In DenseCL dense feature's similarity matrix doesn't compute with right parameter.

Branch

1.x branch (1.x version, such as v1.0.0rc2, or dev-1.x branch)

Prerequisite

[X] I have searched Issues and Discussions but cannot get the expected help.
[X] I have read the documentation but cannot get the expected help.
[X] The bug has not been fixed in the latest version.

Environment

TorchVision : 0.6.1 +cu111 OpenCV：4.6.0 MMEngine : 0.5.0 MMCV:2.0.0rc2 MMCV Compiler : GCC 7.3 MMCV CUDA Compiler: 11.1 MMSelfSup : 1.0.0rc6+48aba52

Describe the bug

I tried to run the densecl model's pretraining. In the original paper, the author said the "num_grid" in "DenseCL_neck" could be set as "1,3,5,7". However, when I run the command: " python tools/train.py configs/selfsup/densecl/densecl_resnet50_8xb32-coslr-200e_in1k.py " And modified the densecl.py as below: " neck=dict( type='DenseCLNeck', in_channels=2048, hid_channels=2048, out_channels=128, num_grid=4), "

It raised the error that: " File "/home/ls/mmselfsupv1/mmselfsup/models/algorithms/base.py", line 127, in forward return self.loss(inputs, data_samples) File "/home/ls/mmselfsupv1/mmselfsup/models/algorithms/densecl.py", line 199, in loss densecl_sim_q = (q_grid * indexed_k_grid).sum(1) # NxS^2 RuntimeError: The size of tensor a (16) must match the size of tensor b (49) at non-singleton dimension 2 "

And then I found that in the file "mmselfsup/models/algorithms/densecl.py" , the code is: "backbone_sim_matrix = torch.matmul(q_b.permute(0,2,1), k_b)"

In my opinion, as the original paper said :"The F1 and F2 are first downsampled to have the spatial shape of S × S by an adaptive average pooling, and then used to calculate the cosine similarity matrix.", maybe it shouldn't use original q_b and k_b to calculate the "backbone_sim_matrix". It should be "downsampled to have the spatial shape of S × S".

I'm not sure my opinion is correct, if not, sorry for my wrong opinion. If the code is right, I want to know how to set the grid size to different number without raising the error mentioned above.

Thanks for your help.

Reproduces the problem - code sample

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Reproduces the problem - error message

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open-mmlab / mmselfsup