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, pred_channels=1
  ):
      """
      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 = 100
  
      # define a simple 3d example
      target = torch.rand((1, 1, 5, 5, 5), device=self.device)
      image = 12 * target + 27
      image = image.to(device=self.device)
  
      # define a one layer model
      class OnelayerNet(nn.Module):
          def __init__(self):
              super().__init__()
              self.layer = nn.Sequential(
                  nn.Conv3d(
                      in_channels=1,
                      out_channels=1,
                      kernel_size=3,
                      padding=1,
                  ),
                  nn.ReLU(),
                  nn.Conv3d(
                      in_channels=1,
                      out_channels=pred_channels,
                      kernel_size=3,
                      padding=1,
                  ),
              )
  
          def forward(self, x):
              return self.layer(x)
  
      # initialise the network
      net = OnelayerNet().to(self.device)
  
      # initialize the loss
      loss = loss_type(**loss_args).to(self.device)
  
      # initialize a SGD optimizer
      optimizer = optim.Adam(net.parameters(), lr=learning_rate)
  
      # train the network
      for it in range(max_iter):
          # set the gradient to zero
          optimizer.zero_grad()
  
          # forward pass
          output = net(image)
          loss_input = {"pred": output, "target": target}
  
          loss_val = loss(**{k: loss_input[k] for k in forward_args})
          if it == 0:
              init_loss = loss_val
  
          # backward pass
          loss_val.backward()
          optimizer.step()
      self.assertTrue(init_loss > loss_val, "loss did not decrease")
  
file_path: MONAI/tests/test_reg_loss_integration.py
imports: from __future__ import annotations
  import unittest
  import torch
  import torch.nn
  import torch.optim
  from parameterized import parameterized
  from monai.losses import BendingEnergyLoss, GlobalMutualInformationLoss, LocalNormalizedCrossCorrelationLoss
  from tests.utils import SkipIfBeforePyTorchVersion
label_desc: Use image node_id 3 for calling the TestRegLossIntegration obj's underlying member method code with example usage: obj.test_convergence(loss_type, loss_args, forward_args, pred_channels) and returns: self