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5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_torch_function_with_multiple_output_on_remote_var
# 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_torch_function_with_multiple_output_on_remote_var(self):
hook = TorchHook(verbose=False)
me = hook.local_worker
remote = VirtualWorker(id=2, hook=hook)
me.add_worker(remote)
x = Var(torch.FloatTensor([[1, 2], [4, 3], [5, 6]]))
x.send(remote)
y, z = torch.max(x, 1)
y.get()
assert torch.equal(y, Var(torch.FloatTensor([2, 4, 6])))
x = Var(torch.FloatTensor([[0, 0], [1, 0]])).send(remote)
y, z = torch.qr(x)
assert (y.get() == Var(torch.FloatTensor([[0, -1], [-1, 0]]))).all()
assert (z.get() == Var(torch.FloatTensor([[-1, 0], [0, 0]]))).all()
x = Var(torch.arange(1, 6)).send(remote)
y, z = torch.kthvalue(x, 4)
assert (y.get() == Var(torch.FloatTensor([4]))).all()
assert (z.get() == Var(torch.LongTensor([3]))).all()
x = Var(torch.FloatTensor([[0, 0], [0, 0]]))
x.send(remote)
y, z = torch.eig(x, True)
assert (y.get() == Var(torch.FloatTensor([[0, 0], [0, 0]]))).all()
assert (z.get() == Var(torch.FloatTensor([[1, 0.], [0, 1]]))).all()
x = Var(torch.zeros(3, 3)).send(remote)
w, y, z = torch.svd(x)
assert (w.get() == Var(torch.FloatTensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]]))).all()
assert (y.get() == Var(torch.FloatTensor([0, 0, 0]))).all()
assert (z.get() == Var(torch.FloatTensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]]))).all()示例2: test_encode_decode_json_python
# 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_encode_decode_json_python(self):
"""
Test that the python objects are correctly encoded and decoded in
json with our encoder/JSONDecoder.
The main focus is on non-serializable objects, such as torch Variable
or tuple, or even slice().
"""
hook = TorchHook(verbose=False)
local = hook.local_worker
remote = VirtualWorker(id=1, hook=hook)
local.add_worker(remote)
encoder = utils.PythonEncoder(retrieve_tensorvar=True)
decoder = utils.PythonJSONDecoder(remote)
x = Var(torch.FloatTensor([[1, -1],[0,1]]))
x.send(remote)
# Note that there is two steps of encoding/decoding because the first
# transforms `Variable containing:[torch.FloatTensor - Locations:[
# <syft.core.workers.virtual.VirtualWorker id:2>]]` into
# Variable containing:[torch.FloatTensor - Locations:[2]]`
obj = [None, ({'marcel': (1, [1.3], x), 'proust': slice(0, 2, None)}, 3)]
enc, t = encoder.encode(obj)
enc = json.dumps(enc)
dec1 = decoder.decode(enc)
enc, t = encoder.encode(dec1)
enc = json.dumps(enc)
dec2 = decoder.decode(enc)
assert dec1 == dec2示例3: test_torch_F_relu_on_remote_var
# 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_torch_F_relu_on_remote_var(self):
hook = TorchHook(verbose=False)
me = hook.local_worker
remote = VirtualWorker(id=2,hook=hook)
me.add_worker(remote)
x = Var(torch.FloatTensor([[1, -1], [-1, 1]]))
x.send(remote)
x = F.relu(x)
x.get()
assert torch.equal(x, Var(torch.FloatTensor([[1, 0], [0, 1]])))示例4: test_torch_nn_conv2d_on_remote_var
# 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_torch_nn_conv2d_on_remote_var(self):
hook = TorchHook(verbose=False)
me = hook.local_worker
remote = VirtualWorker(id=2,hook=hook)
me.add_worker(remote)
x = Var(torch.FloatTensor([[[[1, -1, 2], [-1, 0, 1], [1, 0, -2]]]]))
x.send(remote)
convolute = nn.Conv2d(1, 1, 2, stride=1, padding=0)
convolute.weight = torch.nn.Parameter(torch.FloatTensor([[[[1, -1], [-1, 1]]]]))
convolute.bias = torch.nn.Parameter(torch.FloatTensor([0]))
convolute.send(remote)
conv = convolute(x)
conv.get()
expected_conv = Var(torch.FloatTensor([[[[3, -2], [-2, -3]]]]))
assert torch.equal(conv, expected_conv)示例5: test_torch_F_conv2d_on_remote_var
# 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_torch_F_conv2d_on_remote_var(self):
hook = TorchHook(verbose=False)
me = hook.local_worker
remote = VirtualWorker(id=2,hook=hook)
me.add_worker(remote)
x = Var(torch.FloatTensor([[[[1, -1, 2], [-1, 0, 1], [1, 0, -2]]]]))
x.send(remote)
weight = torch.nn.Parameter(torch.FloatTensor([[[[1, -1], [-1, 1]]]]))
bias = torch.nn.Parameter(torch.FloatTensor([0]))
weight.send(remote)
bias.send(remote)
conv = F.conv2d(x, weight, bias, stride=(1,1))
conv.get()
expected_conv = Var(torch.FloatTensor([[[[3, -2], [-2, -3]]]]))
assert torch.equal(conv, expected_conv)