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