text: import math
  
  from os.path import join as pjoin
  from collections import OrderedDict
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  def np2th(weights, conv=False):
      """Possibly convert HWIO to OIHW."""
      if conv:
          weights = weights.transpose([3, 2, 0, 1])
      return torch.from_numpy(weights)
  
  
  class StdConv2d(nn.Conv2d):
  
      def forward(self, x):
          w = self.weight
          v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
          w = (w - m) / torch.sqrt(v + 1e-5)
          return F.conv2d(x, w, self.bias, self.stride, self.padding,
                          self.dilation, self.groups)
  
  
  def conv3x3(cin, cout, stride=1, groups=1, bias=False):
      return StdConv2d(cin, cout, kernel_size=3, stride=stride,
                       padding=1, bias=bias, groups=groups)
  
  
  def conv1x1(cin, cout, stride=1, bias=False):
      return StdConv2d(cin, cout, kernel_size=1, stride=stride,
                       padding=0, bias=bias)
  
  
  class PreActBottleneck(nn.Module):
      """Pre-activation (v2) bottleneck block.
      """
  
      def __init__(self, cin, cout=None, cmid=None, stride=1):
          super().__init__()
          cout = cout or cin
          cmid = cmid or cout//4
  
          self.gn1 = nn.GroupNorm(32, cmid, eps=1e-6)
          self.conv1 = conv1x1(cin, cmid, bias=False)
          self.gn2 = nn.GroupNorm(32, cmid, eps=1e-6)
          self.conv2 = conv3x3(cmid, cmid, stride, bias=False)  # Original code has it on conv1!!
          self.gn3 = nn.GroupNorm(32, cout, eps=1e-6)
          self.conv3 = conv1x1(cmid, cout, bias=False)
          self.relu = nn.ReLU(inplace=True)
  
          if (stride != 1 or cin != cout):
              # Projection also with pre-activation according to paper.
              self.downsample = conv1x1(cin, cout, stride, bias=False)
              self.gn_proj = nn.GroupNorm(cout, cout)
  
      def forward(self, x):
  
          # Residual branch
          residual = x
          if hasattr(self, 'downsample'):
              residual = self.downsample(x)
              residual = self.gn_proj(residual)
  
          # Unit's branch
          y = self.relu(self.gn1(self.conv1(x)))
          y = self.relu(self.gn2(self.conv2(y)))
          y = self.gn3(self.conv3(y))
  
          y = self.relu(residual + y)
          return y
  
      def load_from(self, weights, n_block, n_unit):
          conv1_weight = np2th(weights[pjoin(n_block, n_unit, "conv1/kernel")], conv=True)
          conv2_weight = np2th(weights[pjoin(n_block, n_unit, "conv2/kernel")], conv=True)
          conv3_weight = np2th(weights[pjoin(n_block, n_unit, "conv3/kernel")], conv=True)
  
          gn1_weight = np2th(weights[pjoin(n_block, n_unit, "gn1/scale")])
          gn1_bias = np2th(weights[pjoin(n_block, n_unit, "gn1/bias")])
  
          gn2_weight = np2th(weights[pjoin(n_block, n_unit, "gn2/scale")])
          gn2_bias = np2th(weights[pjoin(n_block, n_unit, "gn2/bias")])
  
          gn3_weight = np2th(weights[pjoin(n_block, n_unit, "gn3/scale")])
          gn3_bias = np2th(weights[pjoin(n_block, n_unit, "gn3/bias")])
  
          self.conv1.weight.copy_(conv1_weight)
          self.conv2.weight.copy_(conv2_weight)
          self.conv3.weight.copy_(conv3_weight)
  
          self.gn1.weight.copy_(gn1_weight.view(-1))
          self.gn1.bias.copy_(gn1_bias.view(-1))
  
          self.gn2.weight.copy_(gn2_weight.view(-1))
          self.gn2.bias.copy_(gn2_bias.view(-1))
  
          self.gn3.weight.copy_(gn3_weight.view(-1))
          self.gn3.bias.copy_(gn3_bias.view(-1))
  
          if hasattr(self, 'downsample'):
              proj_conv_weight = np2th(weights[pjoin(n_block, n_unit, "conv_proj/kernel")], conv=True)
              proj_gn_weight = np2th(weights[pjoin(n_block, n_unit, "gn_proj/scale")])
              proj_gn_bias = np2th(weights[pjoin(n_block, n_unit, "gn_proj/bias")])
  
              self.downsample.weight.copy_(proj_conv_weight)
              self.gn_proj.weight.copy_(proj_gn_weight.view(-1))
              self.gn_proj.bias.copy_(proj_gn_bias.view(-1))
  
  class ResNetV2(nn.Module):
      """Implementation of Pre-activation (v2) ResNet mode."""
  
      def __init__(self, block_units, width_factor):
          super().__init__()
          width = int(64 * width_factor)
          self.width = width
  
          self.root = nn.Sequential(OrderedDict([
              ('conv', StdConv2d(3, width, kernel_size=7, stride=2, bias=False, padding=3)),
              ('gn', nn.GroupNorm(32, width, eps=1e-6)),
              ('relu', nn.ReLU(inplace=True)),
              # ('pool', nn.MaxPool2d(kernel_size=3, stride=2, padding=0))
          ]))
  
          self.body = nn.Sequential(OrderedDict([
              ('block1', nn.Sequential(OrderedDict(
                  [('unit1', PreActBottleneck(cin=width, cout=width*4, cmid=width))] +
                  [(f'unit{i:d}', PreActBottleneck(cin=width*4, cout=width*4, cmid=width)) for i in range(2, block_units[0] + 1)],
                  ))),
              ('block2', nn.Sequential(OrderedDict(
                  [('unit1', PreActBottleneck(cin=width*4, cout=width*8, cmid=width*2, stride=2))] +
                  [(f'unit{i:d}', PreActBottleneck(cin=width*8, cout=width*8, cmid=width*2)) for i in range(2, block_units[1] + 1)],
                  ))),
              ('block3', nn.Sequential(OrderedDict(
                  [('unit1', PreActBottleneck(cin=width*8, cout=width*16, cmid=width*4, stride=2))] +
                  [(f'unit{i:d}', PreActBottleneck(cin=width*16, cout=width*16, cmid=width*4)) for i in range(2, block_units[2] + 1)],
                  ))),
          ]))
  
      def forward(self, x):
          features = []
          #print(x.size())
          b, c, in_size, _ = x.size()
          x = self.root(x)
          features.append(x)
          x = nn.MaxPool2d(kernel_size=3, stride=2, padding=0)(x)
          for i in range(len(self.body)-1):
              x = self.body[i](x)
              right_size = int(in_size / 4 / (i+1))
              if x.size()[2] != right_size:
                  pad = right_size - x.size()[2]
                  assert pad < 3 and pad > 0, "x {} should {}".format(x.size(), right_size)
                  feat = torch.zeros((b, x.size()[1], right_size, right_size), device=x.device)
                  feat[:, :, 0:x.size()[2], 0:x.size()[3]] = x[:]
              else:
                  feat = x
              features.append(feat)
          x = self.body[-1](x)
          return x, features[::-1]
  