# Copyright 2022-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import logging
from backbone import MammothBackbone, num_flat_features, register_backbone, xavier
[docs]
class BaseMNISTMLP(MammothBackbone):
"""
Network composed of two hidden layers, each containing 100 ReLU activations.
Designed for the MNIST dataset.
"""
def __init__(self, input_size: int, output_size: int, hidden_size=100) -> None:
"""
Instantiates the layers of the network.
Args:
input_size: the size of the input data
output_size: the size of the output
"""
super(BaseMNISTMLP, self).__init__()
self.input_size = input_size
self.output_size = output_size
self.fc1 = nn.Linear(self.input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, hidden_size)
self._features = nn.Sequential(
self.fc1,
nn.ReLU(),
self.fc2,
nn.ReLU(),
)
self.classifier = nn.Linear(hidden_size, self.output_size)
self.net = nn.Sequential(self._features, self.classifier)
self.reset_parameters()
[docs]
def reset_parameters(self) -> None:
"""
Calls the Xavier parameter initialization function.
"""
self.net.apply(xavier)
[docs]
def forward(self, x: torch.Tensor, returnt='out') -> torch.Tensor:
"""
Compute a forward pass.
Args:
x: input tensor (batch_size, input_size)
Returns:
output tensor (output_size)
"""
x = x.view(-1, num_flat_features(x))
feats = self._features(x)
if returnt == 'features':
return feats
out = self.classifier(feats)
if returnt == 'out':
return out
elif returnt == 'full':
return (out, feats)
raise NotImplementedError("Unknown return type")
[docs]
@register_backbone("mnistmlp")
def mnistmlp(mlp_hidden_size: int = 100) -> BaseMNISTMLP:
if mlp_hidden_size != 100:
logging.info(f"hidden size is set to `{mlp_hidden_size}` instead of the default `100`")
return BaseMNISTMLP(28 * 28, 10, hidden_size=mlp_hidden_size)