deeplearning/modulus/modulus-core-v030/_modules/modulus/models/mlp/fully_connected.html

Source code for modulus.models.mlp.fully_connected

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import torch
import torch.nn as nn
import modulus
from modulus.models.layers import FCLayer, get_activation

from torch import Tensor
from dataclasses import dataclass
from typing import Optional, Union, List
from ..meta import ModelMetaData
from ..module import Module


[docs]@dataclass class MetaData(ModelMetaData): name: str = "FullyConnected" # Optimization jit: bool = True cuda_graphs: bool = True amp: bool = True torch_fx: bool = True # Inference onnx: bool = True onnx_runtime: bool = True # Physics informed func_torch: bool = True auto_grad: bool = True
[docs]class FullyConnected(Module): """A densely-connected MLP architecture Parameters ---------- in_features : int, optional Size of input features, by default 512 layer_size : int, optional Size of every hidden layer, by default 512 out_features : int, optional Size of output features, by default 512 num_layers : int, optional Number of hidden layers, by default 6 activation_fn : Union[str, List[str]], optional Activation function to use, by default 'silu' skip_connections : bool, optional Add skip connections every 2 hidden layers, by default False adaptive_activations : bool, optional Use an adaptive activation function, by default False weight_norm : bool, optional Use weight norm on fully connected layers, by default False Example ------- >>> model = modulus.models.mlp.FullyConnected(in_features=32, out_features=64) >>> input = torch.randn(128, 32) >>> output = model(input) >>> output.size() torch.Size([128, 64]) """ def __init__( self, in_features: int = 512, layer_size: int = 512, out_features: int = 512, num_layers: int = 6, activation_fn: Union[str, List[str]] = "silu", skip_connections: bool = False, adaptive_activations: bool = False, weight_norm: bool = False, ) -> None: super().__init__(meta=MetaData()) self.skip_connections = skip_connections if adaptive_activations: activation_par = nn.Parameter(torch.ones(1)) else: activation_par = None if not isinstance(activation_fn, list): activation_fn = [activation_fn] * num_layers if len(activation_fn) < num_layers: activation_fn = activation_fn + [activation_fn[-1]] * ( num_layers - len(activation_fn) ) activation_fn = [get_activation(a) for a in activation_fn] self.layers = nn.ModuleList() layer_in_features = in_features for i in range(num_layers): self.layers.append( FCLayer( layer_in_features, layer_size, activation_fn[i], weight_norm, activation_par, ) ) layer_in_features = layer_size self.final_layer = FCLayer( in_features=layer_size, out_features=out_features, activation_fn=None, weight_norm=False, activation_par=None, )
[docs] def forward(self, x: Tensor) -> Tensor: x_skip: Optional[Tensor] = None for i, layer in enumerate(self.layers): x = layer(x) if self.skip_connections and i % 2 == 0: if x_skip is not None: x, x_skip = x + x_skip, x else: x_skip = x x = self.final_layer(x) return x
© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.