"""This is the slimmed ResNet as used by Lopez et al. in the GEM paper."""
import torch.nn as nn
from torch.nn.functional import relu, avg_pool2d
from avalanche.models import MultiHeadClassifier, MultiTaskModule, DynamicModule
class MLP(nn.Module):
def __init__(self, sizes):
super(MLP, self).__init__()
layers = []
for i in range(0, len(sizes) - 1):
layers.append(nn.Linear(sizes[i], sizes[i + 1]))
if i < (len(sizes) - 2):
layers.append(nn.ReLU())
self.net = nn.Sequential(*layers)
def forward(self, x):
return self.net(x)
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False,
)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(
in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm2d(self.expansion * planes),
)
def forward(self, x):
out = relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes, nf):
super(ResNet, self).__init__()
self.in_planes = nf
self.conv1 = conv3x3(3, nf * 1)
self.bn1 = nn.BatchNorm2d(nf * 1)
self.layer1 = self._make_layer(block, nf * 1, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, nf * 2, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, nf * 4, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, nf * 8, num_blocks[3], stride=2)
self.linear = nn.Linear(nf * 8 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
bsz = x.size(0)
out = relu(self.bn1(self.conv1(x.view(bsz, 3, 32, 32))))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
[docs]def SlimResNet18(nclasses, nf=20):
"""Slimmed ResNet18."""
return ResNet(BasicBlock, [2, 2, 2, 2], nclasses, nf)
[docs]class MTSlimResNet18(MultiTaskModule, DynamicModule):
"""MultiTask Slimmed ResNet18."""
[docs] def __init__(self, nclasses, nf=20):
super().__init__()
self.in_planes = nf
block = BasicBlock
num_blocks = [2, 2, 2, 2]
self.conv1 = conv3x3(3, nf * 1)
self.bn1 = nn.BatchNorm2d(nf * 1)
self.layer1 = self._make_layer(block, nf * 1, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, nf * 2, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, nf * 4, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, nf * 8, num_blocks[3], stride=2)
self.linear = MultiHeadClassifier(nf * 8 * BasicBlock.expansion, nclasses)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x, task_labels):
bsz = x.size(0)
out = relu(self.bn1(self.conv1(x.view(bsz, 3, 32, 32))))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out, task_labels)
return out