def __init__(self, in_ch, out_ch):
super(up, self).__init__()
self.up = ComplexUpsample(scale_factor=2, mode='bilinear')
self.conv = nn.Sequential(
ComplexConv2d(in_ch * 2, in_ch, [3, 3], padding=(1, 1)),
RadialBatchNorm2d(in_ch), Activation(in_ch),
ComplexConv2d(in_ch, out_ch, [3, 3], padding=(1, 1)),
RadialBatchNorm2d(out_ch), Activation(out_ch))
def __init__(self, in_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(
ComplexConv2d(in_ch, out_ch, [3, 3], padding=(1, 1)),
RadialBatchNorm2d(out_ch),
Activation(out_ch),
# ComplexDropout2d(params.dropout_ratio),
ComplexConv2d(out_ch, out_ch, [3, 3], padding=(1, 1)),
RadialBatchNorm2d(out_ch),
Activation(out_ch),
# ComplexDropout2d(params.dropout_ratio)
)
def __init__(self, in_ch, out_ch, residual_connection=True):
super(bottleneck, self).__init__()
self.residual_connection = residual_connection
self.down_conv = down_conv(in_ch)
self.double_conv = nn.Sequential(
# ComplexDropout2d(params.dropout_ratio),
ComplexConv2d(in_ch, 2 * in_ch, [3, 3], padding=(1, 1)),
RadialBatchNorm2d(2 * in_ch),
Activation(2 * in_ch),
# ComplexDropout2d(params.dropout_ratio),
ComplexConv2d(2 * in_ch, out_ch, [3, 3], padding=(1, 1)),
RadialBatchNorm2d(out_ch),
Activation(out_ch))
def __init__(self, in_ch):
super(down_conv, self).__init__()
self.conv = nn.Sequential(
ComplexConv2d(in_ch, in_ch, [3, 3], stride=(2, 2), padding=(1, 1)),
RadialBatchNorm2d(in_ch), Activation(in_ch))