text: #code taken from https://github.com/meliketoy/wide-resnet.pytorch
  import torch
  import torch.nn as nn
  import torch.nn.init as init
  import torch.nn.functional as F
  from torch.autograd import Variable
  from activations import *
  
  import sys
  import numpy as np
  
  #TODO: Some of the things are not equal to the model definition (from the authors)
  # which is here: https://github.com/szagoruyko/functional-zoo/blob/master/wide-resnet-50-2-export.ipynb
  
  def select_act(self, activation, dim, num_bins=None):
      if activation == 'relu':
          act = torch.nn.ReLU()
      elif activation == 'lma':
          act = LMA(num_bins=num_bins)
      elif activation == 'aplu':
          act = APLU(dim=dim, num_bins=num_bins)
      elif activation == 'prelu':
          act = PReLU()
      elif activation == 'swish':
          act = Swish(dim)
      return act
  
  def conv3x3(in_planes, out_planes, stride=1):
      #TODO: Authors use, in their conv2d a padding=0 by default if I am not mistaken
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
  
  def conv_init(m):
      classname = m.__class__.__name__
      if classname.find('Conv') != -1:
          init.xavier_uniform(m.weight, gain=np.sqrt(2))
          init.constant(m.bias, 0)
      elif classname.find('BatchNorm') != -1:
          init.constant(m.weight, 1)
          init.constant(m.bias, 0)
  
  class wide_basic(nn.Module):
      def __init__(self, in_planes, planes, dropout_rate, stride=1, activation='relu', numBins=8):
          super(wide_basic, self).__init__()
          self.bn1 = nn.BatchNorm2d(in_planes)
          self.act1 = select_act(activation, in_planes, numBins)
          self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True)
          self.dropout = nn.Dropout(p=dropout_rate)
          self.bn2 = nn.BatchNorm2d(planes)
          self.act2 = select_act(activation, planes, numBins)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True),
              )
  
      def forward(self, x):
          out = self.dropout(self.conv1(self.act1(self.bn1(x))))
          out = self.conv2(self.act2(self.bn2(out)))
          out += self.shortcut(x)
  
          return out
  
  class Wide_ResNet(nn.Module):
      def __init__(self, depth, widen_factor, dropout_rate, num_classes, activation='relu', numBins=8):
          super(Wide_ResNet, self).__init__()
          self.in_planes = 16
  
          assert ((depth-4)%6 ==0), 'Wide-resnet depth should be 6n+4'
          n = int((depth-4)/6)
          k = widen_factor
  
          nStages = [16, 16*k, 32*k, 64*k]
  
          self.conv1 = conv3x3(3,nStages[0]) #TODO: authors use stride=2, padding=3 in first convolution
          self.layer1 = self._wide_layer(wide_basic, nStages[1], n, dropout_rate, stride=1)
          self.layer2 = self._wide_layer(wide_basic, nStages[2], n, dropout_rate, stride=2)
          self.layer3 = self._wide_layer(wide_basic, nStages[3], n, dropout_rate, stride=2)
          self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9)
          self.act = select_act(activation, nStages[3], numBins)
          self.linear = nn.Linear(nStages[3], num_classes)
          self.apply(conv_init)
  
      def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
          strides = [stride] + [1]*(num_blocks-1)
          layers = []
  
          for stride in strides:
              layers.append(block(self.in_planes, planes, dropout_rate, stride))
              self.in_planes = planes
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          out = self.conv1(x) #TODO: after first layer they use relu and maxpool2d with parameters 3, 2, 1
          out = self.layer1(out)
          out = self.layer2(out)
          out = self.layer3(out)
          out = self.act(self.bn1(out))
          out = F.avg_pool2d(out, 8)
          out = out.view(out.size(0), -1)
          out = self.linear(out)
  
          return out
  