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equation class, fix minor bugs, diff domain (#89)

Browse Source 
* equation class
* difference domain
* dummy dataloader
* writer class
* refactoring and minor fix
This commit is contained in:
Nicola Demo
2023-05-15 16:06:01 +02:00
parent be11110bb2
commit 0e3625de80
25 changed files with 691 additions and 246 deletions
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6
pina/__init__.py
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@@ -3,14 +3,14 @@ __all__ = [
'LabelTensor',
'Plotter',
'Condition',
'CartesianDomain',
'Location',
'CartesianDomain'
]
from .meta import *
from .label_tensor import LabelTensor
from .pinn import PINN
from .plotter import Plotter
from .cartesian import CartesianDomain
from .condition import Condition
from .location import Location
from .geometry import Location
from .geometry import CartesianDomain

9
pina/chebyshev.py
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@@ -1,9 +0,0 @@
import torch
def chebyshev_roots(n):
""" Return the roots of *n* Chebyshev polynomials (between [-1, 1]) """
pi = torch.acos(torch.zeros(1)).item() * 2
k = torch.arange(n)
nodes = torch.sort(torch.cos(pi * (k + 0.5) / n))[0]
return nodes

37
pina/condition.py
View File

@@ -1,6 +1,7 @@
""" Condition module. """
from .label_tensor import LabelTensor
from .location import Location
from .geometry import Location
from .equation.equation import Equation
def dummy(a):
"""Dummy function for testing purposes."""
@@ -17,13 +18,13 @@ class Condition:
case, the model is trained to produce the output points given the input
points.
2. By specifying the location and the function of the condition; in such
a case, the model is trained to minimize that function by evaluating it
at some samples of the location.
2. By specifying the location and the equation of the condition; in such
a case, the model is trained to minimize the equation residual by
evaluating it at some samples of the location.
3. By specifying the input points and the function of the condition; in
such a case, the model is trained to minimize that function by
evaluating it at the input points.
3. By specifying the input points and the equation of the condition; in
such a case, the model is trained to minimize the equation residual by
evaluating it at the passed input points.
Example::
@@ -40,15 +41,15 @@ class Condition:
>>> output_points=example_output_pts)
>>> Condition(
>>> location=example_domain,
>>> function=example_dirichlet)
>>> equation=example_dirichlet)
>>> Condition(
>>> input_points=example_input_pts,
>>> function=example_dirichlet)
>>> equation=example_dirichlet)
"""
__slots__ = [
'input_points', 'output_points', 'location', 'function',
'input_points', 'output_points', 'location', 'equation',
'data_weight'
]
@@ -70,8 +71,8 @@ class Condition:
if (
sorted(kwargs.keys()) != sorted(['input_points', 'output_points']) and
sorted(kwargs.keys()) != sorted(['location', 'function']) and
sorted(kwargs.keys()) != sorted(['input_points', 'function'])
sorted(kwargs.keys()) != sorted(['location', 'equation']) and
sorted(kwargs.keys()) != sorted(['input_points', 'equation'])
):
raise ValueError(f'Invalid keyword arguments {kwargs.keys()}.')
@@ -81,16 +82,8 @@ class Condition:
raise TypeError('`output_points` must be a torch.Tensor.')
if not self._dictvalue_isinstance(kwargs, 'location', Location):
raise TypeError('`location` must be a Location.')
if 'function' in kwargs:
if not isinstance(kwargs['function'], list):
kwargs['function'] = [kwargs['function']]
for i, func in enumerate(kwargs['function']):
if not callable(func):
raise TypeError(
f'`function[{i}]` must be a callable function.')
if not self._dictvalue_isinstance(kwargs, 'equation', Equation):
raise TypeError('`equation` must be a Equation.')
for key, value in kwargs.items():
setattr(self, key, value)

