Method __init__

models/networks.py:722–735

(self, n_upsample, n_res, dim, output_dim, norm='batch', activ='relu', pad_type='zero', nz=0)

class Decoder_all(nn.Module):
    def __init__(self, n_upsample, n_res, dim, output_dim, norm='batch', activ='relu', pad_type='zero', nz=0):
        super(Decoder_all, self).__init__()
        # AdaIN residual blocks
        self.resnet_block = ResBlocks(n_res, dim, norm, activ, pad_type=pad_type, nz=nz)
        self.n_blocks = 0
        # upsampling blocks
        for i in range(n_upsample):
            block = [Upsample2(scale_factor=2), Conv2dBlock(dim + nz, dim // 2, 5, 1, 2, norm='ln', activation=activ, pad_type='reflect')]
            setattr(self, 'block_{:d}'.format(self.n_blocks), nn.Sequential(*block))
            self.n_blocks += 1
            dim //= 2
        # use reflection padding in the last conv layer
        setattr(self, 'block_{:d}'.format(self.n_blocks), Conv2dBlock(dim + nz, output_dim, 7, 1, 3, norm='none', activation='tanh', pad_type='reflect'))
        self.n_blocks += 1

    def forward(self, x, y=None):
        if y is not None: