deeplearning/modulus/modulus-v2209/_modules/modulus/models/radial_basis.html

v22.09

Source code for modulus.models.radial_basis

from typing import Dict
from typing import List

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


import modulus.models.layers as layers
from modulus.models.arch import Arch
from modulus.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)

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