code: """
  ResNet code gently borrowed from
  https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
  """
  from __future__ import print_function, division, absolute_import
  from collections import OrderedDict
  import math
  import torch
  import torch.nn as nn
  from torch.utils import model_zoo
  
  __all__ = ['SENet', 'senet154', 'se_resnet50', 'se_resnet101', 'se_resnet152',
             'se_resnext50_32x4d', 'se_resnext101_32x4d']
  
  pretrained_settings = {
      'senet154': {
          'imagenet': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth',
              'input_space': 'RGB',
              'input_size': [3, 224, 224],
              'input_range': [0, 1],
              'mean': [0.485, 0.456, 0.406],
              'std': [0.229, 0.224, 0.225],
              'num_classes': 1000
          }
      },
      'se_resnet50': {
          'imagenet': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth',
              'input_space': 'RGB',
              'input_size': [3, 224, 224],
              'input_range': [0, 1],
              'mean': [0.485, 0.456, 0.406],
              'std': [0.229, 0.224, 0.225],
              'num_classes': 1000
          }
      },
      'se_resnet101': {
          'imagenet': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth',
              'input_space': 'RGB',
              'input_size': [3, 224, 224],
              'input_range': [0, 1],
              'mean': [0.485, 0.456, 0.406],
              'std': [0.229, 0.224, 0.225],
              'num_classes': 1000
          }
      },
      'se_resnet152': {
          'imagenet': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth',
              'input_space': 'RGB',
              'input_size': [3, 224, 224],
              'input_range': [0, 1],
              'mean': [0.485, 0.456, 0.406],
              'std': [0.229, 0.224, 0.225],
              'num_classes': 1000
          }
      },
      'se_resnext50_32x4d': {
          'imagenet': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth',
              'input_space': 'RGB',
              'input_size': [3, 224, 224],
              'input_range': [0, 1],
              'mean': [0.485, 0.456, 0.406],
              'std': [0.229, 0.224, 0.225],
              'num_classes': 1000
          }
      },
      'se_resnext101_32x4d': {
          'imagenet': {
              'url': 'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth',
              'input_space': 'RGB',
              'input_size': [3, 224, 224],
              'input_range': [0, 1],
              'mean': [0.485, 0.456, 0.406],
              'std': [0.229, 0.224, 0.225],
              'num_classes': 1000
          }
      },
  }
  
  
  class SEModule(nn.Module):
  
      def __init__(self, channels, reduction):
          super(SEModule, self).__init__()
          self.avg_pool = nn.AdaptiveAvgPool2d(1)
          self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
                               padding=0)
          self.relu = nn.ReLU(inplace=True)
          self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1,
                               padding=0)
          self.sigmoid = nn.Sigmoid()
  
      def forward(self, x):
          module_input = x
          x = self.avg_pool(x)
          x = self.fc1(x)
          x = self.relu(x)
          x = self.fc2(x)
          x = self.sigmoid(x)
          return module_input * x
  
  
  class Bottleneck(nn.Module):
      """
      Base class for bottlenecks that implements `forward()` method.
      """
      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 = self.se_module(out) + residual
          out = self.relu(out)
  
          return out
  
  
  class SEBottleneck(Bottleneck):
      """
      Bottleneck for SENet154.
      """
      expansion = 4
  
      def __init__(self, inplanes, planes, groups, reduction, stride=1,
                   downsample=None):
          super(SEBottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes * 2)
          self.conv2 = nn.Conv2d(planes * 2, planes * 4, kernel_size=3,
                                 stride=stride, padding=1, groups=groups,
                                 bias=False)
          self.bn2 = nn.BatchNorm2d(planes * 4)
          self.conv3 = nn.Conv2d(planes * 4, planes * 4, kernel_size=1,
                                 bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.se_module = SEModule(planes * 4, reduction=reduction)
          self.downsample = downsample
          self.stride = stride
  
  
  class SEResNetBottleneck(Bottleneck):
      """
      ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
      implementation and uses `stride=stride` in `conv1` and not in `conv2`
      (the latter is used in the torchvision implementation of ResNet).
      """
      expansion = 4
  
      def __init__(self, inplanes, planes, groups, reduction, stride=1,
                   downsample=None):
          super(SEResNetBottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False,
                                 stride=stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1,
                                 groups=groups, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.se_module = SEModule(planes * 4, reduction=reduction)
          self.downsample = downsample
          self.stride = stride
  
  
  class SEResNeXtBottleneck(Bottleneck):
      """
      ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
      """
      expansion = 4
  
      def __init__(self, inplanes, planes, groups, reduction, stride=1,
                   downsample=None, base_width=4):
          super(SEResNeXtBottleneck, self).__init__()
          width = math.floor(planes * (base_width / 64)) * groups
          self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False,
                                 stride=1)
          self.bn1 = nn.BatchNorm2d(width)
          self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
                                 padding=1, groups=groups, bias=False)
          self.bn2 = nn.BatchNorm2d(width)
          self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.se_module = SEModule(planes * 4, reduction=reduction)
          self.downsample = downsample
          self.stride = stride
  
  
  class SENet(nn.Module):
  
