NVIDIA Modulus Sym v1.2.0
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Source code for modulus.sym.models.siren

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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, 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

© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.