Bilinear¶

class paddle.nn.initializer. Bilinear [source]

This initializer can be used in transposed convolution operator to act as upsampling. Users can upsample a feature map with shape of (B, C, H, W) by any integer factor.

Returns

Bilinear initializer instance objects.

Examples

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)

Where, out_channels=C and groups=C means this is channel-wise transposed convolution. The filter shape will be (C, 1, K, K) where K is kernel_size, This initializer will set a (K, K) interpolation kernel for every channel of the filter identically. The resulting shape of the output feature map will be (B, C, factor * H, factor * W). Note that the learning rate and the weight decay are set to 0 in order to keep coefficient values of bilinear interpolation unchanged during training.

forward ( var, block=None )

forward¶

Initialize the input tensor with Bilinear initialization.

Parameters

• var (Tensor) – Tensor that needs to be initialized.

• block (Block, optional) – The block in which initialization ops should be added. Used in static graph only, default None.

Returns

The initialization op