NVIDIA Modulus Sym v1.1.0
Sym v1.1.0


Source code for modulus.sym.models.radial_basis

# 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
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Dict
from typing import List

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

import modulus.sym.models.layers as layers
from modulus.sym.models.arch import Arch
from modulus.sym.key import Key

[docs]class RadialBasisArch(Arch): """ Radial Basis Neural Network. Parameters ---------- input_keys : List[Key] Input key list output_keys : List[Key] Output key list bounds : Dict[str, Tuple[float, float]] Bounds to to randomly generate radial basis functions in. detach_keys : List[Key], optional List of keys to detach gradients, by default [] nr_centers : int = 128 number of radial basis functions to use. sigma : float = 0.1 Sigma in radial basis kernel. """ def __init__( self, input_keys: List[Key], output_keys: List[Key], bounds: Dict[str, List[float]], detach_keys: List[Key] = [], nr_centers: int = 128, sigma: float = 0.1, ) -> None: super().__init__( input_keys=input_keys, output_keys=output_keys, detach_keys=detach_keys ) out_features = sum(self.output_key_dict.values()) self.nr_centers = nr_centers self.sigma = sigma self.centers = nn.Parameter( torch.empty(nr_centers, len(bounds)), requires_grad=False ) with torch.no_grad(): for idx, bound in enumerate(bounds.values()): self.centers[:, idx].uniform_(bound[0], bound[1]) self.fc_layer = layers.FCLayer( nr_centers, out_features, activation_fn=layers.Activation.IDENTITY, ) def _tensor_forward(self, x: Tensor) -> Tensor: # no op since no scales x = self.process_input(x, input_dict=self.input_key_dict, dim=-1) x = x.unsqueeze(-2) # no need to unsqueeze(0), we could and we have to rely on broadcast to # make BatchedTensor work centers = self.centers radial_activation = torch.exp( -0.5 * torch.square(torch.norm(centers - x, p=2, dim=-1) / self.sigma) ) x = self.fc_layer(radial_activation) x = self.process_output(x) # no op return x
[docs] def forward(self, in_vars: Dict[str, Tensor]) -> Dict[str, Tensor]: x = self.concat_input( in_vars, self.input_key_dict.keys(), detach_dict=self.detach_key_dict, dim=-1, ) y = self._tensor_forward(x) return self.split_output(y, self.output_key_dict, dim=-1)

def _dict_forward(self, in_vars: Dict[str, Tensor]) -> Dict[str, Tensor]: """ This is the original forward function, left here for the correctness test. """ x = self.prepare_input( in_vars, self.input_key_dict.keys(), self.detach_key_dict, -1 ) shape = (x.size(0), self.nr_centers, x.size(1)) x = x.unsqueeze(1).expand(shape) centers = self.centers.expand(shape) radial_activation = torch.exp( -0.5 * torch.square(torch.norm(centers - x, p=2, dim=-1) / self.sigma) ) x = self.fc_layer(radial_activation) return self.prepare_output(x, self.output_key_dict, -1)

© Copyright 2023, NVIDIA Modulus Team. Last updated on Oct 17, 2023.