df673cad4e
* solvers -> solver * adaptive_functions -> adaptive_function * callbacks -> callback * operators -> operator * pinns -> physics_informed_solver * layers -> block
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
import torch
|
|
|
|
|
|
class Integral(object):
|
|
|
|
def __init__(self, param):
|
|
"""Integral class for continous convolution
|
|
|
|
:param param: type of continuous convolution
|
|
:type param: string
|
|
"""
|
|
|
|
if param == "discrete":
|
|
self.make_integral = self.integral_param_disc
|
|
elif param == "continuous":
|
|
self.make_integral = self.integral_param_cont
|
|
else:
|
|
raise TypeError
|
|
|
|
def __call__(self, *args, **kwds):
|
|
return self.make_integral(*args, **kwds)
|
|
|
|
def _prepend_zero(self, x):
|
|
"""Create bins for performing integral
|
|
|
|
:param x: input tensor
|
|
:type x: torch.tensor
|
|
:return: bins for integrals
|
|
:rtype: torch.tensor
|
|
"""
|
|
return torch.cat((torch.zeros(1, dtype=x.dtype, device=x.device), x))
|
|
|
|
def integral_param_disc(self, x, y, idx):
|
|
"""Perform discretize integral
|
|
with discrete parameters
|
|
|
|
:param x: input vector
|
|
:type x: torch.tensor
|
|
:param y: input vector
|
|
:type y: torch.tensor
|
|
:param idx: indeces for different strides
|
|
:type idx: list
|
|
:return: integral
|
|
:rtype: torch.tensor
|
|
"""
|
|
cs_idxes = self._prepend_zero(torch.cumsum(torch.tensor(idx), 0))
|
|
cs = self._prepend_zero(torch.cumsum(x.flatten() * y.flatten(), 0))
|
|
return cs[cs_idxes[1:]] - cs[cs_idxes[:-1]]
|
|
|
|
def integral_param_cont(self, x, y, idx):
|
|
"""Perform discretize integral for continuous convolution
|
|
with continuous parameters
|
|
|
|
:param x: input vector
|
|
:type x: torch.tensor
|
|
:param y: input vector
|
|
:type y: torch.tensor
|
|
:param idx: indeces for different strides
|
|
:type idx: list
|
|
:return: integral
|
|
:rtype: torch.tensor
|
|
"""
|
|
raise NotImplementedError
|