python constant_Python init.constant方法代码示例
本文整理汇总了Python中torch.nn.init.constant方法的典型用法代码示例。如果您正苦于以下问题：Python init.constant方法的具体用法？Python init.constant怎么用？Python init.constant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模块torch.nn.init的用法示例。
在下文中一共展示了init.constant方法的25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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: conv
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# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def conv(in_planes, out_planes, kernel_size=3, stride=1, dilation=1, bias=False, transposed=False):
if transposed:
layer = nn.ConvTranspose2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=1, output_padding=1, dilation=dilation, bias=bias)
# Bilinear interpolation init
w = torch.Tensor(kernel_size, kernel_size)
centre = kernel_size % 2 == 1 and stride - 1 or stride - 0.5
for y in range(kernel_size):
for x in range(kernel_size):
w[y, x] = (1 - abs((x - centre) / stride)) * (1 - abs((y - centre) / stride))
layer.weight.data.copy_(w.div(in_planes).repeat(in_planes, out_planes, 1, 1))
else:
padding = (kernel_size + 2 * (dilation - 1)) // 2
layer = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias)
if bias:
init.constant(layer.bias, 0)
return layer
# Returns 2D batch normalisation layer
开发者ID:Kaixhin，项目名称:FCN-semantic-segmentation，代码行数:21，
示例2: __init__
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# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def __init__(self, num_classes, pretrained_net):
super().__init__()
self.pretrained_net = pretrained_net
self.relu = nn.ReLU(inplace=True)
self.conv5 = conv(512, 256, stride=2, transposed=True)
self.bn5 = bn(256)
self.conv6 = conv(256, 128, stride=2, transposed=True)
self.bn6 = bn(128)
self.conv7 = conv(128, 64, stride=2, transposed=True)
self.bn7 = bn(64)
self.conv8 = conv(64, 64, stride=2, transposed=True)
self.bn8 = bn(64)
self.conv9 = conv(64, 32, stride=2, transposed=True)
self.bn9 = bn(32)
self.conv10 = conv(32, num_classes, kernel_size=7)
init.constant(self.conv10.weight, 0) # Zero init
开发者ID:Kaixhin，项目名称:FCN-semantic-segmentation，代码行数:18，
示例3: init_params
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# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def init_params(net):
'''Init layer parameters.'''
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal(m.weight, mode='fan_out')
if m.bias:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant(m.weight, 1)
init.constant(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal(m.weight, std=1e-3)
if m.bias:
init.constant(m.bias, 0)
#_, term_width = os.popen('stty size', 'r').read().split()
# term_width = int(term_width)
开发者ID:leehomyc，项目名称:mixup_pytorch，代码行数:20，
示例4: reset_parameters
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# 需要导入模块: from torch.nn import init [as 别名]
# 或者: from torch.nn.init import constant [as 别名]
def reset_parameters(self):
"""
Initialize parameters following the way proposed in the paper.
"""
# The input-to-hidden weight matrix is initialized orthogonally.
init.orthogonal(self.weight_ih.data)
# The hidden-to-hidden weight matrix is initialized as an identity
# matrix.
weight_hh_data = torch.eye(self.hidden_size)
weight_hh_data = weight_hh_data.repeat(1, 4)
self.weight_hh.data.set_(weight_hh_data)
# The bias is just set to zero vectors.
init.constant(self.bias.data, val=0)
# Initialization of BN parameters.
self.bn_ih.reset_parameters()
self.bn_hh.reset_parameters()
self.bn_c.reset_parameters()
self.bn_ih.bias.data.fill_(0)
self.bn_hh.bias.data.fill_(0)
self.bn_ih.weight.data.fill_(0.1)
self.bn_h