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

v22.09

Source code for modulus.models.dgm

from typing import List, Dict

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 DGMArch(Arch): """ A variation of the fully connected network. Reference: Sirignano, J. and Spiliopoulos, K., 2018. DGM: A deep learning algorithm for solving partial differential equations. Journal of computational physics, 375, pp.1339-1364. 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 [] 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. activation_fn : layers.Activation = layers.Activation.SILU Activation function used by network. adaptive_activations : bool = False If True then use an adaptive activation function as described here https://arxiv.org/abs/1906.01170. weight_norm : bool = True Use weight norm on fully connected layers. """ def __init__( self, input_keys: List[Key], output_keys: List[Key], detach_keys: List[Key] = [], layer_size: int = 512, nr_layers: int = 6, activation_fn=layers.Activation.SIN, adaptive_activations: bool = False, weight_norm: bool = True, ) -> None: super().__init__( input_keys=input_keys, output_keys=output_keys, detach_keys=detach_keys ) in_features = sum(self.input_key_dict.values()) out_features = sum(self.output_key_dict.values()) if adaptive_activations: activation_par = nn.Parameter(torch.ones(1)) else: activation_par = None self.fc_start = layers.FCLayer( in_features=in_features, out_features=layer_size, activation_fn=activation_fn, weight_norm=weight_norm, ) self.dgm_layers = nn.ModuleList() for _ in range(nr_layers - 1): single_layer = {} for key in ["z", "g", "r", "h"]: single_layer[key] = layers.DGMLayer( in_features_1=in_features, in_features_2=layer_size, out_features=layer_size, activation_fn=activation_fn, weight_norm=weight_norm, activation_par=activation_par, ) self.dgm_layers.append(nn.ModuleDict(single_layer)) self.fc_end = 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, periodicity=self.periodicity, input_dict=self.input_key_dict, dim=-1, ) s = self.fc_start(x) for layer in self.dgm_layers: # TODO: this can be optimized, 'z', 'g', 'r' can be merged into a # single layer with 3x output size z = layer["z"](x, s) g = layer["g"](x, s) r = layer["r"](x, s) h = layer["h"](x, s * r) s = h - g * h + z * s x = self.fc_end(s) 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, ) s = self.fc_start(x) for layer in self.dgm_layers: # TODO: this can be optimized, 'z', 'g', 'r' can be merged into a # single layer with 3x output size z = layer["z"](x, s) g = layer["g"](x, s) r = layer["r"](x, s) h = layer["h"](x, s * r) s = h - g * h + z * s x = self.fc_end(s) return self.prepare_output( x, self.output_key_dict, dim=-1, output_scales=self.output_scales )

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