      def __init__(self, block, layers, groups, reduction, dropout_p=0.2,
                   inplanes=128, input_3x3=True, downsample_kernel_size=3,
                   downsample_padding=1, last_stride=2, last2_stride = 2):
          """
          Parameters
          ----------
          block (nn.Module): Bottleneck class.
              - For SENet154: SEBottleneck
              - For SE-ResNet models: SEResNetBottleneck
              - For SE-ResNeXt models:  SEResNeXtBottleneck
          layers (list of ints): Number of residual blocks for 4 layers of the
              network (layer1...layer4).
          groups (int): Number of groups for the 3x3 convolution in each
              bottleneck block.
              - For SENet154: 64
              - For SE-ResNet models: 1
              - For SE-ResNeXt models:  32
          reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
              - For all models: 16
          dropout_p (float or None): Drop probability for the Dropout layer.
              If `None` the Dropout layer is not used.
              - For SENet154: 0.2
              - For SE-ResNet models: None
              - For SE-ResNeXt models: None
          inplanes (int):  Number of input channels for layer1.
              - For SENet154: 128
              - For SE-ResNet models: 64
              - For SE-ResNeXt models: 64
          input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
              a single 7x7 convolution in layer0.
              - For SENet154: True
              - For SE-ResNet models: False
              - For SE-ResNeXt models: False
          downsample_kernel_size (int): Kernel size for downsampling convolutions
              in layer2, layer3 and layer4.
              - For SENet154: 3
              - For SE-ResNet models: 1
              - For SE-ResNeXt models: 1
          downsample_padding (int): Padding for downsampling convolutions in
              layer2, layer3 and layer4.
              - For SENet154: 1
              - For SE-ResNet models: 0
              - For SE-ResNeXt models: 0
          num_classes (int): Number of outputs in `last_linear` layer.
              - For all models: 1000
          """
          super(SENet, self).__init__()
          self.inplanes = inplanes
          if input_3x3:
              layer0_modules = [
                  ('conv1', nn.Conv2d(3, 64, 3, stride=2, padding=1,
                                      bias=False)),
                  ('bn1', nn.BatchNorm2d(64)),
                  ('relu1', nn.ReLU(inplace=True)),
                  ('conv2', nn.Conv2d(64, 64, 3, stride=1, padding=1,
                                      bias=False)),
                  ('bn2', nn.BatchNorm2d(64)),
                  ('relu2', nn.ReLU(inplace=True)),
                  ('conv3', nn.Conv2d(64, inplanes, 3, stride=1, padding=1,
                                      bias=False)),
                  ('bn3', nn.BatchNorm2d(inplanes)),
                  ('relu3', nn.ReLU(inplace=True)),
              ]
          else:
              layer0_modules = [
                  ('conv1', nn.Conv2d(3, inplanes, kernel_size=7, stride=2,
                                      padding=3, bias=False)),
                  ('bn1', nn.BatchNorm2d(inplanes)),
                  ('relu1', nn.ReLU(inplace=True)),
              ]
          # To preserve compatibility with Caffe weights `ceil_mode=True`
          # is used instead of `padding=1`.
          layer0_modules.append(('pool', nn.MaxPool2d(3, stride=2,
                                                      ceil_mode=True)))
          self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
          self.layer1 = self._make_layer(
              block,
              planes=64,
              blocks=layers[0],
              groups=groups,
              reduction=reduction,
              downsample_kernel_size=1,
              downsample_padding=0
          )
          self.layer2 = self._make_layer(
              block,
              planes=128,
              blocks=layers[1],
              stride=2,
              groups=groups,
              reduction=reduction,
              downsample_kernel_size=downsample_kernel_size,
              downsample_padding=downsample_padding
          )
          self.layer3 = self._make_layer(
              block,
              planes=256,
              blocks=layers[2],
              stride=last2_stride,
              groups=groups,
              reduction=reduction,
              downsample_kernel_size=downsample_kernel_size,
              downsample_padding=downsample_padding
          )
          self.layer4 = self._make_layer(
              block,
              planes=512,
              blocks=layers[3],
              stride=last_stride,
              groups=groups,
              reduction=reduction,
              downsample_kernel_size=downsample_kernel_size,
              downsample_padding=downsample_padding
          )
          self.avg_pool = nn.AvgPool2d(7, stride=1)
          self.dropout = nn.Dropout(dropout_p) if dropout_p is not None else None
  
      def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
                      downsample_kernel_size=1, downsample_padding=0):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = nn.Sequential(
                  nn.Conv2d(self.inplanes, planes * block.expansion,
                            kernel_size=downsample_kernel_size, stride=stride,
                            padding=downsample_padding, bias=False),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, groups, reduction, stride,
                              downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes, groups, reduction))
  
          return nn.Sequential(*layers)
      
      def load_param(self, model_path):
          param_dict = torch.load(model_path)
          for i in param_dict:
              if 'last_linear' in i:
                  continue
              self.state_dict()[i].copy_(param_dict[i])
  
      def forward(self, x):
          x = self.layer0(x)
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
          return x
apis: torch.nn.BatchNorm2d torch.nn.MaxPool2d torch.load torch.nn.AdaptiveAvgPool2d torch.nn.Conv2d torch.nn.Sequential torch.nn.AvgPool2d torch.nn.Sigmoid torch.nn.ReLU torch.nn.Dropout