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Initial commit: DCNv4 custom op mirror setup

Browse Source 
- Add enhanced README with project structure and quick start guide
- Initialize repository with DCNv4 CUDA extension (PyTorch module)
- Include classification, detection, and segmentation subdirectories
- Reference upstream OpenGVLab DCNv4 implementation

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
This commit is contained in:
Pikaliov
2026-06-11 10:30:44 +03:00
commit 1b3206b6a7
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DCNv4_op/DCNv4/__init__.py Normal file
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from .functions import DCNv4Function, FlashDeformAttnFunction
from .modules import DCNv4, DCNv4Strip, FlashDeformAttn

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DCNv4_op/DCNv4/functions/__init__.py Normal file
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# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
# from .ms_flash_deform_attn_func import FlashMSDeformAttnFunction
from .flash_deform_attn_func import FlashDeformAttnFunction
from .dcnv4_func import DCNv4Function

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DCNv4_op/DCNv4/functions/dcnv4_func.py Normal file
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# --------------------------------------------------------
# InternImage
# Copyright (c) 2022 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import math
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
from .table import TABLE, BWDTABLE
from DCNv4 import ext
def factors(N):
res = []
for i in range(1, N+1):
if N % i == 0:
res.append(i)
return res
def findspec(B, H, W, G, C):
key = f"{B}x{H}x{W}x{G}x{C}"
if key in TABLE:
return TABLE[key][0], TABLE[key][1]
d_stride = 8
ms = factors(B*H*W)
multiplier = 1
for m in ms:
if m <= 64 and (m * G * C // d_stride) <= 512:
multiplier = m
n_thread = multiplier * G * C // d_stride
key = f"{B}x{H}x{W}x{G}x{C}"
TABLE[key] = (d_stride, n_thread)
return d_stride, n_thread
def find_spec_bwd(B, H, W, G, C):
key = f"{B}x{H}x{W}x{G}x{C}"
if key in BWDTABLE:
return BWDTABLE[key][0], BWDTABLE[key][1]
if C >= 64:
d_stride = 2
else:
d_stride = 1
ms = factors(B*H*W)
multiplier = 1
for m in ms:
if m <= 64 and (m * G * C // d_stride) <= 256:
multiplier = m
n_thread = multiplier * G * C // d_stride
return d_stride, n_thread
class DCNv4Function(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):
forward_d_stride, forward_block_thread = findspec(input.shape[0], input.shape[1], input.shape[2], group, group_channels)
backward_d_stride, backward_block_thread = find_spec_bwd(input.shape[0], input.shape[1], input.shape[2], group, group_channels)
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
ctx.backward_d_stride = backward_d_stride
ctx.backward_block_thread = backward_block_thread
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,
remove_center,
forward_d_stride,
forward_block_thread,
False,
]
output = ext.dcnv4_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, ctx.im2col_step,
grad_output.contiguous(), ctx.remove_center,
ctx.backward_d_stride, ctx.backward_block_thread,
False
]
grad_input, grad_offset_mask = \
ext.dcnv4_backward(*args)
return grad_input, grad_offset_mask, \
None, None, None, None, None, None, None,\
None, None, None, None, None, None

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DCNv4_op/DCNv4/functions/flash_deform_attn_func.py Normal file
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# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
import numpy as np
from DCNv4 import ext
shm_size_dict = {
"8.0": 163000,
"8.6": 99000,
"8.7": 163000,
"8.9": 99000,
"9.0": 227000,
