deeplearning/modulus/modulus-sym-v110/_modules/modulus/sym/models/dgm.html
Source code for modulus.sym.models.dgm
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import List, Dict
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 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
)