168 lines
5.0 KiB
Python
168 lines
5.0 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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TODO
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"""
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def grad_scalar_output(output_, input_, d):
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"""
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"""
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if len(output_.labels) != 1:
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raise RuntimeError
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if not all([di in input_.labels for di in d]):
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raise RuntimeError
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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()).to(
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dtype=input_.dtype,
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device=input_.device),
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create_graph=True, retain_graph=True, allow_unused=True)[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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TODO
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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 field')
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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)
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# print(grad_output)
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# print('empty', div)
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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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# print(c_fields)
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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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# print('full', div)
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# print(labels)
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return LabelTensor(div, ['+'.join(labels)])
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def nabla(output_, input_, components=None, d=None, method='std'):
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"""
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TODO
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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
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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)
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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] += gg[:, i]
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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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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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return LabelTensor(result, labels)
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def advection(output_, input_):
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"""
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TODO
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"""
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dimension = len(output_.labels)
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for i, label in enumerate(output_.labels):
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# compute u dot gradient in each direction
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gradient_loc = grad(output_.extract([label]),
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input_).extract(input_.labels[:dimension])
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dim_0 = gradient_loc.shape[0]
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dim_1 = gradient_loc.shape[1]
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u_dot_grad_loc = torch.bmm(output_.view(dim_0, 1, dim_1),
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gradient_loc.view(dim_0, dim_1, 1))
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u_dot_grad_loc = LabelTensor(torch.reshape(u_dot_grad_loc,
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(u_dot_grad_loc.shape[0],
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u_dot_grad_loc.shape[1])),
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[input_.labels[i]])
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if i == 0:
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adv_term = u_dot_grad_loc
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else:
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adv_term = adv_term.append(u_dot_grad_loc)
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return adv_term
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