"7.5": 64000,
"7.0": 96000,
}
cuda_capability = f"{torch.cuda.get_device_properties(0).major}.{torch.cuda.get_device_properties(0).minor}"
if cuda_capability not in shm_size_dict:
raise NotImplementedError
shm_size_cap = shm_size_dict[cuda_capability]
def factors(N):
res = []
for i in range(1, N+1):
if N % i == 0:
res.append(i)
return res
def findspec(B, Q, G, C):
d_stride = 8
ms = factors(B*Q)
multiplier = 1
for m in ms:
if m <= 64 and (m * G * C // d_stride) <= 512:
multiplier = m
n_thread = multiplier * G * C // d_stride
return d_stride, n_thread
def findspec_bwd(B, Q, G, C):
if C >= 64:
d_stride = 2
else:
d_stride = 1
ms = factors(B*Q)
multiplier = 1
for m in ms:
if m <= 64 and (m * G * C // d_stride) <= 256:
multiplier = m
n_thread = multiplier * G * C // d_stride
return d_stride, n_thread
class FlashDeformAttnFunction(Function):
@staticmethod
@torch.autocast("cuda", enabled=True, dtype=torch.float16)
def forward(
ctx, value, value_spatial_shapes, value_level_start_index,
sampling_loc_attn, im2col_step, K=8
):
ctx.im2col_step = im2col_step
ctx.K = K
d_stride, blockthread = findspec(value.shape[0], sampling_loc_attn.shape[1], value.shape[2], value.shape[3])
d_stride_backward, blockthread_backward = findspec_bwd(value.shape[0], sampling_loc_attn.shape[1], value.shape[2], value.shape[3])
ctx.d_stride_backward = d_stride_backward
ctx.blockthread_backward = blockthread_backward
output = ext.flash_deform_attn_forward(
value,
value_spatial_shapes,
value_level_start_index,
sampling_loc_attn,
ctx.im2col_step,
K,
d_stride,
blockthread,
)
ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_loc_attn)
return output
@staticmethod
@once_differentiable
def backward(ctx, grad_output):
value, value_spatial_shapes, value_level_start_index, sampling_loc_attn = ctx.saved_tensors
grad_value, grad_sampling_loc_attn = ext.flash_deform_attn_backward(
value,
value_spatial_shapes,
value_level_start_index,
sampling_loc_attn,
grad_output.contiguous(),
ctx.im2col_step,
ctx.K,
ctx.d_stride_backward,
ctx.blockthread_backward,
)
return grad_value, None, None, grad_sampling_loc_attn, None, None

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DCNv4_op/DCNv4/functions/table.py Normal file
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DCNv4_op/DCNv4/modules/__init__.py Normal file
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# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
from .flash_deform_attn import FlashDeformAttn
from .dcnv4 import DCNv4, DCNv4Strip

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DCNv4_op/DCNv4/modules/dcnv4.py Normal file
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# --------------------------------------------------------
# Deformable Convolution v4
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_, constant_
from ..functions import DCNv4Function
class CenterFeatureScaleModule(nn.Module):
def forward(self,
query,
center_feature_scale_proj_weight,
center_feature_scale_proj_bias):
center_feature_scale = F.linear(query,
weight=center_feature_scale_proj_weight,
bias=center_feature_scale_proj_bias).sigmoid()
return center_feature_scale
class DCNv4(nn.Module):
def __init__(
self,
channels=64,
kernel_size=3,
stride=1,
pad=1,
dilation=1,
group=4,
offset_scale=1.0,
dw_kernel_size=None,
center_feature_scale=False,
remove_center=False,
output_bias=True,
without_pointwise=False,
**kwargs):
"""
DCNv4 Module
:param channels
:param kernel_size
:param stride
:param pad
:param dilation
:param group
:param offset_scale
:param act_layer
:param norm_layer
"""
super().__init__()
if channels % group != 0:
raise ValueError(
f'channels must be divisible by group, but got {channels} and {group}')
