text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # Python version: 3.6
  
  from torch import nn
  import torch.nn.functional as F
  
  
  class MLP(nn.Module):
      def __init__(self, dim_in=784, dim_hidden=200, dim_out=10,
                   use_dropout=False, activation='ReLU'):
          super(MLP, self).__init__()
          self.layers = nn.ModuleList([nn.Linear(dim_in, dim_hidden),
                                       nn.Linear(dim_hidden, dim_hidden),
                                       nn.Linear(dim_hidden, dim_out)])
          self.softmax = nn.Softmax(dim=1)
  
          self.relu = getattr(nn, activation)()
          self.dropout = None
          if use_dropout:
              self.dropout = nn.Dropout()
  
      def forward(self, x):                                       # input x of size (batchsize, 1, H, W)
          x = x.view(-1, x.shape[1] * x.shape[-2] * x.shape[-1])  # x of size (batchsize, H*W)
  
          # Compute the forward propagation
          for layer in self.layers:
              x = layer(x)
              if self.dropout != None:
                  x = self.dropout(x)
              x = self.relu(x)
  
          return self.softmax(x)
  
  # class CNNMnist(nn.Module):
  #     def __init__(self, args):
  #         super(CNNMnist, self).__init__()
  #         self.conv1 = nn.Conv2d(args.num_channels, 10, kernel_size=5)
  #         self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
  #         self.conv2_drop = nn.Dropout2d()
  #         self.fc1 = nn.Linear(512, 50)
  #         self.fc2 = nn.Linear(50, args.num_classes)
  
  #     def forward(self, x):
  #         x = F.relu(F.max_pool2d(self.conv1(x), 2))
  #         x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
  #         x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
  #         x = F.relu(self.fc1(x))
  #         x = F.dropout(x, training=self.training)
  #         x = self.fc2(x)
  #         return F.log_softmax(x, dim=1)
  
  class CNNMnist(nn.Module):
      def __init__(self, args):
          super(CNNMnist, self).__init__()
          self.layer1 = nn.Sequential(
              nn.Conv2d(args.num_channels, 32,kernel_size=5, padding=2),
              nn.ReLU(),
              nn.MaxPool2d(2,2))
          
          self.layer2 = nn.Sequential(
              nn.Conv2d(32, 64,kernel_size=5, padding=2),
              nn.ReLU(),
              nn.MaxPool2d(2,2))
          
          self.fc1 = nn.Linear(64*7*7, 512)
          self.fc2 = nn.Linear(512, args.num_classes)
  
  
      def forward(self, x):
          x = self.layer1(x)
          x = self.layer2(x)
          x = x.view(-1, x.shape[1] * x.shape[-2] * x.shape[-1])  # x of size (batchsize, H*W)
          x = F.relu(self.fc1(x))
          x = self.fc2(x)
          return F.softmax(x, dim=1)
  
  class CNNFashion_Mnist(nn.Module):
      def __init__(self, args):
          super(CNNFashion_Mnist, self).__init__()
          self.layer1 = nn.Sequential(
              nn.Conv2d(1, 16, kernel_size=5, padding=2),
              nn.BatchNorm2d(16),
              nn.ReLU(),
              nn.MaxPool2d(2))
          self.layer2 = nn.Sequential(
              nn.Conv2d(16, 32, kernel_size=5, padding=2),
              nn.BatchNorm2d(32),
              nn.ReLU(),
              nn.MaxPool2d(2))
          self.fc = nn.Linear(7*7*32, 10)
  
      def forward(self, x):
          out = self.layer1(x)
          out = self.layer2(out)
          out = out.view(out.size(0), -1)
          out = self.fc(out)
          return out
  
  
  class CNNCifar(nn.Module):
      def __init__(self, args):
          super(CNNCifar, self).__init__()
          self.conv1 = nn.Conv2d(3, 6, 5)
          self.pool = nn.MaxPool2d(2, 2)
          self.conv2 = nn.Conv2d(6, 16, 5)
          self.fc1 = nn.Linear(16 * 5 * 5, 120)
          self.fc2 = nn.Linear(120, 84)
          self.fc3 = nn.Linear(84, args.num_classes)
  
      def forward(self, x):
          x = self.pool(F.relu(self.conv1(x)))
          x = self.pool(F.relu(self.conv2(x)))
          x = x.view(-1, 16 * 5 * 5)
          x = F.relu(self.fc1(x))
          x = F.relu(self.fc2(x))
          x = self.fc3(x)
          return F.log_softmax(x, dim=1)
  
  
  class modelC(nn.Module):
      def __init__(self, input_size, n_classes=10, **kwargs):
          super(AllConvNet, self).__init__()
          self.conv1 = nn.Conv2d(input_size, 96, 3, padding=1)
          self.conv2 = nn.Conv2d(96, 96, 3, padding=1)
          self.conv3 = nn.Conv2d(96, 96, 3, padding=1, stride=2)
          self.conv4 = nn.Conv2d(96, 192, 3, padding=1)
          self.conv5 = nn.Conv2d(192, 192, 3, padding=1)
          self.conv6 = nn.Conv2d(192, 192, 3, padding=1, stride=2)
          self.conv7 = nn.Conv2d(192, 192, 3, padding=1)
          self.conv8 = nn.Conv2d(192, 192, 1)
  
          self.class_conv = nn.Conv2d(192, n_classes, 1)
  
  
      def forward(self, x):
          x_drop = F.dropout(x, .2)
          conv1_out = F.relu(self.conv1(x_drop))
          conv2_out = F.relu(self.conv2(conv1_out))
          conv3_out = F.relu(self.conv3(conv2_out))
          conv3_out_drop = F.dropout(conv3_out, .5)
          conv4_out = F.relu(self.conv4(conv3_out_drop))
          conv5_out = F.relu(self.conv5(conv4_out))
          conv6_out = F.relu(self.conv6(conv5_out))
          conv6_out_drop = F.dropout(conv6_out, .5)
          conv7_out = F.relu(self.conv7(conv6_out_drop))
          conv8_out = F.relu(self.conv8(conv7_out))
  
          class_out = F.relu(self.class_conv(conv8_out))
          pool_out = F.adaptive_avg_pool2d(class_out, 1)
          pool_out.squeeze_(-1)
          pool_out.squeeze_(-1)
          return pool_out
  
  
  # ==========================================================================================================
  # Main function
  # ==========================================================================================================
  if __name__ == "__main__":
      import torch
      from collections import namedtuple
      args = {'num_classes': 10, 'num_channels': 1,
              'num_hidden_channels1': 32, 'num_hidden_channels2': 64}
      args = namedtuple('x', args.keys())(*args.values())
      model = CNNMnist(args)
  
      x = torch.rand((2, 1, 28, 28))
      # model = MLP()
      pytorch_total_params = sum(p.numel() for p in model.parameters())
      print(pytorch_total_params)
      # y = model(x)
  