df673cad4e
* solvers -> solver * adaptive_functions -> adaptive_function * callbacks -> callback * operators -> operator * pinns -> physics_informed_solver * layers -> block
50 lines
1.1 KiB
Python
50 lines
1.1 KiB
Python
import torch
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def check_point(x, current_stride, dim):
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max_stride = current_stride + dim
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indeces = torch.logical_and(
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x[..., :-1] < max_stride, x[..., :-1] >= current_stride
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).all(dim=-1)
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return indeces
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def map_points_(x, filter_position):
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"""Mapping function n dimensional case
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:param x: input data of two dimension
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:type x: torch.tensor
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:param filter_position: position of the filter
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:type dim: list[numeric]
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:return: data mapped inplace
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:rtype: torch.tensor
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"""
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x.add_(-filter_position)
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return x
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def optimizing(f):
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"""Decorator for calling a function just once
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:param f: python function
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:type f: function
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"""
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def wrapper(*args, **kwargs):
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if kwargs["type"] == "forward":
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if not wrapper.has_run_inverse:
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wrapper.has_run_inverse = True
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return f(*args, **kwargs)
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if kwargs["type"] == "inverse":
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if not wrapper.has_run:
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wrapper.has_run = True
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return f(*args, **kwargs)
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wrapper.has_run_inverse = False
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wrapper.has_run = False
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return wrapper
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