convs/modified_represnet.py

import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F

__all__ = ['ResNet', 'resnet18_rep', 'resnet34_rep' ]


def conv3x3(in_planes, out_planes, stride=1):
 "3x3 convolution with padding"
 return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
 padding=1, bias=True)


def conv1x1(in_planes, out_planes, stride=1):
 """1x1 convolution"""
 return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=True)

class conv_block(nn.Module):

 def __init__(self, in_planes, planes, mode, stride=1):
 super(conv_block, self).__init__()
 self.conv = conv3x3(in_planes, planes, stride)
 self.mode = mode
 if mode == 'parallel_adapters':
 self.adapter = conv1x1(in_planes, planes, stride)

 
 def re_init_conv(self):
 nn.init.kaiming_normal_(self.adapter.weight, mode='fan_out', nonlinearity='relu')
 return 
 def forward(self, x):
 y = self.conv(x)
 if self.mode == 'parallel_adapters':
 y = y + self.adapter(x)

 return y


class BasicBlock(nn.Module):
 expansion = 1

 def __init__(self, inplanes, planes, mode, stride=1, downsample=None):
 super(BasicBlock, self).__init__()
 self.conv1 = conv_block(inplanes, planes, mode, stride)
 self.norm1 = nn.BatchNorm2d(planes)
 self.relu = nn.ReLU(inplace=True)
 self.conv2 = conv_block(planes, planes, mode)
 self.norm2 = nn.BatchNorm2d(planes)
 self.mode = mode

 self.downsample = downsample
 self.stride = stride

 def forward(self, x):
 residual = x
 out = self.conv1(x)
 out = self.norm1(out)
 out = self.relu(out)
 out = self.conv2(out)
 out = self.norm2(out)
 if self.downsample is not None:
 residual = self.downsample(x)
 out += residual
 out = self.relu(out)
 return out


class ResNet(nn.Module):

 def __init__(self, block, layers, num_classes=100, args = None):
 self.inplanes = 64
 super(ResNet, self).__init__()
 assert args is not None
 self.mode = args["mode"]
 
 if 'cifar' in args["dataset"]:
 self.conv1 = nn.Sequential(nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False),
 nn.BatchNorm2d(self.inplanes), nn.ReLU(inplace=True))
 print("use cifar")
 elif 'imagenet' in args["dataset"] or 'stanfordcar' in args["dataset"]:
 if args["init_cls"] == args["increment"]:
 self.conv1 = nn.Sequential(
 nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False),
 nn.BatchNorm2d(self.inplanes),
 nn.ReLU(inplace=True),
 nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
 )
 else:
 # Following PODNET implmentation
 self.conv1 = nn.Sequential(
 nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False),
 nn.BatchNorm2d(self.inplanes),
 nn.ReLU(inplace=True),
 nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
 )


 self.layer1 = self._make_layer(block, 64, layers[0])
 self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
 self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
 self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
 self.feature = nn.AvgPool2d(4, stride=1)
 self.out_dim = 512


 for m in self.modules():
 if isinstance(m, nn.Conv2d):
 nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
 elif isinstance(m, nn.BatchNorm2d):
 nn.init.constant_(m.weight, 1)
 nn.init.constant_(m.bias, 0)

 def _make_layer(self, block, planes, blocks, stride=1):
 downsample = None
 if stride != 1 or self.inplanes != planes * block.expansion:
 downsample = nn.Sequential(
 nn.Conv2d(self.inplanes, planes * block.expansion,
 kernel_size=1, stride=stride, bias=True),
 )
 layers = []
 layers.append(block(self.inplanes, planes, self.mode, stride, downsample))
 self.inplanes = planes * block.expansion
 for i in range(1, blocks):
 layers.append(block(self.inplanes, planes, self.mode))

 return nn.Sequential(*layers)

 def switch(self, mode='normal'):
 for name, module in self.named_modules():
 if hasattr(module, 'mode'):
 module.mode = mode
 def re_init_params(self):
 for name, module in self.named_modules():
 if hasattr(module, 're_init_conv'):
 module.re_init_conv()
 def forward(self, x):
 x = self.conv1(x)

 x = self.layer1(x)
 x = self.layer2(x)
 x = self.layer3(x)
 x = self.layer4(x)
 dim = x.size()[-1]
 pool = nn.AvgPool2d(dim, stride=1)
 x = pool(x)
 x = x.view(x.size(0), -1)
 return {"features": x}


def resnet18_rep(pretrained=False, **kwargs):
 """Constructs a ResNet-18 model.
 Args:
 pretrained (bool): If True, returns a model pre-trained on ImageNet
 """
 model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
 if pretrained:
 pretrained_state_dict = model_zoo.load_url(model_urls['resnet18'])
 now_state_dict = model.state_dict()
 now_state_dict.update(pretrained_state_dict)
 model.load_state_dict(now_state_dict)
 return model


def resnet34_rep(pretrained=False, **kwargs):
 """Constructs a ResNet-34 model.
 Args:
 pretrained (bool): If True, returns a model pre-trained on ImageNet
 """
 model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
 if pretrained:
 pretrained_state_dict = model_zoo.load_url(model_urls['resnet34'])
 now_state_dict = model.state_dict()
 now_state_dict.update(pretrained_state_dict)
 model.load_state_dict(now_state_dict)
 return model