Source code for mmcv.ops.deform_conv
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair, _single
from mmcv.utils import deprecated_api_warning
from ..cnn import CONV_LAYERS
from ..utils import ext_loader, print_log
ext_module = ext_loader.load_ext('_ext', [
'deform_conv_forward', 'deform_conv_backward_input',
'deform_conv_backward_parameters'
])
class DeformConv2dFunction(Function):
@staticmethod
def symbolic(g, input, offset, weight, stride, padding, dilation, groups,
deform_groups, bias, im2col_step):
return g.op(
'MMCVDeformConv2d',
input,
offset,
weight,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
deform_groups=deform_groups,
bias=bias,
im2col_step=im2col_step)
@staticmethod
def forward(ctx,
input,
offset,
weight,
stride=1,
padding=0,
dilation=1,
groups=1,
deform_groups=1,
bias=False,
im2col_step=32):
if input is not None and input.dim() != 4:
raise ValueError(
f'Expected 4D tensor as input, got {input.dim()}D tensor \
instead.')
assert bias is False, 'Only support bias is False.'
ctx.stride = _pair(stride)
ctx.padding = _pair(padding)
ctx.dilation = _pair(dilation)
ctx.groups = groups
ctx.deform_groups = deform_groups
ctx.im2col_step = im2col_step
ctx.save_for_backward(input, offset, weight)
output = input.new_empty(
DeformConv2dFunction._output_size(ctx, input, weight))
ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones
cur_im2col_step = min(ctx.im2col_step, input.size(0))
assert (input.size(0) %
cur_im2col_step) == 0, 'im2col step must divide batchsize'
ext_module.deform_conv_forward(
input,
weight,
offset,
output,
ctx.bufs_[0],
ctx.bufs_[1],
kW=weight.size(3),
kH=weight.size(2),
dW=ctx.stride[1],
dH=ctx.stride[0],
padW=ctx.padding[1],
padH=ctx.padding[0],
dilationW=ctx.dilation[1],
dilationH=ctx.dilation[0],
group=ctx.groups,
deformable_group=ctx.deform_groups,
im2col_step=cur_im2col_step)
return output
@staticmethod
@once_differentiable
def backward(ctx, grad_output):
input, offset, weight = ctx.saved_tensors
grad_input = grad_offset = grad_weight = None
cur_im2col_step = min(ctx.im2col_step, input.size(0))
assert (input.size(0) %
cur_im2col_step) == 0, 'im2col step must divide batchsize'
grad_output = grad_output.contiguous()
if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
grad_input = torch.zeros_like(input)
grad_offset = torch.zeros_like(offset)
ext_module.deform_conv_backward_input(
input,
offset,
grad_output,
grad_input,
grad_offset,
weight,
ctx.bufs_[0],
kW=weight.size(3),
kH=weight.size(2),
dW=ctx.stride[1],
dH=ctx.stride[0],
padW=ctx.padding[1],
padH=ctx.padding[0],
dilationW=ctx.dilation[1],
dilationH=ctx.dilation[0],
group=ctx.groups,
deformable_group=ctx.deform_groups,
im2col_step=cur_im2col_step)
if ctx.needs_input_grad[2]:
grad_weight = torch.zeros_like(weight)
ext_module.deform_conv_backward_parameters(
input,
offset,
grad_output,
grad_weight,
ctx.bufs_[0],
ctx.bufs_[1],
kW=weight.size(3),
kH=weight.size(2),
dW=ctx.stride[1],
dH=ctx.stride[0],
padW=ctx.padding[1],
padH=ctx.padding[0],
dilationW=ctx.dilation[1],
dilationH=ctx.dilation[0],
group=ctx.groups,
deformable_group=ctx.deform_groups,
scale=1,
im2col_step=cur_im2col_step)
return grad_input, grad_offset, grad_weight, \
None, None, None, None, None, None, None
@staticmethod
def _output_size(ctx, input, weight):
channels = weight.size(0)
output_size = (input.size(0), channels)
for d in range(input.dim() - 2):
in_size = input.size(d + 2)
pad = ctx.padding[d]
kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
stride_ = ctx.stride[d]
output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
if not all(map(lambda s: s > 0, output_size)):
raise ValueError(
'convolution input is too small (output would be ' +
'x'.join(map(str, output_size)) + ')')