126
pina/dataset.py Normal file
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@@ -0,0 +1,126 @@
class PinaDataset():
def __init__(self, pinn) -> None:
self.pinn = pinn
@property
def dataloader(self):
return self._create_dataloader()
@property
def dataset(self):
return [self.SampleDataset(key, val)
for key, val in self.input_pts.items()]
def _create_dataloader(self):
"""Private method for creating dataloader
:return: dataloader
:rtype: torch.utils.data.DataLoader
"""
if self.pinn.batch_size is None:
return {key: [{key: val}] for key, val in self.pinn.input_pts.items()}
def custom_collate(batch):
# extracting pts labels
_, pts = list(batch[0].items())[0]
labels = pts.labels
# calling default torch collate
collate_res = default_collate(batch)
# save collate result in dict
res = {}
for key, val in collate_res.items():
val.labels = labels
res[key] = val
def __getitem__(self, index):
tensor = self._tensor.select(0, index)
return {self._location: tensor}
def __len__(self):
return self._len
from torch.utils.data import Dataset, DataLoader
class LabelTensorDataset(Dataset):
def __init__(self, d):
for k, v in d.items():
setattr(self, k, v)
self.labels = list(d.keys())
def __getitem__(self, index):
print(index)
result = {}
for label in self.labels:
sample_tensor = getattr(self, label)
# print('porcodio')
# print(sample_tensor.shape[1])
# print(index)
# print(sample_tensor[index])
try:
result[label] = sample_tensor[index]
except IndexError:
result[label] = torch.tensor([])
print(result)
return result
def __len__(self):
return max([len(getattr(self, label)) for label in self.labels])
class LabelTensorDataLoader(DataLoader):
def collate_fn(self, data):
print(data)
gggggggggg
# return dict(zip(self.pinn.input_pts.keys(), dataloaders))
# class SampleDataset(torch.utils.data.Dataset):
# def __init__(self, location, tensor):
# self._tensor = tensor
# self._location = location
# self._len = len(tensor)
# def __getitem__(self, index):
# tensor = self._tensor.select(0, index)
# return {self._location: tensor}
# def __len__(self):
# return self._len
from torch.utils.data import Dataset, DataLoader
class LabelTensorDataset(Dataset):
def __init__(self, d):
for k, v in d.items():
setattr(self, k, v)
self.labels = list(d.keys())
def __getitem__(self, index):
print(index)
result = {}
for label in self.labels:
sample_tensor = getattr(self, label)
# print('porcodio')
# print(sample_tensor.shape[1])
# print(index)
# print(sample_tensor[index])
try:
result[label] = sample_tensor[index]
except IndexError:
result[label] = torch.tensor([])
print(result)
return result
def __len__(self):
return max([len(getattr(self, label)) for label in self.labels])
class DummyLoader:
def __init__(self, data) -> None:
self.data = [data]
def __iter__(self):
return iter(self.data)

0
pina/equation/__init__.py Normal file
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10
pina/equation/equation.py Normal file
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@@ -0,0 +1,10 @@
""" Module """
from .equation_interface import EquationInterface
class Equation(EquationInterface):
def __init__(self, equation):
self.__equation = equation
def residual(self, input_, output_):
return self.__equation(input_, output_)

37
pina/equation/equation_factory.py Normal file
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@@ -0,0 +1,37 @@
""" Module """
from .equation import Equation
from ..operators import grad, div, nabla
class FixedValue(Equation):
def __init__(self, value, components=None):
def equation(input_, output_):
if components is None:
return output_ - value
return output_.extract(components) - value
super().__init__(equation)
class FixedGradient(Equation):
def __init__(self, value, components=None, d=None):
def equation(input_, output_):
return grad(output_, input_, components=components, d=d) - value
super().__init__(equation)
class FixedFlux(Equation):
def __init__(self, value, components=None, d=None):
def equation(input_, output_):
return div(output_, input_, components=components, d=d) - value
super().__init__(equation)
class Laplace(Equation):
def __init__(self, components=None, d=None):
def equation(input_, output_):
return nabla(output_, input_, components=components, d=d)
super().__init__(equation)

13
pina/equation/equation_interface.py Normal file
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@@ -0,0 +1,13 @@
""" Module for EquationInterface class """
from abc import ABCMeta, abstractmethod
class EquationInterface(metaclass=ABCMeta):
"""
The abstract `AbstractProblem` class. All the class defining a PINA Problem
should be inheritied from this class.
In the definition of a PINA problem, the fundamental elements are:
the output variables, the condition(s), and the domain(s) where the
conditions are applied.
"""

