docstr: The goal of this test is to assess if the gradient of the loss function
  is correct by testing if we can train a one layer neural network
  to segment one image.
  We verify that the loss is decreasing in almost all SGD steps.
docstr_tok: ["The","goal","of","this","test","is","to","assess","if","the","gradient","of","the","loss","function","is","correct","by","testing","if","we","can","train","a","one","layer","neural","network","to","segment","one","image",".","We","verify","that","the","loss","is","decreasing","in","almost","all","SGD","steps","."]
code: def test_convergence(self, loss_type, loss_args, forward_args):
      """
      The goal of this test is to assess if the gradient of the loss function
      is correct by testing if we can train a one layer neural network
      to segment one image.
      We verify that the loss is decreasing in almost all SGD steps.
      """
      learning_rate = 0.001
      max_iter = 40
  
      # define a simple 3d example
      target_seg = torch.tensor(
          [
              [
                  # raw 0
                  [
                      [0, 0, 0, 0],
                      [0, 1, 1, 0],
                      [0, 1, 1, 0],
                      [0, 0, 0, 0],
                  ],
                  # raw 1
                  [
                      [0, 0, 0, 0],
                      [0, 1, 1, 0],
                      [0, 1, 1, 0],
                      [0, 0, 0, 0],
                  ],
                  # raw 2
                  [
                      [0, 0, 0, 0],
                      [0, 1, 1, 0],
                      [0, 1, 1, 0],
                      [0, 0, 0, 0],
                  ],
              ]
          ],
          device=self.device,
      )
      target_seg = torch.unsqueeze(target_seg, dim=0)
      image = 12 * target_seg + 27
      image = image.float().to(self.device)
      num_classes = 2
      num_voxels = 3 * 4 * 4
  
      target_onehot = one_hot(target_seg, num_classes=num_classes)
  
      # define a one layer model
      class OnelayerNet(nn.Module):
          def __init__(self):
              super().__init__()
              self.layer_1 = nn.Linear(num_voxels, 200)
              self.acti = nn.ReLU()
              self.layer_2 = nn.Linear(200, num_voxels * num_classes)
  
          def forward(self, x):
              x = x.view(-1, num_voxels)
              x = self.layer_1(x)
              x = self.acti(x)
              x = self.layer_2(x)
              x = x.view(-1, num_classes, 3, 4, 4)
              return x
  
      # initialise the network
      net = OnelayerNet().to(self.device)
  
      # initialize the loss
      loss = loss_type(**loss_args)
  
      # initialize a SGD optimizer
      optimizer = optim.Adam(net.parameters(), lr=learning_rate)
  
      loss_history = []
      init_output = None
  
      # train the network
      for iter_i in range(max_iter):
          # set the gradient to zero
          optimizer.zero_grad()
  
          # forward pass
          output = net(image)
          if init_output is None:
              init_output = (
                  torch.argmax(output, 1).detach().cpu().numpy()
              )
  
          if loss_args["to_onehot_y"] is False:
              loss_val = loss(output, target_onehot, **forward_args)
          else:
              loss_val = loss(output, target_seg, **forward_args)
  
          if iter_i % 10 == 0:
              pred = torch.argmax(output, 1).detach().cpu().numpy()
              gt = target_seg.detach().cpu().numpy()[:, 0]
              print(
                  f"{loss_type.__name__} iter: {iter_i}, acc: {np.sum(pred == gt) / np.prod(pred.shape)}"
              )
  
          # backward pass
          loss_val.backward()
          optimizer.step()
  
          # stats
          loss_history.append(loss_val.item())
  
      pred = torch.argmax(output, 1).detach().cpu().numpy()
      target = target_seg.detach().cpu().numpy()[:, 0]
      # initial predictions are bad
      self.assertTrue(not np.allclose(init_output, target))
      # final predictions are good
      np.testing.assert_allclose(pred, target)
  
file_path: MONAI/tests/test_seg_loss_integration.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from parameterized import parameterized
  from monai.losses import DiceLoss, FocalLoss, GeneralizedDiceLoss, TverskyLoss
  from monai.networks import one_hot
label_desc: Use image node_id 3 for calling the TestSegLossIntegration obj's underlying member method code with example usage: obj.test_convergence(loss_type, loss_args, forward_args) and returns: x