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"""Defines the MLP network."""
from typing import Callable, Dict, List, Optional, Union
from einops.layers.torch import Rearrange
import torch
from torch import nn
from text_recognizer.networks.misc import activation_function
class MLP(nn.Module):
"""Multi layered perceptron network."""
def __init__(
self,
input_size: int = 784,
output_size: int = 10,
hidden_size: Union[int, List] = 128,
num_layers: int = 3,
dropout_rate: float = 0.2,
activation_fn: str = "relu",
) -> None:
"""Initialization of the MLP network.
Args:
input_size (int): The input shape of the network. Defaults to 784.
output_size (int): Number of classes in the dataset. Defaults to 10.
hidden_size (Union[int, List]): The number of `neurons` in each hidden layer. Defaults to 128.
num_layers (int): The number of hidden layers. Defaults to 3.
dropout_rate (float): The dropout rate at each layer. Defaults to 0.2.
activation_fn (str): Name of the activation function in the hidden layers. Defaults to
relu.
"""
super().__init__()
activation_fn = activation_function(activation_fn)
if isinstance(hidden_size, int):
hidden_size = [hidden_size] * num_layers
self.layers = [
Rearrange("b c h w -> b (c h w)"),
nn.Linear(in_features=input_size, out_features=hidden_size[0]),
activation_fn,
]
for i in range(num_layers - 1):
self.layers += [
nn.Linear(in_features=hidden_size[i], out_features=hidden_size[i + 1]),
activation_fn,
]
if dropout_rate:
self.layers.append(nn.Dropout(p=dropout_rate))
self.layers.append(
nn.Linear(in_features=hidden_size[-1], out_features=output_size)
)
self.layers = nn.Sequential(*self.layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""The feedforward pass."""
# If batch dimenstion is missing, it needs to be added.
if len(x.shape) == 3:
x = x.unsqueeze(0)
return self.layers(x)
@property
def __name__(self) -> str:
"""Returns the name of the network."""
return "mlp"
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