mirror of https://github.com/Jittor/Jittor
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
# ***************************************************************
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# Copyright (c) 2020 Jittor. Authors:
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# Dun Liang <randonlang@gmail.com>.
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# All Rights Reserved.
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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import unittest
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import jittor as jt
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import os
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import numpy as np
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import jittor.nn as jnn
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from jittor.test.test_log import find_log_with_re
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skip_this_test = False
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try:
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jt.dirty_fix_pytorch_runtime_error()
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import torch
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import torch.nn as tnn
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except:
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skip_this_test = True
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@unittest.skipIf(skip_this_test, "No Torch found")
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class TestLoss(unittest.TestCase):
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def test_l1_loss(self):
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jt_loss=jnn.L1Loss()
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tc_loss=tnn.L1Loss()
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output=np.random.randn(10,100).astype(np.float32)
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target=np.random.randn(10,100).astype(np.float32)
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jt_y=jt_loss(jt.array(output), jt.array(target))
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tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
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assert np.allclose(jt_y.numpy(), tc_y.numpy())
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def test_mse_loss(self):
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jt_loss=jnn.MSELoss()
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tc_loss=tnn.MSELoss()
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output=np.random.randn(10,100).astype(np.float32)
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target=np.random.randn(10,100).astype(np.float32)
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jt_y=jt_loss(jt.array(output), jt.array(target))
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tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
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assert np.allclose(jt_y.numpy(), tc_y.numpy())
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def test_cross_entropy_loss(self):
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jt_loss=jnn.CrossEntropyLoss()
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tc_loss=tnn.CrossEntropyLoss()
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output=np.random.randn(10,10).astype(np.float32)
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target=np.random.randint(10, size=(10))
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jt_y=jt_loss(jt.array(output), jt.array(target))
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tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
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assert np.allclose(jt_y.numpy(), tc_y.numpy())
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def test_bce_loss(self):
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jt_loss=jnn.BCELoss()
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tc_loss=tnn.BCELoss()
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jt_sig = jnn.Sigmoid()
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tc_sig = tnn.Sigmoid()
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output=np.random.randn(100).astype(np.float32)
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target=np.random.randint(2, size=(100)).astype(np.float32)
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jt_y=jt_loss(jt_sig(jt.array(output)), jt.array(target))
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tc_y=tc_loss(tc_sig(torch.from_numpy(output)), torch.from_numpy(target))
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assert np.allclose(jt_y.numpy(), tc_y.numpy())
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def test_bce_with_logits_loss(self):
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jt_loss=jnn.BCEWithLogitsLoss()
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tc_loss=tnn.BCEWithLogitsLoss()
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output=np.random.randn(100).astype(np.float32)
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target=np.random.randint(2, size=(100)).astype(np.float32)
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jt_y=jt_loss(jt.array(output), jt.array(target))
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tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
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assert np.allclose(jt_y.numpy(), tc_y.numpy())
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if __name__ == "__main__":
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unittest.main()
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