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import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from .deform_conv_func import deform_conv, modulated_deform_conv
class DeformConv(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
deformable_groups=1,
bias=False
):
assert not bias
super(DeformConv, self).__init__()
self.with_bias = 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
self.weight = nn.Parameter(
torch.Tensor(out_channels, in_channels // self.groups,
*self.kernel_size))
self.reset_parameters()
def reset_parameters(self):
n = self.in_channels
for k in self.kernel_size:
n *= k
stdv = 1. / math.sqrt(n)
self.weight.data.uniform_(-stdv, stdv)
def forward(self, input, offset):
return deform_conv(input, offset, self.weight, self.stride,
self.padding, self.dilation, self.groups,
self.deformable_groups)
def __repr__(self):
return "".join([
"{}(".format(self.__class__.__name__),
"in_channels={}, ".format(self.in_channels),
"out_channels={}, ".format(self.out_channels),
"kernel_size={}, ".format(self.kernel_size),
"stride={}, ".format(self.stride),
"dilation={}, ".format(self.dilation),
"padding={}, ".format(self.padding),
"groups={}, ".format(self.groups),
"deformable_groups={}, ".format(self.deformable_groups),
"bias={})".format(self.with_bias),
])
class ModulatedDeformConv(nn.Module):
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
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()
def reset_parameters(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_()
def forward(self, input, offset, mask):
return modulated_deform_conv(
input, offset, mask, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups, self.deformable_groups)
def __repr__(self):
return "".join([
"{}(".format(self.__class__.__name__),
"in_channels={}, ".format(self.in_channels),
"out_channels={}, ".format(self.out_channels),
"kernel_size={}, ".format(self.kernel_size),
"stride={}, ".format(self.stride),
"dilation={}, ".format(self.dilation),
"padding={}, ".format(self.padding),
"groups={}, ".format(self.groups),
"deformable_groups={}, ".format(self.deformable_groups),
"bias={})".format(self.with_bias),
])
class ModulatedDeformConvPack(ModulatedDeformConv):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
deformable_groups=1,
bias=True):
super(ModulatedDeformConvPack, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, deformable_groups, bias)
self.conv_offset_mask = nn.Conv2d(
self.in_channels // self.groups,
self.deformable_groups * 3 * self.kernel_size[0] *
self.kernel_size[1],
kernel_size=self.kernel_size,
stride=_pair(self.stride),
padding=_pair(self.padding),
bias=True)
self.init_offset()
def init_offset(self):
self.conv_offset_mask.weight.data.zero_()
self.conv_offset_mask.bias.data.zero_()
def forward(self, input):
out = self.conv_offset_mask(input)
o1, o2, mask = torch.chunk(out, 3, dim=1)
offset = torch.cat((o1, o2), dim=1)
mask = torch.sigmoid(mask)
return modulated_deform_conv(
input, offset, mask, self.weight, self.bias, self.stride,
self.padding, self.dilation, self.groups, self.deformable_groups)
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