mirror of https://github.com/Jittor/Jittor
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
# ***************************************************************
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# Copyright (c) 2021 Jittor. All Rights Reserved.
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# Maintainers:
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# Dun Liang <randonlang@gmail.com>.
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# Zheng-Ning Liu <lzhengning@gmail.com>
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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 numpy as np
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import jittor.nn as jnn
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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 TestVarFunctions(unittest.TestCase):
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def test_var(self):
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x = np.random.randn(100, 1000).astype(np.float32)
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jt_x = jt.array(x)
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tc_x = torch.from_numpy(x)
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np.testing.assert_allclose(jt_x.var().numpy(), tc_x.var().numpy(), rtol=1e-3, atol=1e-4)
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np.testing.assert_allclose(jt_x.var(dim=1).numpy(), tc_x.var(dim=1).numpy(), rtol=1e-3, atol=1e-4)
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np.testing.assert_allclose(jt_x.var(dim=0, unbiased=True).numpy(), tc_x.var(dim=0, unbiased=True).numpy(), rtol=1e-3, atol=1e-4)
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def test_std(self):
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x=np.random.randn(100, 1000).astype(np.float32)
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jt_x = jt.array(x)
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tc_x = torch.from_numpy(x)
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np.testing.assert_allclose(jt_x.std().numpy(), tc_x.std().numpy(), 1e-4)
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def test_norm(self):
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x = np.random.randn(100, 1000).astype(np.float32)
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jt_x = jt.array(x)
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tc_x = torch.from_numpy(x)
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np.testing.assert_allclose(jt_x.norm(1,1).numpy(), tc_x.norm(1,1).numpy(), atol=1e-6)
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np.testing.assert_allclose(jt_x.norm(1,0).numpy(), tc_x.norm(1,0).numpy(), atol=1e-6)
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np.testing.assert_allclose(jt_x.norm(2,1).numpy(), tc_x.norm(2,1).numpy(), atol=1e-6)
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np.testing.assert_allclose(jt_x.norm(2,0).numpy(), tc_x.norm(2,0).numpy(), atol=1e-6)
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if __name__ == "__main__":
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unittest.main()
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