Python torch.nn.BatchNorm3d() Examples

def __init__(self, inplanes, planes, stride=1,  dilation_ = 1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, stride=stride, bias=False)
        self.bn1 = nn.BatchNorm3d(planes, affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        padding = dilation_
        #if dilation_ == 2:
        #    padding = 2
        #elif dilation_ == 4:
        #    padding = 4
        self.conv2 = nn.Conv3d(planes, planes, kernel_size=3, stride=1, padding=padding, bias=False, dilation = dilation_)
        self.bn2 = nn.BatchNorm3d(planes,affine = affine_par)
        for i in self.bn2.parameters():
            i.requires_grad = False
        self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm3d(planes * 4, affine = affine_par)
        for i in self.bn3.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride 

def __init__(self, inChannels, outChannels, kernelSize = 1, stride = 1, padding = 0):
		super(NewConvBnRelu3D, self).__init__()
		self.inChannels = inChannels
		self.outChannels = outChannels
		self.kernelSize = kernelSize
		self.stride = stride
		self.padding = padding
		self.relu = nn.LeakyReLU()
		self.bn = nn.BatchNorm3d(self.inChannels)
		if (kernelSize == 1):
			self.conv = nn.Conv1d(self.inChannels, self.outChannels, self.kernelSize, self.stride, self.padding)
		elif (isinstance(kernelSize, int)):
			self.conv = nn.Conv3d(self.inChannels, self.outChannels, self.kernelSize, self.stride, self.padding)	
		elif (kernelSize[0] == 1):
			self.conv = nn.Conv2d(self.inChannels, self.outChannels, self.kernelSize[1:], self.stride, self.padding)
		else :
			self.conv = nn.Conv3d(self.inChannels, self.outChannels, self.kernelSize, self.stride, self.padding) 

def __init__(self, inplanes, planes, stride=1, downsample=None, track_running_stats=True, use_final_relu=True):
        super(Bottleneck2d, self).__init__()
        bias = False
        self.use_final_relu = use_final_relu
        self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=bias)
        self.bn1 = nn.BatchNorm3d(planes, track_running_stats=track_running_stats)
        
        self.conv2 = nn.Conv3d(planes, planes, kernel_size=(1,3,3), stride=(1,stride,stride), padding=(0,1,1), bias=bias)
        self.bn2 = nn.BatchNorm3d(planes, track_running_stats=track_running_stats)
        
        self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=bias)
        self.bn3 = nn.BatchNorm3d(planes * 4, track_running_stats=track_running_stats)
        
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride 

def __call__(self, module):
        if isinstance(module, (nn.Conv2d, nn.Conv3d)):
            self.initializer(
                module.weight.data,
                self.slope,
                self.mode,
                self.nonlinearity)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, (nn.BatchNorm2d, nn.BatchNorm3d, nn.GroupNorm)):
            if module.weight is not None:
                module.weight.data.fill_(1)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Linear):
            self.initializer(
                module.weight.data,
                self.slope,
                self.mode,
                self.nonlinearity)
            if module.bias is not None:
                module.bias.data.zero_() 

def __init__(self, inplanes):
        super(hourglass, self).__init__()

        self.conv1 = nn.Sequential(convbn_3d(inplanes, inplanes*2, kernel_size=3, stride=2, pad=1),
                                   nn.ReLU(inplace=True))

        self.conv2 = convbn_3d(inplanes*2, inplanes*2, kernel_size=3, stride=1, pad=1)

        self.conv3 = nn.Sequential(convbn_3d(inplanes*2, inplanes*2, kernel_size=3, stride=2, pad=1),
                                   nn.ReLU(inplace=True))

        self.conv4 = nn.Sequential(convbn_3d(inplanes*2, inplanes*2, kernel_size=3, stride=1, pad=1),
                                   nn.ReLU(inplace=True))

