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Filippo0.2 (#361)

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* Add summation and remove deepcopy (only for tensors) in LabelTensor class
* Update operators for compatibility with updated LabelTensor implementation
* Implement labels.setter in LabelTensor class
* Update LabelTensor

---------

Co-authored-by: FilippoOlivo <filippo@filippoolivo.com>
This commit is contained in:
Dario Coscia
2024-10-04 15:59:09 +02:00
committed by Nicola Demo
parent 1d3df2a127
commit fdb8f65143
4 changed files with 212 additions and 92 deletions
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114
pina/operators.py
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@@ -1,13 +1,13 @@
"""
Module for operators vectorize implementation. Differential operators are used to write any differential problem.
These operators are implemented to work on different accellerators: CPU, GPU, TPU or MPS.
These operators are implemented to work on different accelerators: CPU, GPU, TPU or MPS.
All operators take as input a tensor onto which computing the operator, a tensor with respect
to which computing the operator, the name of the output variables to calculate the operator
for (in case of multidimensional functions), and the variables name on which the operator is calculated.
"""
import torch
from copy import deepcopy
from pina.label_tensor import LabelTensor
@@ -49,12 +49,12 @@ def grad(output_, input_, components=None, d=None):
:rtype: LabelTensor
"""
if len(output_.labels) != 1:
if len(output_.labels[output_.tensor.ndim-1]['dof']) != 1:
raise RuntimeError("only scalar function can be differentiated")
if not all([di in input_.labels for di in d]):
if not all([di in input_.labels[input_.tensor.ndim-1]['dof'] for di in d]):
raise RuntimeError("derivative labels missing from input tensor")
output_fieldname = output_.labels[0]
output_fieldname = output_.labels[output_.ndim-1]['dof'][0]
gradients = torch.autograd.grad(
output_,
input_,
@@ -65,41 +65,35 @@ def grad(output_, input_, components=None, d=None):
retain_graph=True,
allow_unused=True,
)[0]
gradients.labels = input_.labels
new_labels = deepcopy(input_.labels)
gradients.labels = new_labels
gradients = gradients.extract(d)
gradients.labels = [f"d{output_fieldname}d{i}" for i in d]
new_labels[input_.tensor.ndim - 1]['dof'] = [f"d{output_fieldname}d{i}" for i in d]
gradients.labels = new_labels
return gradients
if not isinstance(input_, LabelTensor):
raise TypeError
if d is None:
d = input_.labels
d = input_.labels[input_.tensor.ndim-1]['dof']
if components is None:
components = output_.labels
components = output_.labels[output_.tensor.ndim-1]['dof']
if output_.shape[1] == 1: # scalar output ################################
if output_.shape[output_.ndim-1] == 1: # scalar output ################################
if components != output_.labels:
if components != output_.labels[output_.tensor.ndim-1]['dof']:
raise RuntimeError
gradients = grad_scalar_output(output_, input_, d)
elif output_.shape[1] >= 2: # vector output ##############################
elif output_.shape[output_.ndim-1] >= 2: # vector output ##############################
tensor_to_cat = []
for i, c in enumerate(components):
c_output = output_.extract([c])
if i == 0:
gradients = grad_scalar_output(c_output, input_, d)
else:
gradients = gradients.append(
grad_scalar_output(c_output, input_, d)
)
tensor_to_cat.append(grad_scalar_output(c_output, input_, d))
gradients = LabelTensor.cat(tensor_to_cat, dim=output_.tensor.ndim-1)
else:
raise NotImplementedError
return gradients
@@ -130,27 +124,29 @@ def div(output_, input_, components=None, d=None):
raise TypeError
if d is None:
d = input_.labels
d = input_.labels[input_.tensor.ndim-1]['dof']
if components is None:
