data/networks/RepUXNet_3D/network_backbone.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 10 15:04:06 2022

@author: leeh43
"""

from typing import Tuple

import torch.nn as nn

from monai.networks.blocks.dynunet_block import UnetOutBlock
from monai.networks.blocks.unetr_block import UnetrBasicBlock, UnetrUpBlock
from typing import Union
import torch.nn.functional as F
from lib.utils.tools.logger import Logger as Log
from lib.models.tools.module_helper import ModuleHelper
from networks.RepUXNet_3D.repuxnet_encoder import repuxnet_conv

class ProjectionHead(nn.Module):
    def __init__(self, dim_in, proj_dim=256, proj='convmlp', bn_type='torchbn'):
        super(ProjectionHead, self).__init__()

        Log.info('proj_dim: {}'.format(proj_dim))

        if proj == 'linear':
            self.proj = nn.Conv2d(dim_in, proj_dim, kernel_size=1)
        elif proj == 'convmlp':
            self.proj = nn.Sequential(
                nn.Conv3d(dim_in, dim_in, kernel_size=1),
                ModuleHelper.BNReLU(dim_in, bn_type=bn_type),
                nn.Conv3d(dim_in, proj_dim, kernel_size=1)
            )

    def forward(self, x):
        return F.normalize(self.proj(x), p=2, dim=1)


# class ResBlock(nn.Module):
#     expansion = 1
#
#     def __init__(self,
#                  in_planes: int,
#                  planes: int,
#                  spatial_dims: int = 3,
#                  stride: int = 1,
#                  downsample: Union[nn.Module, partial, None] = None,
#     ) -> None:
#         """
#         Args:
#             in_planes: number of input channels.
#             planes: number of output channels.
#             spatial_dims: number of spatial dimensions of the input image.
#             stride: stride to use for first conv layer.
#             downsample: which downsample layer to use.
#         """
#
#         super().__init__()
#
#         conv_type: Callable = Conv[Conv.CONV, spatial_dims]
#         norm_type: Callable = Norm[Norm.BATCH, spatial_dims]
#
#         self.conv1 = conv_type(in_planes, planes, kernel_size=3, padding=1, stride=stride, bias=False)
#         self.bn1 = norm_type(planes)
#         self.relu = nn.ReLU(inplace=True)
#         self.conv2 = conv_type(planes, planes, kernel_size=3, padding=1, bias=False)
#         self.bn2 = norm_type(planes)
#         self.downsample = downsample
#         self.stride = stride
#
#     def forward(self, x:torch.Tensor) -> torch.Tensor:
#         residual = x
#
#         out: torch.Tensor = self.conv1(x)
#         out = self.bn1(out)
#         out = self.relu(out)
#
#         out = self.conv2(out)
#         out = self.bn2(out)
#
#         if self.downsample is not None:
#             residual = self.downsample(x)
#
#         out += residual
#         out = self.relu(out)
#
#         return out


class REPUXNET(nn.Module):

    def __init__(
        self,
        in_chans=1,
        out_chans=13,
        depths=[2, 2, 2, 2],
        feat_size=[48, 96, 192, 384],
        ks = 21,
        a = 1,
        drop_path_rate=0,
        layer_scale_init_value=1e-6,
        hidden_size: int = 768,
        norm_name: Union[Tuple, str] = "instance",
        conv_block: bool = True,
        res_block: bool = True,
        spatial_dims=3,
        deploy=False,
    ) -> None:
        """
        Args:
            in_channels: dimension of input channels.
            out_channels: dimension of output channels.
            img_size: dimension of input image.
            feature_size: dimension of network feature size.
            hidden_size: dimension of hidden layer.
            mlp_dim: dimension of feedforward layer.
            num_heads: number of attention heads.
            pos_embed: position embedding layer type.
            norm_name: feature normalization type and arguments.
            conv_block: bool argument to determine if convolutional block is used.
            res_block: bool argument to determine if residual block is used.
            dropout_rate: faction of the input units to drop.
            spatial_dims: number of spatial dims.

