本文整理汇总了Python中torch.nn.functional.linear方法的典型用法代码示例。如果您正苦于以下问题：Python functional.linear方法的具体用法？Python functional.linear怎么用？Python functional.linear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.functional的用法示例。

在下文中一共展示了functional.linear方法的24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: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, x):

"""

forward pass of the layer

:param x: input

:return: y => output

"""

from torch.nn.functional import linear

return linear(x, self.weight * self.scale,

self.bias if self.use_bias else None)

# -----------------------------------------------------------------------------------

# Pixelwise feature vector normalization.

# reference:

# https://github.com/tkarras/progressive_growing_of_gans/blob/master/networks.py#L120

# -----------------------------------------------------------------------------------

开发者ID:akanimax，项目名称:BMSG-GAN，代码行数:18，

示例2: inverse_no_cache

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def inverse_no_cache(self, inputs):

"""Cost:

output = O(D^3 + D^2N)

logabsdet = O(D^3)

where:

D = num of features

N = num of inputs

"""

batch_size = inputs.shape[0]

outputs = inputs - self.bias

outputs, lu = torch.gesv(outputs.t(), self._weight) # Linear-system solver.

outputs = outputs.t()

# The linear-system solver returns the LU decomposition of the weights, which we

# can use to obtain the log absolute determinant directly.

logabsdet = -torch.sum(torch.log(torch.abs(torch.diag(lu))))

logabsdet = logabsdet * torch.ones(batch_size)

return outputs, logabsdet

开发者ID:bayesiains，项目名称:nsf，代码行数:19，

示例3: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, input, hx, att_score):

"""

References

----------

https://github.com/pytorch/pytorch/blob/v0.4.1/torch/nn/_functions/rnn.py#L49

"""

gi = F.linear(input, self.weight_ih, self.bias_ih)

gh = F.linear(hx, self.weight_hh, self.bias_hh)

i_r, i_z, i_n = gi.chunk(3, 1)

h_r, h_z, h_n = gh.chunk(3, 1)

resetgate = torch.sigmoid(i_r + h_r)

# updategate = torch.sigmoid(i_z + h_z)

newgate = torch.tanh(i_n + resetgate * h_n)

# hy = newgate + updategate * (hx - newgate)

att_score = att_score.view(-1, 1)

hy = (1. - att_score) * hx + att_score * newgate

return hy

开发者ID:GitHub-HongweiZhang，项目名称:prediction-flow，代码行数:25，

示例4: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, inputs, labels):

cos_th = F.linear(inputs, F.normalize(self.weight))

cos_th = cos_th.clamp(-1, 1)

sin_th = torch.sqrt(1.0 - torch.pow(cos_th, 2))

cos_th_m = cos_th * self.cos_m - sin_th * self.sin_m

cos_th_m = torch.where(cos_th > self.th, cos_th_m, cos_th - self.mm)

cond_v = cos_th - self.th

cond = cond_v <= 0

cos_th_m[cond] = (cos_th - self.mm)[cond]

if labels.dim() == 1:

labels = labels.unsqueeze(-1)

onehot = torch.zeros(cos_th.size()).cuda()

onehot.scatter_(1, labels, 1)

outputs = onehot * cos_th_m + (1.0 - onehot) * cos_th

outputs = outputs * self.s

return outputs

开发者ID:pudae，项目名称:kaggle-humpback，代码行数:20，

示例5: get_loadings

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def get_loadings(self) -> np.ndarray:

"""Extract per-gene weights (for each Z, shape is genes by dim(Z)) in the linear decoder."""

# This is BW, where B is diag(b) batch norm, W is weight matrix

if self.use_batch_norm is True:

w = self.decoder.factor_regressor.fc_layers[0][0].weight

bn = self.decoder.factor_regressor.fc_layers[0][1]

sigma = torch.sqrt(bn.running_var + bn.eps)

gamma = bn.weight

b = gamma / sigma

bI = torch.diag(b)

loadings = torch.matmul(bI, w)

else:

loadings = self.decoder.factor_regressor.fc_layers[0][0].weight

loadings = loadings.detach().cpu().numpy()

if self.n_batch > 1:

loadings = loadings[:, : -self.n_batch]

return loadings

开发者ID:YosefLab，项目名称:scVI，代码行数:20，

示例6: LSTMCell

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def LSTMCell(input, hidden, w_ih, w_hh, b_ih=None, b_hh=None):

"""

A modified LSTM cell with hard sigmoid activation on the input, forget and output gates.

