text: from __future__ import absolute_import
  
  from scipy.stats import pearsonr
  import numpy as np
  import torch.nn.functional as F
  
  '''Resnet for cifar dataset. 
  Ported form 
  https://github.com/facebook/fb.resnet.torch
  and
  https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
  (c) YANG, Wei 
  '''
  
  
  import torch.nn as nn
  import math
  
  
  __all__ = ['resnet']
  
  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, dropRate=0.0):
          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.droprate = dropRate
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          if self.droprate > 0:
              out = F.dropout(out, p=self.droprate, training=self.training)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None, dropRate=0.0):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                 padding=1, 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.downsample = downsample
          self.stride = stride
  
          self.droprate = dropRate
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          if self.droprate > 0:
              print('dropped 1')
              out = F.dropout(out, p=self.droprate, training=self.training)
  
          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 ResNet(nn.Module):
  
      def __init__(self, depth, num_classes=1000, dropRate=0.0):
          super(ResNet, self).__init__()
          # Model type specifies number of layers for CIFAR-10 model
          assert (depth - 2) % 6 == 0, 'depth should be 6n+2'
          n = (depth - 2) // 6
  
          block = Bottleneck if depth >=44 else BasicBlock
  
          self.inplanes = 16
          self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(16)
          self.relu = nn.ReLU(inplace=True)
          self.layer1 = self._make_layer(block, 16, n, dropRate=dropRate)
          self.layer2 = self._make_layer(block, 32, n, stride=2, dropRate=dropRate)
          self.layer3 = self._make_layer(block, 64, n, stride=2, dropRate=dropRate)
          self.avgpool = nn.AvgPool2d(8)
          self.fc = nn.Linear(64 * block.expansion, num_classes)
  
          self.cors = []
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, stride=1, dropRate=0.0):
          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, dropRate))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def forward(self, x, to_cal_cor=False):
          x = self.conv1(x)
  
          if to_cal_cor:
              correlations_tuples = []
              a_bch, a_ker_num, a_y, a_x = x.shape
              avg_pool_x = nn.AvgPool2d(a_y)(x).squeeze()
              for a_ker_1 in range(a_ker_num-1):
                  for a_ker_2 in range(a_ker_1, a_ker_num):
                      correlations_tuples.append(pearsonr(avg_pool_x[:, a_ker_1].detach().cpu().numpy(),
                                                          avg_pool_x[:, a_ker_2].detach().cpu().numpy()))
  
              correlations = list([x[0] for x in correlations_tuples])
              no_nan_correlations = list([abs(value) for value in correlations if not math.isnan(value)])
              # print('no nan correlations: ', no_nan_correlations[:20])
              self.cors.append(np.mean(no_nan_correlations))
  
          x = self.bn1(x)
          x = self.relu(x)    # 32x32
  
          x = self.layer1(x)  # 32x32
          x = self.layer2(x)  # 16x16
          x = self.layer3(x)  # 8x8
  
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
  
          return x
  
  
  def resnet(**kwargs):
      """
      Constructs a ResNet model.
      """
      return ResNet(**kwargs)
  