0e3625de80
* equation class * difference domain * dummy dataloader * writer class * refactoring and minor fix
245 lines
9.2 KiB
Python
245 lines
9.2 KiB
Python
"""Module for operators vectorize implementation"""
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import torch
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from pina.label_tensor import LabelTensor
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def grad(output_, input_, components=None, d=None):
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"""
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Perform gradient operation. The operator works for vectorial and scalar
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functions, with multiple input coordinates.
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:param LabelTensor output_: the output tensor onto which computing the
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gradient.
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:param LabelTensor input_: the input tensor with respect to which computing
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the gradient.
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:param list(str) components: the name of the output variables to calculate
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the gradient for. It should be a subset of the output labels. If None,
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all the output variables are considered. Default is None.
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:param list(str) d: the name of the input variables on which the gradient is
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calculated. d should be a subset of the input labels. If None, all the
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input variables are considered. Default is None.
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:return: the gradient tensor.
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:rtype: LabelTensor
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"""
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def grad_scalar_output(output_, input_, d):
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"""
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Perform gradient operation for a scalar output.
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:param LabelTensor output_: the output tensor onto which computing the
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gradient. It has to be a column tensor.
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:param LabelTensor input_: the input tensor with respect to which
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computing the gradient.
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:param list(str) d: the name of the input variables on which the
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gradient is calculated. d should be a subset of the input labels. If
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None, all the input variables are considered. Default is None.
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:raises RuntimeError: a vectorial function is passed.
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:raises RuntimeError: missing derivative labels.
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:return: the gradient tensor.
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:rtype: LabelTensor
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"""
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if len(output_.labels) != 1:
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raise RuntimeError('only scalar function can be differentiated')
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if not all([di in input_.labels for di in d]):
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raise RuntimeError('derivative labels missing from input tensor')
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output_fieldname = output_.labels[0]
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gradients = torch.autograd.grad(
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output_,
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input_,
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grad_outputs=torch.ones(output_.size(), dtype=output_.dtype,
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device=output_.device),
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create_graph=True,
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retain_graph=True,
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allow_unused=True
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)[0]
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gradients.labels = input_.labels
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gradients = gradients.extract(d)
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gradients.labels = [f'd{output_fieldname}d{i}' for i in d]
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return gradients
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if not isinstance(input_, LabelTensor):
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raise TypeError
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if d is None:
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d = input_.labels
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if components is None:
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components = output_.labels
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if output_.shape[1] == 1: # scalar output ################################
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if components != output_.labels:
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raise RuntimeError
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gradients = grad_scalar_output(output_, input_, d)
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elif output_.shape[1] >= 2: # vector output ##############################
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for i, c in enumerate(components):
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c_output = output_.extract([c])
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if i == 0:
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gradients = grad_scalar_output(c_output, input_, d)
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else:
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gradients = gradients.append(
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grad_scalar_output(c_output, input_, d))
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else:
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raise NotImplementedError
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return gradients
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def div(output_, input_, components=None, d=None):
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"""
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Perform divergence operation. The operator works for vectorial functions,
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with multiple input coordinates.
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:param LabelTensor output_: the output tensor onto which computing the
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divergence.
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:param LabelTensor input_: the input tensor with respect to which computing
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the divergence.
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:param list(str) components: the name of the output variables to calculate
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the divergence for. It should be a subset of the output labels. If None,
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all the output variables are considered. Default is None.
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:param list(str) d: the name of the input variables on which the divergence
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is calculated. d should be a subset of the input labels. If None, all
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the input variables are considered. Default is None.
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:raises TypeError: div operator works only for LabelTensor.
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:raises ValueError: div operator works only for vector fields.
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:raises ValueError: div operator must derive all components with
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respect to all coordinates.
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:return: the divergenge tensor.
