################################################################################
# Copyright (c) 2021 ContinualAI. #
# Copyrights licensed under the MIT License. #
# See the accompanying LICENSE file for terms. #
# #
# Date: 1-05-2020 #
# Author(s): Vincenzo Lomonaco, Antonio Carta #
# E-mail: contact@continualai.org #
# Website: avalanche.continualai.org #
################################################################################
import torch.nn as nn
from avalanche.models.dynamic_modules import MultiTaskModule, \
MultiHeadClassifier
[docs]class SimpleCNN(nn.Module):
"""
Convolutional Neural Network
"""
[docs] def __init__(self, num_classes=10):
super(SimpleCNN, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(32, 32, kernel_size=3, padding=0),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(p=0.25),
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=0),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Dropout(p=0.25),
nn.Conv2d(64, 64, kernel_size=1, padding=0),
nn.ReLU(inplace=True),
nn.AdaptiveMaxPool2d(1),
nn.Dropout(p=0.25)
)
self.classifier = nn.Sequential(
nn.Linear(64, num_classes)
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
[docs]class MTSimpleCNN(SimpleCNN, MultiTaskModule):
"""Convolutional Neural Network
with multi-head classifier"""
[docs] def __init__(self):
super().__init__()
self.classifier = MultiHeadClassifier(64)
def forward(self, x, task_labels):
x = self.features(x)
x = x.squeeze()
x = self.classifier(x, task_labels)
return x
__all__ = [
'SimpleCNN',
'MTSimpleCNN'
]