"""

hx, cx = hidden

gates = F.linear(input, w_ih, b_ih) + F.linear(hx, w_hh, b_hh)

ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)

ingate = hard_sigmoid(ingate)

forgetgate = hard_sigmoid(forgetgate)

cellgate = F.tanh(cellgate)

outgate = hard_sigmoid(outgate)

cy = (forgetgate * cx) + (ingate * cellgate)

hy = outgate * F.tanh(cy)

return hy, cy

开发者ID:chenyangh，项目名称:SemEval2019Task3，代码行数:20，

示例7: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, x):

# Pass Add bias.

x += self.bias

# Evaluate activation function.

if self.act == "linear":

pass

elif self.act == 'lrelu':

x = F.leaky_relu(x, self.alpha, inplace=True)

x = x * np.sqrt(2) # original repo def_gain=np.sqrt(2).

# Scale by gain.

if self.gain != 1:

x = x * self.gain

return x

开发者ID:tomguluson92，项目名称:StyleGAN2_PyTorch，代码行数:18，

示例8: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, x):

if self.zero_mean:

lrt_mean = 0.0

else:

lrt_mean = F.linear(x, self.W)

if self.bias is not None:

lrt_mean = lrt_mean + self.bias

sigma2 = Variable.exp(self.log_alpha) * self.W * self.W

if self.permute_sigma:

sigma2 = sigma2.view(-1)[torch.randperm(self.in_features * self.out_features).cuda()].view(self.out_features, self.in_features)

lrt_std = Variable.sqrt(1e-16 + F.linear(x * x, sigma2))

if self.training:

eps = Variable(lrt_std.data.new(lrt_std.size()).normal_())

else:

eps = 0.0

return lrt_mean + lrt_std * eps

开发者ID:da-molchanov，项目名称:variance-networks，代码行数:20，

示例9: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, x):

"""Forward feature from the regression head to get integral result of

bounding box location.

Args:

x (Tensor): Features of the regression head, shape (N, 4*(n+1)),

n is self.reg_max.

Returns:

x (Tensor): Integral result of box locations, i.e., distance

offsets from the box center in four directions, shape (N, 4).

"""

x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)

x = F.linear(x, self.project).reshape(-1, 4)

return x

开发者ID:open-mmlab，项目名称:mmdetection，代码行数:17，

示例10: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, x, lens, hidden=None):

emb_x = self.dp1(self.emb(x))

if not self.training:

self.rnn.flatten_parameters()

packed = nn.utils.rnn.pack_padded_sequence(

emb_x, lens, batch_first=True, enforce_sorted=False)

# output: (seq_len, batch, hidden)

outputs, hidden = self.rnn(packed, hidden)

outputs, _ = nn.utils.rnn.pad_packed_sequence(

outputs, batch_first=True)

if self.emb_tying:

outputs = F.linear(self.dp2(outputs), self.emb.weight)

else:

outputs = self.trans(self.dp2(outputs))

return outputs, hidden

开发者ID:Alexander-H-Liu，项目名称:End-to-end-ASR-Pytorch，代码行数:17，代码来源:lm.py

示例11: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, input):

if self.masked_weight is None:

return F.linear(input, self.mask * self.weight, self.bias)

else:

# ~17% speedup for Prog Sampling.

return F.linear(input, self.masked_weight, self.bias)

开发者ID:naru-project，项目名称:naru，代码行数:8，

示例12: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, x):

if self.groups == 1:

out = super(Linear, self).forward(x)

else:

x_g = x.chunk(self.groups, dim=-1)

w_g = self.weight.chunk(self.groups, dim=-1)

out = torch.cat([F.linear(x_g[i], w_g[i])

for i in range(self.groups)], -1)

if self.bias is not None:

out += self.bias

return out

开发者ID:nadavbh12，项目名称:Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch，代码行数:13，

示例13: forward_no_cache

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward_no_cache(self, inputs):

"""Cost:

output = O(D^2N)

logabsdet = O(D)

where:

D = num of features

N = num of inputs

"""

lower, upper = self._create_lower_upper()

outputs = F.linear(inputs, upper)

outputs = F.linear(outputs, lower, self.bias)

logabsdet = self.logabsdet() * inputs.new_ones(outputs.shape[0])

return outputs, logabsdet

开发者ID:bayesiains，项目名称:nsf，代码行数:15，代码来源:lu.py

示例14: forward

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​

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, inputs, context=None):

if not self.training and self.using_cache:

self._check_forward_cache()

outputs = F.linear(inputs, self.cache.weight, self.bias)

logabsdet = self.cache.logabsdet * torch.ones(outputs.shape[0])

return outputs, logabsdet

else:

return self.forward_no_cache(inputs)