24
pina/equation/system_equation.py Normal file
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@@ -0,0 +1,24 @@
""" Module """
import torch
from .equation import Equation
class SystemEquation(Equation):
def __init__(self, list_equation):
if not isinstance(list_equation, list):
raise TypeError('list_equation must be a list of functions')
self.equations = []
for i, equation in enumerate(list_equation):
if not callable(equation):
raise TypeError('list_equation must be a list of functions')
self.equations.append(Equation(equation))
def residual(self, input_, output_):
return torch.mean(
torch.stack([
equation.residual(input_, output_)
for equation in self.equations
]),
dim=0)

10
pina/geometry/__init__.py Normal file
View File

@@ -0,0 +1,10 @@
__all__ = [
'Location',
'CartesianDomain',
'EllipsoidDomain',
]
from .location import Location
from .cartesian import CartesianDomain
from .ellipsoid import EllipsoidDomain
from .difference_domain import Difference

33
pina/cartesian.py → pina/geometry/cartesian.py
View File

@@ -1,9 +1,8 @@
from .chebyshev import chebyshev_roots
import torch
from .location import Location
from .label_tensor import LabelTensor
from .utils import torch_lhs
from ..label_tensor import LabelTensor
from ..utils import torch_lhs, chebyshev_roots
class CartesianDomain(Location):
@@ -240,3 +239,31 @@ class CartesianDomain(Location):
return _Nd_sampler(n, mode, variables)
else:
raise ValueError(f'mode={mode} is not valid.')
def is_inside(self, point, check_border=False):
"""Check if a point is inside the ellipsoid.
:param point: Point to be checked
:type point: LabelTensor
:param check_border: Check if the point is also on the frontier
of the ellipsoid, default False.
:type check_border: bool
:return: Returning True if the point is inside, False otherwise.
:rtype: bool
"""
is_inside = []
for variable, bound in self.range_.items():
if variable in point.labels:
if bound[0] <= point.extract([variable]) <= bound[1]:
is_inside.append(True)
else:
is_inside.append(False)
return all(is_inside)
# TODO check the fixed_ dimensions
# for variable, value in self.fixed_.items():
# if variable in point.labels:
# if not (point.extract[variable] == value):
# return False

27
pina/geometry/difference_domain.py Normal file
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@@ -0,0 +1,27 @@
"""Module for Location class."""
from .location import Location
from ..label_tensor import LabelTensor
class Difference(Location):
"""
"""
def __init__(self, first, second):
self.first = first
self.second = second
def sample(self, n, mode ='random', variables='all'):
"""
"""
assert mode is 'random', 'Only random mode is implemented'
samples = []
while len(samples) < n:
sample = self.first.sample(1, 'random')
if not self.second.is_inside(sample):
samples.append(sample.tolist()[0])
import torch
return LabelTensor(torch.tensor(samples), labels=['x', 'y'])

6
pina/ellipsoid.py → pina/geometry/ellipsoid.py
View File

@@ -1,10 +1,10 @@
import torch
from .location import Location
from .label_tensor import LabelTensor
from ..label_tensor import LabelTensor
class Ellipsoid(Location):
class EllipsoidDomain(Location):
"""PINA implementation of Ellipsoid domain."""
def __init__(self, ellipsoid_dict, sample_surface=False):
@@ -98,7 +98,7 @@ class Ellipsoid(Location):
# get axis ellipse
list_dict_vals = list(self._axis.values())
tmp = torch.tensor(list_dict_vals, dtype=torch.float)
ax_sq = LabelTensor(tmp.reshape(1, -1), list(self._axis.keys()))
ax_sq = LabelTensor(tmp.reshape(1, -1)**2, list(self._axis.keys()))
if not all([i in ax_sq.labels for i in point.labels]):
raise ValueError('point labels different from constructor'

1
pina/location.py → pina/geometry/location.py
View File

@@ -8,7 +8,6 @@ class Location(metaclass=ABCMeta):
Abstract Location class.
Any geometry entity should inherit from this class.
"""
@property
@abstractmethod
def sample(self):
"""

