import math
from typing import Optional, Tuple
import torch
from torch import nn, Tensor
from torch.nn import init
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
from torchvision.extension import _assert_has_ops
from ..utils import _log_api_usage_once
def deform_conv2d(
input: Tensor,
offset: Tensor,
weight: Tensor,
bias: Optional[Tensor] = None,
stride: Tuple[int, int] = (1, 1),
padding: Tuple[int, int] = (0, 0),
dilation: Tuple[int, int] = (1, 1),
mask: Optional[Tensor] = None,
) -> Tensor:
r"""
Performs Deformable Convolution v2, described in
`Deformable ConvNets v2: More Deformable, Better Results
<https://arxiv.org/abs/1811.11168>`__ if :attr:`mask` is not ``None`` and
Performs Deformable Convolution, described in
`Deformable Convolutional Networks
<https://arxiv.org/abs/1703.06211>`__ if :attr:`mask` is ``None``.
Args:
input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]):
offsets to be applied for each position in the convolution kernel.
weight (Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]): convolution weights,
split into groups of size (in_channels // groups)
bias (Tensor[out_channels]): optional bias of shape (out_channels,). Default: None
stride (int or Tuple[int, int]): distance between convolution centers. Default: 1
padding (int or Tuple[int, int]): height/width of padding of zeroes around
each image. Default: 0
dilation (int or Tuple[int, int]): the spacing between kernel elements. Default: 1
mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]):
masks to be applied for each position in the convolution kernel. Default: None
Returns:
Tensor[batch_sz, out_channels, out_h, out_w]: result of convolution
Examples::
>>> input = torch.rand(4, 3, 10, 10)
>>> kh, kw = 3, 3
>>> weight = torch.rand(5, 3, kh, kw)
>>> # offset and mask should have the same spatial size as the output
>>> # of the convolution. In this case, for an input of 10, stride of 1
>>> # and kernel size of 3, without padding, the output size is 8
>>> offset = torch.rand(4, 2 * kh * kw, 8, 8)
>>> mask = torch.rand(4, kh * kw, 8, 8)
>>> out = deform_conv2d(input, offset, weight, mask=mask)
>>> print(out.shape)
>>> # returns
>>> torch.Size([4, 5, 8, 8])
"""
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(deform_conv2d)
_assert_has_ops()
out_channels = weight.shape[0]
use_mask = mask is not None
if mask is None:
mask = torch.zeros((input.shape[0], 1), device=input.device, dtype=input.dtype)
if bias is None:
bias = torch.zeros(out_channels, device=input.device, dtype=input.dtype)
stride_h, stride_w = _pair(stride)
pad_h, pad_w = _pair(padding)
dil_h, dil_w = _pair(dilation)
weights_h, weights_w = weight.shape[-2:]
_, n_in_channels, _, _ = input.shape
n_offset_grps = offset.shape[1] // (2 * weights_h * weights_w)
n_weight_grps = n_in_channels // weight.shape[1]
if n_offset_grps == 0:
raise RuntimeError(
"the shape of the offset tensor at dimension 1 is not valid. It should "
"be a multiple of 2 * weight.size[2] * weight.size[3].\n"
f"Got offset.shape[1]={offset.shape[1]}, while 2 * weight.size[2] * weight.size[3]={2 * weights_h * weights_w}"
)
return torch.ops.torchvision.deform_conv2d(
input,
weight,
offset,
mask,
bias,
stride_h,
stride_w,
pad_h,
pad_w,
dil_h,
dil_w,
n_weight_grps,
n_offset_grps,
use_mask,
)
class DeformConv2d(nn.Module):
"""
See :func:`deform_conv2d`.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
padding: int = 0,
dilation: int = 1,
groups: int = 1,
bias: bool = True,
):
super().__init__()
_log_api_usage_once(self)
if in_channels % groups != 0:
raise ValueError("in_channels must be divisible by groups")
if out_channels % groups != 0:
raise ValueError("out_channels must be divisible by 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.weight = Parameter(
torch.empty(out_channels, in_channels // groups, self.kernel_size[0], self.kernel_size[1])
)
if bias:
self.bias = Parameter(torch.empty(out_channels))
else:
self.register_parameter("bias", None)
self.reset_parameters()
def reset_parameters(self) -> None:
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
def forward(self, input: Tensor, offset: Tensor, mask: Optional[Tensor] = None) -> Tensor:
"""
Args:
input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]):
offsets to be applied for each position in the convolution kernel.
mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]):
masks to be applied for each position in the convolution kernel.
"""
return deform_conv2d(
input,
offset,
self.weight,
self.bias,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
mask=mask,
)
def __repr__(self) -> str:
s = (
f"{self.__class__.__name__}("
f"{self.in_channels}"
f", {self.out_channels}"
f", kernel_size={self.kernel_size}"
f", stride={self.stride}"
)
s += f", padding={self.padding}" if self.padding != (0, 0) else ""
s += f", dilation={self.dilation}" if self.dilation != (1, 1) else ""
s += f", groups={self.groups}" if self.groups != 1 else ""
s += ", bias=False" if self.bias is None else ""
s += ")"
return s