text: # coding: utf-8
  
  #############################################
  # Consistent Cumulative Logits with ResNet-34
  #############################################
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import argparse
  
  from torchvision import transforms
  from PIL import Image
  
  torch.backends.cudnn.deterministic = True
  
  parser = argparse.ArgumentParser()
  parser.add_argument('-i', '--image_path',
                      type=str,
                      required=True)
  
  parser.add_argument('-s', '--state_dict_path',
                      type=str,
                      required=True)
  
  parser.add_argument('-d', '--dataset',
                      help="Options: 'afad', 'morph2', or 'cacd'.",
                      type=str,
                      required=True)
  
  args = parser.parse_args()
  IMAGE_PATH = args.image_path
  STATE_DICT_PATH = args.state_dict_path
  GRAYSCALE = False
  
  if args.dataset == 'afad':
      NUM_CLASSES = 26
      ADD_CLASS = 15
  
  elif args.dataset == 'morph2':
      NUM_CLASSES = 55
      ADD_CLASS = 16
  
  elif args.dataset == 'cacd':
      NUM_CLASSES = 49
      ADD_CLASS = 14
  
  else:
      raise ValueError("args.dataset must be 'afad',"
                       " 'morph2', or 'cacd'. Got %s " % (args.dataset))
  
  
  ############################
  ### Load image
  ############################
  
  
  image = Image.open(IMAGE_PATH)
  custom_transform = transforms.Compose([transforms.Resize((128, 128)),
                                         transforms.CenterCrop((120, 120)),
                                         transforms.ToTensor()])
  image = custom_transform(image)
  DEVICE = torch.device('cpu')
  image = image.to(DEVICE)
  
  
  ##########################
  # MODEL
  ##########################
  
  
  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):
          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
  
      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)
  
          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, block, layers, num_classes, grayscale):
          self.num_classes = num_classes
          self.inplanes = 64
          if grayscale:
              in_dim = 1
          else:
              in_dim = 3
          super(ResNet, self).__init__()
          self.conv1 = nn.Conv2d(in_dim, 64, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bn1 = nn.BatchNorm2d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, 64, layers[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
          self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
          self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
          self.avgpool = nn.AvgPool2d(4)
          self.fc = nn.Linear(512, 1, bias=False)
          self.linear_1_bias = nn.Parameter(torch.zeros(self.num_classes-1).float())
  
          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, (2. / n)**.5)
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, stride=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),
                  nn.BatchNorm2d(planes * block.expansion),
              )
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample))
          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):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(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)
          logits = self.fc(x)
          logits = logits + self.linear_1_bias
          probas = torch.sigmoid(logits)
          return logits, probas
  
  
  def resnet34(num_classes, grayscale):
      """Constructs a ResNet-34 model."""
      model = ResNet(block=BasicBlock,
                     layers=[3, 4, 6, 3],
                     num_classes=num_classes,
                     grayscale=grayscale)
      return model
  
  
  #######################
  ### Initialize Model
  #######################
  
  model = resnet34(NUM_CLASSES, GRAYSCALE)
  model.load_state_dict(torch.load(STATE_DICT_PATH, map_location=DEVICE))
  model.eval()
  
  image = image.unsqueeze(0)
  
  with torch.set_grad_enabled(False):
      logits, probas = model(image)
      predict_levels = probas > 0.5
      predicted_label = torch.sum(predict_levels, dim=1)
      print('Class probabilities:', probas)
      print('Predicted class label:', predicted_label.item())
      print('Predicted age in years:', predicted_label.item() + ADD_CLASS)