_d_per_group = channels // group
# you'd better set _d_per_group to a power of 2 which is more efficient in our CUDA implementation
assert _d_per_group % 16 == 0
self.offset_scale = offset_scale
self.channels = channels
self.kernel_size = kernel_size
self.stride = stride
self.dilation = dilation
self.pad = pad
self.group = group
self.group_channels = channels // group
self.offset_scale = offset_scale
self.dw_kernel_size = dw_kernel_size
self.center_feature_scale = center_feature_scale
self.remove_center = int(remove_center)
self.without_pointwise = without_pointwise
self.K = group * (kernel_size * kernel_size - self.remove_center)
if dw_kernel_size is not None:
self.offset_mask_dw = nn.Conv2d(channels, channels, dw_kernel_size, stride=1, padding=(dw_kernel_size - 1) // 2, groups=channels)
self.offset_mask = nn.Linear(channels, int(math.ceil((self.K * 3)/8)*8))
if not without_pointwise:
self.value_proj = nn.Linear(channels, channels)
self.output_proj = nn.Linear(channels, channels, bias=output_bias)
self._reset_parameters()
if center_feature_scale:
self.center_feature_scale_proj_weight = nn.Parameter(
torch.zeros((group, channels), dtype=torch.float))
self.center_feature_scale_proj_bias = nn.Parameter(
torch.tensor(0.0, dtype=torch.float).view((1,)).repeat(group, ))
self.center_feature_scale_module = CenterFeatureScaleModule()
def _reset_parameters(self):
constant_(self.offset_mask.weight.data, 0.)
constant_(self.offset_mask.bias.data, 0.)
if not self.without_pointwise:
xavier_uniform_(self.value_proj.weight.data)
constant_(self.value_proj.bias.data, 0.)
xavier_uniform_(self.output_proj.weight.data)
if self.output_proj.bias is not None:
constant_(self.output_proj.bias.data, 0.)
def forward(self, input, shape=None):
"""
:param query (N, H, W, C)
:return output (N, H, W, C)
"""
N, L, C = input.shape
if shape is not None:
H, W = shape
else:
H, W = int(L**0.5), int(L**0.5)
x = input
if not self.without_pointwise:
x = self.value_proj(x)
x = x.reshape(N, H, W, -1)
if self.dw_kernel_size is not None:
offset_mask_input = self.offset_mask_dw(input.view(N, H, W, C).permute(0, 3, 1, 2))
offset_mask_input = offset_mask_input.permute(0, 2, 3, 1).view(N, L, C)
else:
offset_mask_input = input
offset_mask = self.offset_mask(offset_mask_input).reshape(N, H, W, -1)
x_proj = x
x = DCNv4Function.apply(
x, offset_mask,
self.kernel_size, self.kernel_size,
self.stride, self.stride,
self.pad, self.pad,
self.dilation, self.dilation,
self.group, self.group_channels,
self.offset_scale,
256,
self.remove_center
)
if self.center_feature_scale:
center_feature_scale = self.center_feature_scale_module(
x, self.center_feature_scale_proj_weight, self.center_feature_scale_proj_bias)
center_feature_scale = center_feature_scale[..., None].repeat(
1, 1, 1, 1, self.channels // self.group).flatten(-2)
x = x * (1 - center_feature_scale) + x_proj * center_feature_scale
x = x.view(N, L, -1)
if not self.without_pointwise:
x = self.output_proj(x)
return x
# Kernel-point counts (kernel_h * kernel_w) that have compiled template
# instantiations in dcnv4_im2col_cuda.cuh / dcnv4_col2im_cuda.cuh
# (``switch (K) { case 9 / 25 / 49 }``). Any other K requires adding a case
# to those switches and rebuilding the extension.
_COMPILED_K = (9, 25, 49)
class DCNv4Strip(nn.Module):
"""Deformable STRIP convolution: a (1, k) or (k, 1) DCNv4 sampling line.
SOFIA "strip-DCN" (O2 in RECOMMENDATIONS Дополнение 3): the deformable
neighbourhood is a line of ``k`` points instead of a k×k square, so the
offset/mask predictor shrinks by ~k× (e.g. K: 49 → 9 per group) while the
receptive field along the strip is preserved and offsets let the line bend
along image structures (roads, field boundaries).