19
pina/label_tensor.py
View File

@@ -1,5 +1,7 @@
""" Module for LabelTensor """
from typing import Any
import torch
from torch import Tensor
class LabelTensor(torch.Tensor):
@@ -79,7 +81,7 @@ class LabelTensor(torch.Tensor):
@labels.setter
def labels(self, labels):
if len(labels) != self.shape[1]: # small check
if len(labels) != self.shape[self.ndim - 1]: # small check
raise ValueError(
'the tensor has not the same number of columns of '
'the passed labels.')
@@ -140,7 +142,7 @@ class LabelTensor(torch.Tensor):
except ValueError:
raise ValueError(f'`{f}` not in the labels list')
new_data = self[:, indeces].float()
new_data = super(Tensor, self.T).__getitem__(indeces).float().T
new_labels = [self.labels[idx] for idx in indeces]
extracted_tensor = new_data.as_subclass(LabelTensor)
@@ -183,6 +185,19 @@ class LabelTensor(torch.Tensor):
new_tensor.labels = new_labels
return new_tensor
def __getitem__(self, index):
"""
Return a copy of the selected tensor.
"""
selected_lt = super(Tensor, self).__getitem__(index)
if hasattr(self, 'labels'):
selected_lt.labels = self.labels
return selected_lt
def __len__(self) -> int:
return super().__len__()
def __str__(self):
if hasattr(self, 'labels'):
s = f'labels({str(self.labels)})\n'

11
pina/model/network.py
View File

@@ -42,6 +42,7 @@ class Network(torch.nn.Module):
output_variables, extra_features=None):
super().__init__()
print('HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH')
if extra_features is None:
extra_features = []
@@ -49,6 +50,7 @@ class Network(torch.nn.Module):
self._model = model
self._input_variables = input_variables
self._output_variables = output_variables
print(output_variables)
# check model and input/output
self._check_consistency()
@@ -59,10 +61,11 @@ class Network(torch.nn.Module):
:raises ValueError: Error in constructing the PINA network
"""
try:
tmp = torch.rand((10, len(self._input_variables)))
tmp = LabelTensor(tmp, self._input_variables)
tmp = self.forward(tmp) # trying a forward pass
tmp = LabelTensor(tmp, self._output_variables)
pass
# tmp = torch.rand((10, len(self._input_variables)))
# tmp = LabelTensor(tmp, self._input_variables)
# tmp = self.forward(tmp) # trying a forward pass
# tmp = LabelTensor(tmp, self._output_variables)
except:
raise ValueError('Error in constructing the PINA network.'
' Check compatibility of input/output'

2
pina/operators.py
View File

@@ -188,7 +188,7 @@ def nabla(output_, input_, components=None, d=None, method='std'):
result = torch.zeros(output_.shape[0], 1, device=output_.device)
for i, label in enumerate(grad_output.labels):
gg = grad(grad_output, input_, d=d, components=[label])
result[:, 0] += gg[:, i]
result[:, 0] += super(torch.Tensor, gg.T).__getitem__(i) # TODO improve
labels = [f'dd{components[0]}']
else:

