Errors when using generate label

[sunwell1994]

Dear author, I have several questions on generating labels

1. in ./mobile_crnn/generat_label.py,

• should there be the ident for the for loop?
• should we use the Spec.h5 instead of Audio.h5
• why is the aggregation process different generate_labelv.py?

2. for generate_labelv.py

• which resnet to use, the one in ./resnet or pretrained resnet from torch
• why set top k to be 0?

Thanks for your answer. It is not trivial to run the files directly. It will be best if the labels are provided. Thanks.

[zjsong]

Hi @shvdiwnkozbw , thanks for sharing the code.

In the following command, do we need to insert keyword largest=False into torch.topk? Because the default setting is largest=True...

Multi-Source-Sound-Localization/generate_labelv.py

Line 16 in 8fa5856

prob[torch.topk(prob, dim=1, k=990)] = 0

[shvdiwnkozbw]

Dear author, I have several questions on generating labels

1. in ./mobile_crnn/generat_label.py,

• should there be the ident for the for loop?
• should we use the Spec.h5 instead of Audio.h5
• why is the aggregation process different generate_labelv.py?

1. for generate_labelv.py

• which resnet to use, the one in ./resnet or pretrained resnet from torch
• why set top k to be 0?

Thanks for your answer. It is not trivial to run the files directly. It will be best if the labels are provided. Thanks.

Thanks for pointing out, I made some mistakes when reimplementing this code for label generation.
Yes, there should be indent, and use spec.h5 for label generation.
When generate label_v, we use imagenet-pretrained resnet-50 in torchvision for label prediction. In order to align the audio and visual categories, we need to aggregate the category predictions from 1000 imagenet categories into 7 general classes in audioset.

[shvdiwnkozbw]

Hi @shvdiwnkozbw , thanks for sharing the code.

In the following command, do we need to insert keyword largest=False into torch.topk? Because the default setting is largest=True...

Multi-Source-Sound-Localization/generate_labelv.py

Line 16 in 8fa5856

prob[torch.topk(prob, dim=1, k=990)] = 0

Yes, it is mistake in reimplementing this script, it should be largest=False. Thanks!