本文整理匯總了Python中torch.nn.ConstantPad3d方法的典型用法代碼示例。如果您正苦於以下問題：Python nn.ConstantPad3d方法的具體用法？Python nn.ConstantPad3d怎麽用？Python nn.ConstantPad3d使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類torch.nn的用法示例。
在下文中一共展示了nn.ConstantPad3d方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _make_layer
# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import ConstantPad3d [as 別名]
def _make_layer(self, layer_gates, block, planes, blocks, stride=1, conv_downsample=False):
downsample = None
outplanes = planes * block.expansion
if stride != 1 or self.inplanes != outplanes:
if conv_downsample:
downsample = nn.Conv2d(self.inplanes, outplanes,
kernel_size=1, stride=stride, bias=False)
else:
# Identity downsample uses strided average pooling + padding instead of convolution
pad_amount = int(self.inplanes / 2)
downsample = nn.Sequential(
nn.AvgPool2d(2),
nn.ConstantPad3d((0, 0, 0, 0, pad_amount, pad_amount), 0)
)
layers = []
layers.append(block(layer_gates[0], self.inplanes, planes, stride, downsample, conv_downsample))
self.inplanes = outplanes
for i in range(1, blocks):
layers.append(block(layer_gates[i], self.inplanes, planes))
return nn.Sequential(*layers) 示例2: get_pad_operation
# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import ConstantPad3d [as 別名]
def get_pad_operation(self):
if self.op in ['Conv2d']:
lr = (self.dilation[1]) * (self.kernel_size[1] // 2)
hw = (self.dilation[0]) * (self.kernel_size[0] // 2)
self.pad_op = nn.ConstantPad2d((lr, lr, hw, hw), 0)
if self.op in ['Conv3d']:
lr = (self.dilation[2]) * (self.kernel_size[2] // 2)
hw = (self.dilation[1]) * (self.kernel_size[1] // 2)
fb = (self.dilation[0]) * (self.kernel_size[0] // 2) # (front, back) => depth dimension
self.pad_op = nn.ConstantPad3d((lr, lr, hw, hw, fb, fb), 0) 示例3: __init__
# 需要導入模塊: from torch import nn [as 別名]
# 或者: from torch.nn import ConstantPad3d [as 別名]
def __init__(self, kernel_size, stride, return_indices=False, return_pad=False):
super(PadMaxPool3d, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.pool = nn.MaxPool3d(kernel_size, stride, return_indices=return_indices)
self.pad = nn.ConstantPad3d(padding=0, value=0)
self.return_indices = return_indices
self.return_pad = return_pad