        self.conv5 = nn.Sequential(nn.ConvTranspose3d(inplanes*2, inplanes*2, kernel_size=3, padding=1, output_padding=1, stride=2,bias=False),
                                   nn.BatchNorm3d(inplanes*2)) #+conv2

        self.conv6 = nn.Sequential(nn.ConvTranspose3d(inplanes*2, inplanes, kernel_size=3, padding=1, output_padding=1, stride=2,bias=False),
                                   nn.BatchNorm3d(inplanes)) #+x 

def __init__(self, inplanes):
        super(hourglass, self).__init__()

        self.conv1 = nn.Sequential(convbn_3d(inplanes, inplanes*2, kernel_size=3, stride=2, pad=1),
                                   nn.ReLU(inplace=True))

        self.conv2 = convbn_3d(inplanes*2, inplanes*2, kernel_size=3, stride=1, pad=1)

        self.conv3 = nn.Sequential(convbn_3d(inplanes*2, inplanes*2, kernel_size=3, stride=2, pad=1),
                                   nn.ReLU(inplace=True))

        self.conv4 = nn.Sequential(convbn_3d(inplanes*2, inplanes*2, kernel_size=3, stride=1, pad=1),
                                   nn.ReLU(inplace=True))

        self.conv5 = nn.Sequential(nn.ConvTranspose3d(inplanes*2, inplanes*2, kernel_size=3, padding=1, output_padding=1, stride=2,bias=False),
                                   nn.BatchNorm3d(inplanes*2)) #+conv2

        self.conv6 = nn.Sequential(nn.ConvTranspose3d(inplanes*2, inplanes, kernel_size=3, padding=1, output_padding=1, stride=2,bias=False),
                                   nn.BatchNorm3d(inplanes)) #+x 

def __init__(self,dilation_series,padding_series,NoLabels):
        super(Classifier_Module, self).__init__()
        self.conv3d_list = nn.ModuleList()
        self.bn3d_list = nn.ModuleList()
        for dilation,padding in zip(dilation_series,padding_series):
            self.conv3d_list.append(nn.Conv3d(2048, 256,kernel_size=3,stride=1, padding =padding, dilation = dilation,bias = True))
            self.bn3d_list.append(nn.BatchNorm3d(256, affine = affine_par))
        self.num_concats = len(self.conv3d_list) + 2
        #add global pooling, add batchnorm
        self.conv1x1_1 = nn.Conv3d(2048, 256, kernel_size=1, stride=1)
        self.conv1x1_2 = nn.Conv3d(2048, 256, kernel_size=1, stride=1)
        self.conv1x1_3 = nn.Conv3d(256*self.num_concats, 256, kernel_size=1, stride=1)
        self.conv1x1_4 = nn.Conv3d(256, NoLabels, kernel_size=1, stride=1)

        self.bn1 = nn.BatchNorm3d(256, affine = affine_par)
        self.bn2 = nn.BatchNorm3d(256*self.num_concats, affine= affine_par)
        self.bn3 = nn.BatchNorm3d(256, affine= affine_par)
        #global avg pool
        #input = 1x512xdim1xdim2xdim3
        #output = 1x512x1x1x1
        #XXX check

        for m in self.conv3d_list:
            m.weight.data.normal_(0, 0.01) 

def _make_layer(self, block, planes, blocks, stride=1,dilation__ = 1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion or dilation__ == 2 or dilation__ == 4:
            downsample = nn.Sequential(
                nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm3d(planes * block.expansion,affine = affine_par),
            )
            for i in downsample._modules['1'].parameters():
                i.requires_grad = False
        
        layers = []
        layers.append(block(self.inplanes, planes, stride, dilation_=dilation__, downsample = downsample ))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes,dilation_=dilation__))

        return nn.Sequential(*layers) 

def __init__(self, inplanes, outplanes, padding_=1, stride=1, dilation_ = 1):
        super(HighResNetBlock, self).__init__()