        """

        super().__init__()

        # in_channels: int,
        # out_channels: int,
        # img_size: Union[Sequence[int], int],
        # feature_size: int = 16,
        # if not (0 <= dropout_rate <= 1):
        #     raise ValueError("dropout_rate should be between 0 and 1.")
        #
        # if hidden_size % num_heads != 0:
        #     raise ValueError("hidden_size should be divisible by num_heads.")
        self.hidden_size = hidden_size
        # self.feature_size = feature_size
        self.in_chans = in_chans
        self.out_chans = out_chans
        self.depths = depths
        self.drop_path_rate = drop_path_rate
        self.feat_size = feat_size
        self.ks = ks
        self.a = a
        self.deploy = deploy
        self.layer_scale_init_value = layer_scale_init_value
        self.out_indice = []
        for i in range(len(self.feat_size)):
            self.out_indice.append(i)

        self.spatial_dims = spatial_dims

        # self.classification = False
        # self.vit = ViT(
        #     in_channels=in_channels,
        #     img_size=img_size,
        #     patch_size=self.patch_size,
        #     hidden_size=hidden_size,
        #     mlp_dim=mlp_dim,
        #     num_layers=self.num_layers,
        #     num_heads=num_heads,
        #     pos_embed=pos_embed,
        #     classification=self.classification,
        #     dropout_rate=dropout_rate,
        #     spatial_dims=spatial_dims,
        # )
        self.repuxnet_3d = repuxnet_conv(
            in_chans= self.in_chans,
            depths=self.depths,
            dims=self.feat_size,
            ks=self.ks,
            a=self.a,
            drop_path_rate=self.drop_path_rate,
            layer_scale_init_value=1e-6,
            out_indices=self.out_indice,
            deploy=self.deploy
        )
        self.encoder1 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.in_chans,
            out_channels=self.feat_size[0],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.encoder2 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[0],
            out_channels=self.feat_size[1],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.encoder3 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[1],
            out_channels=self.feat_size[2],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.encoder4 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[2],
            out_channels=self.feat_size[3],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )

        self.encoder5 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[3],
            out_channels=self.hidden_size,
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )

        self.decoder5 = UnetrUpBlock(
            spatial_dims=spatial_dims,
            in_channels=self.hidden_size,
            out_channels=self.feat_size[3],
            kernel_size=3,
            upsample_kernel_size=2,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.decoder4 = UnetrUpBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[3],
            out_channels=self.feat_size[2],
            kernel_size=3,
            upsample_kernel_size=2,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.decoder3 = UnetrUpBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[2],
            out_channels=self.feat_size[1],
            kernel_size=3,
            upsample_kernel_size=2,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.decoder2 = UnetrUpBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[1],
            out_channels=self.feat_size[0],
            kernel_size=3,
            upsample_kernel_size=2,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.decoder1 = UnetrBasicBlock(
            spatial_dims=spatial_dims,
            in_channels=self.feat_size[0],
            out_channels=self.feat_size[0],
            kernel_size=3,
            stride=1,
            norm_name=norm_name,
            res_block=res_block,
        )
        self.out = UnetOutBlock(spatial_dims=spatial_dims, in_channels=48, out_channels=self.out_chans)
        # self.conv_proj = ProjectionHead(dim_in=hidden_size)


    def proj_feat(self, x, hidden_size, feat_size):
        new_view = (x.size(0), *feat_size, hidden_size)
        x = x.view(new_view)
        new_axes = (0, len(x.shape) - 1) + tuple(d + 1 for d in range(len(feat_size)))
        x = x.permute(new_axes).contiguous()
        return x
    
    def forward(self, x_in):
        outs = self.repuxnet_3d(x_in)
        # print(outs[0].size())
        # print(outs[1].size())
        # print(outs[2].size())
        # print(outs[3].size())
        enc1 = self.encoder1(x_in)
        # print(enc1.size())
        x2 = outs[0]
        enc2 = self.encoder2(x2)
        # print(enc2.size())
        x3 = outs[1]
        enc3 = self.encoder3(x3)
        # print(enc3.size())
        x4 = outs[2]
        enc4 = self.encoder4(x4)
        # print(enc4.size())
        # dec4 = self.proj_feat(outs[3], self.hidden_size, self.feat_size)
        enc_hidden = self.encoder5(outs[3])
        dec3 = self.decoder5(enc_hidden, enc4)
        dec2 = self.decoder4(dec3, enc3)
        dec1 = self.decoder3(dec2, enc2)
        dec0 = self.decoder2(dec1, enc1)
        out = self.decoder1(dec0)
        
        # feat = self.conv_proj(dec4)
        
        return self.out(out)