""
return self.models[0]
@property
def trunk_net(self):
"""
The trunk net for DeepONet.
"""
return self.models[1]