components = output_.labels
components = output_.labels[output_.tensor.ndim-1]['dof']
if output_.shape[1] < 2 or len(components) < 2:
if output_.shape[output_.ndim-1] < 2 or len(components) < 2:
raise ValueError("div supported only for vector fields")
if len(components) != len(d):
raise ValueError
grad_output = grad(output_, input_, components, d)
div = torch.zeros(input_.shape[0], 1, device=output_.device)
labels = [None] * len(components)
last_dim_dof = [None] * len(components)
to_sum_tensors = []
for i, (c, d) in enumerate(zip(components, d)):
c_fields = f"d{c}d{d}"
div[:, 0] += grad_output.extract(c_fields).sum(axis=1)
labels[i] = c_fields
last_dim_dof[i] = c_fields
to_sum_tensors.append(grad_output.extract(c_fields))
div = div.as_subclass(LabelTensor)
div.labels = ["+".join(labels)]
div = LabelTensor.summation(to_sum_tensors)
new_labels = deepcopy(input_.labels)
new_labels[input_.tensor.ndim-1]['dof'] = ["+".join(last_dim_dof)]
div.labels = new_labels
return div
@@ -205,10 +201,10 @@ def laplacian(output_, input_, components=None, d=None, method="std"):
return result
if d is None:
d = input_.labels
d = input_.labels[input_.tensor.ndim-1]['dof']
if components is None:
components = output_.labels
components = output_.labels[output_.tensor.ndim-1]['dof']
if method == "divgrad":
raise NotImplementedError("divgrad not implemented as method")
@@ -218,25 +214,43 @@ def laplacian(output_, input_, components=None, d=None, method="std"):
elif method == "std":
if len(components) == 1:
result = scalar_laplace(output_, input_, components, d)
# result = scalar_laplace(output_, input_, components, d) # TODO check (from 0.1)
grad_output = grad(output_, input_, components=components, d=d)
to_append_tensors = []
for i, label in enumerate(grad_output.labels[grad_output.ndim-1]['dof']):
gg = grad(grad_output, input_, d=d, components=[label])
to_append_tensors.append(gg.extract([gg.labels[gg.tensor.ndim-1]['dof'][i]]))
labels = [f"dd{components[0]}"]
result = LabelTensor.summation(tensors=to_append_tensors)
result.labels = labels
else:
result = torch.empty(
size=(input_.shape[0], len(components)),
dtype=output_.dtype,
device=output_.device,
)
labels = [None] * len(components)
for idx, c in enumerate(components):
result[:, idx] = scalar_laplace(output_, input_, c, d).flatten()
labels[idx] = f"dd{c}"
# result = torch.empty( # TODO check (from 0.1)
# size=(input_.shape[0], len(components)),
# dtype=output_.dtype,
# device=output_.device,
# )
# labels = [None] * len(components)
# for idx, c in enumerate(components):
# result[:, idx] = scalar_laplace(output_, input_, c, d).flatten()
# labels[idx] = f"dd{c}"
result = result.as_subclass(LabelTensor)
result.labels = labels
# result = result.as_subclass(LabelTensor)
# result.labels = labels
labels = [None] * len(components)
to_append_tensors = [None] * len(components)
for idx, (ci, di) in enumerate(zip(components, d)):
if not isinstance(ci, list):
ci = [ci]
if not isinstance(di, list):
di = [di]
grad_output = grad(output_, input_, components=ci, d=di)
to_append_tensors[idx] = grad(grad_output, input_, d=di)
labels[idx] = f"dd{ci[0]}dd{di[0]}"
result = LabelTensor.cat(tensors=to_append_tensors, dim=output_.tensor.ndim-1)
result.labels = labels
return result
# TODO Fix advection operator
def advection(output_, input_, velocity_field, components=None, d=None):
"""
Perform advection operation. The operator works for vectorial functions,
@@ -258,10 +272,10 @@ def advection(output_, input_, velocity_field, components=None, d=None):
:rtype: LabelTensor
"""
if d is None:
d = input_.labels
d = input_.labels[input_.tensor.ndim-1]['dof']
if components is None:
components = output_.labels
components = output_.labels[output_.tensor.ndim-1]['dof']
tmp = (
grad(output_, input_, components, d)