| # This is the CMakeCache file. | |
| # For build in directory: /home/shauren/projects/TrinityCore/build | |
| # It was generated by CMake: /usr/bin/cmake | |
| # You can edit this file to change values found and used by cmake. | |
| # If you do not want to change any of the values, simply exit the editor. | |
| # If you do want to change a value, simply edit, save, and exit the editor. | |
| # The syntax for the file is as follows: | |
| # KEY:TYPE=VALUE | |
| # KEY is the name of a variable in the cache. | |
| # TYPE is a hint to GUIs for the type of VALUE, DO NOT EDIT TYPE!. |
| """ | |
| PyTorch has pack_padded_sequence this doesn’t work with dense layers. For sequence data with high variance in its length | |
| the best way to minimize padding and masking within a batch is by feeding in data that is already grouped by sequence length | |
| (while still shuffling it somewhat). Here is my current solution in numpy. | |
| I will need to convert every function over to torch to allow it to run on the GPU and am sure there are many other | |
| ways to optimize it further. Hope this helps others and that maybe it can become a new PyTorch Batch Sampler someday. | |
| General approach to how it works: | |
| Decide what your bucket boundaries for the data are. |
