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A10G
Running
on
A10G
| import torch | |
| import torch.nn as nn | |
| from torch.nn import init as init | |
| from torch.nn.modules.utils import _pair, _single | |
| import math | |
| class ModulatedDeformConv2d(nn.Module): | |
| 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 = stride | |
| self.padding = padding | |
| self.dilation = dilation | |
| self.groups = groups | |
| self.deform_groups = deform_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() | |
| def init_weights(self): | |
| n = self.in_channels | |
| for k in self.kernel_size: | |
| n *= k | |
| stdv = 1. / math.sqrt(n) | |
| self.weight.data.uniform_(-stdv, stdv) | |
| if self.bias is not None: | |
| self.bias.data.zero_() | |
| if hasattr(self, 'conv_offset'): | |
| self.conv_offset.weight.data.zero_() | |
| self.conv_offset.bias.data.zero_() | |
| def forward(self, x, offset, mask): | |
| pass |