>>> x = torch.tensor([1.], requires_grad=True)>>> with torch.no_grad():...   y = x * 2>>> y.requires_gradOut[12]:False

>>> @torch.no_grad()... def doubler(x):...     return x * 2>>> z = doubler(x)>>> z.requires_gradOut[13]:False

>>> x = torch.tensor([1.], requires_grad=True)>>> with torch.no_grad():...   with torch.enable_grad():...     y = x * 2>>> y.requires_gradOut[14]:True >>> y.backward()  # 计算梯度>>> x.gradOut[15]: tensor([2.])

>>> @torch.enable_grad()... def doubler(x):...     return x * 2 >>> with torch.no_grad():...     z = doubler(x)>>> z.requires_grad Out[16]:True

torch.autograd.set_grad_enable()

>>> x = torch.tensor([1.], requires_grad=True)>>> is_train = False>>> with torch.set_grad_enabled(is_train):...   y = x * 2>>> y.requires_gradOut[17]:False >>> torch.set_grad_enabled(True)>>> y = x * 2>>> y.requires_gradOut[18]:True >>> torch.set_grad_enabled(False)>>> y = x * 2>>> y.requires_gradOut[19]:False

x = torch.tensor([1.], requires_grad=True) with torch.enable_grad():    torch.set_grad_enabled(False)    y = x * 2    print(y.requires_grad)Out[20]: False torch.set_grad_enabled(True)with torch.no_grad():    z = x * 2    print(z.requires_grad)Out[21]:False

Locally disabling gradient computation在局部区域内关闭(禁用)梯度的计算.The context managers torch.no_grad(), torch.enable_grad(), and torch.set_grad_enabled() are helpful for locally disabling and enabling gradient computation. See Locally disabling gradient computation for more details on their usage. These context managers are thread local, so they won"t work if you send work to another thread using the threading module, etc.上下文管理器torch.no_grad()、torch.enable_grad()和torch.set_grad_enabled()可以用来在局部范围内启用或禁用梯度计算.在Locally disabling gradient computation章节中详细介绍了局部禁用梯度计算的使用方式.这些上下文管理器具有线程局部性,因此,如果你使用threading模块来将工作负载发送到另一个线程,这些上下文管理器将不会起作用.no_grad   Context-manager that disabled gradient calculation.no_grad   用于禁用梯度计算的上下文管理器.enable_grad  Context-manager that enables gradient calculation.enable_grad  用于启用梯度计算的上下文管理器.set_grad_enabled  Context-manager that sets gradient calculation to on or off.set_grad_enabled  用于设置梯度计算打开或关闭状态的上下文管理器.

Microsoft Windows [版本 10.0.18363.1440](c) 2019 Microsoft Corporation。保留所有权利。C:Userschenxuqi>conda activate pytorch_1.7.1_cu102(pytorch_1.7.1_cu102) C:Userschenxuqi>pythonPython 3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32Type "help", "copyright", "credits" or "license" for more information.>>> import torch>>> torch.manual_seed(seed=20200910)<torch._C.Generator object at 0x000001A2E55A8870>>>> a = torch.randn(3,4,requires_grad=True)>>> atensor([[ 0.2824, -0.3715,  0.9088, -1.7601],        [-0.1806,  2.0937,  1.0406, -1.7651],        [ 1.1216,  0.8440,  0.1783,  0.6859]], requires_grad=True)>>> b = a * 2>>> btensor([[ 0.5648, -0.7430,  1.8176, -3.5202],        [-0.3612,  4.1874,  2.0812, -3.5303],        [ 2.2433,  1.6879,  0.3567,  1.3718]], grad_fn=<MulBackward0>)>>> b.requires_gradTrue>>> b.grad__main__:1: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won"t be populated during autograd.backward(). If you indeed want the gradient for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations.>>> print(b.grad)None>>> a.requires_gradTrue>>> a.grad>>> print(a.grad)None>>>>>> with torch.no_grad():...     c = a * 2...>>> ctensor([[ 0.5648, -0.7430,  1.8176, -3.5202],        [-0.3612,  4.1874,  2.0812, -3.5303],        [ 2.2433,  1.6879,  0.3567,  1.3718]])>>> c.requires_gradFalse>>> print(c.grad)None>>> a.grad>>>>>> print(a.grad)None>>> c.sum()tensor(6.1559)>>>>>> c.sum().backward()Traceback (most recent call last):  File "<stdin>", line 1, in <module>  File "D:Anaconda3envspytorch_1.7.1_cu102libsite-packages	orch	ensor.py", line 221, in backward    torch.autograd.backward(self, gradient, retain_graph, create_graph)  File "D:Anaconda3envspytorch_1.7.1_cu102libsite-packages	orchautograd\__init__.py", line 132, in backward    allow_unreachable=True)  # allow_unreachable flagRuntimeError: element 0 of tensors does not require grad and does not have a grad_fn>>>>>>>>> b.sum()tensor(6.1559, grad_fn=<SumBackward0>)>>> b.sum().backward()>>>>>>>>> a.gradtensor([[2., 2., 2., 2.],        [2., 2., 2., 2.],        [2., 2., 2., 2.]])>>> a.requires_gradTrue>>>>>>

