deeplearning/modulus/modulus-core-v021/_modules/modulus/models/mlp/fully_connected.html

Core v0.2.1

Source code for modulus.models.mlp.fully_connected

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import torch
import torch.nn as nn
import modulus

from torch import Tensor
from dataclasses import dataclass
from typing import Optional, Union, List
from modulus.models.layers import FCLayer
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[nn.Module, List[nn.Module]], optional Activation function to use, by default nn.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[nn.Module, List[nn.Module]] = nn.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) ) 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 Sep 21, 2023.