deeplearning/modulus/modulus-sym-v120/_modules/modulus/sym/models/radial_basis.html

Sym v1.2.0

Source code for modulus.sym.models.radial_basis

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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 Jan 25, 2024.