deepatlas/network/network.py

import monai
import torch
import itk
import numpy as np
import matplotlib.pyplot as plt
from typing import Optional, Sequence, Tuple, Union
from monai.networks.layers.factories import Act, Norm


def segNet(
    spatial_dim: int,
    in_channel: int,
    out_channel: int,
    channel: Sequence[int],
    stride: Sequence[int],
    num_res_unit: int = 0,
    acts: Union[Tuple, str] = Act.PRELU,
    norms: Union[Tuple, str] = Norm.INSTANCE,
    dropouts: float = 0.0,
):
    seg_net = monai.networks.nets.UNet(
        spatial_dims=spatial_dim,  # spatial dims
        in_channels=in_channel,  # input channels
        out_channels=out_channel,  # output channels
        channels=channel,  # channel sequence
        strides=stride,  # convolutional strides
        dropout=dropouts,
        act=acts,
        norm=norms,
        num_res_units=num_res_unit
    )
    return seg_net

def regNet(
    spatial_dim: int,
    in_channel: int,
    out_channel: int,
    channel: Sequence[int],
    stride: Sequence[int],
    num_res_unit: int = 0,
    acts: Union[Tuple, str] = Act.PRELU,
    norms: Union[Tuple, str] = Norm.INSTANCE,
    dropouts: float = 0.0,
):
    reg_net = monai.networks.nets.UNet(
        spatial_dims=spatial_dim,  # spatial dims
        in_channels=in_channel,  # input channels
        out_channels=out_channel,  # output channels
        channels=channel,  # channel sequence
        strides=stride,  # convolutional strides
        dropout=dropouts,
        act=acts,
        norm=norms,
        num_res_units=num_res_unit
    )
    return reg_net