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:rtype: LabelTensor
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"""
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if not isinstance(input_, LabelTensor):
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raise TypeError
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if d is None:
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d = input_.labels
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if components is None:
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components = output_.labels
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if output_.shape[1] < 2 or len(components) < 2:
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raise ValueError('div supported only for vector fields')
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if len(components) != len(d):
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raise ValueError
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grad_output = grad(output_, input_, components, d)
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div = torch.zeros(input_.shape[0], 1, device=output_.device)
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labels = [None] * len(components)
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for i, (c, d) in enumerate(zip(components, d)):
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c_fields = f'd{c}d{d}'
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div[:, 0] += grad_output.extract(c_fields).sum(axis=1)
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labels[i] = c_fields
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div = div.as_subclass(LabelTensor)
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div.labels = ['+'.join(labels)]
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return div
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def nabla(output_, input_, components=None, d=None, method='std'):
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"""
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Perform nabla (laplace) operator. The operator works for vectorial and
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scalar functions, with multiple input coordinates.
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:param LabelTensor output_: the output tensor onto which computing the
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nabla.
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:param LabelTensor input_: the input tensor with respect to which computing
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the nabla.
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:param list(str) components: the name of the output variables to calculate
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the nabla for. It should be a subset of the output labels. If None,
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all the output variables are considered. Default is None.
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:param list(str) d: the name of the input variables on which the nabla
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is calculated. d should be a subset of the input labels. If None, all
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the input variables are considered. Default is None.
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:param str method: used method to calculate nabla, defaults to 'std'.
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:raises ValueError: for vectorial field derivative with respect to
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all coordinates must be performed.
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:raises NotImplementedError: 'divgrad' not implemented as method.
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:return: The tensor containing the result of the nabla operator.
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:rtype: LabelTensor
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"""
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if d is None:
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d = input_.labels
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if components is None:
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components = output_.labels
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if len(components) != len(d) and len(components) != 1:
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raise ValueError
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if method == 'divgrad':
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raise NotImplementedError('divgrad not implemented as method')
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# TODO fix
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# grad_output = grad(output_, input_, components, d)
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# result = div(grad_output, input_, d=d)
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elif method == 'std':
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if len(components) == 1:
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grad_output = grad(output_, input_, components=components, d=d)
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result = torch.zeros(output_.shape[0], 1, device=output_.device)
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for i, label in enumerate(grad_output.labels):
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gg = grad(grad_output, input_, d=d, components=[label])
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result[:, 0] += super(torch.Tensor, gg.T).__getitem__(i) # TODO improve
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labels = [f'dd{components[0]}']
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else:
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result = torch.empty(input_.shape[0], len(components),
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device=output_.device)
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labels = [None] * len(components)
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for idx, (ci, di) in enumerate(zip(components, d)):
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if not isinstance(ci, list):
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ci = [ci]
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if not isinstance(di, list):
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di = [di]
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grad_output = grad(output_, input_, components=ci, d=di)
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result[:, idx] = grad(grad_output, input_, d=di).flatten()
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labels[idx] = f'dd{ci}dd{di}'
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result = result.as_subclass(LabelTensor)
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result.labels = labels
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return result
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def advection(output_, input_, velocity_field, components=None, d=None):
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"""
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Perform advection operation. The operator works for vectorial functions,
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with multiple input coordinates.
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:param LabelTensor output_: the output tensor onto which computing the
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nabla.
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:param LabelTensor input_: the input tensor with respect to which computing
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the nabla.
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:param str velocity_field: the name of the output variables which is used
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as velocity field. It should be a subset of the output labels.
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:param list(str) components: the name of the output variables to calculate
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the nabla for. It should be a subset of the output labels. If None,
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all the output variables are considered. Default is None.
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:param list(str) d: the name of the input variables on which the nabla
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is calculated. d should be a subset of the input labels. If None, all
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the input variables are considered. Default is None.
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:return: the tensor containing the result of the advection operator.
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:rtype: LabelTensor
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"""
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if d is None:
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d = input_.labels
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if components is None:
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components = output_.labels
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tmp = grad(output_, input_, components, d
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).reshape(-1, len(components), len(d)).transpose(0, 1)
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tmp *= output_.extract(velocity_field)
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return tmp.sum(dim=2).T
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