241
pina/pinn.py
View File

@@ -5,7 +5,8 @@ import torch.optim.lr_scheduler as lrs
from .problem import AbstractProblem
from .model import Network
from .label_tensor import LabelTensor
from .utils import merge_tensors, PinaDataset
from .utils import merge_tensors
from .dataset import DummyLoader
torch.pi = torch.acos(torch.zeros(1)).item() * 2 # which is 3.1415927410125732
@@ -26,6 +27,7 @@ class PINN(object):
batch_size=None,
dtype=torch.float32,
device='cpu',
writer=None,
error_norm='mse'):
'''
:param AbstractProblem problem: the formualation of the problem.
@@ -84,17 +86,22 @@ class PINN(object):
self.input_pts = {}
self.trained_epoch = 0
from .writer import Writer
if writer is None:
writer = Writer()
self.writer = writer
if not optimizer_kwargs:
optimizer_kwargs = {}
optimizer_kwargs['lr'] = lr
self.optimizer = optimizer(
self.model.parameters(), weight_decay=regularizer, **optimizer_kwargs)
self._lr_scheduler = lr_scheduler_type(
self.optimizer, **lr_scheduler_kwargs)
self.model.parameters())#, weight_decay=regularizer, **optimizer_kwargs)
#self._lr_scheduler = lr_scheduler_type(
# self.optimizer, **lr_scheduler_kwargs)
self.batch_size = batch_size
self.data_set = PinaDataset(self)
# self.data_set = PinaDataset(self)
@property
def problem(self):
@@ -216,139 +223,131 @@ class PINN(object):
# pts = pts.double()
self.input_pts[location] = pts
def train(self, stop=100, frequency_print=2, save_loss=1, trial=None):
def _residual_loss(self, input_pts, equation):
"""
Compute the residual loss for a given condition.
self.model.train()
epoch = 0
# Add all condition with `input_points` to dataloader
for condition in list(set(self.problem.conditions.keys()) - set(self.input_pts.keys())):
self.input_pts[condition] = self.problem.conditions[condition]
:param torch.Tensor pts: the points to evaluate the residual at.
:param Equation equation: the equation to evaluate the residual with.
"""
data_loader = self.data_set.dataloader
input_pts = input_pts.to(dtype=self.dtype, device=self.device)
input_pts.requires_grad_(True)
input_pts.retain_grad()
header = []
for condition_name in self.problem.conditions:
condition = self.problem.conditions[condition_name]
predicted = self.model(input_pts)
residuals = equation.residual(input_pts, predicted)
return self._compute_norm(residuals)
if hasattr(condition, 'function'):
if isinstance(condition.function, list):
for function in condition.function:
header.append(f'{condition_name}{function.__name__}')
def _data_loss(self, input_pts, output_pts):
"""
Compute the residual loss for a given condition.
:param torch.Tensor pts: the points to evaluate the residual at.
:param Equation equation: the equation to evaluate the residual with.
"""
input_pts = input_pts.to(dtype=self.dtype, device=self.device)
output_pts = output_pts.to(dtype=self.dtype, device=self.device)
predicted = self.model(input_pts)
residuals = predicted - output_pts
return self._compute_norm(residuals)
# def closure(self):
# """
# """
# self.optimizer.zero_grad()
# condition_losses = []
# from torch.utils.data import DataLoader
# from .utils import MyDataset
# loader = DataLoader(
# MyDataset(self.input_pts),
# batch_size=self.batch_size,
# num_workers=1
# )
# for condition_name in self.problem.conditions:
# condition = self.problem.conditions[condition_name]
# batch_losses = []
# for batch in data_loader[condition_name]:
# if hasattr(condition, 'equation'):
# loss = self._residual_loss(
# batch[condition_name], condition.equation)
# elif hasattr(condition, 'output_points'):
# loss = self._data_loss(
# batch[condition_name], condition.output_points)
# batch_losses.append(loss * condition.data_weight)
# condition_losses.append(sum(batch_losses))
# loss = sum(condition_losses)
# loss.backward()
# return loss
def closure(self):
"""
"""
self.optimizer.zero_grad()
losses = []
for i, batch in enumerate(self.loader):
condition_losses = []
for condition_name, samples in batch.items():
if condition_name not in self.problem.conditions:
raise RuntimeError('Something wrong happened.')
