minor changes
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@@ -19,7 +19,8 @@ class DeepONet(torch.nn.Module):
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<https://doi.org/10.1038/s42256-021-00302-5>`_
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"""
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def __init__(self, branch_net, trunk_net, output_variables, inner_size=10):
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def __init__(self, branch_net, trunk_net, output_variables, inner_size=10,
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features=None, features_net=None):
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"""
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:param torch.nn.Module branch_net: the neural network to use as branch
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model. It has to take as input a :class:`LabelTensor`. The number
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@@ -64,17 +65,48 @@ class DeepONet(torch.nn.Module):
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self.output_variables = output_variables
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self.output_dimension = len(output_variables)
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trunk_out_dim = trunk_net.layers[-1].out_features
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branch_out_dim = branch_net.layers[-1].out_features
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if trunk_out_dim != branch_out_dim:
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raise ValueError('Branch and trunk networks have not the same '
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'output dimension.')
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self.trunk_net = trunk_net
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self.branch_net = branch_net
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if isinstance(self.branch_net.output_variables, int) and isinstance(self.branch_net.output_variables, int):
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if self.branch_net.output_dimension == self.trunk_net.output_dimension:
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self.inner_size = self.branch_net.output_dimension
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else:
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raise ValueError('Branch and trunk networks have not the same output dimension.')
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else:
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warnings.warn("The output dimension of the branch and trunk networks has been imposed by default as 10 for each output variable. To set it change the output_variable of networks to an integer.")
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self.inner_size = self.output_dimension*inner_size
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if features:
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# if len(features) != features_net.layers[0].in_features:
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# raise ValueError('Incompatible features')
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# if trunk_out_dim != features_net.layers[-1].out_features:
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# raise ValueError('Incompatible features')
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self.features = features
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# self.features_net = nn.Sequential(
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# nn.Linear(len(features), 10), nn.Softplus(),
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# # nn.Linear(10, 10), nn.Softplus(),
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# nn.Linear(10, trunk_out_dim)
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# )
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self.features_net = nn.Sequential(
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nn.Linear(len(features), trunk_out_dim)
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)
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self.reduction = nn.Linear(trunk_out_dim, self.output_dimension)
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# print(self.branch_net.output_variables)
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# print(self.trunk_net.output_variables)
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# if isinstance(self.branch_net.output_variables, int) and isinstance(self.branch_net.output_variables, int):
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# if self.branch_net.output_dimension == self.trunk_net.output_dimension:
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# self.inner_size = self.branch_net.output_dimension
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# print('qui')
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# else:
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# raise ValueError('Branch and trunk networks have not the same output dimension.')
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# else:
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# warnings.warn("The output dimension of the branch and trunk networks has been imposed by default as 10 for each output variable. To set it change the output_variable of networks to an integer.")
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# self.inner_size = self.output_dimension*inner_size
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@property
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def input_variables(self):
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@@ -89,17 +121,27 @@ class DeepONet(torch.nn.Module):
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:return: the output computed by the model.
