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I wanna share the cause of memory increase in train.py code (however, currently this code is already deleted). The root cause is in this code part:
Putting tf.assign operation inside the training loop will create new additional graph repeatedly. So, I change those three tf.assign by using placeholder, and do feed_dict to them using rec, prec and mAP. The training time is also faster afterward.
Hi,
I try to train, and the used RAM memory gradually increases until it kills the process due to out of RAM memory. I test it tf 1.12 and 1.10. Thanks.
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