text: # ***************************************************************
  # Copyright (c) 2021 Jittor. All Rights Reserved. 
  # Maintainers: 
  #     Dun Liang <randonlang@gmail.com>. 
  # 
  # This file is subject to the terms and conditions defined in
  # file 'LICENSE.txt', which is part of this source code package.
  # ***************************************************************
  import unittest
  import jittor as jt
  import os
  import numpy as np
  import jittor.nn as jnn
  
  skip_this_test = False
  
  try:
      jt.dirty_fix_pytorch_runtime_error()
      import torch
      import torch.nn as tnn
  except:
      skip_this_test = True
  
  @unittest.skipIf(skip_this_test, "No Torch found")
  class TestLoss(unittest.TestCase):
      def test_l1_loss(self):
          jt_loss=jnn.L1Loss()
          tc_loss=tnn.L1Loss()
          output=np.random.randn(10,100).astype(np.float32)
          target=np.random.randn(10,100).astype(np.float32)
          jt_y=jt_loss(jt.array(output), jt.array(target))
          tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
          
      def test_mse_loss(self):
          jt_loss=jnn.MSELoss()
          tc_loss=tnn.MSELoss()
          output=np.random.randn(10,100).astype(np.float32)
          target=np.random.randn(10,100).astype(np.float32)
          jt_y=jt_loss(jt.array(output), jt.array(target))
          tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
          
      def test_nll_loss(self):
          tc_loss = tnn.functional.nll_loss
          jt_loss = jnn.nll_loss
          output=np.random.randn(10,10).astype(np.float32)
          target=np.random.randint(10, size=(10))
          jt_y=jt_loss(jt.array(output), jt.array(target),reduction='mean')
          tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target),reduction='mean')
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
          output=np.random.randn(10,10).astype(np.float32)
          target=np.random.randint(10, size=(10))
          weight=np.random.randn(10,).astype(np.float32)
          jt_y=jt_loss(jt.array(output), jt.array(target),jt.array(weight),reduction='mean')
          tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target),torch.from_numpy(weight),reduction='mean')
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
  
      def test_cross_entropy_loss(self):
          jt_loss=jnn.CrossEntropyLoss()
          tc_loss=tnn.CrossEntropyLoss()
          output=np.random.randn(10,10).astype(np.float32)
          target=np.random.randint(10, size=(10))
          jt_y=jt_loss(jt.array(output), jt.array(target))
          tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
  
      def test_cross_entropy_ignore_index(self):
          ignore_index = np.random.randint(0, 10)
          jt_loss = jnn.CrossEntropyLoss(ignore_index=ignore_index)
          tc_loss = tnn.CrossEntropyLoss(ignore_index=ignore_index)
          output = np.random.rand(100, 10).astype(np.float32)
          target = np.random.randint(10, size=(100))
          jt_y=jt_loss(jt.array(output), jt.array(target))
          tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
  
      def test_cross_entropy_weight(self):
          weight = np.random.rand(10).astype('float32')
          jt_loss = jnn.CrossEntropyLoss(weight=jt.array(weight))
          tc_loss = tnn.CrossEntropyLoss(weight=torch.from_numpy(weight))
          output = np.random.rand(100, 10).astype(np.float32)
          target = np.random.randint(10, size=(100))
          jt_y=jt_loss(jt.array(output), jt.array(target))
          tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
  
      def test_bce_loss(self):
          jt_loss=jnn.BCELoss()
          tc_loss=tnn.BCELoss()
          jt_sig = jnn.Sigmoid()
          tc_sig = tnn.Sigmoid()
          output=np.random.randn(100).astype(np.float32)
          target=np.random.randint(2, size=(100)).astype(np.float32)
          jt_y=jt_loss(jt_sig(jt.array(output)), jt.array(target))
          tc_y=tc_loss(tc_sig(torch.from_numpy(output)), torch.from_numpy(target))
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
  
          weight=np.random.randn(100).astype(np.float32)
          jt_loss=jnn.BCELoss(weight=jt.array(weight), size_average=False)
          tc_loss=tnn.BCELoss(weight=torch.Tensor(weight), size_average=False)
          jt_y=jt_loss(jt_sig(jt.array(output)), jt.array(target))
          tc_y=tc_loss(tc_sig(torch.from_numpy(output)), torch.from_numpy(target))
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
          
      def test_bce_with_logits_loss(self):
          jt_loss=jnn.BCEWithLogitsLoss()
          tc_loss=tnn.BCEWithLogitsLoss()
          output=np.random.randn(100).astype(np.float32)
          target=np.random.randint(2, size=(100)).astype(np.float32)
          jt_y=jt_loss(jt.array(output), jt.array(target))
          tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
          assert np.allclose(jt_y.numpy(), tc_y.numpy())
          
  if __name__ == "__main__":
      unittest.main()
  