From 098fdbdb17c993bee5fccd172a158f8f4e3feeb1 Mon Sep 17 00:00:00 2001 From: bkerbl Date: Sun, 16 Jul 2023 15:49:06 +0200 Subject: [PATCH] Added LR example --- README.md | 4 ++++ assets/better.png | Bin 0 -> 621572 bytes assets/worse.png | Bin 0 -> 349734 bytes 3 files changed, 4 insertions(+) create mode 100644 assets/better.png create mode 100644 assets/worse.png diff --git a/README.md b/README.md index 8c58ab4..e79683a 100644 --- a/README.md +++ b/README.md @@ -468,6 +468,10 @@ python convert.py -s --skip_matching [--resize] #If not resizing, Ima - *How can I use this for a much larger dataset, like a city district?* The current method was not designed for these, but given enough memory, it should work out. However, the approach can struggle in multi-scale detail scenes (extreme close-ups, mixed with far-away shots). This is usually the case in, e.g., driving data sets (cars close up, buildings far away). For such scenes, you will want to lower the ```--position_lr_init/final``` and ```--scaling_lr``` (x0.3, x0.1, ...). +Default Learning Rates | Using ```--position_lr_init 0.000016 --scaling_lr 0.001``` +:--- | ---: +![Default learning rate result](assets/worse.png "title-1") | ![Reduced learning rate result](assets/better.png "title-2") + - *I don't have 24 GB of VRAM for training, what do I do?* The VRAM consumption is determined by the number of points that are being optimized, which increases over time. If you only want to train to 7k iterations, you will need significantly less. To do the full training routine and avoid running out of memory, you can increase the ```--densify_grad_threshold```, ```--densification_interval``` or reduce the value of ```--densify_until_iter```. Note however that this will affect the quality of the result. Also try setting ```--test_iterations``` to ```-1``` to avoid memory spikes during testing. If ```--densify_grad_threshold``` is very high, no densification should occur and training should complete if the scene itself loads successfully. - *24 GB of VRAM for reference quality training is still a lot! Can't we do it with less?* Yes, most likely. By our calculations it should be possible with **way** less memory (~8GB). If we can find the time we will try to achieve this. If some PyTorch veteran out there wants to tackle this, we look forward to your pull request! diff --git a/assets/better.png b/assets/better.png new file mode 100644 index 0000000000000000000000000000000000000000..019c143829caa4e182c055307dd43c013329f88c GIT binary patch literal 621572 zcmZ7d1yGyM*FFwY3KVxMQY>gG6!%b^;845;ibHXCDDD=56bn$KcyWgomjVTf2DcOf z1b2D!yfgoq@8`FfOlHn(b~k5Fj$G%uW3@Dt2ykg|(a_KcK+5tuXlT!O(9qBsu(41Q z%B~<8G_+?C_HuGsAUQckS9cd1dq-+7uR4L`YcXR`0?@O59;Cd6gVH@Ug$Ha-M zP{e;KY@{&udfRr6mn{h3NDAIy}At!=vqQ!$^(`b!Yy1_rX=sAWvON4ElTs1d2QCkx{o%t5GD{?Kj@uD z1f+8W4XP|T|p}-}KmY3+GY3ANE?@5t&ek#>{^y%wO)e`L@pR z;8R<;(TG-TSu9Ijg?4v}m(fP&fKl~dLqz_4^LX#y;vDjXXDhtT4}7>7-k~-t%{|FZ z9e(K7(9qY_hqc0A7B#04PH)^X&Yhl9%{!^DKIifI@{8SsAE0zPLyn8N{*chk`2v3# zz01Mbr8+1Vi1u>-^9`1$D8^tMr0eM9D1T!0QEFx%`67?fy>)vmpbvE|SQe^E@@TB6 z7aQFk%>zn;-iqmR9(9itLRUCSVpkO~cr5e+0SqwBkL&;?4i2KndTEVxEVv5ceC3w0N+W00-jt;+%nU=rB_OtY7f{$n+_?8tKykqkp{T0xh*q;`r-L>-zy(ep 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