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:rtype: LabelTensor
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"""
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input_feature = []
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for feature in self.features:
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#print(feature)
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input_feature.append(feature(x))
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input_feature = torch.cat(input_feature, dim=1)
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branch_output = self.branch_net(
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x.extract(self.branch_net.input_variables))
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trunk_output = self.trunk_net(
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x.extract(self.trunk_net.input_variables))
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local_size = int(self.inner_size/self.output_dimension)
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for i, var in enumerate(self.output_variables):
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start = i*local_size
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stop = (i+1)*local_size
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local_output = LabelTensor(torch.sum(branch_output[:, start:stop] * trunk_output[:, start:stop], dim=1).reshape(-1, 1), var)
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if i==0:
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output_ = local_output
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else:
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output_ = output_.append(local_output)
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feat_output = self.features_net(input_feature)
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output_ = self.reduction(branch_output * trunk_output * feat_output)
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output_ = self.reduction(trunk_output * feat_output)
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output_ = LabelTensor(output_, self.output_variables)
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# local_size = int(trunk_output.shape[1]/self.output_dimension)
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# for i, var in enumerate(self.output_variables):
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# start = i*local_size
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# stop = (i+1)*local_size
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# local_output = LabelTensor(torch.sum(branch_output[:, start:stop] * trunk_output[:, start:stop], dim=1).reshape(-1, 1), var)
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# if i==0:
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# output_ = local_output
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# else:
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# output_ = output_.append(local_output)
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return output_
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59
pina/pinn.py
59
pina/pinn.py
@@ -120,21 +120,53 @@ class PINN(object):
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return self
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def span_pts(self, n_spatial, n_params=0, mode_spatial='grid', mode_param='random', locations='all'):
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def span_pts(self, *args, **kwargs):
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"""
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>>> pinn.span_pts(n=10, mode='grid')
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>>> pinn.span_pts(n=10, mode='grid', variables=['x'])
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"""
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def merge_tensors(tensors): # name to be changed
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if len(tensors) == 2:
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tensor1 = tensors[0]
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tensor2 = tensors[1]
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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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return tensor1.append(tensor2)
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else:
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pass
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if isinstance(args[0], int) and isinstance(args[1], str):
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pass
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variables = self.problem.input_variables
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elif all(isinstance(arg, dict) for arg in args):
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print(args)
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arguments = args
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pass
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elif all(key in kwargs for key in ['n', 'mode']):
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variables = self.problem.input_variables
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pass
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else:
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raise RuntimeError
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locations = kwargs.get('locations', 'all')
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if locations == 'all':
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locations = [condition for condition in self.problem.conditions]
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for location in locations:
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condition = self.problem.conditions[location]
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try:
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pts = condition.location.sample(n_spatial, mode_spatial, variables=self.problem.spatial_variables)
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if n_params != 0:
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pts_params = condition.location.sample(n_params, mode_param, variables=self.problem.parameters)
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pts = LabelTensor(pts.repeat(n_params, 1), pts.labels)
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pts_params = LabelTensor(pts_params.repeat_interleave(n_spatial).reshape((n_spatial*n_params, len(self.problem.parameters))), pts_params.labels)
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pts = pts.append(pts_params)
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except:
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pts = condition.input_points
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pts = merge_tensors([
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condition.location.sample(
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argument['n'],
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argument['mode'],
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variables=argument['variables'])
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for argument in arguments])
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self.input_pts[location] = pts #.double() # TODO
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self.input_pts[location] = (
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self.input_pts[location].to(dtype=self.dtype,
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@@ -168,9 +200,10 @@ class PINN(object):
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for condition_name in self.problem.conditions:
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condition = self.problem.conditions[condition_name]
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if hasattr(condition, 'function'):
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pts = self.input_pts[condition_name]
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predicted = self.model(pts)
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if hasattr(condition, 'function'):
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if isinstance(condition.function, list):
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for function in condition.function:
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residuals = function(pts, predicted)
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@@ -181,6 +214,10 @@ class PINN(object):
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local_loss = condition.data_weight*self._compute_norm(residuals)
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losses.append(local_loss)
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elif hasattr(condition, 'output_points'):
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pts = condition.input_points
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# print(pts)
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predicted = self.model(pts)
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# print(predicted)
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residuals = predicted - condition.output_points
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local_loss = condition.data_weight*self._compute_norm(residuals)
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losses.append(local_loss)
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@@ -106,6 +106,7 @@ class Plotter:
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ind_dict[location] = ind_to_exclude
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import functools
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from functools import reduce
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final_inds = reduce(np.intersect1d, ind_dict.values())
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predicted_output = obj.model(pts)
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predicted_output = predicted_output.extract([component])
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@@ -122,7 +123,7 @@ class Plotter:
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fig.colorbar(cb, ax=axes[2])
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else:
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))
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cb = getattr(axes, method)(*grids_container, predicted_output.reshape(res, res).detach())
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cb = getattr(axes, method)(*grids_container, predicted_output.reshape(res, res).detach(), levels=32)
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fig.colorbar(cb, ax=axes)
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if filename:
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@@ -71,7 +71,7 @@ class Span(Location):
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if not len(spatial_fixed_)==0:
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pts_fixed_ = torch.ones(pts.shape[0], len(spatial_fixed_),
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dtype=pts.dtype) * fixed
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pts_fixed_ = pts_fixed_.float()
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pts_fixed_ = LabelTensor(pts_fixed_, spatial_fixed_)
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pts_range_ = pts_range_.append(pts_fixed_)
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