text: #!/usr/bin/env python
  # Copyright (c) 2018 DeNA Co., Ltd.
  # Licensed under The MIT License [see LICENSE for details]
  #
  # Original code forked from PyTorch licensed pytorch project
  # https://github.com/pytorch/examples/blob/master/mnist/main.py
  
  from __future__ import print_function
  import logging
  import argparse
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  from torchvision import datasets, transforms
  
  from chainerpruner.pruning.psfp import ProgressiveSoftFilterPruning
  
  
  class Net(nn.Module):
      def __init__(self):
          super(Net, self).__init__()
  
          class Lambda(nn.Module):
              def __init__(self, func):
                  super().__init__()
                  self.func = func
  
              def forward(self, x):
                  return self.func(x)
  
          self.net = nn.Sequential(
              nn.Conv2d(1, 20, 5, 1),
              nn.ReLU(),
              nn.MaxPool2d(2, 2),
              nn.Conv2d(20, 50, 5, 1),
              nn.ReLU(),
              nn.MaxPool2d(2, 2),
              Lambda(lambda x: x.view(-1, 4 * 4 * 50)),
              nn.Linear(4 * 4 * 50, 500),
              nn.ReLU(),
              nn.Linear(500, 10),
          )
  
      def forward(self, x):
          x = self.net(x)
          return F.log_softmax(x, dim=1)
  
  
  def train(args, model, device, train_loader, optimizer, epoch, psfp):
      model.train()
      for batch_idx, (data, target) in enumerate(train_loader):
          data, target = data.to(device), target.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          # psfp(epoch)  # soft pruning per batch
          if batch_idx % args.log_interval == 0:
              print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                  epoch, batch_idx * len(data), len(train_loader.dataset),
                         100. * batch_idx / len(train_loader), loss.item()))
      psfp(epoch)  # soft pruning per epoch
  
  
  def test(args, model, device, test_loader):
      model.eval()
      test_loss = 0
      correct = 0
      with torch.no_grad():
          for data, target in test_loader:
              data, target = data.to(device), target.to(device)
              output = model(data)
              test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
              pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
              correct += pred.eq(target.view_as(pred)).sum().item()
  
      test_loss /= len(test_loader.dataset)
  
      print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
          test_loss, correct, len(test_loader.dataset),
          100. * correct / len(test_loader.dataset)))
  
  
  def main():
      # Training settings
      parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
      parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                          help='input batch size for training (default: 64)')
      parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                          help='input batch size for testing (default: 1000)')
      parser.add_argument('--epochs', type=int, default=10, metavar='N',
                          help='number of epochs to train (default: 10)')
      parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                          help='learning rate (default: 0.01)')
      parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                          help='SGD momentum (default: 0.5)')
      parser.add_argument('--no-cuda', action='store_true', default=False,
                          help='disables CUDA training')
      parser.add_argument('--seed', type=int, default=1, metavar='S',
                          help='random seed (default: 1)')
      parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                          help='how many batches to wait before logging training status')
      parser.add_argument('--save-model', action='store_true', default=False,
                          help='For Saving the current Model')
  
      # for channel pruning
      parser.add_argument('--pruning-percent', type=float, default=0.8,
                          help='pruning percent.')
  
      args = parser.parse_args()
      use_cuda = not args.no_cuda and torch.cuda.is_available()
  
      torch.manual_seed(args.seed)
  
      device = torch.device("cuda" if use_cuda else "cpu")
  
      kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
      train_loader = torch.utils.data.DataLoader(
          datasets.MNIST('./data', train=True, download=True,
                         transform=transforms.Compose([
                             transforms.ToTensor(),
                             transforms.Normalize((0.1307,), (0.3081,))
                         ])),
          batch_size=args.batch_size, shuffle=True, **kwargs)
      test_loader = torch.utils.data.DataLoader(
          datasets.MNIST('./data', train=False, transform=transforms.Compose([
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,))
          ])),
          batch_size=args.test_batch_size, shuffle=True, **kwargs)
  
      model = Net().to(device)
      print(model)
      optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
  
      # initialize pruning extension
      # TODO(tkat0) cant pruning net.3 (conv-fc)
      target_layers = ['net.0']
      x = torch.randn((1, 1, 28, 28), requires_grad=False).to(device)
      psfp = ProgressiveSoftFilterPruning(model, x, target_layers, pruning_rate=args.pruning_percent,
                                          stop_trigger=args.epochs)
  
      for epoch in range(1, args.epochs + 1):
          train(args, model, device, train_loader, optimizer, epoch, psfp)
          test(args, model, device, test_loader)
  
      model = model.to('cpu')
  
      if (args.save_model):
          torch.onnx.export(model, x, "mnist_cnn.onnx", verbose=False,
                            input_names=['input'],
                            output_names=['output'])
  
      psfp.rebuild()
      print(model)
  
      model.to(device)(x)  # testing
  
      if (args.save_model):
          torch.onnx.export(model, x, "mnist_cnn_rebuild.onnx", verbose=False,
                            input_names=['input'],
                            output_names=['output'])
          torch.save(model.state_dict(), "mnist_cnn.pt")
  
  
  if __name__ == '__main__':
      logging.basicConfig(level=logging.INFO)
      main()
  