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Universal Representation Learning from Multiple Domains for Few-shot Classification - ICCV 2021, Cross-domain Few-shot Learning with Task-specific Adapters - CVPR 2022
MIT License
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关于使用episodic training #7

Closed koko6677 closed 2 years ago

koko6677 commented 2 years ago

您好!很抱歉再次来打扰您,希望能得到您的一些帮助或者建议。这几天我使用您以及您上次提到的Cnaps作者发布的代码进行episodic training,采取meta-learner是protonet。但是我在使用时都发现一个问题,就是似乎训练的时间代价很大。当我使用一张RTX3090进行训练时,仅在ImageNet上进行episodic training,每进行500次迭代的时间大约是30分钟。我使用的tensorflow版本是1.15,训练时有一些警告,我列举了一部分,如下所示: OMP: Info #171: KMP_AFFINITY: 05 proc 77 maps to package 1 core 5 thread 1 OMP: Info #171: KMP_AFFINITY: os proc 30 maps to package 1 core 8 thread 0 OMP: Info #171: KMP_AFFINITY: 0S proc 78 maps to package 1 core 8 thread 1 OMP: Info #171: KMP_AFFINITY: oS proc 31 maps to package 1 core 9 thread 0 OMP: Info #171: KMP_AFFINITY: 0s proc 79 maps to package 1 core 9 thread 1 OMP: Info #171: KMP_AFFINITY: os proc 32 naps to package 1 core 10 thread 0 OMP: Info #171: KMP_AFFINITY: os proc 80 naps to package 1 core 10 thread 1 OMP: Info #171: KNP_AFFINITY: os proc 33 naps to package 1 core 11 thread 0 OMP: Info #171: KMP_AFFINITY: 0S proc 81 maps to package 1 core 11 thread 1 OMP: Info #250: KNP_AFFINITY: pid 137352 tid 137558 thread 1 bound to oS proc set 1 OMP: Info #250:KNP_AFFINITY: pid 137352 tid 137561 thread 2 bound to oS proc set 2 ONP: Info #250:KNP_AFFINITY: pid 137352 tid 137565 thread 3 bound to os proc set 3 如果您也遇到过类似的情况,是否能给我一些建议呢?感谢您!