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# --------------------------------------------------------
# InternImage
# Copyright (c) 2025 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from __future__ import absolute_import, division, print_function
try:
import DCNv3
dcn_version = float(pkg_resources.get_distribution('DCNv3').version)
has_cuda_kernel = True
except:
has_cuda_kernel = False
import pkg_resources
import torch
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.cuda.amp import custom_bwd, custom_fwd
class DCNv3Function(Function):
@staticmethod
@custom_fwd
def forward(
ctx, input, offset, mask,
kernel_h, kernel_w, stride_h, stride_w,
pad_h, pad_w, dilation_h, dilation_w,
group, group_channels, offset_scale, im2col_step, remove_center):
ctx.kernel_h = kernel_h
ctx.kernel_w = kernel_w
ctx.stride_h = stride_h
ctx.stride_w = stride_w
ctx.pad_h = pad_h
ctx.pad_w = pad_w
ctx.dilation_h = dilation_h
ctx.dilation_w = dilation_w
ctx.group = group
ctx.group_channels = group_channels
ctx.offset_scale = offset_scale
ctx.im2col_step = im2col_step
ctx.remove_center = remove_center
args = [
input, offset, mask, kernel_h,
kernel_w, stride_h, stride_w, pad_h,
pad_w, dilation_h, dilation_w, group,
group_channels, offset_scale, ctx.im2col_step
]
if remove_center or dcn_version > 1.0:
args.append(remove_center)
output = DCNv3.dcnv3_forward(*args)
ctx.save_for_backward(input, offset, mask)
return output
@staticmethod
@once_differentiable
@custom_bwd
def backward(ctx, grad_output):
input, offset, mask = ctx.saved_tensors
args = [
input, offset, mask, ctx.kernel_h,
ctx.kernel_w, ctx.stride_h, ctx.stride_w, ctx.pad_h,
ctx.pad_w, ctx.dilation_h, ctx.dilation_w, ctx.group,
ctx.group_channels, ctx.offset_scale, grad_output.contiguous(), ctx.im2col_step
]
if ctx.remove_center or dcn_version > 1.0:
args.append(ctx.remove_center)
grad_input, grad_offset, grad_mask = \
DCNv3.dcnv3_backward(*args)
return grad_input, grad_offset, grad_mask, \
None, None, None, None, None, None, None, None, None, None, None, None, None
@staticmethod
def symbolic(g, input, offset, mask, kernel_h, kernel_w, stride_h,
stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
group_channels, offset_scale, im2col_step, remove_center):
"""Symbolic function for mmdeploy::DCNv3.
Returns:
DCNv3 op for onnx.
"""
return g.op(
'mmdeploy::TRTDCNv3',
input,
offset,
mask,
kernel_h_i=int(kernel_h),
kernel_w_i=int(kernel_w),
stride_h_i=int(stride_h),
stride_w_i=int(stride_w),
pad_h_i=int(pad_h),
pad_w_i=int(pad_w),
dilation_h_i=int(dilation_h),
dilation_w_i=int(dilation_w),
group_i=int(group),
group_channels_i=int(group_channels),
offset_scale_f=float(offset_scale),
im2col_step_i=int(im2col_step),
remove_center_i=int(remove_center),
)
def _get_reference_points(spatial_shapes, device, kernel_h, kernel_w, dilation_h, dilation_w, pad_h=0, pad_w=0, stride_h=1, stride_w=1):
_, H_, W_, _ = spatial_shapes
H_out = (H_ - (dilation_h * (kernel_h - 1) + 1)) // stride_h + 1
W_out = (W_ - (dilation_w * (kernel_w - 1) + 1)) // stride_w + 1
ref_y, ref_x = torch.meshgrid(
torch.linspace(
# pad_h + 0.5,
# H_ - pad_h - 0.5,
(dilation_h * (kernel_h - 1)) // 2 + 0.5,
(dilation_h * (kernel_h - 1)) // 2 + 0.5 + (H_out - 1) * stride_h,
H_out,
dtype=torch.float32,
device=device),
torch.linspace(
# pad_w + 0.5,
# W_ - pad_w - 0.5,
(dilation_w * (kernel_w - 1)) // 2 + 0.5,
(dilation_w * (kernel_w - 1)) // 2 + 0.5 + (W_out - 1) * stride_w,
W_out,
dtype=torch.float32,
device=device))
ref_y = ref_y.reshape(-1)[None] / H_
ref_x = ref_x.reshape(-1)[None] / W_