        self.conv1 = nn.Conv3d(inplanes, outplanes, kernel_size=3, stride=1, 
                                padding=padding_, bias=False, dilation = dilation_)
        self.conv2 = nn.Conv3d(outplanes, outplanes, kernel_size=3, stride=1, 
                                padding=padding_, bias=False, dilation = dilation_)
        #2 convolutions of same dilation. residual block
        self.bn1 = nn.BatchNorm3d(outplanes, affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False

        self.bn2 = nn.BatchNorm3d(outplanes, affine = affine_par)
        for i in self.bn2.parameters():
            i.requires_grad = False

        self.relu = nn.PReLU()
        self.diff_dims = (inplanes != outplanes)

        self.downsample = nn.Sequential(
            nn.Conv3d(inplanes, outplanes, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm3d(outplanes, affine = affine_par)
        )
        for i in self.downsample._modules['1'].parameters():
                i.requires_grad = False 

def __init__(self,NoLabels):
        super(SmallHighResNet,self).__init__()
        self.conv1 = nn.Conv3d(1, 8, kernel_size=3, stride=8, padding=1, bias=False)
        self.bn1 = nn.BatchNorm3d(8, affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        self.relu = nn.PReLU()

        self.block1_1 = HighResNetBlock(inplanes=8, outplanes=8, padding_=1, dilation_=1)

        self.block2_1 = HighResNetBlock(inplanes=8, outplanes=16,padding_=2, dilation_=2)
        self.block2_2 = HighResNetBlock(inplanes=16, outplanes=16, padding_=2, dilation_=2)

        self.block3_1 = HighResNetBlock(inplanes=16, outplanes=16, padding_=4, dilation_=4)
        self.block3_2 = HighResNetBlock(inplanes=16, outplanes=16, padding_=4, dilation_=4)

        self.conv2 = nn.Conv3d(16, NoLabels, kernel_size=1, stride=1, padding=0, bias=False) 

def __init__(self, inplanes, outplanes, padding_=1, stride=1, dilation_ = 1):
        super(HighResNetBlock, self).__init__()

        self.conv1 = nn.Conv3d(inplanes, outplanes, kernel_size=3, stride=1, 
                                padding=padding_, bias=False, dilation = dilation_)
        self.conv2 = nn.Conv3d(outplanes, outplanes, kernel_size=3, stride=1, 
                                padding=padding_, bias=False, dilation = dilation_)
        #2 convolutions of same dilation. residual block
        self.bn1 = nn.BatchNorm3d(outplanes, affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False

        self.bn2 = nn.BatchNorm3d(outplanes, affine = affine_par)
        for i in self.bn2.parameters():
            i.requires_grad = False

        self.relu = nn.PReLU()
        self.diff_dims = (inplanes != outplanes)

        self.downsample = nn.Sequential(
            nn.Conv3d(inplanes, outplanes, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm3d(outplanes, affine = affine_par)
        )
        for i in self.downsample._modules['1'].parameters():
                i.requires_grad = False 

def init_weights(model):
    if isinstance(model, nn.Linear):
        if model.weight is not None:
            init.kaiming_uniform_(model.weight.data)
        if model.bias is not None:
            init.normal_(model.bias.data)
    elif isinstance(model, nn.BatchNorm1d):
        if model.weight is not None:
            init.normal_(model.weight.data, mean=1, std=0.02)
        if model.bias is not None:
            init.constant_(model.bias.data, 0)
    elif isinstance(model, nn.BatchNorm2d):
        if model.weight is not None:
            init.normal_(model.weight.data, mean=1, std=0.02)
        if model.bias is not None:
            init.constant_(model.bias.data, 0)
    elif isinstance(model, nn.BatchNorm3d):
        if model.weight is not None:
            init.normal_(model.weight.data, mean=1, std=0.02)
        if model.bias is not None:
            init.constant_(model.bias.data, 0)
    else:
        pass 

def __init__(self,NoLabels):
        super(HighResNet,self).__init__()
        self.conv1 = nn.Conv3d(1, 16, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm3d(16, affine = affine_par)
        for i in self.bn1.parameters():
            i.requires_grad = False
        self.relu = nn.PReLU()

        self.block1_1 = HighResNetBlock(inplanes=16, outplanes=16, padding_=1, dilation_=1)

        self.block2_1 = HighResNetBlock(inplanes=16, outplanes=32, padding_=2, dilation_=2)
        self.block2_2 = HighResNetBlock(inplanes=32, outplanes=32, padding_=2, dilation_=2)

        self.block3_1 = HighResNetBlock(inplanes=32, outplanes=64, padding_=4, dilation_=4)
        self.block3_2 = HighResNetBlock(inplanes=64, outplanes=64, padding_=4, dilation_=4)

        self.conv2 = nn.Conv3d(64, 80, kernel_size=1, stride=1, padding=0, bias=False)
        self.upsample = nn.ConvTranspose3d(80, 80, kernel_size=2, stride=2, bias=False)
        self.conv3 = nn.Conv3d(80, NoLabels, kernel_size=1, stride=1, padding=0, bias=False) 

def __init__(self,dilation_series, padding_series, inplanes, midplanes, outplanes):
        super(ASPP_Module, self).__init__()
        self.conv3d_list = nn.ModuleList()
        self.bn3d_list = nn.ModuleList()
        for dilation,padding in zip(dilation_series,padding_series):
            self.conv3d_list.append(nn.Conv3d(inplanes, midplanes,kernel_size=3,stride=1, padding =padding, dilation = dilation,bias = True))
            self.bn3d_list.append(nn.BatchNorm3d(midplanes, affine = affine_par))
        self.num_concats = len(self.conv3d_list) + 2
        #add global pooling, add batchnorm
        self.conv1x1_1 = nn.Conv3d(inplanes, midplanes, kernel_size=1, stride=1)
        self.conv1x1_2 = nn.Conv3d(inplanes, midplanes, kernel_size=1, stride=1)
        self.conv1x1_3 = nn.Conv3d(midplanes*self.num_concats, outplanes, kernel_size=1, stride=1)

        self.relu = nn.PReLU()

        self.bn1 = nn.BatchNorm3d(midplanes, affine = affine_par)
        self.bn2 = nn.BatchNorm3d(midplanes*self.num_concats, affine= affine_par)
        self.bn3 = nn.BatchNorm3d(midplanes, affine= affine_par) 

def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
        super(Bottleneck, self).__init__()
        self.out_planes = out_planes
        self.dense_depth = dense_depth
        self.last_planes = last_planes
        self.in_planes = in_planes

        self.conv1 = nn.Conv3d(last_planes, in_planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm3d(in_planes)
        self.conv2 = nn.Conv3d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=8, bias=False)
        self.bn2 = nn.BatchNorm3d(in_planes)
        self.conv3 = nn.Conv3d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm3d(out_planes+dense_depth)

        self.shortcut = nn.Sequential()
        if first_layer:
            self.shortcut = nn.Sequential(
                nn.Conv3d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm3d(out_planes+dense_depth)
            ) 

def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
        super(_DenseLayer, self).__init__()
        self.add_module('norm1', nn.BatchNorm3d(num_input_features))
        self.add_module('relu1', nn.ReLU(inplace=True))
        self.add_module('conv1',
                        nn.Conv3d(
                            num_input_features,
                            bn_size * growth_rate,
                            kernel_size=1,
                            stride=1,
                            bias=False))
        self.add_module('norm2', nn.BatchNorm3d(bn_size * growth_rate))
        self.add_module('relu2', nn.ReLU(inplace=True))
        self.add_module('conv2',
                        nn.Conv3d(
                            bn_size * growth_rate,
                            growth_rate,
                            kernel_size=3,
                            stride=1,
                            padding=1,
                            bias=False))
        self.drop_rate = drop_rate 

def __init__(self, inplanes, planes, stride=1, downsample=None, track_running_stats=True, use_final_relu=True):
        super(Bottleneck3d, self).__init__()
        bias = False
        self.use_final_relu = use_final_relu
        self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=bias)
        self.bn1 = nn.BatchNorm3d(planes, track_running_stats=track_running_stats)
        
        self.conv2 = nn.Conv3d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias)
        self.bn2 = nn.BatchNorm3d(planes, track_running_stats=track_running_stats)
        
        self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=bias)
        self.bn3 = nn.BatchNorm3d(planes * 4, track_running_stats=track_running_stats)
        
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride 

def __init__(self, cfg):
        super(DPN, self).__init__()
        in_planes, out_planes = cfg['in_planes'], cfg['out_planes']
        num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth']

        # self.in_planes = in_planes
        # self.out_planes = out_planes
        # self.num_blocks = num_blocks
        # self.dense_depth = dense_depth

        self.conv1 = nn.Conv3d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm3d(64)
        self.last_planes = 64
        self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1)
        self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2)
        self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2)
        self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2)
        self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)*dense_depth[3], 2)#10) 

def __init__(self, block, layers, track_running_stats=True):
        super(ResNet2d3d_full, self).__init__()
        self.inplanes = 64
        self.track_running_stats = track_running_stats
        bias = False
        self.conv1 = nn.Conv3d(3, 64, kernel_size=(1,7,7), stride=(1, 2, 2), padding=(0, 3, 3), bias=bias)
        self.bn1 = nn.BatchNorm3d(64, track_running_stats=track_running_stats)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1))
        
        if not isinstance(block, list):
            block = [block] * 4

        self.layer1 = self._make_layer(block[0], 64, layers[0])
        self.layer2 = self._make_layer(block[1], 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block[2], 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block[3], 256, layers[3], stride=2, is_final=True)
        # modify layer4 from exp=512 to exp=256
        for m in self.modules():
            if isinstance(m, nn.Conv3d):
                m.weight = nn.init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None: m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm3d):
                m.weight.data.fill_(1)
                m.bias.data.zero_() 

def __init__(self, inplanes, planes, cardinality, stride=1,
                 downsample=None):
        super(ResNeXtBottleneck, self).__init__()
        mid_planes = cardinality * int(planes / 32)
        self.conv1 = nn.Conv3d(inplanes, mid_planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm3d(mid_planes)
        self.conv2 = nn.Conv3d(
            mid_planes,
            mid_planes,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=cardinality,
            bias=False)
        self.bn2 = nn.BatchNorm3d(mid_planes)
        self.conv3 = nn.Conv3d(
            mid_planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm3d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride 

def __init__(self, in_size, gate_size, inter_size, nonlocal_mode, sub_sample_factor):
        super(MultiAttentionBlock, self).__init__()
        self.gate_block_1 = GridAttentionBlock3D(in_channels=in_size, gating_channels=gate_size,
                                                 inter_channels=inter_size, mode=nonlocal_mode,
                                                 sub_sample_factor= sub_sample_factor)
        self.gate_block_2 = GridAttentionBlock3D(in_channels=in_size, gating_channels=gate_size,
                                                 inter_channels=inter_size, mode=nonlocal_mode,
                                                 sub_sample_factor=sub_sample_factor)
        self.combine_gates = nn.Sequential(nn.Conv3d(in_size*2, in_size, kernel_size=1, stride=1, padding=0),
                                           nn.BatchNorm3d(in_size),
                                           nn.ReLU(inplace=True)
                                           )

