import typing
import torch
from torch import nn
[docs]class Critic(nn.Module):
[docs] def __init__(self, x_input: int, critic_layers: typing.List[int]) -> None:
"""
Non-conditional Critic's constructor.
Parameters
----------
x_input : int
The dimension of the input tensor.
critic_layers : typing.List[int]
List of integers corresponding to the number of neurons
at each hidden layer of the critic.
"""
super(Critic, self).__init__()
self.x_input = x_input
self.critic_layers = critic_layers
self._create_critic()
[docs] def forward(self, data: torch.Tensor, *args, **kwargs) -> torch.Tensor:
"""
Function for completing a forward pass of the critic.
Parameters
----------
data : torch.Tensor
Tensor containing gene expression of (fake/real) cells.
*args
Variable length argument list.
**kwargs
Arbitrary keyword arguments.
Returns
-------
torch.Tensor
1-dimensional tensor representing fake/real cells.
"""
return self._critic(data)
[docs] def _create_critic(self) -> None:
"""Method for creating the Critic's network."""
layers = []
input_size = self.x_input
for output_size in self.critic_layers:
layers.append(self._create_critic_block(input_size, output_size))
input_size = output_size # update input size for the next layer
# outermost layer
layers.append(self._create_critic_block(input_size, 1, final_layer=True))
self._critic = nn.Sequential(*layers)
[docs] @staticmethod
def _create_critic_block(
input_dim: int, output_dim: int, final_layer: typing.Optional[bool] = False
) -> nn.Sequential:
"""
Function for creating a sequence of operations corresponding to
a Critic block; a linear layer, and ReLU (except in the final block).
Parameters
----------
input_dim : int
The block's input dimensions.
output_dim : int
The block's output dimensions.
final_layer : typing.Optional[bool], optional
Indicates if the block contains the final layer, by default False.
Returns
-------
nn.Sequential
Sequential container containing the modules.
"""
linear_layer = nn.Linear(input_dim, output_dim)
torch.nn.init.zeros_(linear_layer.bias)
if not final_layer:
torch.nn.init.kaiming_normal_(
linear_layer.weight, mode="fan_in", nonlinearity="relu"
)
return nn.Sequential(linear_layer, nn.ReLU(inplace=True))
# don't use an activation function at the
# outermost layer of the critic's network
else:
torch.nn.init.xavier_uniform_(linear_layer.weight)
return nn.Sequential(linear_layer)
[docs]class ConditionalCritic(Critic):
[docs] def __init__(
self, x_input: int, critic_layers: typing.List[int], num_classes: int
) -> None:
"""
Conditional Critic's constructor - Projection Discriminator (Miyato et al.,2018).
Parameters
----------
x_input : int
The dimension of the input tensor.
critic_layers : typing.List[int]
List of integers corresponding to the number of neurons
at each hidden layer of the critic.
num_classes : int
Number of clusters.
"""
self.num_classes = num_classes
super(ConditionalCritic, self).__init__(x_input, critic_layers)
[docs] def forward(
self, data: torch.Tensor, labels: torch.Tensor = None, *args, **kwargs
) -> torch.Tensor:
"""
Function for completing a forward pass of the conditional critic.
Parameters
----------
data : torch.Tensor
Tensor containing gene expression of (fake/real) cells.
labels : torch.Tensor
Tensor containing labels corresponding to cells (data parameter).
*args
Variable length argument list.
**kwargs
Arbitrary keyword arguments.
Returns
-------
torch.Tensor
1-dimensional tensor representing fake/real cells.
"""
y = data
for layer in self._critic[:-2]:
y = layer(y)
output = self._critic[-2](y)
proj = self._critic[-1](labels)
output += torch.sum(proj * y, dim=1, keepdim=True)
return output
[docs] def _create_critic(self) -> None:
"""Method for creating the Conditional Critic's network."""
self._critic = nn.ModuleList()
input_size = self.x_input
for output_size in self.critic_layers:
self._critic.append(self._create_critic_block(input_size, output_size))
input_size = output_size # update input size for the next layer
# outermost layer
self._critic.append(self._create_critic_block(input_size, 1, final_layer=True))
# projection layer
proj_layer = nn.Embedding(self.num_classes, input_size)
nn.init.xavier_uniform_(proj_layer.weight)
self._critic.append(proj_layer)
[docs]class ConditionalCriticProj(Critic):
[docs] def __init__(
self, x_input: int, critic_layers: typing.List[int], num_classes: int
) -> None:
"""
Conditional Critic's constructor using a modified implementation of
Projection Discriminator (Marouf et al, 2020).
Parameters
----------
x_input : int
The dimension of the input tensor.
critic_layers : typing.List[int]
List of integers corresponding to the number of neurons
at each hidden layer of the critic.
num_classes : int
Number of clusters.
"""
self.num_classes = num_classes
super(ConditionalCriticProj, self).__init__(x_input, critic_layers)
[docs] def forward(
self, data: torch.Tensor, labels: torch.Tensor = None, *args, **kwargs
) -> torch.Tensor:
"""
Function for completing a forward pass of the conditional critic.
Parameters
----------
data : torch.Tensor
Tensor containing gene expression of (fake/real) cells.
labels : torch.Tensor
Tensor containing labels corresponding to cells (data parameter).
*args
Variable length argument list.
**kwargs
Arbitrary keyword arguments.
Returns
-------
torch.Tensor
1-dimensional tensor representing fake/real cells.
"""
y = data
for layer in self._critic[:-2]:
y = layer(y)
output = self._critic[-2](labels)
proj = self._critic[-1](labels)
output += torch.sum(proj * y, dim=1, keepdim=True)
return output
[docs] def _create_critic(self) -> None:
"""Method for creating the Conditional Critic's network."""
self._critic = nn.ModuleList()
input_size = self.x_input
for output_size in self.critic_layers:
self._critic.append(self._create_critic_block(input_size, output_size))
input_size = output_size # update input size for the next layer
# bias layer (replaces the output linear layer)
proj_bias = nn.Embedding(self.num_classes, 1)
torch.nn.init.zeros_(proj_bias.weight)
self._critic.append(proj_bias)
# projection layer
proj_layer = nn.Embedding(self.num_classes, input_size)
nn.init.xavier_uniform_(proj_layer.weight)
self._critic.append(proj_layer)