开发者ID:bayesiains，项目名称:nsf，代码行数:10，

示例15: inverse

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def inverse(self, inputs, context=None):

if not self.training and self.using_cache:

self._check_inverse_cache()

outputs = F.linear(inputs - self.bias, self.cache.inverse)

logabsdet = (-self.cache.logabsdet) * torch.ones(outputs.shape[0])

return outputs, logabsdet

else:

return self.inverse_no_cache(inputs)

开发者ID:bayesiains，项目名称:nsf，代码行数:10，

示例16: forward_no_cache

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward_no_cache(self, inputs):

"""Cost:

output = O(D^2N)

logabsdet = O(D^3)

where:

D = num of features

N = num of inputs

"""

batch_size = inputs.shape[0]

outputs = F.linear(inputs, self._weight, self.bias)

logabsdet = utils.logabsdet(self._weight)

logabsdet = logabsdet * torch.ones(batch_size)

return outputs, logabsdet

开发者ID:bayesiains，项目名称:nsf，代码行数:15，

示例17: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, x):

return F.linear(x, self.weight * self.mask, self.bias)

开发者ID:bayesiains，项目名称:nsf，代码行数:4，

示例18: __init__

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def __init__(self,

in_degrees,

autoregressive_features,

context_features=None,

random_mask=False,

activation=F.relu,

dropout_probability=0.,

use_batch_norm=False):

super().__init__()

features = len(in_degrees)

# Batch norm.

if use_batch_norm:

self.batch_norm = nn.BatchNorm1d(features, eps=1e-3)

else:

self.batch_norm = None

if context_features is not None:

raise NotImplementedError()

# Masked linear.

self.linear = MaskedLinear(

in_degrees=in_degrees,

out_features=features,

autoregressive_features=autoregressive_features,

random_mask=random_mask,

is_output=False,

)

self.degrees = self.linear.degrees

# Activation and dropout.

self.activation = activation

self.dropout = nn.Dropout(p=dropout_probability)

开发者ID:bayesiains，项目名称:nsf，代码行数:35，

示例19: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, x, init=False):

if init is True:

# out_features * in_features

self.V.data.copy_(torch.randn(self.V.data.size()).type_as(

self.V.data) * 0.05)

# norm is out_features * 1

v_norm = self.V.data / \

self.V.data.norm(2, 1).expand_as(self.V.data)

# batch_size * out_features

x_init = F.linear(x, Variable(v_norm)).data

# out_features

m_init, v_init = x_init.mean(0).squeeze(

0), x_init.var(0).squeeze(0)

# out_features

scale_init = self.init_scale / \

torch.sqrt(v_init + 1e-10)

self.g.data.copy_(scale_init)

self.b.data.copy_(-m_init * scale_init)

x_init = scale_init.view(1, -1).expand_as(x_init) \

* (x_init - m_init.view(1, -1).expand_as(x_init))

self.V_avg.copy_(self.V.data)

self.g_avg.copy_(self.g.data)

self.b_avg.copy_(self.b.data)

return Variable(x_init)

else:

V, g, b = get_vars_maybe_avg(self, ['V', 'g', 'b'],

self.training,

polyak_decay=self.polyak_decay)

# batch_size * out_features

x = F.linear(x, V)

scalar = g / torch.norm(V, 2, 1).squeeze(1)

x = scalar.view(1, -1).expand_as(x) * x + \

b.view(1, -1).expand_as(x)

return x

开发者ID:xiadingZ，项目名称:video-caption-openNMT.pytorch，代码行数:36，

示例20: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, features):

cosine = F.linear(F.normalize(features), F.normalize(self.weight))

return cosine

开发者ID:maciej-sypetkowski，项目名称:kaggle-rcic-1st，代码行数:5，

示例21: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, input):

weight = self.compute_weight(update=self.training)

return F.linear(input, weight, self.bias)

开发者ID:rtqichen，项目名称:residual-flows，代码行数:5，

示例22: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, input):

weight = self.compute_weight(update=False)

return F.linear(input, weight, self.bias)

开发者ID:rtqichen，项目名称:residual-flows，代码行数:5，

示例23: forward

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def forward(self, x, logpx=None):

y = F.linear(x, self.weight)

if logpx is None:

return y

else:

return y, logpx - self._logdetgrad

开发者ID:rtqichen，项目名称:residual-flows，代码行数:8，

示例24: linear_classifier

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# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import linear [as 别名]

def linear_classifier(x, param_dict):

"""

Classifier.

"""

return F.linear(x, param_dict['weight_mean'], param_dict['bias_mean'])

开发者ID:cambridge-mlg，项目名称:cnaps，代码行数:7，

注：本文中的torch.nn.functional.linear方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

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