        # initialise the blocks
        for m in self.children():
            if m.__class__.__name__.find('GridAttentionBlock3D') != -1: continue
            init_weights(m, init_type='kaiming') 

def __init__(self, in_channels, middle_channels, out_channels, deconv_channels, dropout=False):
        super(Decoder3D, self).__init__()

        layers = [
            nn.Conv3d(in_channels, middle_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(middle_channels),
            nn.ReLU(inplace=True),
            nn.Conv3d(middle_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(out_channels),
            nn.ReLU(inplace=True),
            nn.ConvTranspose3d(out_channels, deconv_channels, kernel_size=2, stride=2)
        ]

        if dropout:
            assert 0 <= dropout <= 1, 'dropout must be between 0 and 1'
            layers.append(nn.Dropout3d(p=dropout))

        self.decoder = nn.Sequential(*layers) 

def __init__(self, in_size, out_size, is_batchnorm, kernel_size=(3,3,1), padding_size=(1,1,0), init_stride=(1,1,1)):
        super(UnetConv3, self).__init__()

        if is_batchnorm:
            self.conv1 = nn.Sequential(nn.Conv3d(in_size, out_size, kernel_size, init_stride, padding_size),
                                       nn.BatchNorm3d(out_size),
                                       nn.ReLU(inplace=True),)
            self.conv2 = nn.Sequential(nn.Conv3d(out_size, out_size, kernel_size, 1, padding_size),
                                       nn.BatchNorm3d(out_size),
                                       nn.ReLU(inplace=True),)
        else:
            self.conv1 = nn.Sequential(nn.Conv3d(in_size, out_size, kernel_size, init_stride, padding_size),
                                       nn.ReLU(inplace=True),)
            self.conv2 = nn.Sequential(nn.Conv3d(out_size, out_size, kernel_size, 1, padding_size),
                                       nn.ReLU(inplace=True),)

        # initialise the blocks
        for m in self.children():
            init_weights(m, init_type='kaiming') 

def __init__(self, in_size, out_size, is_batchnorm, kernel_size=(3,3,1), padding_size=(1,1,0), init_stride=(1,1,1)):
        super(FCNConv3, self).__init__()

        if is_batchnorm:
            self.conv1 = nn.Sequential(nn.Conv3d(in_size, out_size, kernel_size, init_stride, padding_size),
                                       nn.BatchNorm3d(out_size),
                                       nn.ReLU(inplace=True),)
            self.conv2 = nn.Sequential(nn.Conv3d(out_size, out_size, kernel_size, 1, padding_size),
                                       nn.BatchNorm3d(out_size),
                                       nn.ReLU(inplace=True),)
            self.conv3 = nn.Sequential(nn.Conv3d(out_size, out_size, kernel_size, 1, padding_size),
                                       nn.BatchNorm3d(out_size),
                                       nn.ReLU(inplace=True),)
        else:
            self.conv1 = nn.Sequential(nn.Conv3d(in_size, out_size, kernel_size, init_stride, padding_size),
                                       nn.ReLU(inplace=True),)
            self.conv2 = nn.Sequential(nn.Conv3d(out_size, out_size, kernel_size, 1, padding_size),
                                       nn.ReLU(inplace=True),)
            self.conv3 = nn.Sequential(nn.Conv3d(out_size, out_size, kernel_size, 1, padding_size),
                                       nn.ReLU(inplace=True),)

        # initialise the blocks
        for m in self.children():
            init_weights(m, init_type='kaiming') 

def __init__(self, in_size, out_size, is_batchnorm):
        super(UnetGatingSignal3, self).__init__()
        self.fmap_size = (4, 4, 4)

        if is_batchnorm:
            self.conv1 = nn.Sequential(nn.Conv3d(in_size, in_size//2, (1,1,1), (1,1,1), (0,0,0)),
                                       nn.BatchNorm3d(in_size//2),
                                       nn.ReLU(inplace=True),
                                       nn.AdaptiveAvgPool3d(output_size=self.fmap_size),
                                       )
            self.fc1 = nn.Linear(in_features=(in_size//2) * self.fmap_size[0] * self.fmap_size[1] * self.fmap_size[2],
                                 out_features=out_size, bias=True)
        else:
            self.conv1 = nn.Sequential(nn.Conv3d(in_size, in_size//2, (1,1,1), (1,1,1), (0,0,0)),
                                       nn.ReLU(inplace=True),
                                       nn.AdaptiveAvgPool3d(output_size=self.fmap_size),
                                       )
            self.fc1 = nn.Linear(in_features=(in_size//2) * self.fmap_size[0] * self.fmap_size[1] * self.fmap_size[2],
                                 out_features=out_size, bias=True)

