Sym v1.0.0

Source code for modulus.sym.models.moving_time_window

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# limitations under the License.

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

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

import modulus.sym.models.layers as layers
from .interpolation import smooth_step_1, smooth_step_2
from modulus.sym.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)

© Copyright 2023, NVIDIA Modulus Team. Last updated on Aug 8, 2023.