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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import config_task
import math
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
def conv1x1_fonc(in_planes, out_planes=None, stride=1, bias=False):
if out_planes is None:
return nn.Conv2d(in_planes, in_planes, kernel_size=1, stride=stride, padding=0, bias=bias)
else:
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=bias)
class conv1x1(nn.Module):
def __init__(self, planes, out_planes=None, stride=1):
super(conv1x1, self).__init__()
if config_task.mode == 'series_adapters':
self.conv = nn.Sequential(nn.BatchNorm2d(planes), conv1x1_fonc(planes))
elif config_task.mode == 'parallel_adapters':
self.conv = conv1x1_fonc(planes, out_planes, stride)
else:
self.conv = conv1x1_fonc(planes)
def forward(self, x):
y = self.conv(x)
if config_task.mode == 'series_adapters':
y += x
return y
class conv_task(nn.Module):
def __init__(self, in_planes, planes, stride=1, nb_tasks=1, is_proj=1, second=0):
super(conv_task, self).__init__()
self.is_proj = is_proj
self.second = second
self.conv = conv3x3(in_planes, planes, stride)
if config_task.mode == 'series_adapters' and is_proj:
self.bns = nn.ModuleList([nn.Sequential(conv1x1(planes), nn.BatchNorm2d(planes)) for i in range(nb_tasks)])
elif config_task.mode == 'parallel_adapters' and is_proj:
self.parallel_conv = nn.ModuleList([conv1x1(in_planes, planes, stride) for i in range(nb_tasks)])
self.bns = nn.ModuleList([nn.BatchNorm2d(planes) for i in range(nb_tasks)])
else:
self.bns = nn.ModuleList([nn.BatchNorm2d(planes) for i in range(nb_tasks)])
def forward(self, x):
task = config_task.task
y = self.conv(x)
'''
if self.second == 0:
if config_task.isdropout1:
x = F.dropout2d(x, p=0.5, training = self.training)
else:
if config_task.isdropout2:
x = F.dropout2d(x, p=0.5, training = self.training)
if config_task.mode == 'parallel_adapters' and self.is_proj:
y = y + self.parallel_conv[task](x)
'''
y = self.bns[task](y)
return y
# No projection: identity shortcut
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, shortcut=0, nb_tasks=1):
super(BasicBlock, self).__init__()
self.conv1 = conv_task(in_planes, planes, stride, nb_tasks, is_proj=int(config_task.proj[0]))
self.conv2 = nn.Sequential(nn.ReLU(True), conv_task(planes, planes, 1, nb_tasks, is_proj=int(config_task.proj[1]), second=1))
self.shortcut = shortcut
if self.shortcut == 1:
self.avgpool = nn.AvgPool2d(2)
def forward(self, x):
residual = x
y = self.conv1(x)
y = self.conv2(y)
if self.shortcut == 1:
residual = self.avgpool(x)
residual = torch.cat((residual, residual*0),1)
y += residual
y = F.relu(y)
return y
class ResNet(nn.Module):
def __init__(self, block, nblocks, num_classes=[10]):
super(ResNet, self).__init__()
nb_tasks = len(num_classes)
blocks = [block, block, block]
factor = config_task.factor
self.in_planes = int(32*factor)
self.pre_layers_conv = conv_task(3,int(32*factor), 1, nb_tasks)
self.layer1 = self._make_layer(blocks[0], int(64*factor), nblocks[0], stride=2, nb_tasks=nb_tasks)
self.layer2 = self._make_layer(blocks[1], int(128*factor), nblocks[1], stride=2, nb_tasks=nb_tasks)
self.layer3 = self._make_layer(blocks[2], int(256*factor), nblocks[2], stride=2, nb_tasks=nb_tasks)
self.end_bns = nn.ModuleList([nn.Sequential(nn.BatchNorm2d(int(256*factor)),nn.ReLU(True)) for i in range(nb_tasks)])
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.linears = nn.ModuleList([nn.Linear(int(256*factor), num_classes[i]) for i in range(nb_tasks)])
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, nblocks, stride=1, nb_tasks=1):
shortcut = 0
if stride != 1 or self.in_planes != planes * block.expansion:
shortcut = 1
layers = []
layers.append(block(self.in_planes, planes, stride, shortcut, nb_tasks=nb_tasks))
self.in_planes = planes * block.expansion
for i in range(1, nblocks):
layers.append(block(self.in_planes, planes, nb_tasks=nb_tasks))
return nn.Sequential(*layers)
def forward(self, x):
x = self.pre_layers_conv(x)
task = config_task.task
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.end_bns[task](x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.linears[task](x)
return x
def resnet26(num_classes=10, blocks=BasicBlock):
return ResNet(blocks, [4,4,4],num_classes)