def initialize_network(self):
    if self.threeD:
        cfg = get_default_network_config(3, None, norm_type='in')
    else:
        cfg = get_default_network_config(1, None, norm_type='in')
    stage_plans = self.plans['plans_per_stage'][self.stage]
    conv_kernel_sizes = stage_plans['conv_kernel_sizes']
    blocks_per_stage_encoder = stage_plans['num_blocks_encoder']
    blocks_per_stage_decoder = stage_plans['num_blocks_decoder']
    pool_op_kernel_sizes = stage_plans['pool_op_kernel_sizes']
    self.network = FabiansUNet(self.num_input_channels, self.base_num_features, blocks_per_stage_encoder, 2, pool_op_kernel_sizes, conv_kernel_sizes, cfg, self.num_classes, blocks_per_stage_decoder, True, False, 320, InitWeights_He(0.01))
    if torch.cuda.is_available():
        self.network.cuda()
    self.network.inference_apply_nonlin = softmax_helper
def initialize_network(self):
    if self.threeD:
        cfg = get_default_network_config(3, None, norm_type='in')
    else:
        cfg = get_default_network_config(1, None, norm_type='in')
    stage_plans = self.plans['plans_per_stage'][self.stage]
    conv_kernel_sizes = stage_plans['conv_kernel_sizes']
    blocks_per_stage_encoder = stage_plans['num_blocks_encoder']
    blocks_per_stage_decoder = stage_plans['num_blocks_decoder']
    pool_op_kernel_sizes = stage_plans['pool_op_kernel_sizes']
    self.network = FabiansUNet(self.num_input_channels, self.base_num_features, blocks_per_stage_encoder, 2, pool_op_kernel_sizes, conv_kernel_sizes, cfg, self.num_classes, blocks_per_stage_decoder, True, False, 320, InitWeights_He(0.01))
    if torch.cuda.is_available():
        self.network.cuda()
    self.network.inference_apply_nonlin = softmax_helper