mirror of https://github.com/Jittor/Jittor
75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
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# ***************************************************************
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# Copyright (c) 2021 Jittor. All Rights Reserved.
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# Maintainers:
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# Wenyang Zhou <576825820@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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#
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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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import torchvision
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except:
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torch = None
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tnn = None
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torchvision = None
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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 TestHook(unittest.TestCase):
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def test_bhook(self):
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a = jnn.ReLU()
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hooked = False
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def hook(mod, grad_input, grad_output):
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nonlocal hooked
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hooked = True
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assert len(grad_input) == 1
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assert len(grad_output) == 1
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np.testing.assert_allclose(grad_input[0].numpy(), [0, 1])
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np.testing.assert_allclose(grad_output[0].numpy(), [1, 1])
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return (jt.array([-1.0, -2.0]), )
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a.register_backward_hook(hook)
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x = jt.array([-1.0,2])
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y = a(x)
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dx = jt.grad(y, x)
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assert hooked
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np.testing.assert_allclose(dx.numpy(), [-1.0, -2.0])
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def test_register_hook(self):
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x = jt.array([0.0, 0.0])
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y = x * [1,2]
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y.register_hook(lambda g: g*2)
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dx = jt.grad(y, x)
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np.testing.assert_allclose(dx.data, [2,4])
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def test_requires_grads_(self):
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class Mod(jt.nn.Module):
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def execute(self, x):
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return x*2
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x = jt.random((100,))
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mod = Mod()
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mod.requires_grad_(True)
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y = mod(x)
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y = y*10
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dx = jt.grad(y, x)
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np.testing.assert_allclose(dx.data, 20)
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mod.requires_grad_(False)
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y = mod(x)
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y = y*10
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dx = jt.grad(y, x)
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np.testing.assert_allclose(dx.data, 0)
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if __name__ == "__main__":
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unittest.main() |