49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
import torch
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import torch.nn as nn
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import time
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from torchdiffeq import odeint
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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class DiffeqSolver(nn.Module):
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def __init__(self, odefunc, method, latent_dim,
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odeint_rtol = 1e-4, odeint_atol = 1e-5):
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nn.Module.__init__(self)
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self.ode_method = method
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self.odefunc = odefunc
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self.latent_dim = latent_dim
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self.rtol = odeint_rtol
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self.atol = odeint_atol
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def forward(self, first_point, time_steps_to_pred):
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"""
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Decoder the trajectory through the ODE Solver.
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:param time_steps_to_pred: horizon
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:param first_point: (n_traj_samples, batch_size, num_nodes * latent_dim)
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:return: pred_y: # shape (horizon, n_traj_samples, batch_size, self.num_nodes * self.output_dim)
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"""
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n_traj_samples, batch_size = first_point.size()[0], first_point.size()[1]
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first_point = first_point.reshape(n_traj_samples * batch_size, -1) # reduce the complexity by merging dimension
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# pred_y shape: (horizon, n_traj_samples * batch_size, num_nodes * latent_dim)
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start_time = time.time()
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self.odefunc.nfe = 0
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pred_y = odeint(self.odefunc,
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first_point,
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time_steps_to_pred,
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rtol=self.rtol,
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atol=self.atol,
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method=self.ode_method)
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time_fe = time.time() - start_time
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# pred_y shape: (horizon, n_traj_samples, batch_size, num_nodes * latent_dim)
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pred_y = pred_y.reshape(pred_y.size()[0], n_traj_samples, batch_size, -1)
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# assert(pred_y.size()[1] == n_traj_samples)
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# assert(pred_y.size()[2] == batch_size)
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return pred_y, (self.odefunc.nfe, time_fe)
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