199 lines
5.7 KiB
Python
199 lines
5.7 KiB
Python
"""Utils module"""
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from torch.utils.data import Dataset, DataLoader
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from functools import reduce
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import types
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import torch
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from torch.utils.data import DataLoader, default_collate, ConcatDataset
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from .label_tensor import LabelTensor
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import torch
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def check_consistency(object, object_instance, subclass=False):
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"""Helper function to check object inheritance consistency.
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Given a specific ``'object'`` we check if the object is
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instance of a specific ``'object_instance'``, or in case
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``'subclass=True'`` we check if the object is subclass
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if the ``'object_instance'``.
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:param (iterable or class object) object: The object to check the inheritance
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:param Object object_instance: The parent class from where the object
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is expected to inherit
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:param str object_name: The name of the object
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:param bool subclass: Check if is a subclass and not instance
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:raises ValueError: If the object does not inherit from the
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specified class
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"""
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if not isinstance(object, (list, set, tuple)):
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object = [object]
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for obj in object:
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try:
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if not subclass:
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assert isinstance(obj, object_instance)
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else:
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assert issubclass(obj, object_instance)
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except AssertionError:
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raise ValueError(f"{type(obj).__name__} must be {object_instance}.")
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def number_parameters(
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model, aggregate=True, only_trainable=True
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): # TODO: check
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"""
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Return the number of parameters of a given `model`.
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:param torch.nn.Module model: the torch module to inspect.
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:param bool aggregate: if True the return values is an integer corresponding
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to the total amount of parameters of whole model. If False, it returns a
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dictionary whose keys are the names of layers and the values the
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corresponding number of parameters. Default is True.
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:param bool trainable: if True, only trainable parameters are count,
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otherwise no. Default is True.
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:return: the number of parameters of the model
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:rtype: dict or int
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"""
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tmp = {}
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for name, parameter in model.named_parameters():
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if only_trainable and not parameter.requires_grad:
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continue
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tmp[name] = parameter.numel()
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if aggregate:
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tmp = sum(tmp.values())
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return tmp
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def merge_tensors(tensors): # name to be changed
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if tensors:
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return reduce(merge_two_tensors, tensors[1:], tensors[0])
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raise ValueError("Expected at least one tensor")
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def merge_two_tensors(tensor1, tensor2):
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n1 = tensor1.shape[0]
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n2 = tensor2.shape[0]
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tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels)
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tensor2 = LabelTensor(
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tensor2.repeat_interleave(n1, dim=0), labels=tensor2.labels
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)
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return tensor1.append(tensor2)
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def torch_lhs(n, dim):
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"""Latin Hypercube Sampling torch routine.
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Sampling in range $[0, 1)^d$.
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:param int n: number of samples
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:param int dim: dimensions of latin hypercube
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:return: samples
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:rtype: torch.tensor
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"""
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if not isinstance(n, int):
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raise TypeError("number of point n must be int")
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if not isinstance(dim, int):
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raise TypeError("dim must be int")
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if dim < 1:
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raise ValueError("dim must be greater than one")
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samples = torch.rand(size=(n, dim))
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perms = torch.tile(torch.arange(1, n + 1), (dim, 1))
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for row in range(dim):
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idx_perm = torch.randperm(perms.shape[-1])
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perms[row, :] = perms[row, idx_perm]
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perms = perms.T
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samples = (perms - samples) / n
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return samples
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def is_function(f):
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"""
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Checks whether the given object `f` is a function or lambda.
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:param object f: The object to be checked.
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:return: `True` if `f` is a function, `False` otherwise.
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:rtype: bool
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"""
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return type(f) == types.FunctionType or type(f) == types.LambdaType
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def chebyshev_roots(n):
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"""
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Return the roots of *n* Chebyshev polynomials (between [-1, 1]).
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:param int n: number of roots
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:return: roots
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:rtype: torch.tensor
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"""
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pi = torch.acos(torch.zeros(1)).item() * 2
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k = torch.arange(n)
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nodes = torch.sort(torch.cos(pi * (k + 0.5) / n))[0]
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return nodes
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# class PinaDataset():
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# def __init__(self, pinn) -> None:
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# self.pinn = pinn
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# @property
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# def dataloader(self):
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# return self._create_dataloader()
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# @property
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# def dataset(self):
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# return [self.SampleDataset(key, val)
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# for key, val in self.input_pts.items()]
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# def _create_dataloader(self):
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# """Private method for creating dataloader
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# :return: dataloader
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# :rtype: torch.utils.data.DataLoader
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# """
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# if self.pinn.batch_size is None:
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# return {key: [{key: val}] for key, val in self.pinn.input_pts.items()}
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# def custom_collate(batch):
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# # extracting pts labels
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# _, pts = list(batch[0].items())[0]
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# labels = pts.labels
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# # calling default torch collate
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# collate_res = default_collate(batch)
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# # save collate result in dict
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# res = {}
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# for key, val in collate_res.items():
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# val.labels = labels
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# res[key] = val
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# __init__(self, location, tensor):
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# self._tensor = tensor
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# self._location = location
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# self._len = len(tensor)
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# def __getitem__(self, index):
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# tensor = self._tensor.select(0, index)
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# return {self._location: tensor}
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# def __len__(self):
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# return self._len
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class LabelTensorDataLoader(DataLoader):
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def collate_fn(self, data):
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pass
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