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"""Defines the LeNet network."""
from typing import Callable, Dict, Optional, Tuple
from einops.layers.torch import Rearrange
import torch
from torch import nn
class LeNet(nn.Module):
"""LeNet network."""
def __init__(
self,
channels: Tuple[int, ...] = (1, 32, 64),
kernel_sizes: Tuple[int, ...] = (3, 3, 2),
hidden_size: Tuple[int, ...] = (9216, 128),
dropout_rate: float = 0.2,
output_size: int = 10,
activation_fn: Optional[Callable] = None,
activation_fn_args: Optional[Dict] = None,
) -> None:
"""The LeNet network.
Args:
channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64).
kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2).
hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers.
Defaults to (9216, 128).
dropout_rate (float): The dropout rate. Defaults to 0.2.
output_size (int): Number of classes. Defaults to 10.
activation_fn (Optional[Callable]): The non-linear activation function. Defaults to
nn.ReLU(inplace).
activation_fn_args (Optional[Dict]): The arguments for the activation function. Defaults to None.
"""
super().__init__()
if activation_fn is not None:
activation_fn_args = activation_fn_args or {}
activation_fn = getattr(nn, activation_fn)(**activation_fn_args)
else:
activation_fn = nn.ReLU(inplace=True)
self.layers = [
nn.Conv2d(
in_channels=channels[0],
out_channels=channels[1],
kernel_size=kernel_sizes[0],
),
activation_fn,
nn.Conv2d(
in_channels=channels[1],
out_channels=channels[2],
kernel_size=kernel_sizes[1],
),
activation_fn,
nn.MaxPool2d(kernel_sizes[2]),
nn.Dropout(p=dropout_rate),
Rearrange("b c h w -> b (c h w)"),
nn.Linear(in_features=hidden_size[0], out_features=hidden_size[1]),
activation_fn,
nn.Dropout(p=dropout_rate),
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."""
# If batch dimenstion is missing, it needs to be added.
if len(x.shape) == 3:
x = x.unsqueeze(0)
return self.layers(x)
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