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

Source code for modulus.sym.models.modified_fourier_net

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from typing import Dict, List, Optional

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 ModifiedFourierNetArch(Arch): """ A modified Fourier Network which enables multiplicative interactions betweeen the Fourier features and hidden layers. References: (1) Tancik, M., Srinivasan, P.P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J.T. and Ng, R., 2020. Fourier features let networks learn high frequency functions in low dimensional domains. arXiv preprint arXiv:2006.10739. (2) Wang, S., Teng, Y. and Perdikaris, P., 2020. Understanding and mitigating gradient pathologies in physics-informed neural networks. arXiv preprint arXiv:2001.04536. Parameters ---------- input_keys : List[Key] Input key list output_keys : List[Key] Output key list detach_keys : List[Key], optional List of keys to detach gradients, by default [] frequencies : Tuple[str, List[float]] = ("axis", [i for i in range(10)]) A tuple that describes the Fourier encodings to use any inputs in the list `['x', 'y', 'z', 't']`. The first element describes the type of frequency encoding with options, `'gaussian', 'full', 'axis', 'diagonal'`. `'gaussian'` samples frequency of Fourier series from Gaussian. `'axis'` samples along axis of spectral space with the given list range of frequencies. `'diagonal'` samples along diagonal of spectral space with the given list range of frequencies. `'full'` samples along entire spectral space for all combinations of frequencies in given list. frequencies_params : Tuple[str, List[float]] = ("axis", [i for i in range(10)]) Same as `frequencies` except these are used for encodings on any inputs not in the list `['x', 'y', 'z', 't']`. activation_fn : layers.Activation = layers.Activation.SILU Activation function used by network. layer_size : int = 512 Layer size for every hidden layer of the model. nr_layers : int = 6 Number of hidden layers of the model. skip_connections : bool = False If true then apply skip connections every 2 hidden layers. weight_norm : bool = True Use weight norm on fully connected layers. adaptive_activations : bool = False If True then use an adaptive activation function as described here https://arxiv.org/abs/1906.01170. """ def __init__( self, input_keys: List[Key], output_keys: List[Key], detach_keys: List[Key] = [], frequencies=("axis", [i for i in range(10)]), frequencies_params=("axis", [i for i in range(10)]), activation_fn=layers.Activation.SILU, layer_size: int = 512, nr_layers: int = 6, skip_connections: bool = False, weight_norm: bool = True, adaptive_activations: bool = False, ) -> None: super().__init__( input_keys=input_keys, output_keys=output_keys, detach_keys=detach_keys ) self.skip_connections = skip_connections if adaptive_activations: activation_par = nn.Parameter(torch.ones(1)) else: activation_par = None self.xyzt_var = [x for x in self.input_key_dict if x in ["x", "y", "z", "t"]] # Prepare slice index xyzt_slice_index = self.prepare_slice_index(self.input_key_dict, self.xyzt_var) self.register_buffer("xyzt_slice_index", xyzt_slice_index, persistent=False) self.params_var = [ x for x in self.input_key_dict if x not in ["x", "y", "z", "t"] ] params_slice_index = self.prepare_slice_index( self.input_key_dict, self.params_var ) self.register_buffer("params_slice_index", params_slice_index, persistent=False) in_features_xyzt = sum( (v for k, v in self.input_key_dict.items() if k in self.xyzt_var) ) in_features_params = sum( (v for k, v in self.input_key_dict.items() if k in self.params_var) ) in_features = in_features_xyzt + in_features_params out_features = sum(self.output_key_dict.values()) in_features = in_features_xyzt + in_features_params if in_features_xyzt > 0: self.fourier_layer_xyzt = layers.FourierLayer( in_features=in_features_xyzt, frequencies=frequencies ) in_features += self.fourier_layer_xyzt.out_features() else: self.fourier_layer_xyzt = None if in_features_params > 0: self.fourier_layer_params = layers.FourierLayer( in_features=in_features_params, frequencies=frequencies_params ) in_features += self.fourier_layer_params.out_features() else: self.fourier_layer_params = None self.fc_u = layers.FCLayer( in_features=in_features, out_features=layer_size, activation_fn=activation_fn, weight_norm=weight_norm, activation_par=activation_par, ) self.fc_v = layers.FCLayer( in_features=in_features, out_features=layer_size, activation_fn=activation_fn, weight_norm=weight_norm, activation_par=activation_par, ) self.fc_0 = layers.FCLayer( in_features, layer_size, activation_fn, weight_norm, activation_par=activation_par, ) self.fc_layers = nn.ModuleList() for i in range(nr_layers - 1): self.fc_layers.append( layers.FCLayer( layer_size, layer_size, activation_fn, weight_norm, activation_par=activation_par, ) ) self.final_layer = layers.FCLayer( in_features=layer_size, out_features=out_features, activation_fn=layers.Activation.IDENTITY, weight_norm=False, activation_par=None, ) def _tensor_forward(self, x: Tensor) -> Tensor: x = self.process_input( x, self.input_scales_tensor, input_dict=self.input_key_dict, dim=-1 ) if self.fourier_layer_xyzt is not None: in_xyzt_var = self.slice_input(x, self.xyzt_slice_index, dim=-1) fourier_xyzt = self.fourier_layer_xyzt(in_xyzt_var) x = torch.cat((x, fourier_xyzt), dim=-1) if self.fourier_layer_params is not None: in_params_var = self.slice_input(x, self.params_slice_index, dim=-1) fourier_params = self.fourier_layer_params(in_params_var) x = torch.cat((x, fourier_params), dim=-1) xu = self.fc_u(x) xv = self.fc_v(x) x = self.fc_0(x) x_skip: Optional[Tensor] = None for i, layer in enumerate(self.fc_layers, 1): x = layer(x) x = xu - x * xu + x * xv if self.skip_connections and i % 2 == 0: if x_skip is not None: x, x_skip = x + x_skip, x else: x_skip = x x = self.final_layer(x) x = self.process_output(x, self.output_scales_tensor) 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(), detach_dict=self.detach_key_dict, dim=-1, input_scales=self.input_scales, ) if self.fourier_layer_xyzt is not None: in_xyzt_var = self.prepare_input( in_vars, self.xyzt_var, detach_dict=self.detach_key_dict, dim=-1, input_scales=self.input_scales, ) fourier_xyzt = self.fourier_layer_xyzt(in_xyzt_var) x = torch.cat((x, fourier_xyzt), dim=-1) if self.fourier_layer_params is not None: in_params_var = self.prepare_input( in_vars, self.params_var, detach_dict=self.detach_key_dict, dim=-1, input_scales=self.input_scales, ) fourier_params = self.fourier_layer_params(in_params_var) x = torch.cat((x, fourier_params), dim=-1) xu = self.fc_u(x) xv = self.fc_v(x) x = self.fc_0(x) x_skip: Optional[Tensor] = None for i, layer in enumerate(self.fc_layers, 1): x = layer(x) x = xu - x * xu + x * xv if self.skip_connections and i % 2 == 0: if x_skip is not None: x, x_skip = x + x_skip, x else: x_skip = x x = self.final_layer(x) return self.prepare_output( x, self.output_key_dict, dim=-1, output_scales=self.output_scales )

© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.