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import torch |
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import torch.nn as nn |
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from torch.nn import init as init |
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from torch.nn.modules.utils import _pair, _single |
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import math |
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class ModulatedDeformConv2d(nn.Module): |
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def __init__(self, |
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in_channels, |
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out_channels, |
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kernel_size, |
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stride=1, |
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padding=0, |
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dilation=1, |
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groups=1, |
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deform_groups=1, |
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bias=True): |
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super(ModulatedDeformConv2d, self).__init__() |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.kernel_size = _pair(kernel_size) |
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self.stride = stride |
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self.padding = padding |
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self.dilation = dilation |
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self.groups = groups |
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self.deform_groups = deform_groups |
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self.with_bias = bias |
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self.transposed = False |
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self.output_padding = _single(0) |
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self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)) |
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if bias: |
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self.bias = nn.Parameter(torch.Tensor(out_channels)) |
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else: |
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self.register_parameter('bias', None) |
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self.init_weights() |
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def init_weights(self): |
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n = self.in_channels |
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for k in self.kernel_size: |
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n *= k |
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stdv = 1. / math.sqrt(n) |
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self.weight.data.uniform_(-stdv, stdv) |
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if self.bias is not None: |
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self.bias.data.zero_() |
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if hasattr(self, 'conv_offset'): |
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self.conv_offset.weight.data.zero_() |
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self.conv_offset.bias.data.zero_() |
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def forward(self, x, offset, mask): |
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pass |
