deeplearning/modulus/modulus-sym-v120/_modules/modulus/sym/models/fourier_net.html
Source code for modulus.sym.models.fourier_net
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import torch
from torch import Tensor
from typing import Dict, List, Tuple
import modulus.sym.models.fully_connected as fully_connected
import modulus.sym.models.layers as layers
from modulus.sym.models.layers import Activation
from modulus.sym.models.arch import Arch
from modulus.sym.key import Key
[docs]class FourierNetArch(Arch):
"""Fourier encoding fully-connected neural network.
This network is a fully-connected neural network that encodes the input features
into Fourier space using sinesoidal activation functions. This helps reduce spectal
bias during training.
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, optional
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'`, by default
("axis", [i for i in range(10)])
:obj:`'gaussian'` samples frequency of Fourier series from Gaussian.
:obj:`'axis'` samples along axis of spectral space with the given list range of
frequencies.
:obj:`'diagonal'` samples along diagonal of spectral space with the given list range
of frequencies.
:obj:`'full'` samples along entire spectral space for all combinations of frequencies
in given list.
frequencies_params : Tuple, optional
Same as `frequencies` used for encodings of any inputs not in the list
`['x', 'y', 'z', 't']`.
By default ("axis", [i for i in range(10)])
activation_fn : Activation, optional
Activation function, by default :obj:`Activation.SILU`
layer_size : int, optional
Layer size for every hidden layer of the model, by default 512
nr_layers : int, optional
Number of hidden layers of the model, by default 6
skip_connections : bool, optional
Apply skip connections every 2 hidden layers, by default False
weight_norm : bool, optional
Use weight norm on fully connected layers, by default True
adaptive_activations : bool, optional
Use an adaptive activation functions, by default False
Variable Shape
--------------
- Input variable tensor shape: :math:`[N, size]`
- Output variable tensor shape: :math:`[N, size]`
Example
-------
Gaussian frequencies
>>> std = 1.0; num_freq = 10
>>> model = .fourier_net.FourierNetArch(
>>> [Key("x", size=2)],
>>> [Key("y", size=2)],
>>> frequencies=("gaussian", std, num_freq))
Diagonal frequencies
>>> frequencies = [1.0, 2.0, 3.0, 4.0]
>>> model = .fourier_net.FourierNetArch(
>>> [Key("x", size=2)],
>>> [Key("y", size=2)],
>>> frequencies=("diagonal", frequencies))
Full frequencies
>>> frequencies = [1.0, 2.0, 3.0, 4.0]
>>> model = .fourier_net.FourierNetArch(
>>> [Key("x", size=2)],
>>> [Key("y", size=2)],
>>> frequencies=("full", frequencies))
Note
----
For information regarding adaptive activations please refer to
https://arxiv.org/abs/1906.01170.
"""
def __init__(
self,
input_keys: List[Key],
output_keys: List[Key],
detach_keys: List[Key] = [],
frequencies: Tuple = ("axis", [i for i in range(10)]),
frequencies_params: Tuple = ("axis", [i for i in range(10)]),
activation_fn: Activation = 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
)
if frequencies_params is None:
frequencies_params = frequencies
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())
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 = fully_connected.FullyConnectedArchCore(
in_features=in_features,
layer_size=layer_size,
out_features=out_features,
nr_layers=nr_layers,
skip_connections=skip_connections,
activation_fn=activation_fn,
adaptive_activations=adaptive_activations,
weight_norm=weight_norm,
)
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)
x = self.fc(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)
x = self.fc(x)
return self.prepare_output(
x, self.output_key_dict, dim=-1, output_scales=self.output_scales
)