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2019-11-25 17:24:05 -10:00
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2019-11-25 11:45:28 -10:00
26e3a28bee
Update train.py for distributive programming ( #655 )
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When attempting to running this function in a multi-GPU environment I kept on getting a runtime issue. I was able to solve this problem by passing this keyword. I first found the solution here:
https://github.com/pytorch/pytorch/issues/22436
and in the pytorch tutorial
'RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by (1) passing the keyword argument find_unused_parameters=True to torch.nn.parallel.DistributedDataParallel; (2) making sure all forward function outputs participate in calculating loss. If you already have done the above two steps, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's forward function. Please include the loss function and the structure of the return value of forward of your module when reporting this issue (e.g. list, dict, iterable). '
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...
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-04 14:58:18 +02:00
6cd98c46d8
updates
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-04 09:20:03 +02:00
447292eb36
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-03 16:20:55 +02:00
2877ac9286
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-02 14:28:13 +02:00
ea61b46b31
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-02 14:02:08 +02:00
8f913ba82a
updates
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-02 11:59:13 +02:00
360a32811c
weight_decay fix
2019-08-31 17:55:19 +02:00
8eb381dc88
updates
2019-08-29 15:49:51 +02:00
408baf66e2
weight_decay fix
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weight_decay fix
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weight_decay fix
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weight_decay fix
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2019-08-27 12:57:19 +02:00
798a7396f1
weight_decay fix
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weight_decay fix
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2019-08-24 21:39:25 +02:00
1064c37600
removed xy/wh loss reporting
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ca38c9050f
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2019-08-24 21:20:25 +02:00
790e25592f
removed xy/wh loss reporting
2019-08-24 20:55:01 +02:00
25a579e417
removed xy/wh loss reporting
2019-08-24 17:16:20 +02:00
195adaea7d
removed xy/wh loss reporting
2019-08-24 16:45:49 +02:00
39dcf0d561
removed xy/wh loss reporting
2019-08-24 16:43:43 +02:00
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5ff6e6b3a5
tensorboard updates
2019-08-09 19:35:02 +02:00
f9755f8dac
tensorboard updates
2019-08-09 19:29:36 +02:00
298463e530
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2019-08-09 18:22:27 +02:00
933f85f632
tensorboard updates
2019-08-09 16:37:19 +02:00
fdd5afa229
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2019-08-08 22:37:54 +02:00
22f75469ac
Tensorboard support ( #435 )
2019-08-08 22:30:34 +02:00
a21b9891b9
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2019-08-08 21:16:09 +02:00
25bc5e5392
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2019-08-08 19:49:15 +02:00
37c3e762e1
@ktian08 50.5 mAP evolved hyperparameters
2019-08-08 19:41:26 +02:00
f43170817c
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2019-08-07 16:45:13 +02:00
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2019-08-01 18:29:57 +02:00
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2019-08-01 03:31:12 +02:00
7b3d9f02ec
prevent failure when no training_results available ( #409 )
...
Use `chkpt.get('training_results')` instead of `chkpt.get('training_results')` so if the dict doesn't contain this key it won't throw a `KeyError
2019-07-31 14:12:27 +02:00
97edba0b7a
Remove Unused Argument of parser : --var ( #408 )
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Remove Unused Argument of parser : --var
2019-07-31 14:05:43 +02:00
07ffc4e7f9
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Glenn Jocher
Francisco Reveriano
Marc
idow09
Yonghye Kwon