Python torch.nn.modules.utils._single() Examples

The following are 16 code examples of torch.nn.modules.utils._single(). 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.modules.utils , or try the search function .
Example #1
Source File: deform_conv.py    From mmdetection with Apache License 2.0 5 votes vote down vote up 
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 deformable_groups=1,
                 bias=False):
        super(DeformConv, self).__init__()

        assert not bias
        assert in_channels % groups == 0, \
            f'in_channels {in_channels} is not divisible by groups {groups}'
        assert out_channels % groups == 0, \
            f'out_channels {out_channels} is not divisible ' \
            f'by groups {groups}'

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.deformable_groups = deformable_groups
        # enable compatibility with nn.Conv2d
        self.transposed = False
        self.output_padding = _single(0)

        self.weight = nn.Parameter(
            torch.Tensor(out_channels, in_channels // self.groups,
                         *self.kernel_size))

        self.reset_parameters()
Example #2
Source File: deform_conv.py    From mmdetection with Apache License 2.0 5 votes vote down vote up 
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 deformable_groups=1,
                 bias=True):
        super(ModulatedDeformConv, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.deformable_groups = deformable_groups
        self.with_bias = bias
        # enable compatibility with nn.Conv2d
        self.transposed = False
        self.output_padding = _single(0)

        self.weight = nn.Parameter(
            torch.Tensor(out_channels, in_channels // groups,
                         *self.kernel_size))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.init_weights()
Example #3
Source File: conv_tbc.py    From crosentgec with GNU General Public License v3.0 5 votes vote down vote up 
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
        super(ConvTBC, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _single(kernel_size)
        self.padding = _single(padding)

        self.weight = torch.nn.Parameter(torch.Tensor(
            self.kernel_size[0], in_channels, out_channels))
        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
Example #4
Source File: deform_conv.py    From DenseMatchingBenchmark with MIT License 5 votes vote down vote up 
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 deformable_groups=1,
                 bias=False):
        super(DeformConv, self).__init__()

        assert not bias
        assert in_channels % groups == 0, \
            'in_channels {} cannot be divisible by groups {}'.format(
                in_channels, groups)
        assert out_channels % groups == 0, \
            'out_channels {} cannot be divisible by groups {}'.format(
                out_channels, groups)

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.deformable_groups = deformable_groups
        # enable compatibility with nn.Conv2d
        self.transposed = False
        self.output_padding = _single(0)

        self.weight = nn.Parameter(
            torch.Tensor(out_channels, in_channels // self.groups,
                         *self.kernel_size))

        self.reset_parameters()
Example #5
Source File: deform_conv.py    From DenseMatchingBenchmark with MIT License 5 votes vote down vote up 
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 deformable_groups=1,
                 bias=True):
        super(ModulatedDeformConv, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.deformable_groups = deformable_groups
        self.with_bias = bias
        # enable compatibility with nn.Conv2d
        self.transposed = False
        self.output_padding = _single(0)

        self.weight = nn.Parameter(
            torch.Tensor(out_channels, in_channels // groups,
                         *self.kernel_size))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()
Example #6
Source File: conv_tbc.py    From fairseq with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
        super(ConvTBC, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _single(kernel_size)
        self.padding = _single(padding)

        self.weight = torch.nn.Parameter(torch.Tensor(
            self.kernel_size[0], in_channels, out_channels))
        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
Example #7
Source File: conv_tbc.py    From training_results_v0.5 with Apache License 2.0 5 votes vote down vote up 
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
        super(ConvTBC, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _single(kernel_size)
        self.padding = _single(padding)

        self.weight = torch.nn.Parameter(torch.Tensor(
            self.kernel_size[0], in_channels, out_channels))
        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
Example #8
Source File: deform_conv.py    From mmcv with Apache License 2.0 5 votes vote down vote up 
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 deform_groups=1,
                 bias=False):
        super(DeformConv2d, self).__init__()

        assert in_channels % groups == 0, \
            f'in_channels {in_channels} cannot be divisible by groups {groups}'
        assert out_channels % groups == 0, \
            f'out_channels {out_channels} cannot be divisible by groups \
              {groups}'

