A quasi-UNet block, using `PixelShuffle_ICNR upsampling`.
UnetBlock(
up_in_c,
x_in_c,
hook,
final_div = TRUE,
blur = FALSE,
act_cls = nn()$ReLU,
self_attention = FALSE,
init = nn()$init$kaiming_normal_,
norm_type = NULL,
ks = 3,
stride = 1,
padding = NULL,
bias = NULL,
ndim = 2,
bn_1st = TRUE,
transpose = FALSE,
xtra = NULL,
bias_std = 0.01,
dilation = 1,
groups = 1,
padding_mode = "zeros"
)
up_in_c parameter
x_in_c parameter
The hook is set to this intermediate layer to store the output needed for this block.
final div
blur is used to avoid checkerboard artifacts at each layer.
activation
self_attention determines if we use a self-attention layer
initializer
normalization type
kernel size
stride
padding mode
bias
number of dimensions
batch normalization 1st
transpose
xtra
bias standard deviation
dilation
groups
The mode of padding
None