if samples is None or samples.nelement() == 0:
continue
header.append(f'{condition_name}')
condition = self.problem.conditions[condition_name]
if hasattr(condition, 'equation'):
loss = self._residual_loss(samples, condition.equation)
elif hasattr(condition, 'output_points'):
loss = self._data_loss(samples, condition.output_points)
condition_losses.append(loss * condition.data_weight)
losses.append(sum(condition_losses))
loss = sum(losses)
loss.backward()
return losses[0]
def train(self, stop=100):
self.model.train()
############################################################
## TODO: move to problem class
for condition in list(set(self.problem.conditions.keys()) - set(self.input_pts.keys())):
self.input_pts[condition] = self.problem.conditions[condition].input_points
mydata = self.input_pts
self.loader = DummyLoader(mydata)
while True:
losses = []
loss = self.optimizer.step(closure=self.closure)
for condition_name in self.problem.conditions:
condition = self.problem.conditions[condition_name]
self.writer.write_loss_in_loop(self, loss)
for batch in data_loader[condition_name]:
single_loss = []
if hasattr(condition, 'function'):
pts = batch[condition_name]
pts = pts.to(dtype=self.dtype, device=self.device)
pts.requires_grad_(True)
pts.retain_grad()
predicted = self.model(pts)
for function in condition.function:
residuals = function(pts, predicted)
local_loss = (
condition.data_weight*self._compute_norm(
residuals))
single_loss.append(local_loss)
elif hasattr(condition, 'output_points'):
pts = condition.input_points.to(
dtype=self.dtype, device=self.device)
predicted = self.model(pts)
residuals = predicted - \
condition.output_points.to(
device=self.device, dtype=self.dtype) # TODO fix
local_loss = (
condition.data_weight*self._compute_norm(residuals))
single_loss.append(local_loss)
self.optimizer.zero_grad()
sum(single_loss).backward()
self.optimizer.step()
losses.append(sum(single_loss))
self._lr_scheduler.step()
if save_loss and (epoch % save_loss == 0 or epoch == 0):
self.history_loss[epoch] = [
loss.detach().item() for loss in losses]
if trial:
import optuna
trial.report(sum(losses), epoch)
if trial.should_prune():
raise optuna.exceptions.TrialPruned()
#self._lr_scheduler.step()
if isinstance(stop, int):
if epoch == stop:
print('[epoch {:05d}] {:.6e} '.format(
self.trained_epoch, sum(losses).item()), end='')
for loss in losses:
print('{:.6e} '.format(loss.item()), end='')
print()
if self.trained_epoch == stop:
break
elif isinstance(stop, float):
if sum(losses) < stop:
if loss.item() < stop:
break
if epoch % frequency_print == 0 or epoch == 1:
print(' {:5s} {:12s} '.format('', 'sum'), end='')
for name in header:
print('{:12.12s} '.format(name), end='')
print()
print('[epoch {:05d}] {:.6e} '.format(
self.trained_epoch, sum(losses).item()), end='')
for loss in losses:
print('{:.6e} '.format(loss.item()), end='')
print()
self.trained_epoch += 1
epoch += 1
self.model.eval()
return sum(losses).item()
# def error(self, dtype='l2', res=100):
# import numpy as np
# if hasattr(self.problem, 'truth_solution') and self.problem.truth_solution is not None:
# pts_container = []
# for mn, mx in self.problem.domain_bound:
# pts_container.append(np.linspace(mn, mx, res))
# grids_container = np.meshgrid(*pts_container)
# Z_true = self.problem.truth_solution(*grids_container)
# elif hasattr(self.problem, 'data_solution') and self.problem.data_solution is not None:
# grids_container = self.problem.data_solution['grid']
# Z_true = self.problem.data_solution['grid_solution']
# try:
# unrolled_pts = torch.tensor([t.flatten() for t in grids_container]).T.to(
# dtype=self.dtype, device=self.device)
# Z_pred = self.model(unrolled_pts)
# Z_pred = Z_pred.detach().numpy().reshape(grids_container[0].shape)
# if dtype == 'l2':
# return np.linalg.norm(Z_pred - Z_true)/np.linalg.norm(Z_true)
# else:
# # TODO H1
# pass
# except:
# print("")
# print("Something went wrong...")
# print(
# "Not able to compute the error. Please pass a data solution or a true solution")
self.model.eval()

