本文整理汇总了Python中torch.addbmm方法的典型用法代码示例。如果您正苦于以下问题：Python torch.addbmm方法的具体用法？Python torch.addbmm怎么用？Python torch.addbmm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的用法示例。
在下文中一共展示了torch.addbmm方法的10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: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def forward(self, add_matrix, batch1, batch2):
self.save_for_backward(batch1, batch2)
output = self._get_output(add_matrix)
return torch.addbmm(output, self.alpha, add_matrix, self.beta,
batch1, batch2) 示例2: test_addbmm
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def test_addbmm(self):
# num_batches = 10
# M, N, O = 12, 8, 5
num_batches = 2
M, N, O = 2, 3, 4
b1 = torch.randn(num_batches, M, N)
b2 = torch.randn(num_batches, N, O)
res = torch.bmm(b1, b2)
res2 = torch.Tensor().resize_as_(res[0]).zero_()
res2.addbmm_(b1,b2)
self.assertEqual(res2, res.sum(0)[0])
res2.addbmm_(1,b1,b2)
self.assertEqual(res2, res.sum(0)[0]*2)
res2.addbmm_(1.,.5,b1,b2)
self.assertEqual(res2, res.sum(0)[0]*2.5)
res3 = torch.addbmm(1,res2,0,b1,b2)
self.assertEqual(res3, res2)
res4 = torch.addbmm(1,res2,.5,b1,b2)
self.assertEqual(res4, res.sum(0)[0]*3)
res5 = torch.addbmm(0,res2,1,b1,b2)
self.assertEqual(res5, res.sum(0)[0])
res6 = torch.addbmm(.1,res2,.5,b1,b2)
self.assertEqual(res6, res2 * .1 + res.sum(0) * .5) 示例3: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def forward(ctx, add_matrix, batch1, batch2, alpha=1, beta=1, inplace=False):
ctx.alpha = alpha
ctx.beta = beta
ctx.save_for_backward(batch1, batch2)
output = _get_output(ctx, add_matrix, inplace=inplace)
return torch.addbmm(alpha, add_matrix, beta,
batch1, batch2, out=output) 示例4: test_functional_blas
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def test_functional_blas(self):
def compare(fn, *args):
unpacked_args = tuple(arg.data if isinstance(arg, Variable) else arg
for arg in args)
self.assertEqual(fn(*args).data, fn(*unpacked_args))
def test_blas_add(fn, x, y, z):
# Checks all signatures
compare(fn, x, y, z)
compare(fn, 0.5, x, y, z)
compare(fn, 0.5, x, 0.25, y, z)
def test_blas(fn, x, y):
compare(fn, x, y)
test_blas(torch.mm, Variable(torch.randn(2, 10)),
Variable(torch.randn(10, 4)))
test_blas_add(torch.addmm, Variable(torch.randn(2, 4)),
Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4)))
test_blas(torch.bmm, Variable(torch.randn(4, 2, 10)),
Variable(torch.randn(4, 10, 4)))
test_blas_add(torch.addbmm, Variable(torch.randn(2, 4)),
Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4)))
test_blas_add(torch.baddbmm, Variable(torch.randn(4, 2, 4)),
Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4)))
test_blas(torch.mv, Variable(torch.randn(2, 10)),
Variable(torch.randn(10)))
test_blas_add(torch.addmv, Variable(torch.randn(2)),
Variable(torch.randn(2, 10)), Variable(torch.randn(10)))
test_blas(torch.ger, Variable(torch.randn(5)),
Variable(torch.randn(6)))
test_blas_add(torch.addr, Variable(torch.randn(5, 6)),
Variable(torch.randn(5)), Variable(torch.randn(6))) 示例5: test_addbmm
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def test_addbmm(self):
# num_batches = 10
# M, N, O = 12, 8, 5
num_batches = 2
