283 lines
10 KiB
Python
283 lines
10 KiB
Python
from .problem import AbstractProblem
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import torch
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import matplotlib.pyplot as plt
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import numpy as np
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from pina.label_tensor import LabelTensor
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torch.pi = torch.acos(torch.zeros(1)).item() * 2 # which is 3.1415927410125732
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class PINN(object):
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def __init__(self,
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problem,
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model,
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optimizer=torch.optim.Adam,
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lr=0.001,
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regularizer=0.00001,
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dtype=torch.float32,
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device='cpu',
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error_norm='mse'):
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'''
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:param Problem problem: the formualation of the problem.
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:param torch.nn.Module model: the neural network model to use.
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:param float lr: the learning rate; default is 0.001.
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:param float regularizer: the coefficient for L2 regularizer term.
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:param type dtype: the data type to use for the model. Valid option are
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`torch.float32` and `torch.float64` (`torch.float16` only on GPU);
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default is `torch.float64`.
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'''
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if dtype == torch.float64:
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raise NotImplementedError('only float for now')
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self.problem = problem
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# self._architecture = architecture if architecture else dict()
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# self._architecture['input_dimension'] = self.problem.domain_bound.shape[0]
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# self._architecture['output_dimension'] = len(self.problem.variables)
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# if hasattr(self.problem, 'params_domain'):
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# self._architecture['input_dimension'] += self.problem.params_domain.shape[0]
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self.error_norm = error_norm
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if device == 'cuda' and not torch.cuda.is_available():
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raise RuntimeError
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self.device = torch.device(device)
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self.dtype = dtype
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self.history = []
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self.model = model
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self.model.to(dtype=self.dtype, device=self.device)
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self.truth_values = {}
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self.input_pts = {}
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self.trained_epoch = 0
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self.optimizer = optimizer(
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self.model.parameters(), lr=lr, weight_decay=regularizer)
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@property
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def problem(self):
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return self._problem
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@problem.setter
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def problem(self, problem):
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if not isinstance(problem, AbstractProblem):
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raise TypeError
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self._problem = problem
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def _compute_norm(self, vec):
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"""
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Compute the norm of the `vec` one-dimensional tensor based on the
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`self.error_norm` attribute.
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.. todo: complete
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:param vec torch.tensor: the tensor
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"""
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if isinstance(self.error_norm, int):
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return torch.sum(torch.abs(vec**self.error_norm))**(1./self.error_norm)
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elif self.error_norm == 'mse':
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return torch.mean(vec**2)
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elif self.error_norm == 'me':
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return torch.mean(torch.abs(vec))
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else:
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raise RuntimeError
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def save_state(self, filename):
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checkpoint = {
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'epoch': self.trained_epoch,
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'model_state': self.model.state_dict(),
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'optimizer_state' : self.optimizer.state_dict(),
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'optimizer_class' : self.optimizer.__class__,
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'history' : self.history,
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'input_points_dict' : self.input_pts,
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}
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# TODO save also architecture param?
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#if isinstance(self.model, DeepFeedForward):
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# checkpoint['model_class'] = self.model.__class__
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# checkpoint['model_structure'] = {
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# }
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torch.save(checkpoint, filename)
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def load_state(self, filename):
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checkpoint = torch.load(filename)
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self.model.load_state_dict(checkpoint['model_state'])
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self.optimizer = checkpoint['optimizer_class'](self.model.parameters())
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self.optimizer.load_state_dict(checkpoint['optimizer_state'])
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self.trained_epoch = checkpoint['epoch']
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self.history = checkpoint['history']
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self.input_pts = checkpoint['input_points_dict']
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return self
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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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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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device=self.device))
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self.input_pts[location].requires_grad_(True)
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self.input_pts[location].retain_grad()
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def plot_pts(self, locations='all'):
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import matplotlib
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# matplotlib.use('GTK3Agg')
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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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x = self.input_pts[location].extract(['x'])
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y = self.input_pts[location].extract(['y'])
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plt.plot(x.detach(), y.detach(), '.', label=location)
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# np.savetxt('burgers_{}_pts.txt'.format(location), self.input_pts[location].tensor.detach(), header='x y', delimiter=' ')
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plt.legend()
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plt.show()
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def train(self, stop=100, frequency_print=2, trial=None):
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epoch = 0
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while True:
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losses = []
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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 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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local_loss = condition.data_weight*self._compute_norm(residuals)
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losses.append(local_loss)
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else:
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residuals = condition.function(pts, predicted)
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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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self.optimizer.zero_grad()
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sum(losses).backward()
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self.optimizer.step()
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self.trained_epoch += 1
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if epoch % 50 == 0:
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self.history.append([loss.detach().item() for loss in losses])
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epoch += 1
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if trial:
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import optuna
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trial.report(sum(losses), epoch)
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if trial.should_prune():
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raise optuna.exceptions.TrialPruned()
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if isinstance(stop, int):
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if epoch == stop:
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break
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elif isinstance(stop, float):
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if sum(losses) < stop:
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break
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if epoch % frequency_print == 0:
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print('[epoch {:05d}] {:.6e} '.format(self.trained_epoch, sum(losses).item()), end='')
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for loss in losses:
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print('{:.6e} '.format(loss), end='')
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print()
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return sum(losses).item()
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def error(self, dtype='l2', res=100):
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import numpy as np
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if hasattr(self.problem, 'truth_solution') and self.problem.truth_solution is not None:
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pts_container = []
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for mn, mx in self.problem.domain_bound:
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pts_container.append(np.linspace(mn, mx, res))
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grids_container = np.meshgrid(*pts_container)
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Z_true = self.problem.truth_solution(*grids_container)
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elif hasattr(self.problem, 'data_solution') and self.problem.data_solution is not None:
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grids_container = self.problem.data_solution['grid']
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Z_true = self.problem.data_solution['grid_solution']
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try:
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unrolled_pts = torch.tensor([t.flatten() for t in grids_container]).T.to(dtype=self.dtype, device=self.device)
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Z_pred = self.model(unrolled_pts)
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Z_pred = Z_pred.detach().numpy().reshape(grids_container[0].shape)
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if dtype == 'l2':
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return np.linalg.norm(Z_pred - Z_true)/np.linalg.norm(Z_true)
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else:
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# TODO H1
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pass
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except:
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print("")
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print("Something went wrong...")
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print("Not able to compute the error. Please pass a data solution or a true solution")
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