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. The usage is:
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.