本文整理汇总了Python中torch.nn.modules.utils._ntuple方法的典型用法代码示例。如果您正苦于以下问题：Python utils._ntuple方法的具体用法？Python utils._ntuple怎么用？Python utils._ntuple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torch.nn.modules.utils的用法示例。
在下文中一共展示了utils._ntuple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: interpolate
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _ntuple [as 别名]
def interpolate(
input, size=None, scale_factor=None, mode="nearest", align_corners=None
):
if input.numel() > 0:
return torch.nn.functional.interpolate(
input, size, scale_factor, mode, align_corners
)
def _check_size_scale_factor(dim):
if size is None and scale_factor is None:
raise ValueError("either size or scale_factor should be defined")
if size is not None and scale_factor is not None:
raise ValueError("only one of size or scale_factor should be defined")
if (
scale_factor is not None
and isinstance(scale_factor, tuple)
and len(scale_factor) != dim
):
raise ValueError(
"scale_factor shape must match input shape. "
"Input is {}D, scale_factor size is {}".format(dim, len(scale_factor))
)
def _output_size(dim):
_check_size_scale_factor(dim)
if size is not None:
return size
scale_factors = _ntuple(dim)(scale_factor)
# math.floor might return float in py2.7
return [
int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)
]
output_shape = tuple(_output_size(2))
output_shape = input.shape[:-2] + output_shape
return _NewEmptyTensorOp.apply(input, output_shape) 示例2: interpolate
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _ntuple [as 别名]
def interpolate(x, size=None, scale_factor=None, mode="nearest", align_corners=None):
if x.numel() > 0:
return torch.nn.functional.interpolate(
x, size, scale_factor, mode, align_corners
)
def _check_size_scale_factor(dim):
if size is None and scale_factor is None:
raise ValueError("either size or scale_factor should be defined")
if size is not None and scale_factor is not None:
raise ValueError("only one of size or scale_factor should be defined")
if (
scale_factor is not None
and isinstance(scale_factor, tuple)
and len(scale_factor) != dim
):
raise ValueError(
"scale_factor shape must match input shape. "
"Input is {}D, scale_factor size is {}".format(dim, len(scale_factor))
)
def _output_size(dim):
_check_size_scale_factor(dim)
if size is not None:
return size
scale_factors = _ntuple(dim)(scale_factor)
# math.floor might return float in py2.7
return [
int(math.floor(x.size(i + 2) * scale_factors[i])) for i in range(dim)
]
output_shape = tuple(_output_size(2))
output_shape = x.shape[:-2] + output_shape
return _NewEmptyTensorOp.apply(x, output_shape) 示例3: interpolate
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _ntuple [as 别名]
def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None):
if input.numel() > 0:
return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
from torch.nn.modules.utils import _ntuple
import math
def _check_size_scale_factor(dim):
if size is None and scale_factor is None:
raise ValueError('either size or scale_factor should be defined')
if size is not None and scale_factor is not None:
raise ValueError('only one of size or scale_factor should be defined')
if scale_factor is not None and isinstance(scale_factor, tuple)\
and len(scale_factor) != dim:
raise ValueError('scale_factor shape must match input shape. '
'Input is {}D, scale_factor size is {}'.format(dim, len(scale_factor)))
def _output_size(dim):
_check_size_scale_factor(dim)
if size is not None:
return size
scale_factors = _ntuple(dim)(scale_factor)
# math.floor might return float in py2.7
return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)]
output_shape = tuple(_output_size(2))
output_shape = input.shape[:-2] + output_shape
return _NewEmptyTensorOp.apply(input, output_shape) 示例4: interpolate
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _ntuple [as 别名]
def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
"""
A wrapper around :func:`torch.nn.functional.interpolate` to support zero-size tensor.
"""
if TORCH_VERSION > (1, 4) or input.numel() > 0:
return torch.nn.functional.interpolate(
input, size, scale_factor, mode, align_corners=align_corners
)
def _check_size_scale_factor(dim):
if size is None and scale_factor is None:
raise ValueError("either size or scale_factor should be defined")
if size is not None and scale_factor is not None:
raise ValueError("only one of size or scale_factor should be defined")
if (
scale_factor is not None
and isinstance(scale_factor, tuple)
and len(scale_factor) != dim
):
raise ValueError(
"scale_factor shape must match input shape. "
"Input is {}D, scale_factor size is {}".format(dim, len(scale_factor))
)
def _output_size(dim):
_check_size_scale_factor(dim)
if size is not None:
return size
scale_factors = _ntuple(dim)(scale_factor)
# math.floor might return float in py2.7
return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)]
output_shape = tuple(_output_size(2))
output_shape = input.shape[:-2] + output_shape
return _NewEmptyTensorOp.apply(input, output_shape) 示例5: interpolate
# 需要导入模块: from torch.nn.modules import utils [as 别名]
# 或者: from torch.nn.modules.utils import _ntuple [as 别名]
def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
"""
A wrapper around :func:`torch.nn.functional.interpolate` to support zero-size tensor.
"""
if input.numel() > 0:
return torch.nn.functional.interpolate(
input, size, scale_factor, mode, align_corners=align_corners
)
def _check_size_scale_factor(dim):
if size is None and scale_factor is None:
raise ValueError("either size or scale_factor should be defined")
if size is not None and scale_factor is not None:
raise ValueError("only one of size or scale_factor should be defined")
if (
scale_factor is not None
and isinstance(scale_factor, tuple)
and len(scale_factor) != dim
):
raise ValueError(
"scale_factor shape must match input shape. "
"Input is {}D, scale_factor size is {}".format(dim, len(scale_factor))
)
def _output_size(dim):
_check_size_scale_factor(dim)
if size is not None:
return size
scale_factors = _ntuple(dim)(scale_factor)
# math.floor might return float in py2.7
return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)]
output_shape = tuple(_output_size(2))
output_shape = input.shape[:-2] + output_shape
return _NewEmptyTensorOp.apply(input, output_shape)