M, N, O = 2, 3, 4
b1 = torch.randn(num_batches, M, N)
b2 = torch.randn(num_batches, N, O)
res = torch.bmm(b1, b2)
res2 = torch.Tensor().resize_as_(res[0]).zero_()
res2.addbmm_(b1, b2)
self.assertEqual(res2, res.sum(0)[0])
res2.addbmm_(1, b1, b2)
self.assertEqual(res2, res.sum(0)[0] * 2)
res2.addbmm_(1., .5, b1, b2)
self.assertEqual(res2, res.sum(0)[0] * 2.5)
res3 = torch.addbmm(1, res2, 0, b1, b2)
self.assertEqual(res3, res2)
res4 = torch.addbmm(1, res2, .5, b1, b2)
self.assertEqual(res4, res.sum(0)[0] * 3)
res5 = torch.addbmm(0, res2, 1, b1, b2)
self.assertEqual(res5, res.sum(0)[0])
res6 = torch.addbmm(.1, res2, .5, b1, b2)
self.assertEqual(res6, res2 * .1 + res.sum(0) * .5) 示例6: test_addbmm
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def test_addbmm(self):
# num_batches = 10
# M, N, O = 12, 8, 5
num_batches = 2
M, N, O = 2, 3, 4
b1 = torch.randn(num_batches, M, N)
b2 = torch.randn(num_batches, N, O)
res = torch.bmm(b1, b2)
res2 = torch.Tensor().resize_as_(res[0]).zero_()
res2.addbmm_(b1, b2)
self.assertEqual(res2, res.sum(0, False))
res2.addbmm_(1, b1, b2)
self.assertEqual(res2, res.sum(0, False) * 2)
res2.addbmm_(1., .5, b1, b2)
self.assertEqual(res2, res.sum(0, False) * 2.5)
res3 = torch.addbmm(1, res2, 0, b1, b2)
self.assertEqual(res3, res2)
res4 = torch.addbmm(1, res2, .5, b1, b2)
self.assertEqual(res4, res.sum(0, False) * 3)
res5 = torch.addbmm(0, res2, 1, b1, b2)
self.assertEqual(res5, res.sum(0, False))
res6 = torch.addbmm(.1, res2, .5, b1, b2)
self.assertEqual(res6, res2 * .1 + res.sum(0) * .5) 示例7: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def forward(ctx, add_matrix, batch1, batch2, alpha=1, beta=1, inplace=False):
ctx.alpha = alpha
ctx.beta = beta
ctx.add_matrix_size = add_matrix.size()
ctx.save_for_backward(batch1, batch2)
output = _get_output(ctx, add_matrix, inplace=inplace)
return torch.addbmm(alpha, add_matrix, beta,
batch1, batch2, out=output) 示例8: test_addbmm
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def test_addbmm(self):
# num_batches = 10
# M, N, O = 12, 8, 5
num_batches = 2
M, N, O = 2, 3, 4
b1 = torch.randn(num_batches, M, N)
b2 = torch.randn(num_batches, N, O)
res = torch.bmm(b1, b2)
res2 = torch.Tensor().resize_as_(res[0]).zero_()
res2.addbmm_(b1, b2)
self.assertEqual(res2, res.sum(0, False))
res2.addbmm_(1, b1, b2)
self.assertEqual(res2, res.sum(0, False) * 2)
res2.addbmm_(1., .5, b1, b2)
self.assertEqual(res2, res.sum(0, False) * 2.5)
res3 = torch.addbmm(1, res2, 0, b1, b2)
self.assertEqual(res3, res2)
res4 = torch.addbmm(1, res2, .5, b1, b2)
self.assertEqual(res4, res.sum(0, False) * 3)
res5 = torch.addbmm(0, res2, 1, b1, b2)
self.assertEqual(res5, res.sum(0, False))
res6 = torch.addbmm(.1, res2, .5, b1, b2)
self.assertEqual(res6, res2 * .1 + (res.sum(0) * .5)) 示例9: _test_broadcast_fused_matmul
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def _test_broadcast_fused_matmul(self, cast):
fns = ["baddbmm", "addbmm", "addmm", "addmv", "addr"]
for fn in fns:
batch_dim = random.randint(1, 8)
n_dim = random.randint(1, 8)
m_dim = random.randint(1, 8)
p_dim = random.randint(1, 8)
def dims_full_for_fn():
if fn == "baddbmm":
