本文整理汇总了Python中torch.autograd.Variable.send_方法的典型用法代码示例。如果您正苦于以下问题：Python Variable.send_方法的具体用法？Python Variable.send_怎么用？Python Variable.send_使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch.autograd.Variable的用法示例。
在下文中一共展示了Variable.send_方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_var_gradient_keeps_id_during_send_
# 需要导入模块: from torch.autograd import Variable [as 别名]
# 或者: from torch.autograd.Variable import send_ [as 别名]
def test_var_gradient_keeps_id_during_send_(self):
# PyTorch has a tendency to delete var.grad python objects
# and re-initialize them (resulting in new/random ids)
# we have fixed this bug and recorded how it was fixed
# as well as the creation of this unit test in the following
# video (1:50:00 - 2:00:00) ish
# https://www.twitch.tv/videos/275838386
# this is our hook
hook = TorchHook(verbose=False)
local = hook.local_worker
local.verbose = False
remote = VirtualWorker(id=1, hook=hook, verbose=False)
local.add_worker(remote)
data = Var(torch.FloatTensor([[0, 0], [0, 1], [1, 0], [1, 1]]))
target = Var(torch.FloatTensor([[0], [0], [1], [1]]))
model = Var(torch.zeros(2, 1), requires_grad=True)
# generates grad objects on model
pred = data.mm(model)
loss = ((pred - target)**2).sum()
loss.backward()
# the grad's true id
original_data_id = model.data.id + 0
original_grad_id = model.grad.data.id + 0
model.send_(remote)
assert model.data.id == original_data_id
assert model.grad.data.id == original_grad_id示例2: test_send_var_with_gradient
# 需要导入模块: from torch.autograd import Variable [as 别名]
# 或者: from torch.autograd.Variable import send_ [as 别名]
def test_send_var_with_gradient(self):
# previously, there was a bug involving sending variables with graidents
# to remote tensors. This bug was documented in Issue 1350
# https://github.com/OpenMined/PySyft/issues/1350
# this is our hook
hook = TorchHook(verbose=False)
local = hook.local_worker
local.verbose = False
remote = VirtualWorker(id=1, hook=hook, verbose=False)
local.add_worker(remote)
data = Var(torch.FloatTensor([[0, 0], [0, 1], [1, 0], [1, 1]]))
target = Var(torch.FloatTensor([[0], [0], [1], [1]]))
model = Var(torch.zeros(2, 1), requires_grad=True)
# generates grad objects on model
pred = data.mm(model)
loss = ((pred - target)**2).sum()
loss.backward()
# ensure that model and all (grand)children are owned by the local worker
assert model.owners[0].id == local.id
assert model.data.owners[0].id == local.id
# if you get a failure here saying that model.grad.owners does not exist
# check in hooks.py - _hook_new_grad(). self.grad_backup has probably either
# been deleted or is being run at the wrong time (see comments there)
assert model.grad.owners[0].id == local.id
assert model.grad.data.owners[0].id == local.id
# ensure that objects are not yet pointers (haven't sent it yet)
assert not model.is_pointer
assert not model.data.is_pointer
assert not model.grad.is_pointer
assert not model.grad.data.is_pointer
model.send_(remote)
# ensures that object ids do not change during the sending process
assert model.owners[0].id == remote.id
assert model.data.owners[0].id == remote.id
assert model.grad.owners[0].id == remote.id
assert model.grad.data.owners[0].id == remote.id
# ensures that all local objects are now pointers
assert model.is_pointer
assert model.data.is_pointer
assert model.grad.is_pointer
assert model.grad.data.is_pointer
# makes sure that tensors actually get sent to remote worker
assert model.id in remote._objects
assert model.data.id in remote._objects
assert model.grad.id in remote._objects
assert model.grad.data.id in remote._objects