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.deform_groups = deform_groups
        # enable compatibility with nn.Conv2d
        self.transposed = False
        self.output_padding = _single(0)

        # only weight, no bias
        self.weight = nn.Parameter(
            torch.Tensor(out_channels, in_channels // self.groups,
                         *self.kernel_size))

        self.reset_parameters()
Example #9
Source File: modulated_deform_conv.py    From mmcv with Apache License 2.0 5 votes vote down vote up 
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 deform_groups=1,
                 bias=True):
        super(ModulatedDeformConv2d, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.deform_groups = deform_groups
        # enable compatibility with nn.Conv2d
        self.transposed = False
        self.output_padding = _single(0)

        self.weight = nn.Parameter(
            torch.Tensor(out_channels, in_channels // groups,
                         *self.kernel_size))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.init_weights()
Example #10
Source File: conv_tbc.py    From XSum with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
        super(ConvTBC, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _single(kernel_size)
        self.padding = _single(padding)

        self.weight = torch.nn.Parameter(torch.Tensor(
            self.kernel_size[0], in_channels, out_channels))
        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
Example #11
Source File: conv_tbc.py    From XSum with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
        super(ConvTBC, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _single(kernel_size)
        self.padding = _single(padding)

        self.weight = torch.nn.Parameter(torch.Tensor(
            self.kernel_size[0], in_channels, out_channels))
        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
Example #12
Source File: BayesianLayers.py    From Tutorial_BayesianCompressionForDL with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True,
                 cuda=False, init_weight=None, init_bias=None, clip_var=None):
        kernel_size = utils._single(kernel_size)
        stride = utils._single(stride)
        padding = utils._single(padding)
        dilation = utils._single(dilation)

        super(Conv1dGroupNJ, self).__init__(
            in_channels, out_channels, kernel_size, stride, padding, dilation,
            False, utils._pair(0), groups, bias, init_weight, init_bias, cuda, clip_var)
Example #13
Source File: conv_tbc.py    From attn2d with MIT License 5 votes vote down vote up 
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
        super(ConvTBC, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _single(kernel_size)
        self.padding = _single(padding)

        self.weight = torch.nn.Parameter(torch.Tensor(
            self.kernel_size[0], in_channels, out_channels))
        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
Example #14
Source File: deform_conv.py    From FasterRCNN.pytorch with MIT License 5 votes vote down vote up 
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 deformable_groups=1,
                 bias=False):
        super(DeformConv, self).__init__()

        assert not bias
        assert in_channels % groups == 0, \
            'in_channels {} cannot be divisible by groups {}'.format(
                in_channels, groups)
        assert out_channels % groups == 0, \
            'out_channels {} cannot be divisible by groups {}'.format(
                out_channels, groups)

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.deformable_groups = deformable_groups
        # enable compatibility with nn.Conv2d
        self.transposed = False
        self.output_padding = _single(0)

        self.weight = nn.Parameter(
            torch.Tensor(out_channels, in_channels // self.groups,
                         *self.kernel_size))

        self.reset_parameters()
Example #15
Source File: deform_conv.py    From FasterRCNN.pytorch with MIT License 5 votes vote down vote up 
def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 deformable_groups=1,
                 bias=True):
        super(ModulatedDeformConv, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.deformable_groups = deformable_groups
        self.with_bias = bias
        # enable compatibility with nn.Conv2d
        self.transposed = False
        self.output_padding = _single(0)

        self.weight = nn.Parameter(
            torch.Tensor(out_channels, in_channels // groups,
                         *self.kernel_size))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()
Example #16
Source File: conv_tbc.py    From helo_word with Apache License 2.0 5 votes vote down vote up 
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
        super(ConvTBC, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _single(kernel_size)
        self.padding = _single(padding)

        self.weight = torch.nn.Parameter(torch.Tensor(
            self.kernel_size[0], in_channels, out_channels))
        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))