Python torch.nn.Conv2DBatchNorm() Examples

The following are 7 code examples of torch.nn.Conv2DBatchNorm(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module torch.nn , or try the search function .
Example #1
Source File: utils.py    From Attention-Gated-Networks with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, n_filters, stride=1, downsample=None):
        super(residualBottleneck, self).__init__()
        self.convbn1 = nn.Conv2DBatchNorm(in_channels,  n_filters, k_size=1, bias=False)
        self.convbn2 = nn.Conv2DBatchNorm(n_filters,  n_filters, k_size=3, padding=1, stride=stride, bias=False)
        self.convbn3 = nn.Conv2DBatchNorm(n_filters,  n_filters * 4, k_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
Example #2
Source File: utils.py    From PLARD with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, n_filters, stride=1, downsample=None):
        super(residualBottleneck, self).__init__()
        self.convbn1 = nn.Conv2DBatchNorm(in_channels,  n_filters, k_size=1, bias=False)
        self.convbn2 = nn.Conv2DBatchNorm(n_filters,  n_filters, k_size=3, padding=1, stride=stride, bias=False)
        self.convbn3 = nn.Conv2DBatchNorm(n_filters,  n_filters * 4, k_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
Example #3
Source File: unet_utils.py    From UnsupervisedGeometryAwareRepresentationLearning with GNU General Public License v3.0 5 votes vote down vote up 
def __init__(self, in_channels, n_filters, stride=1, downsample=None):
        super(residualBottleneck, self).__init__()
        self.convbn1 = nn.Conv2DBatchNorm(in_channels,  n_filters, k_size=1, bias=False)
        self.convbn2 = nn.Conv2DBatchNorm(n_filters,  n_filters, k_size=3, padding=1, stride=stride, bias=False)
        self.convbn3 = nn.Conv2DBatchNorm(n_filters,  n_filters * 4, k_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
Example #4
Source File: layers.py    From LiverCancerSeg with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, n_filters, stride=1, downsample=None):
        super(residualBottleneck, self).__init__()
        self.convbn1 = nn.Conv2DBatchNorm(in_channels,  n_filters, k_size=1, bias=False)
        self.convbn2 = nn.Conv2DBatchNorm(n_filters,  n_filters, k_size=3, padding=1, stride=stride, bias=False)
        self.convbn3 = nn.Conv2DBatchNorm(n_filters,  n_filters * 4, k_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
Example #5
Source File: utils.py    From CAG_UDA with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, n_filters, stride=1, downsample=None):
        super(residualBottleneck, self).__init__()
        self.convbn1 = nn.Conv2DBatchNorm(in_channels, n_filters, k_size=1, bias=False)
        self.convbn2 = nn.Conv2DBatchNorm(
            n_filters, n_filters, k_size=3, padding=1, stride=stride, bias=False
        )
        self.convbn3 = nn.Conv2DBatchNorm(
            n_filters, n_filters * 4, k_size=1, bias=False
        )
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
Example #6
Source File: utils.py    From pytorch-semseg with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, n_filters, stride=1, downsample=None):
        super(residualBottleneck, self).__init__()
        self.convbn1 = nn.Conv2DBatchNorm(in_channels, n_filters, k_size=1, bias=False)
        self.convbn2 = nn.Conv2DBatchNorm(
            n_filters, n_filters, k_size=3, padding=1, stride=stride, bias=False
        )
        self.convbn3 = nn.Conv2DBatchNorm(n_filters, n_filters * 4, k_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
Example #7
Source File: unet_utils.py    From NeuralSceneDecomposition with GNU General Public License v3.0 5 votes vote down vote up 
def __init__(self, in_channels, n_filters, stride=1, downsample=None):
        super(residualBottleneck, self).__init__()
        self.convbn1 = nn.Conv2DBatchNorm(in_channels,  n_filters, k_size=1, bias=False)
        self.convbn2 = nn.Conv2DBatchNorm(n_filters,  n_filters, k_size=3, padding=1, stride=stride, bias=False)
        self.convbn3 = nn.Conv2DBatchNorm(n_filters,  n_filters * 4, k_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride