lib/net/VE.py

# -*- coding: utf-8 -*-

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# You can only use this computer program if you have closed
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# Any use of the computer program without a valid license is prohibited and
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# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: [email protected]


import torch.nn as nn
import pytorch_lightning as pl


class BaseNetwork(pl.LightningModule):
    def __init__(self):
        super(BaseNetwork, self).__init__()

    def init_weights(self, init_type='xavier', gain=0.02):
        '''
        initializes network's weights
        init_type: normal | xavier | kaiming | orthogonal
        https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39
        '''
        def init_func(m):
            classname = m.__class__.__name__
            if hasattr(m, 'weight') and (classname.find('Conv') != -1
                                         or classname.find('Linear') != -1):
                if init_type == 'normal':
                    nn.init.normal_(m.weight.data, 0.0, gain)
                elif init_type == 'xavier':
                    nn.init.xavier_normal_(m.weight.data, gain=gain)
                elif init_type == 'kaiming':
                    nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
                elif init_type == 'orthogonal':
                    nn.init.orthogonal_(m.weight.data, gain=gain)

                if hasattr(m, 'bias') and m.bias is not None:
                    nn.init.constant_(m.bias.data, 0.0)

            elif classname.find('BatchNorm2d') != -1:
                nn.init.normal_(m.weight.data, 1.0, gain)
                nn.init.constant_(m.bias.data, 0.0)

        self.apply(init_func)


class Residual3D(BaseNetwork):
    def __init__(self, numIn, numOut):
        super(Residual3D, self).__init__()
        self.numIn = numIn
        self.numOut = numOut
        self.with_bias = True
        # self.bn = nn.GroupNorm(4, self.numIn)
        self.bn = nn.BatchNorm3d(self.numIn)
        self.relu = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv3d(self.numIn,
                               self.numOut,
                               bias=self.with_bias,
                               kernel_size=3,
                               stride=1,
                               padding=2,
                               dilation=2)
        # self.bn1 = nn.GroupNorm(4, self.numOut)
        self.bn1 = nn.BatchNorm3d(self.numOut)
        self.conv2 = nn.Conv3d(self.numOut,
                               self.numOut,
                               bias=self.with_bias,
                               kernel_size=3,
                               stride=1,
                               padding=1)
        # self.bn2 = nn.GroupNorm(4, self.numOut)
        self.bn2 = nn.BatchNorm3d(self.numOut)
        self.conv3 = nn.Conv3d(self.numOut,
                               self.numOut,
                               bias=self.with_bias,
                               kernel_size=3,
                               stride=1,
                               padding=1)

        if self.numIn != self.numOut:
            self.conv4 = nn.Conv3d(self.numIn,
                                   self.numOut,
                                   bias=self.with_bias,
                                   kernel_size=1)
        self.init_weights()

    def forward(self, x):
        residual = x
        # out = self.bn(x)
        # out = self.relu(out)
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        # out = self.conv3(out)
        # out = self.relu(out)

        if self.numIn != self.numOut:
            residual = self.conv4(x)

        return out + residual


class VolumeEncoder(BaseNetwork):
    """CycleGan Encoder"""

    def __init__(self, num_in=3, num_out=32, num_stacks=2):
        super(VolumeEncoder, self).__init__()
        self.num_in = num_in
        self.num_out = num_out
        self.num_inter = 8
        self.num_stacks = num_stacks
        self.with_bias = True

        self.relu = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv3d(self.num_in,
                               self.num_inter,
                               bias=self.with_bias,
                               kernel_size=5,
                               stride=2,
                               padding=4,
                               dilation=2)
        # self.bn1 = nn.GroupNorm(4, self.num_inter)
        self.bn1 = nn.BatchNorm3d(self.num_inter)
        self.conv2 = nn.Conv3d(self.num_inter,
                               self.num_out,
                               bias=self.with_bias,
                               kernel_size=5,
                               stride=2,
                               padding=4,
                               dilation=2)
        # self.bn2 = nn.GroupNorm(4, self.num_out)
        self.bn2 = nn.BatchNorm3d(self.num_out)

        self.conv_out1 = nn.Conv3d(self.num_out,
                                   self.num_out,
                                   bias=self.with_bias,
                                   kernel_size=3,
                                   stride=1,
                                   padding=1,
                                   dilation=1)
        self.conv_out2 = nn.Conv3d(self.num_out,
                                   self.num_out,
                                   bias=self.with_bias,
                                   kernel_size=3,
                                   stride=1,
                                   padding=1,
                                   dilation=1)

        for idx in range(self.num_stacks):
            self.add_module("res" + str(idx),
                            Residual3D(self.num_out, self.num_out))

        self.init_weights()

    def forward(self, x, intermediate_output=True):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out_lst = []
        for idx in range(self.num_stacks):
            out = self._modules["res" + str(idx)](out)
            out_lst.append(out)

        if intermediate_output:
            return out_lst
        else:
            return [out_lst[-1]]