return ([batch_dim, n_dim, p_dim], [batch_dim, n_dim, m_dim], [batch_dim, m_dim, p_dim])
elif fn == "addbmm":
return ([n_dim, p_dim], [batch_dim, n_dim, m_dim], [batch_dim, m_dim, p_dim])
elif fn == "addmm":
return ([n_dim, p_dim], [n_dim, m_dim], [m_dim, p_dim])
elif fn == "addmv":
return ([n_dim], [n_dim, m_dim], [m_dim])
elif fn == "addr":
return ([n_dim, m_dim], [n_dim], [m_dim])
else:
raise AssertionError("unknown function")
(t0_dims_full, t1_dims, t2_dims) = dims_full_for_fn()
(t0_dims_small, _, _) = self._select_broadcastable_dims(t0_dims_full)
t0_small = cast(torch.randn(*t0_dims_small).float())
t1 = cast(torch.randn(*t1_dims).float())
t2 = cast(torch.randn(*t2_dims).float())
t0_full = cast(t0_small.expand(*t0_dims_full))
fntorch = getattr(torch, fn)
r0 = fntorch(t0_small, t1, t2)
r1 = fntorch(t0_full, t1, t2)
self.assertEqual(r0, r1) 示例10: test_functional_blas
# 需要导入模块: import torch [as 别名]
# 或者: from torch import addbmm [as 别名]
def test_functional_blas(self):
def compare(fn, *args):
unpacked_args = tuple(arg.data if isinstance(arg, Variable) else arg
for arg in args)
unpacked_result = fn(*unpacked_args)
packed_result = fn(*args).data
# if non-Variable torch function returns a scalar, compare to scalar
if not torch.is_tensor(unpacked_result):
assert packed_result.dim() == 1
assert packed_result.nelement() == 1
packed_result = packed_result[0]
self.assertEqual(packed_result, unpacked_result)
def test_blas_add(fn, x, y, z):
# Checks all signatures
compare(fn, x, y, z)
compare(fn, 0.5, x, y, z)
compare(fn, 0.5, x, 0.25, y, z)
def test_blas(fn, x, y):
compare(fn, x, y)
test_blas(torch.mm, Variable(torch.randn(2, 10)),
Variable(torch.randn(10, 4)))
test_blas_add(torch.addmm, Variable(torch.randn(2, 4)),
Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4)))
test_blas(torch.bmm, Variable(torch.randn(4, 2, 10)),
Variable(torch.randn(4, 10, 4)))
test_blas_add(torch.addbmm, Variable(torch.randn(2, 4)),
Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4)))
test_blas_add(torch.baddbmm, Variable(torch.randn(4, 2, 4)),
Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4)))
test_blas(torch.mv, Variable(torch.randn(2, 10)),
Variable(torch.randn(10)))
test_blas_add(torch.addmv, Variable(torch.randn(2)),
Variable(torch.randn(2, 10)), Variable(torch.randn(10)))
test_blas(torch.ger, Variable(torch.randn(5)),
Variable(torch.randn(6)))
test_blas_add(torch.addr, Variable(torch.randn(5, 6)),
Variable(torch.randn(5)), Variable(torch.randn(6)))
test_blas(torch.matmul, Variable(torch.randn(6)), Variable(torch.randn(6)))
test_blas(torch.matmul, Variable(torch.randn(10, 4)), Variable(torch.randn(4)))
test_blas(torch.matmul, Variable(torch.randn(5)), Variable(torch.randn(5, 6)))
test_blas(torch.matmul, Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4)))
test_blas(torch.matmul, Variable(torch.randn(5, 2, 10)), Variable(torch.randn(5, 10, 4)))
test_blas(torch.matmul, Variable(torch.randn(3, 5, 2, 10)), Variable(torch.randn(3, 5, 10, 4)))
test_blas(torch.matmul, Variable(torch.randn(3, 5, 2, 10)), Variable(torch.randn(10)))
test_blas(torch.matmul, Variable(torch.randn(10)), Variable(torch.randn(3, 5, 10, 4)))