The CUDA kernels are generic over (kernel_h, kernel_w) at runtime but
template-dispatch on K = kernel_h * kernel_w with compiled cases
{9, 25, 49} — therefore ``k`` defaults to 9 (a (1, 9) strip reuses the
existing K=9 instantiation; no rebuild needed). For other ``k`` extend the
switch in the .cuh files first.
Args:
channels: Input/output channels (sequence layout [N, L, C]).
k: Number of sampling points along the strip. Must be in {9, 25, 49}
unless ``allow_uncompiled_k=True`` (then you must have rebuilt the
extension with the extra case).
orientation: 'h' → kernel (1, k); 'v' → kernel (k, 1).
group: Offset groups; ``channels // group`` must be divisible by 16.
offset_scale: DCNv4 offset scale.
without_pointwise: Skip value/output projections (default True — in
SOFIA the surrounding MBConv 1×1s already mix channels).
output_bias: Bias for output projection (used only if pointwise on).
allow_uncompiled_k: Permit k outside the compiled set (see above).
"""
def __init__(
self,
channels=64,
k=9,
orientation='h',
group=4,
offset_scale=1.0,
without_pointwise=True,
output_bias=True,
allow_uncompiled_k=False,
**kwargs):
super().__init__()
if channels % group != 0:
raise ValueError(
f'channels must be divisible by group, but got {channels} and {group}')
_d_per_group = channels // group
assert _d_per_group % 16 == 0, (
f'channels // group must be divisible by 16, got {_d_per_group}')
if orientation not in ('h', 'v'):
raise ValueError(f"orientation must be 'h' or 'v', got {orientation!r}")
if k not in _COMPILED_K and not allow_uncompiled_k:
raise ValueError(
f'k={k} has no compiled CUDA instantiation (K must be in '
f'{_COMPILED_K}); add a `case {k}:` to dcnv4_im2col_cuda.cuh / '
f'dcnv4_col2im_cuda.cuh and rebuild, then pass '
f'allow_uncompiled_k=True')
self.channels = channels
self.k = k
self.orientation = orientation
if orientation == 'h':
self.kernel_h, self.kernel_w = 1, k
self.pad_h, self.pad_w = 0, (k - 1) // 2
else:
self.kernel_h, self.kernel_w = k, 1
self.pad_h, self.pad_w = (k - 1) // 2, 0
self.stride = 1
self.dilation = 1
self.group = group
self.group_channels = channels // group
self.offset_scale = offset_scale
self.without_pointwise = without_pointwise
# Total points across groups; offsets (2K) + masks (K), padded to /8.
self.K = group * k
self.offset_mask = nn.Linear(channels, int(math.ceil((self.K * 3) / 8) * 8))
if not without_pointwise:
self.value_proj = nn.Linear(channels, channels)
self.output_proj = nn.Linear(channels, channels, bias=output_bias)
self._reset_parameters()
def _reset_parameters(self):
# Zero-init offsets/masks: the strip starts as a uniform static line
# (stable start; masks=0 → zero branch output, residual/other branches
# carry the signal until offsets learn).
constant_(self.offset_mask.weight.data, 0.)
constant_(self.offset_mask.bias.data, 0.)
if not self.without_pointwise:
xavier_uniform_(self.value_proj.weight.data)
constant_(self.value_proj.bias.data, 0.)
xavier_uniform_(self.output_proj.weight.data)
if self.output_proj.bias is not None:
constant_(self.output_proj.bias.data, 0.)
def forward(self, input, shape=None):
"""input: [N, L, C] (sequence layout, as DCNv4); returns [N, L, C]."""
