本文整理汇总了Python中chainer.cuda.available方法的典型用法代码示例。如果您正苦于以下问题：Python cuda.available方法的具体用法？Python cuda.available怎么用？Python cuda.available使用的例子？那么, 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chainer.cuda的用法示例。
在下文中一共展示了cuda.available方法的3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: __call__
# 需要导入模块: from chainer import cuda [as 别名]
# 或者: from chainer.cuda import available [as 别名]
def __call__(self, opt):
if cuda.available:
kernel = cuda.elementwise(
'T p, T decay', 'T g', 'g += decay * p', 'weight_decay')
rate = self.rate
for name, param in opt.target.namedparams():
if name == 'b' or name.endswith('/b'):
continue
p, g = param.data, param.grad
with cuda.get_device(p) as dev:
if int(dev) == -1:
g += rate * p
else:
kernel(p, rate, g) 示例2: __call__
# 需要导入模块: from chainer import cuda [as 别名]
# 或者: from chainer.cuda import available [as 别名]
def __call__(self, opt):
if cuda.available:
kernel = cuda.elementwise(
'T low, T high',
'T p',
'p = (p < low) ? low : (p > high) ? high : p',
'weight_clip')
for param in opt.target.params():
p = param.data
with cuda.get_device(p) as dev:
if int(dev) == -1:
numpy.clip(p, self.low, self.high)
else:
kernel(self.low, self.high, p) 示例3: _apply_categorical_projection
# 需要导入模块: from chainer import cuda [as 别名]
# 或者: from chainer.cuda import available [as 别名]
def _apply_categorical_projection(y, y_probs, z):
"""Apply categorical projection.
See Algorithm 1 in https://arxiv.org/abs/1707.06887.
Args:
y (ndarray): Values of atoms before projection. Its shape must be
(batch_size, n_atoms).
y_probs (ndarray): Probabilities of atoms whose values are y.
Its shape must be (batch_size, n_atoms).
z (ndarray): Values of atoms after projection. Its shape must be
(n_atoms,). It is assumed that the values are sorted in ascending
order and evenly spaced.
Returns:
ndarray: Probabilities of atoms whose values are z.
"""
batch_size, n_atoms = y.shape
assert z.shape == (n_atoms,)
assert y_probs.shape == (batch_size, n_atoms)
delta_z = z[1] - z[0]
v_min = z[0]
v_max = z[-1]
xp = cuda.get_array_module(z)
y = xp.clip(y, v_min, v_max)
# bj: (batch_size, n_atoms)
bj = (y - v_min) / delta_z
assert bj.shape == (batch_size, n_atoms)
# Avoid the error caused by inexact delta_z
bj = xp.clip(bj, 0, n_atoms - 1)
# l, u: (batch_size, n_atoms)
l, u = xp.floor(bj), xp.ceil(bj)
assert l.shape == (batch_size, n_atoms)
assert u.shape == (batch_size, n_atoms)
if cuda.available and xp is cuda.cupy:
scatter_add = cuda.cupyx.scatter_add
else:
scatter_add = np.add.at
z_probs = xp.zeros((batch_size, n_atoms), dtype=xp.float32)
offset = xp.arange(
0, batch_size * n_atoms, n_atoms, dtype=xp.int32)[..., None]
# Accumulate m_l
# Note that u - bj in the original paper is replaced with 1 - (bj - l) to
# deal with the case when bj is an integer, i.e., l = u = bj
scatter_add(
z_probs.ravel(),
(l.astype(xp.int32) + offset).ravel(),
(y_probs * (1 - (bj - l))).ravel())
# Accumulate m_u
scatter_add(
z_probs.ravel(),
(u.astype(xp.int32) + offset).ravel(),
(y_probs * (bj - l)).ravel())
return z_probs