Source code for modulus.models.moving_time_window

from typing import Optional, Dict, Tuple
from modulus.key import Key
import copy

import torch
import torch.nn as nn
from torch import Tensor

import modulus.models.layers as layers
from .interpolation import smooth_step_1, smooth_step_2
from modulus.models.arch import Arch

from typing import List

[docs]class MovingTimeWindowArch(Arch): """ Moving time window model the keeps track of current time window and previous window. Parameters ---------- arch : Arch Modulus architecture to use for moving time window. window_size : float Size of the time window. This will be used to slide the window forward every iteration. """ def __init__( self, arch: Arch, window_size: float, ) -> None: output_keys = ( arch.output_keys + [Key( + "_prev_step") for x in arch.output_keys] + [Key( + "_prev_step_diff") for x in arch.output_keys] ) super().__init__( input_keys=arch.input_keys, output_keys=output_keys, periodicity=arch.periodicity, ) # set networks for current and prev time window self.arch_prev_step = arch self.arch = copy.deepcopy(arch) # store time window parameters self.window_size = window_size self.window_location = nn.Parameter(torch.empty(1), requires_grad=False) self.reset_parameters()
[docs] def forward(self, in_vars: Dict[str, Tensor]) -> Dict[str, Tensor]: with torch.no_grad(): in_vars["t"] += self.window_location y_prev_step = self.arch_prev_step.forward(in_vars) y = self.arch.forward(in_vars) y_keys = list(y.keys()) for key in y_keys: y_prev = y_prev_step[key] y[key + "_prev_step"] = y_prev y[key + "_prev_step_diff"] = y[key] - y_prev return y

def move_window(self): += self.window_size for param, param_prev_step in zip( self.arch.parameters(), self.arch_prev_step.parameters() ): = param.detach().clone().data param_prev_step.requires_grad = False def reset_parameters(self) -> None: nn.init.constant_(self.window_location, 0)

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