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Running
on
Zero
File size: 2,439 Bytes
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import torch
import torch.nn as nn
from torchvision.ops import deform_conv2d
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:]
#max_offset = max(h, w)/4.
offset = self.offset_conv(x)#.clamp(-max_offset, max_offset)
modulator = 2. * torch.sigmoid(self.modulator_conv(x))
x = deform_conv2d(
input=x,
offset=offset,
weight=self.regular_conv.weight,
bias=self.regular_conv.bias,
padding=self.padding,
mask=modulator,
stride=self.stride,
)
return x
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