text: ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  ## Created by: Yaoyao Liu
  ## Modified from: https://github.com/hshustc/CVPR19_Incremental_Learning
  ## Max Planck Institute for Informatics
  ## yaoyao.liu@mpi-inf.mpg.de
  ## Copyright (c) 2021
  ##
  ## This source code is licensed under the MIT-style license found in the
  ## LICENSE file in the root directory of this source tree
  ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  import torch.nn as nn
  import math
  import torch.utils.model_zoo as model_zoo
  import models.modified_linear as modified_linear
  
  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=False)
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, last=False):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes)
          self.downsample = downsample
          self.stride = stride
          self.last = last
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          if not self.last: 
              out = self.relu(out)
  
          return out
  
  class ResNet(nn.Module):
  
      def __init__(self, block, layers, num_classes=10):
          self.inplanes = 16
          super(ResNet, self).__init__()
          self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(16)
          self.relu = nn.ReLU(inplace=True)
          self.layer1 = self._make_layer(block, 16, layers[0])
          self.layer2 = self._make_layer(block, 32, layers[1], stride=2)
          self.layer3 = self._make_layer(block, 64, layers[2], stride=2, last_phase=True)
          self.avgpool = nn.AvgPool2d(8, stride=1)
          self.fc = modified_linear.CosineLinear(64 * block.expansion, num_classes)
  
          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, last_phase=False):
          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=False),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample))
          self.inplanes = planes * block.expansion
          if last_phase:
              for i in range(1, blocks-1):
                  layers.append(block(self.inplanes, planes))
              layers.append(block(self.inplanes, planes, last=True))
          else: 
              for i in range(1, blocks):
                  layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def forward(self, x, return_z_also=False, return_gap_also=False):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
          inner_x = self.layer3(x)
  
          x = self.avgpool(inner_x)
          z = x.view(x.size(0), -1)
          x = self.fc(z)
  
          if return_gap_also:
              # Returns Global Average Pool of the third conv layer, along with x and z.
              gap_features = inner_x.mean(dim=(2, 3))
              return x, z, gap_features
  
          if return_z_also:
              return x, z
  
          return x
  
  
  def resnet18(pretrained=False, **kwargs):
      model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
      return model
  
  
  def resnet20(pretrained=False, **kwargs):
      n = 3
      model = ResNet(BasicBlock, [n, n, n], **kwargs)
      return model
  
  
  def resnet32(pretrained=False, **kwargs):
      n = 5
      model = ResNet(BasicBlock, [n, n, n], **kwargs)
      return model
  