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copy.py
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copy.py
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from chainer.backends import cuda
from chainer import function_node
from chainer.utils import type_check
class Copy(function_node.FunctionNode):
"""Copies the input variable onto the specified device."""
def __init__(self, out_device):
self.out_device = out_device
def check_type_forward(self, in_types):
type_check.expect(
in_types.size() == 1
)
def forward(self, inputs):
x, = inputs
self._in_device = cuda.get_device_from_array(x).id
if int(self.out_device) == -1:
return cuda.to_cpu(x),
else:
return cuda.to_gpu(x, self.out_device),
def backward(self, indexes, grad_outputs):
return Copy(self._in_device).apply(grad_outputs)
def copy(x, dst):
"""Copies the input variable onto the specified device.
This function copies the array of input variable onto the device specified
by ``dst``. When ``dst == -1``, it copies the array onto the host memory.
This function supports copies from host to host, from host to device,
from device to device and from device to host.
Args:
x (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`):
Variable to be copied.
dst (int): Target device specifier.
Returns:
~chainer.Variable: Output variable.
.. admonition:: Example
>>> import chainer.cuda as cuda
>>> x = np.random.uniform(-1, 1, (5, 10))
>>> cuda.get_device_from_array(x).id
-1
>>> y = F.copy(x, 0) # from host to device0
>>> cuda.get_device_from_array(y.data).id
0
>>> z = F.copy(y, -1) # from device0 to host
>>> cuda.get_device_from_array(z.data).id
-1
"""
y, = Copy(dst).apply((x,))
return y