N, L, C = input.shape
if shape is not None:
H, W = shape
else:
H, W = int(L ** 0.5), int(L ** 0.5)
x = input
if not self.without_pointwise:
x = self.value_proj(x)
x = x.reshape(N, H, W, -1)
offset_mask = self.offset_mask(input).reshape(N, H, W, -1)
x = DCNv4Function.apply(
x, offset_mask,
self.kernel_h, self.kernel_w,
self.stride, self.stride,
self.pad_h, self.pad_w,
self.dilation, self.dilation,
self.group, self.group_channels,
self.offset_scale,
256,
0, # remove_center: keep the centre point of the strip
)
x = x.view(N, L, -1)
if not self.without_pointwise:
x = self.output_proj(x)
return x

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# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import warnings
import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_, constant_
from ..functions import FlashDeformAttnFunction
def _is_power_of_2(n):
if (not isinstance(n, int)) or (n < 0):
raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
return (n & (n - 1) == 0) and n != 0
class FlashDeformAttn(nn.Module):
def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
"""
Multi-Scale Deformable Attention Module
:param d_model hidden dimension
:param n_levels number of feature levels
:param n_heads number of attention heads
:param n_points number of sampling points per attention head per feature level
"""
super().__init__()
if d_model % n_heads != 0:
raise ValueError("d_model must be divisible by n_heads, but got {} and {}".format(d_model, n_heads))
_d_per_head = d_model // n_heads
# you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
if not _is_power_of_2(_d_per_head):
warnings.warn(
"You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
"which is more efficient in our CUDA implementation."
)
self.im2col_step = 64
self.d_model = d_model
self.n_levels = n_levels
self.n_heads = n_heads
self.n_points = n_points
self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
self.value_proj = nn.Linear(d_model, d_model)
self.output_proj = nn.Linear(d_model, d_model)
self._reset_parameters()
def _reset_parameters(self):
constant_(self.sampling_offsets.weight.data, 0.0)
thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
grid_init = (
(grid_init / grid_init.abs().max(-1, keepdim=True)[0])
.view(self.n_heads, 1, 1, 2)
.repeat(1, self.n_levels, self.n_points, 1)
)
for i in range(self.n_points):
grid_init[:, :, i, :] *= i + 1
with torch.no_grad():
self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
constant_(self.attention_weights.weight.data, 0.0)
constant_(self.attention_weights.bias.data, 0.0)
xavier_uniform_(self.value_proj.weight.data)
constant_(self.value_proj.bias.data, 0.0)
xavier_uniform_(self.output_proj.weight.data)
constant_(self.output_proj.bias.data, 0.0)
def forward(
self,
query,
reference_points,
input_flatten,
input_spatial_shapes,
input_level_start_index,
input_padding_mask=None,
):
"""
:param query (N, Length_{query}, C)
:param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area
or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes
:param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C)
:param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
:param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]
:param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
:return output (N, Length_{query}, C)
"""
N, Len_q, _ = query.shape
N, Len_in, _ = input_flatten.shape
assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in
value = self.value_proj(input_flatten)
if input_padding_mask is not None:
value = value.masked_fill(input_padding_mask[..., None], float(0))
value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)
attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
# N, Len_q, n_heads, n_levels, n_points, 2
if reference_points.shape[-1] == 2:
offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)
sampling_locations = (
reference_points[:, :, None, :, None, :]
+ sampling_offsets / offset_normalizer[None, None, None, :, None, :]
)
elif reference_points.shape[-1] == 4:
sampling_locations = (
reference_points[:, :, None, :, None, :2]
+ sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
)
else:
raise ValueError(
"Last dim of reference_points must be 2 or 4, but get {} instead.".format(reference_points.shape[-1])
)
# Cat sampling_offsets and attention_weights, generate sampling_loc_attn:
sampling_locations = sampling_locations.flatten(-3).half()
sampling_loc_attn = torch.cat([sampling_locations, attention_weights], dim=-1)
output = FlashDeformAttnFunction.apply(
value,
input_spatial_shapes,
input_level_start_index,
sampling_loc_attn,
self.im2col_step,
self.n_points
)
output = self.output_proj(output)
return output