text: import torch.nn as nn
  import math
  from .utils import load_url
  from lib.nn import SynchronizedBatchNorm2d
  BatchNorm2d = SynchronizedBatchNorm2d
  import torch
  
  __all__ = ['ResNeXt', 'resnext101'] # support resnext 101
  
  
  model_urls = {
      #'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-imagenet.pth',
      'resnext101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext101-imagenet.pth'
  }
  #Mish function
  from torch.nn import functional as F
  class Mish(nn.Module):
      def __init__(self):
          super().__init__()
          print("Mish Resnext activation loaded...")
  
      def forward(self, x):  
          #save 1 second per epoch with no x= x*() and then return x...just inline it.
          x = x *( torch.tanh(F.softplus(x)))
          return x 
  act_fn = Mish() #nn.ReLU(inplace=True)
  
  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 GroupBottleneck(nn.Module):
      expansion = 2
  
      def __init__(self, inplanes, planes, stride=1, groups=1, downsample=None):
          super(GroupBottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=1, groups=groups, bias=False)
          self.bn2 = BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=False)
          self.bn3 = BatchNorm2d(planes * 2)
          self.relu = act_fn
          self.downsample = downsample
          self.stride = stride
  
      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)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class ResNeXt(nn.Module):
  
      def __init__(self, block, layers, groups=32, num_classes=1000):
          self.inplanes = 128
          super(ResNeXt, self).__init__()
          self.conv1 = conv3x3(3, 64, stride=2)
          self.bn1 = BatchNorm2d(64)
          self.relu1 = act_fn
          self.conv2 = conv3x3(64, 64)
          self.bn2 = BatchNorm2d(64)
          self.relu2 = act_fn
          self.conv3 = conv3x3(64, 128)
          self.bn3 = BatchNorm2d(128)
          self.relu3 = act_fn
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
          self.layer1 = self._make_layer(block, 128, layers[0], groups=groups)
          self.layer2 = self._make_layer(block, 256, layers[1], stride=2, groups=groups)
          self.layer3 = self._make_layer(block, 512, layers[2], stride=2, groups=groups)
          self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, groups=groups)
          self.avgpool = nn.AvgPool2d(7, stride=1)
          self.fc = nn.Linear(1024 * block.expansion, num_classes)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels // m.groups
                  m.weight.data.normal_(0, math.sqrt(2. / n))
              elif isinstance(m, BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, stride=1, groups=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=False),
                  BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, groups, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, groups=groups))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.relu1(self.bn1(self.conv1(x)))
          x = self.relu2(self.bn2(self.conv2(x)))
          x = self.relu3(self.bn3(self.conv3(x)))
          x = self.maxpool(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
  
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
  
          return x
  
  
  '''
  def resnext50(pretrained=False, **kwargs):
      """Constructs a ResNet-50 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on Places
      """
      model = ResNeXt(GroupBottleneck, [3, 4, 6, 3], **kwargs)
      if pretrained:
          model.load_state_dict(load_url(model_urls['resnext50']), strict=False)
      return model
  '''
  
  
  def resnext101(pretrained=False, **kwargs):
      """Constructs a ResNet-101 model.
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on Places
      """
      model = ResNeXt(GroupBottleneck, [3, 4, 23, 3], **kwargs)
      if pretrained:
          model.load_state_dict(load_url(model_urls['resnext101']), strict=False)
      return model
  
  
  # def resnext152(pretrained=False, **kwargs):
  #     """Constructs a ResNeXt-152 model.
  #
  #     Args:
  #         pretrained (bool): If True, returns a model pre-trained on Places
  #     """
  #     model = ResNeXt(GroupBottleneck, [3, 8, 36, 3], **kwargs)
  #     if pretrained:
  #         model.load_state_dict(load_url(model_urls['resnext152']))
  #     return model
  