Method __init__

models/modules/deform_conv.py:7–48

(self,
                 in_channels,
                 out_channels,
                 kernel_size=3,
                 stride=1,
                 padding=1,
                 bias=False)

class DeformableConv2d(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size=3,
                 stride=1,
                 padding=1,
                 bias=False):

        super(DeformableConv2d, self).__init__()
        
        assert type(kernel_size) == tuple or type(kernel_size) == int

        kernel_size = kernel_size if type(kernel_size) == tuple else (kernel_size, kernel_size)
        self.stride = stride if type(stride) == tuple else (stride, stride)
        self.padding = padding
        
        self.offset_conv = nn.Conv2d(in_channels,
                                     2 * kernel_size[0] * kernel_size[1],
                                     kernel_size=kernel_size,
                                     stride=stride,
                                     padding=self.padding,
                                     bias=True)

        nn.init.constant_(self.offset_conv.weight, 0.)
        nn.init.constant_(self.offset_conv.bias, 0.)
        
        self.modulator_conv = nn.Conv2d(in_channels,
                                     1 * kernel_size[0] * kernel_size[1],
                                     kernel_size=kernel_size,
                                     stride=stride,
                                     padding=self.padding,
                                     bias=True)

        nn.init.constant_(self.modulator_conv.weight, 0.)
        nn.init.constant_(self.modulator_conv.bias, 0.)

        self.regular_conv = nn.Conv2d(in_channels,
                                      out_channels=out_channels,
                                      kernel_size=kernel_size,
                                      stride=stride,
                                      padding=self.padding,
                                      bias=bias)

    def forward(self, x):
        #h, w = x.shape[2:]