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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
"
May I get some help about why it couldn't set as the paper said? Thanks you for your help.
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-
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
"
May I get some help about why it couldn't set as the paper said? Thanks you for your help.
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