Source code for pina.model.block.residual
"""Module for residual blocks and enhanced linear layers."""
import torch
from torch import nn
from ...utils import check_consistency
[docs]
class ResidualBlock(nn.Module):
"""
Residual block class.
.. seealso::
**Original reference**: He, Kaiming, et al.
*Deep residual learning for image recognition.*
Proceedings of the IEEE conference on computer vision and pattern
recognition. 2016.
DOI: `<https://arxiv.org/pdf/1512.03385.pdf>`_.
"""
def __init__(
self,
input_dim,
output_dim,
hidden_dim,
spectral_norm=False,
activation=torch.nn.ReLU(),
):
"""
Initialization of the :class:`ResidualBlock` class.
:param int input_dim: The input dimension.
:param int output_dim: The output dimension.
:param int hidden_dim: The hidden dimension.
:param bool spectral_norm: If ``True``, the spectral normalization is
applied to the feedforward layers. Default is ``False``.
:param torch.nn.Module activation: The activation function.
Default is :class:`torch.nn.ReLU`.
"""
super().__init__()
# check consistency
check_consistency(spectral_norm, bool)
check_consistency(input_dim, int)
check_consistency(output_dim, int)
check_consistency(hidden_dim, int)
check_consistency(activation, torch.nn.Module)
# assign variables
self._spectral_norm = spectral_norm
self._input_dim = input_dim
self._output_dim = output_dim
self._hidden_dim = hidden_dim
self._activation = activation
# create layers
self._l1 = self._spect_norm(nn.Linear(input_dim, hidden_dim))
self._l2 = self._spect_norm(nn.Linear(hidden_dim, output_dim))
self._l3 = self._spect_norm(nn.Linear(input_dim, output_dim))
[docs]
def forward(self, x):
"""
Forward pass.
:param torch.Tensor x: The input tensor.
:return: The output tensor.
:rtype: torch.Tensor
"""
y = self._activation(self._l1(x))
y = self._l2(y)
x = self._l3(x)
return y + x
def _spect_norm(self, x):
"""
Perform spectral normalization on the network layers.
:param torch.nn.Module x: A :class:`torch.nn.Linear` layer.
:return: The spectral norm of the layer
:rtype: torch.nn.Module
"""
return nn.utils.spectral_norm(x) if self._spectral_norm else x
[docs]
class EnhancedLinear(torch.nn.Module):
"""
Enhanced Linear layer class.
This class is a wrapper for enhancing a linear layer with activation and/or
dropout.
"""
def __init__(self, layer, activation=None, dropout=None):
"""
Initialization of the :class:`EnhancedLinear` class.
:param torch.nn.Module layer: The linear layer to be enhanced.
:param torch.nn.Module activation: The activation function. Default is
``None``.
:param float dropout: The dropout probability. Default is ``None``.
:Example:
>>> linear_layer = torch.nn.Linear(10, 20)
>>> activation = torch.nn.ReLU()
>>> dropout_prob = 0.5
>>> enhanced_linear = EnhancedLinear(
... linear_layer,
... activation,
... dropout_prob
... )
"""
super().__init__()
# check consistency
check_consistency(layer, nn.Module)
if activation is not None:
check_consistency(activation, nn.Module)
if dropout is not None:
check_consistency(dropout, float)
# assign forward
if (dropout is None) and (activation is None):
self._model = torch.nn.Sequential(layer)
elif (dropout is None) and (activation is not None):
self._model = torch.nn.Sequential(layer, activation)
elif (dropout is not None) and (activation is None):
self._model = torch.nn.Sequential(layer, self._drop(dropout))
elif (dropout is not None) and (activation is not None):
self._model = torch.nn.Sequential(
layer, activation, self._drop(dropout)
)
[docs]
def forward(self, x):
"""
Forward pass.
:param torch.Tensor x: The input tensor.
:return: The output tensor.
:rtype: torch.Tensor
"""
return self._model(x)
def _drop(self, p):
"""
Apply dropout with probability p.
:param float p: Dropout probability.
:return: Dropout layer with the specified probability.
:rtype: torch.nn.Dropout
"""
return torch.nn.Dropout(p)