return output_size
deform_conv2d = DeformConv2dFunction.apply
[docs]class DeformConv2d(nn.Module):
@deprecated_api_warning({'deformable_groups': 'deform_groups'},
cls_name='DeformConv2d')
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
deform_groups=1,
bias=False):
super(DeformConv2d, self).__init__()
assert in_channels % groups == 0, \
f'in_channels {in_channels} cannot be divisible by groups {groups}'
assert out_channels % groups == 0, \
f'out_channels {out_channels} cannot be divisible by groups \
{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.deform_groups = deform_groups
# enable compatibility with nn.Conv2d
self.transposed = False
self.output_padding = _single(0)
# only weight, no bias
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, x, offset):
# To fix an assert error in deform_conv_cuda.cpp:128
# input image is smaller than kernel
input_pad = (x.size(2) < self.kernel_size[0]) or (x.size(3) <
self.kernel_size[1])
if input_pad:
pad_h = max(self.kernel_size[0] - x.size(2), 0)
pad_w = max(self.kernel_size[1] - x.size(3), 0)
x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0)
offset = offset.contiguous()
out = deform_conv2d(x, offset, self.weight, self.stride, self.padding,
self.dilation, self.groups, self.deform_groups)
if input_pad:
out = out[:, :, :out.size(2) - pad_h, :out.size(3) -
pad_w].contiguous()
return out
[docs]@CONV_LAYERS.register_module('DCN')
class DeformConv2dPack(DeformConv2d):
"""A Deformable Conv Encapsulation that acts as normal Conv layers.
The offset tensor is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`.
The spatial arrangement is like:
.. code:: text
(x0, y0) (x1, y1) (x2, y2)
(x3, y3) (x4, y4) (x5, y5)
(x6, y6) (x7, y7) (x8, y8)
Args:
in_channels (int): Same as nn.Conv2d.
out_channels (int): Same as nn.Conv2d.
kernel_size (int or tuple[int]): Same as nn.Conv2d.
stride (int or tuple[int]): Same as nn.Conv2d.
padding (int or tuple[int]): Same as nn.Conv2d.
dilation (int or tuple[int]): Same as nn.Conv2d.
groups (int): Same as nn.Conv2d.
bias (bool or str): If specified as `auto`, it will be decided by the
norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
False.
"""
_version = 2
def __init__(self, *args, **kwargs):
super(DeformConv2dPack, self).__init__(*args, **kwargs)
self.conv_offset = nn.Conv2d(
self.in_channels,
self.deform_groups * 2 * self.kernel_size[0] * self.kernel_size[1],
kernel_size=self.kernel_size,
stride=_pair(self.stride),
padding=_pair(self.padding),
dilation=_pair(self.dilation),
bias=True)
self.init_offset()
def init_offset(self):
self.conv_offset.weight.data.zero_()
self.conv_offset.bias.data.zero_()
def forward(self, x):
offset = self.conv_offset(x)
return deform_conv2d(x, offset, self.weight, self.stride, self.padding,
self.dilation, self.groups, self.deform_groups)
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
version = local_metadata.get('version', None)
if version is None or version < 2:
# the key is different in early versions
# In version < 2, DeformConvPack loads previous benchmark models.
if (prefix + 'conv_offset.weight' not in state_dict
and prefix[:-1] + '_offset.weight' in state_dict):
state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
prefix[:-1] + '_offset.weight')
if (prefix + 'conv_offset.bias' not in state_dict
and prefix[:-1] + '_offset.bias' in state_dict):
state_dict[prefix +
'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
'_offset.bias')
if version is not None and version > 1:
print_log(
f'DeformConv2dPack {prefix.rstrip(".")} is upgraded to '
'version 2.',
logger='root')
super()._load_from_state_dict(state_dict, prefix, local_metadata,
strict, missing_keys, unexpected_keys,
error_msgs)