Microsoft Windows [版本 10.0.18363.1440](c) 2019 Microsoft Corporation。保留所有权利。C:Userschenxuqi>conda activate pytorch_1.7.1_cu102(pytorch_1.7.1_cu102) C:Userschenxuqi>pythonPython 3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32Type "help", "copyright", "credits" or "license" for more information.>>> import torch>>> torch.manual_seed(seed=20200910)<torch._C.Generator object at 0x000002109ABC8870>>>>>>> a = torch.randn(3,4,requires_grad=True)>>> atensor([[ 0.2824, -0.3715,  0.9088, -1.7601],        [-0.1806,  2.0937,  1.0406, -1.7651],        [ 1.1216,  0.8440,  0.1783,  0.6859]], requires_grad=True)>>> a.requires_gradTrue>>>>>> with torch.set_grad_enabled(False):...     b = a * 2...>>> btensor([[ 0.5648, -0.7430,  1.8176, -3.5202],        [-0.3612,  4.1874,  2.0812, -3.5303],        [ 2.2433,  1.6879,  0.3567,  1.3718]])>>> b.requires_gradFalse>>>>>> with torch.set_grad_enabled(True):...     c = a * 3...>>> ctensor([[ 0.8472, -1.1145,  2.7263, -5.2804],        [-0.5418,  6.2810,  3.1219, -5.2954],        [ 3.3649,  2.5319,  0.5350,  2.0576]], grad_fn=<MulBackward0>)>>> c.requires_gradTrue>>>>>> d = a * 4>>> d.requires_gradTrue>>>>>> torch.set_grad_enabled(True)  # this can also be used as a function<torch.autograd.grad_mode.set_grad_enabled object at 0x00000210983982C8>>>>>>> # 以函数调用的方式来使用>>>>>> e = a * 5>>> etensor([[ 1.4119, -1.8574,  4.5439, -8.8006],        [-0.9030, 10.4684,  5.2031, -8.8257],        [ 5.6082,  4.2198,  0.8917,  3.4294]], grad_fn=<MulBackward0>)>>> e.requires_gradTrue>>>>>> dtensor([[ 1.1296, -1.4859,  3.6351, -7.0405],        [-0.7224,  8.3747,  4.1625, -7.0606],        [ 4.4866,  3.3759,  0.7133,  2.7435]], grad_fn=<MulBackward0>)>>>>>> torch.set_grad_enabled(False) # 以函数调用的方式来使用<torch.autograd.grad_mode.set_grad_enabled object at 0x0000021098394C48>>>>>>> f = a * 6>>> ftensor([[  1.6943,  -2.2289,   5.4527, -10.5607],        [ -1.0836,  12.5621,   6.2437, -10.5908],        [  6.7298,   5.0638,   1.0700,   4.1153]])>>> f.requires_gradFalse>>>>>>>>>