手动实现pixel_unshuffle时使用 F.conv2d报错

Inizia eseguendo la riproduzione segnalata di MegEngine 1.11.1 con F.conv2d, usando le forme dell’input e del kernel indicate nell’issue. Leggi megengine/functional/nn.py intorno a conv2d e l’assertion in convolution.cpp per i filtri raggruppati. Il lavoro è completato quando la convoluzione raggruppata segnalata funziona con queste forme oppure quando la limitazione e i requisiti per le forme supportate sono documentati chiaramente.

我希望使用megengine手动实现pixel_unshuffle方法
使用F.conv2d(input, kernel, stride=downscale_factor, groups=c)代码中
input.shape=(2, 3, 1980, 2880)
kernel.shape=(48,1,4,4)
downscale_factor=4
c=3

同样的方法在pytorch中有效，输出shape为(2, 48, 495, 720),但是在MegEngine的F.conv2d会爆出错误

环境

1.系统环境：
2.MegEngine版本：1.11.1+cu111
3.python版本：3.8

复现步骤

pytorch代码：
import torch
import torch.nn as nn
import torch.nn.functional as F

def pixel_unshuffle(input, downscale_factor):
'''
input: batchSize * c * kw * kh
kdownscale_factor: k

batchSize * c * k*w * k*h -> batchSize * k*k*c * w * h
'''
c = input.shape[1]

kernel = torch.zeros(size=[downscale_factor * downscale_factor * c,
                           1, downscale_factor, downscale_factor],
                     device=input.device)
for y in range(downscale_factor):
    for x in range(downscale_factor):
        kernel[x + y * downscale_factor::downscale_factor*downscale_factor, 0, y, x] = 1
return F.conv2d(input, kernel, stride=downscale_factor, groups=c)

x = torch.zeros(size=(2, 3, 1980, 2880))

x = pixel_unshuffle(x, 4)
print(x.shape)

megengine代码：
def pixel_unshuffle(input, downscale_factor):
'''
input: (batchSize, c, kw, kh)
downscale_factor: k

(batchSize, c, k*w, k*h) -> (batchSize, k*k*c, w, h)
'''
c = input.shape[1]

# 创建一个展开操作符（类似于PyTorch中的卷积核）
kernel = F.zeros((downscale_factor * downscale_factor * c, 1, downscale_factor, downscale_factor), device=input.device)
for y in range(downscale_factor):
    for x in range(downscale_factor):
        kernel[x + y * downscale_factor::downscale_factor*downscale_factor, 0, y, x] = 1
return F.conv2d(input, kernel, stride=downscale_factor, groups=c)

im = F.zeros((2, 3, 1980, 2880))
isn = pixel_unshuffle(im, 4)
print(isn)

请提供完整的日志及报错信息

Traceback (most recent call last):
File "/home/kyoron/.pycharm_helpers/pydev/pydevd.py", line 1500, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "/home/kyoron/.pycharm_helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/mnt/e/多媒体/桌面/WZ/code/CREStereo_swinT/CREStereo/nets/utils/utils.py", line 65, in
python-BaseException
isn = pixel_unshuffle(im, 4)
File "/mnt/e/多媒体/桌面/WZ/code/CREStereo_swinT/CREStereo/nets/utils/utils.py", line 62, in pixel_unshuffle
return F.conv2d(input, kernel, stride=downscale_factor, groups=c)
File "/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/functional/nn.py", line 265, in conv2d
(output,) = apply(op, inp, weight)
RuntimeError: assertion `filter.ndim == img_ndim + 3 || filter.ndim == img_ndim + 5' failed at ../../../../../../dnn/src/common/convolution.cpp:56: void {anonymous}::make_canonized_filter_meta_nchw_nhwc(size_t, const megdnn::TensorLayout&, const Param&, typename megdnn::ConvolutionBase::CanonizedFilterMeta&) [with Parameter = megdnn::param::Convolution; Param = megdnn::param::Convolution; size_t = long unsigned int; typename megdnn::ConvolutionBase::CanonizedFilterMeta = megdnn::ConvolutionBasemegdnn::param::Convolution::CanonizedFilterMeta]
extra message: bad filter ndim for group convolution: spatial_ndim=2 filter_ndim=4

backtrace:
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN3mgb13MegBrainErrorC1ERKSs+0x4a) [0x7feefd2d76aa]
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/lib/libmegengine_shared.so(+0x2ab2557) [0x7feefd339557]
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN6megdnn12ErrorHandler15on_megdnn_errorERKSs+0x14) [0x7fef00addb34]
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN6megdnn12ErrorHandler15on_megdnn_errorEPKc+0x22) [0x7fef00adf632]
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/lib/libmegengine_shared.so(_ZN6megdnn15__assert_fail__EPKciS1_S1_S1_z+0x190) [0x7fef00b80c50]
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/lib/libmegengine_shared.so(_ZNK6megdnn15ConvolutionBaseINS_5param11ConvolutionEE26make_canonized_filter_metaEmRKNS_12TensorLayoutE+0x12d6) [0x7fef00ae8a46]
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/lib/libmegengine_shared.so(ZNK6megdnn15ConvolutionBaseINS_5param11ConvolutionEE17deduce_layout_fwdERKNS_12TensorLayoutES6_RS4+0xa3) [0x7fef00af18c3]
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/lib/libmegengine_shared.so(ZN6megdnn18ConvolutionForward13deduce_layoutERKNS_12TensorLayoutES3_RS1+0x1b) [0x7fef00af388b]
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/_imperative_rt.cpython-38-x86_64-linux-gnu.so(+0x33d733) [0x7fef99e1c733]
/home/kyoron/miniconda3/envs/dl/lib/python3.8/site-packages/megengine/core/_imperative_rt.cpython-38-x86_64-linux-gnu.so(+0x2e5bff) [0x7fef99dc4bff]

ERROR conda.cli.main_run:execute(124): conda run python /home/kyoron/.pycharm_helpers/pydev/pydevd.py --multiprocess --qt-support=auto --client 127.0.0.1 --port 59297 --file /mnt/e/多媒体/桌面/WZ/code/CREStereo_swinT/CREStereo/nets/utils/utils.py failed. (See above for error)