        # initialise the blocks
        for m in self.children():
            init_weights(m, init_type='kaiming') 

def __init__(self, in_size, out_size, kernel_size=(1,1,1), is_batchnorm=True):
        super(UnetGridGatingSignal3, self).__init__()

        if is_batchnorm:
            self.conv1 = nn.Sequential(nn.Conv3d(in_size, out_size, kernel_size, (1,1,1), (0,0,0)),
                                       nn.BatchNorm3d(out_size),
                                       nn.ReLU(inplace=True),
                                       )
        else:
            self.conv1 = nn.Sequential(nn.Conv3d(in_size, out_size, kernel_size, (1,1,1), (0,0,0)),
                                       nn.ReLU(inplace=True),
                                       )

        # initialise the blocks
        for m in self.children():
            init_weights(m, init_type='kaiming') 

def __init__(self, in_channels, middle_channels, out_channels, deconv_channels, dropout=False):
        super(Center3D, self).__init__()

        layers = [
            nn.MaxPool3d(kernel_size=2),
            nn.Conv3d(in_channels, middle_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(middle_channels),
            nn.ReLU(inplace=True),
            nn.Conv3d(middle_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(out_channels),
            nn.ReLU(inplace=True),
            nn.ConvTranspose3d(out_channels, deconv_channels, kernel_size=2, stride=2)
        ]

        if dropout:
            assert 0 <= dropout <= 1, 'dropout must be between 0 and 1'
            layers.append(nn.Dropout3d(p=dropout))

        self.center = nn.Sequential(*layers) 

def __init__(
            self, in_channels, middle_channels, out_channels,
            dropout=False, downsample_kernel=2
    ):
        super(Encoder3D, self).__init__()

        layers = [
            nn.MaxPool3d(kernel_size=downsample_kernel),
            nn.Conv3d(in_channels, middle_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(middle_channels),
            nn.ReLU(inplace=True),
            nn.Conv3d(middle_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(out_channels),
            nn.ReLU(inplace=True)
        ]

        if dropout:
            assert 0 <= dropout <= 1, 'dropout must be between 0 and 1'
            layers.append(nn.Dropout3d(p=dropout))

        self.encoder = nn.Sequential(*layers) 

def __init__(self, in_channels, middle_channels, out_channels, dropout=False):
        super(First3D, self).__init__()

        layers = [
            nn.Conv3d(in_channels, middle_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(middle_channels),
            nn.ReLU(inplace=True),
            nn.Conv3d(middle_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(out_channels),
            nn.ReLU(inplace=True)
        ]

        if dropout:
            assert 0 <= dropout <= 1, 'dropout must be between 0 and 1'
            layers.append(nn.Dropout3d(p=dropout))

        self.first = nn.Sequential(*layers) 

def __init__(self, inChannels, outChannels, kernelSize = 1, stride = 1, padding = 0):
		super(NewConvBnRelu3D, self).__init__()
		self.inChannels = inChannels
		self.outChannels = outChannels
		self.kernelSize = kernelSize
		self.stride = stride
		self.padding = padding
		self.relu = nn.LeakyReLU()
		self.bn = nn.BatchNorm3d(self.inChannels)
		if (kernelSize == 1):
			self.conv = nn.Conv1d(self.inChannels, self.outChannels, self.kernelSize, self.stride, self.padding)
		elif (isinstance(kernelSize, int)):
			self.conv = nn.Conv3d(self.inChannels, self.outChannels, self.kernelSize, self.stride, self.padding)	
		elif (kernelSize[0] == 1):
			self.conv = nn.Conv2d(self.inChannels, self.outChannels, self.kernelSize[1:], self.stride, self.padding)
		else :
			self.conv = nn.Conv3d(self.inChannels, self.outChannels, self.kernelSize, self.stride, self.padding)