ref = torch.stack((ref_x, ref_y), -1).reshape(
1, H_out, W_out, 1, 2)
return ref
def _generate_dilation_grids(spatial_shapes, kernel_h, kernel_w, dilation_h, dilation_w, group, device):
_, H_, W_, _ = spatial_shapes
points_list = []
x, y = torch.meshgrid(
torch.linspace(
-((dilation_w * (kernel_w - 1)) // 2),
-((dilation_w * (kernel_w - 1)) // 2) + (kernel_w - 1) * dilation_w,
kernel_w,
dtype=torch.float32,
device=device),
torch.linspace(
-((dilation_h * (kernel_h - 1)) // 2),
-((dilation_h * (kernel_h - 1)) // 2) + (kernel_h - 1) * dilation_h,
kernel_h,
dtype=torch.float32,
device=device))
points_list.extend([x / W_, y / H_])
grid = torch.stack(points_list, -1).reshape(-1, 1, 2).\
repeat(1, group, 1).permute(1, 0, 2)
grid = grid.reshape(1, 1, 1, group * kernel_h * kernel_w, 2)
return grid
def remove_center_sampling_locations(sampling_locations, kernel_w, kernel_h):
idx = list(range(sampling_locations.shape[-2]))
C = (kernel_w * kernel_h - 1)//2
idx = [i for i in idx if i != C and (i-C) % (C*2+1) != 0]
sampling_locations = sampling_locations[:,:,:,idx, :]
return sampling_locations
def dcnv3_core_pytorch(
input, offset, mask, kernel_h,
kernel_w, stride_h, stride_w, pad_h,
pad_w, dilation_h, dilation_w, group,
group_channels, offset_scale, remove_center):
# for debug and test only,
# need to use cuda version instead
if remove_center and (kernel_h % 2 == 0 or kernel_w % 2 == 0 or kernel_w != kernel_h):
raise ValueError('remove_center is only compatible with square odd kernel size.')
input = F.pad(
input,
[0, 0, pad_h, pad_h, pad_w, pad_w])
N_, H_in, W_in, _ = input.shape
_, H_out, W_out, _ = offset.shape
ref = _get_reference_points(
input.shape, input.device, kernel_h, kernel_w, dilation_h, dilation_w, pad_h, pad_w, stride_h, stride_w)
grid = _generate_dilation_grids(
input.shape, kernel_h, kernel_w, dilation_h, dilation_w, group, input.device)
spatial_norm = torch.tensor([W_in, H_in]).reshape(1, 1, 1, 2).\
repeat(1, 1, 1, group*(kernel_h*kernel_w-remove_center)).to(input.device)
sampling_locations = (ref + grid * offset_scale).repeat(N_, 1, 1, 1, 1)
if remove_center:
sampling_locations = remove_center_sampling_locations(sampling_locations, kernel_w=kernel_w, kernel_h=kernel_h)
sampling_locations = sampling_locations.flatten(3, 4)
sampling_locations = sampling_locations + offset * offset_scale / spatial_norm
P_ = kernel_h * kernel_w - remove_center
sampling_grids = 2 * sampling_locations - 1
# N_, H_in, W_in, group*group_channels -> N_, H_in*W_in, group*group_channels -> N_, group*group_channels, H_in*W_in -> N_*group, group_channels, H_in, W_in
input_ = input.view(N_, H_in*W_in, group*group_channels).transpose(1, 2).\
reshape(N_*group, group_channels, H_in, W_in)
# N_, H_out, W_out, group*P_*2 -> N_, H_out*W_out, group, P_, 2 -> N_, group, H_out*W_out, P_, 2 -> N_*group, H_out*W_out, P_, 2
sampling_grid_ = sampling_grids.view(N_, H_out*W_out, group, P_, 2).transpose(1, 2).\
flatten(0, 1)
# N_*group, group_channels, H_out*W_out, P_
sampling_input_ = F.grid_sample(
input_, sampling_grid_, mode='bilinear', padding_mode='zeros', align_corners=False)
# (N_, H_out, W_out, group*P_) -> N_, H_out*W_out, group, P_ -> (N_, group, H_out*W_out, P_) -> (N_*group, 1, H_out*W_out, P_)
mask = mask.view(N_, H_out*W_out, group, P_).transpose(1, 2).\
reshape(N_*group, 1, H_out*W_out, P_)
output = (sampling_input_ * mask).sum(-1).view(N_,
group*group_channels, H_out*W_out)
return output.transpose(1, 2).reshape(N_, H_out, W_out, -1).contiguous()
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