deeplearning/modulus/modulus-sym-v120/_modules/modulus/sym/models/siren.html
Source code for modulus.sym.models.siren
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#
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from typing import List, Dict, Tuple, Optional, Union
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
from modulus.sym.constants import NO_OP_NORM
[docs]class SirenArch(Arch):
"""Sinusoidal Representation Network (SIREN).
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, 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
first_omega : float, optional
Scales first weight matrix by this factor, by default 30
omega : float, optional
Scales the weight matrix of all hidden layers by this factor, by default 30
normalization : Dict[str, Tuple[float, float]], optional
Normalization of input to network, by default None
Variable Shape
--------------
- Input variable tensor shape: :math:`[N, size]`
- Output variable tensor shape: :math:`[N, size]`
Example
-------
Siren model (2 -> 64 -> 64 -> 2)
>>> arch = .siren.SirenArch(
>>> [Key("x", size=2)],
>>> [Key("y", size=2)],
>>> layer_size = 64,
>>> nr_layers = 2)
>>> model = arch.make_node()
>>> input = {"x": torch.randn(64, 2)}
>>> output = model.evaluate(input)
Note
----
Reference: Sitzmann, Vincent, et al.
Implicit Neural Representations with Periodic Activation Functions.
https://arxiv.org/abs/2006.09661.
"""
def __init__(
self,
input_keys: List[Key],
output_keys: List[Key],
detach_keys: List[Key] = [],
layer_size: int = 512,
nr_layers: int = 6,
first_omega: float = 30.0,
omega: float = 30.0,
normalization: Dict[str, Tuple[float, float]] = None,
) -> 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())
layers_list = []
layers_list.append(
layers.SirenLayer(
in_features,
layer_size,
layers.SirenLayerType.FIRST,
first_omega,
)
)
for _ in range(nr_layers - 1):
layers_list.append(
layers.SirenLayer(
layer_size, layer_size, layers.SirenLayerType.HIDDEN, omega
)
)
layers_list.append(
layers.SirenLayer(
layer_size, out_features, layers.SirenLayerType.LAST, omega
)
)
self.layers = nn.Sequential(*layers_list)
self.normalization: Optional[Dict[str, Tuple[float, float]]] = normalization
# iterate input keys and add NO_OP_NORM if it is not specified
if self.normalization is not None:
for key in self.input_key_dict:
if key not in self.normalization:
self.normalization[key] = NO_OP_NORM
self.register_buffer(
"normalization_tensor",
self._get_normalization_tensor(self.input_key_dict, self.normalization),
persistent=False,
)
def _tensor_forward(self, x: Tensor) -> Tensor:
x = self._tensor_normalize(x, self.normalization_tensor)
x = self.process_input(
x, self.input_scales_tensor, input_dict=self.input_key_dict, dim=-1
)
x = self.layers(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(
self._normalize(in_vars, self.normalization),
self.input_key_dict.keys(),
detach_dict=self.detach_key_dict,
dim=-1,
input_scales=self.input_scales,
)
x = self.layers(x)
return self.prepare_output(
x, self.output_key_dict, dim=-1, output_scales=self.output_scales
)
def _normalize(
self,
in_vars: Dict[str, Tensor],
norms: Optional[Dict[str, Tuple[float, float]]],
) -> Dict[str, Tensor]:
if norms is None:
return in_vars
normalized_in_vars = {}
for k, v in in_vars.items():
if k in norms:
v = (v - norms[k][0]) / (norms[k][1] - norms[k][0])
v = 2 * v - 1
normalized_in_vars[k] = v
return normalized_in_vars