text: # Copyright 2020 MONAI Consortium
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #     http://www.apache.org/licenses/LICENSE-2.0
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import sys
  import logging
  import numpy as np
  import os
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.tensorboard import SummaryWriter
  
  import monai
  from monai.data import NiftiDataset
  from monai.transforms import Compose, AddChannel, ScaleIntensity, Resize, RandRotate90, ToTensor
  
  
  def main():
      monai.config.print_config()
      logging.basicConfig(stream=sys.stdout, level=logging.INFO)
  
      # IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
      images = [
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI314-IOP-0889-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI249-Guys-1072-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI609-HH-2600-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI173-HH-1590-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI020-Guys-0700-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI342-Guys-0909-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI134-Guys-0780-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI577-HH-2661-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI066-Guys-0731-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI130-HH-1528-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI607-Guys-1097-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI175-HH-1570-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI385-HH-2078-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI344-Guys-0905-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI409-Guys-0960-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI584-Guys-1129-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI253-HH-1694-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI092-HH-1436-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI574-IOP-1156-T1.nii.gz"]),
          os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI585-Guys-1130-T1.nii.gz"]),
      ]
  
      # 2 binary labels for gender classification: man and woman
      labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)
  
      # Define transforms
      train_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), RandRotate90(), ToTensor()])
      val_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), ToTensor()])
  
      # Define nifti dataset, data loader
      check_ds = NiftiDataset(image_files=images, labels=labels, transform=train_transforms)
      check_loader = DataLoader(check_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
      im, label = monai.utils.misc.first(check_loader)
      print(type(im), im.shape, label)
  
      # create a training data loader
      train_ds = NiftiDataset(image_files=images[:10], labels=labels[:10], transform=train_transforms)
      train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2, pin_memory=torch.cuda.is_available())
  
      # create a validation data loader
      val_ds = NiftiDataset(image_files=images[-10:], labels=labels[-10:], transform=val_transforms)
      val_loader = DataLoader(val_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())
  
      # Create DenseNet121, CrossEntropyLoss and Adam optimizer
      device = torch.device("cuda:0")
      model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2,).to(device)
      loss_function = torch.nn.CrossEntropyLoss()
      optimizer = torch.optim.Adam(model.parameters(), 1e-5)
  
      # start a typical PyTorch training
      val_interval = 2
      best_metric = -1
      best_metric_epoch = -1
      epoch_loss_values = list()
      metric_values = list()
      writer = SummaryWriter()
      for epoch in range(5):
          print("-" * 10)
          print(f"epoch {epoch + 1}/{5}")
          model.train()
          epoch_loss = 0
          step = 0
          for batch_data in train_loader:
              step += 1
              inputs, labels = batch_data[0].to(device), batch_data[1].to(device)
              optimizer.zero_grad()
              outputs = model(inputs)
              loss = loss_function(outputs, labels)
              loss.backward()
              optimizer.step()
              epoch_loss += loss.item()
              epoch_len = len(train_ds) // train_loader.batch_size
              print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
              writer.add_scalar("train_loss", loss.item(), epoch_len * epoch + step)
          epoch_loss /= step
          epoch_loss_values.append(epoch_loss)
          print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
  
          if (epoch + 1) % val_interval == 0:
              model.eval()
              with torch.no_grad():
                  num_correct = 0.0
                  metric_count = 0
                  for val_data in val_loader:
                      val_images, val_labels = val_data[0].to(device), val_data[1].to(device)
                      val_outputs = model(val_images)
                      value = torch.eq(val_outputs.argmax(dim=1), val_labels)
                      metric_count += len(value)
                      num_correct += value.sum().item()
                  metric = num_correct / metric_count
                  metric_values.append(metric)
                  if metric > best_metric:
                      best_metric = metric
                      best_metric_epoch = epoch + 1
                      torch.save(model.state_dict(), "best_metric_model.pth")
                      print("saved new best metric model")
                  print(
                      "current epoch: {} current accuracy: {:.4f} best accuracy: {:.4f} at epoch {}".format(
                          epoch + 1, metric, best_metric, best_metric_epoch
                      )
                  )
                  writer.add_scalar("val_accuracy", metric, epoch + 1)
      print(f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}")
      writer.close()
  
  
  if __name__ == "__main__":
      main()
  