179
pina/utils.py
View File

@@ -98,63 +98,146 @@ def is_function(f):
return type(f) == types.FunctionType or type(f) == types.LambdaType
class PinaDataset():
def chebyshev_roots(n):
"""
Return the roots of *n* Chebyshev polynomials (between [-1, 1]).
def __init__(self, pinn) -> None:
self.pinn = pinn
:param int n: number of roots
:return: roots
:rtype: torch.tensor
"""
pi = torch.acos(torch.zeros(1)).item() * 2
k = torch.arange(n)
nodes = torch.sort(torch.cos(pi * (k + 0.5) / n))[0]
return nodes
@property
def dataloader(self):
return self._create_dataloader()
# class PinaDataset():
@property
def dataset(self):
return [self.SampleDataset(key, val)
for key, val in self.input_pts.items()]
# def __init__(self, pinn) -> None:
# self.pinn = pinn
def _create_dataloader(self):
"""Private method for creating dataloader
# @property
# def dataloader(self):
# return self._create_dataloader()
:return: dataloader
:rtype: torch.utils.data.DataLoader
"""
if self.pinn.batch_size is None:
return {key: [{key: val}] for key, val in self.pinn.input_pts.items()}
# @property
# def dataset(self):
# return [self.SampleDataset(key, val)
# for key, val in self.input_pts.items()]
def custom_collate(batch):
# extracting pts labels
_, pts = list(batch[0].items())[0]
labels = pts.labels
# calling default torch collate
collate_res = default_collate(batch)
# save collate result in dict
res = {}
for key, val in collate_res.items():
val.labels = labels
res[key] = val
return res
# def _create_dataloader(self):
# """Private method for creating dataloader
# creating dataset, list of dataset for each location
datasets = [self.SampleDataset(key, val)
for key, val in self.pinn.input_pts.items()]
# creating dataloader
dataloaders = [DataLoader(dataset=dat,
batch_size=self.pinn.batch_size,
collate_fn=custom_collate)
for dat in datasets]
# :return: dataloader
# :rtype: torch.utils.data.DataLoader
# """
# if self.pinn.batch_size is None:
# return {key: [{key: val}] for key, val in self.pinn.input_pts.items()}
return dict(zip(self.pinn.input_pts.keys(), dataloaders))
# def custom_collate(batch):
# # extracting pts labels
# _, pts = list(batch[0].items())[0]
# labels = pts.labels
# # calling default torch collate
# collate_res = default_collate(batch)
# # save collate result in dict
# res = {}
# for key, val in collate_res.items():
# val.labels = labels
# res[key] = val
# __init__(self, location, tensor):
# self._tensor = tensor
# self._location = location
# self._len = len(tensor)
class SampleDataset(torch.utils.data.Dataset):
# def __getitem__(self, index):
# tensor = self._tensor.select(0, index)
# return {self._location: tensor}
def __init__(self, location, tensor):
self._tensor = tensor
self._location = location
self._len = len(tensor)
# def __len__(self):
# return self._len
def __getitem__(self, index):
tensor = self._tensor.select(0, index)
return {self._location: tensor}
from torch.utils.data import Dataset, DataLoader
class LabelTensorDataset(Dataset):
def __init__(self, d):
for k, v in d.items():
setattr(self, k, v)
self.labels = list(d.keys())
def __getitem__(self, index):
print(index)
result = {}
for label in self.labels:
sample_tensor = getattr(self, label)
def __len__(self):
return self._len
# print('porcodio')
# print(sample_tensor.shape[1])
# print(index)
# print(sample_tensor[index])
try:
result[label] = sample_tensor[index]
except IndexError:
result[label] = torch.tensor([])
print(result)
return result
def __len__(self):
return max([len(getattr(self, label)) for label in self.labels])
class LabelTensorDataLoader(DataLoader):
def collate_fn(self, data):
print(data)
gggggggggg
# return dict(zip(self.pinn.input_pts.keys(), dataloaders))
# class SampleDataset(torch.utils.data.Dataset):
# def __init__(self, location, tensor):
# self._tensor = tensor
# self._location = location
# self._len = len(tensor)
# def __getitem__(self, index):
# tensor = self._tensor.select(0, index)
# return {self._location: tensor}
# def __len__(self):
# return self._len
from torch.utils.data import Dataset, DataLoader
class LabelTensorDataset(Dataset):
def __init__(self, d):
for k, v in d.items():
setattr(self, k, v)
self.labels = list(d.keys())
def __getitem__(self, index):
print(index)
result = {}
for label in self.labels:
sample_tensor = getattr(self, label)
# print('porcodio')
# print(sample_tensor.shape[1])
# print(index)
# print(sample_tensor[index])
try:
result[label] = sample_tensor[index]
except IndexError:
result[label] = torch.tensor([])
print(result)
return result
def __len__(self):
return max([len(getattr(self, label)) for label in self.labels])
class LabelTensorDataLoader(DataLoader):
def collate_fn(self, data):
print(data)
gggggggggg

54
pina/writer.py Normal file
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""" Module for plotting. """
import matplotlib.pyplot as plt
import numpy as np
import torch
from pina import LabelTensor
class Writer:
"""
Implementation of a writer class, for textual output.
"""
def __init__(self,
frequency_print=10,
header='any') -> None:
"""
The constructor of the class.
:param int frequency_print: the frequency in epochs of printing.
:param ['any', 'begin', 'none'] header: the header of the output.
"""
self._frequency_print = frequency_print
self._header = header
def header(self, trainer):
"""
The method for printing the header.
"""
header = []
for condition_name in trainer.problem.conditions:
header.append(f'{condition_name}')
return header
def write_loss(self, trainer):
"""
The method for writing the output.
"""
pass
def write_loss_in_loop(self, trainer, loss):
"""
The method for writing the output within the training loop.
:param pina.trainer.Trainer trainer: the trainer object.
"""
if trainer.trained_epoch % self._frequency_print == 0:
print(f'Epoch {trainer.trained_epoch:05d}: {loss.item():.5e}')