Bilinear¶

class paddle.nn. Bilinear ( ) ) ¶

该接口为参数初始化函数，用于转置卷积函数中，对输入进行上采样。用户通过任意整型因子放大shape为(B，C，H，W)的特征图。

返回：对象

用法如下：

代码示例:

import math

import paddle
import paddle.nn as nn
from paddle.regularizer import L2Decay

factor = 2
C = 2
B = 8
H = W = 32
w_attr = paddle.ParamAttr(learning_rate=0.,
                          regularizer=L2Decay(0.),
                          initializer=nn.initializer.Bilinear())
data = paddle.rand([B, 3, H, W], dtype='float32')
conv_up = nn.Conv2DTranspose(3,
                             out_channels=C,
                             kernel_size=2 * factor - factor % 2,
                             padding=int(math.ceil((factor - 1) / 2.)),
                             stride=factor,
                             weight_attr=w_attr,
                             bias_attr=False)
x = conv_up(data)

上述代码实现的是将输入x（shape=[-1, 4, H, W]）经过转置卷积得到shape=[-1, C, H*factor, W*factor]的输出，out_channels = C和groups = C 表示这是按通道转置的卷积函数，输出通道为C，转置卷积的groups为C。滤波器shape为(C,1,K,K)，K为kernel_size。该初始化函数为滤波器的每个通道设置(K,K)插值核。输出特征图的最终输出shape为(B,C,factor*H,factor*W)。注意学习率和权重衰减设为0，以便在训练过程中双线性插值的系数值保持不变

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paddle.nn / initializer / Bilinear

Bilinear¶

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paddle.nn / initializer / Bilinear

Bilinear¶