本文整理汇总了Python中torch.nn.modules.utils._single方法的典型用法代码示例。如果您正苦于以下问题：Python utils._single方法的具体用法？Python utils._single怎么用？Python utils._single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.nn.modules.utils的用法示例。
在下文中一共展示了utils._single方法的6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: __init__
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _single [as 别名]
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
deformable_groups=1,
bias=False):
super(DeformConv, self).__init__()
assert not bias
assert in_channels % groups == 0, \
f'in_channels {in_channels} is not divisible by groups {groups}'
assert out_channels % groups == 0, \
f'out_channels {out_channels} is not divisible ' \
f'by groups {groups}'
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _pair(kernel_size)
self.stride = _pair(stride)
self.padding = _pair(padding)
self.dilation = _pair(dilation)
self.groups = groups
self.deformable_groups = deformable_groups
# enable compatibility with nn.Conv2d
self.transposed = False
self.output_padding = _single(0)
self.weight = nn.Parameter(
torch.Tensor(out_channels, in_channels // self.groups,
*self.kernel_size))
self.reset_parameters() 示例2: __init__
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _single [as 别名]
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
super(ConvTBC, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _single(kernel_size)
self.padding = _single(padding)
self.weight = torch.nn.Parameter(torch.Tensor(
self.kernel_size[0], in_channels, out_channels))
self.bias = torch.nn.Parameter(torch.Tensor(out_channels)) 示例3: __init__
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _single [as 别名]
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
deformable_groups=1,
bias=False):
super(DeformConv, self).__init__()
assert not bias
assert in_channels % groups == 0, \
'in_channels {} cannot be divisible by groups {}'.format(
in_channels, groups)
assert out_channels % groups == 0, \
'out_channels {} cannot be divisible by groups {}'.format(
out_channels, groups)
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _pair(kernel_size)
self.stride = _pair(stride)
self.padding = _pair(padding)
self.dilation = _pair(dilation)
self.groups = groups
self.deformable_groups = deformable_groups
# enable compatibility with nn.Conv2d
self.transposed = False
self.output_padding = _single(0)
self.weight = nn.Parameter(
torch.Tensor(out_channels, in_channels // self.groups,
*self.kernel_size))
self.reset_parameters() 示例4: __init__
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _single [as 别名]
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
deform_groups=1,
bias=False):
super(DeformConv2d, self).__init__()
assert in_channels % groups == 0, \
f'in_channels {in_channels} cannot be divisible by groups {groups}'
assert out_channels % groups == 0, \
f'out_channels {out_channels} cannot be divisible by groups \
{groups}'
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _pair(kernel_size)
self.stride = _pair(stride)
self.padding = _pair(padding)
self.dilation = _pair(dilation)
self.groups = groups
self.deform_groups = deform_groups
# enable compatibility with nn.Conv2d
self.transposed = False
self.output_padding = _single(0)
# only weight, no bias
self.weight = nn.Parameter(
torch.Tensor(out_channels, in_channels // self.groups,
*self.kernel_size))
self.reset_parameters() 示例5: __init__
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _single [as 别名]
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
deform_groups=1,
bias=True):
super(ModulatedDeformConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _pair(kernel_size)
self.stride = _pair(stride)
self.padding = _pair(padding)
self.dilation = _pair(dilation)
self.groups = groups
self.deform_groups = deform_groups
# enable compatibility with nn.Conv2d
self.transposed = False
self.output_padding = _single(0)
self.weight = nn.Parameter(
torch.Tensor(out_channels, in_channels // groups,
*self.kernel_size))
if bias:
self.bias = nn.Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.init_weights() 示例6: __init__
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _single [as 别名]
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True,
cuda=False, init_weight=None, init_bias=None, clip_var=None):
kernel_size = utils._single(kernel_size)
stride = utils._single(stride)
padding = utils._single(padding)
dilation = utils._single(dilation)
super(Conv1dGroupNJ, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, utils._pair(0), groups, bias, init_weight, init_bias, cuda, clip_var)