Utility for syncing training, validation, and evaluation data.

Nobody has claimed this yet.

On most of the datasets I'm putting together, there is not always a 1-1 matching of masks to tiles. At the very least there should be clarification that the trainer needs a directory where all files are in sync. Even better would be to provide a simple pre-processing script for syncing the masks/tiles or in `rs_trainer` provide an option to ignore or remove un-synced masks/tiles.

Currently I just use a simple python script to sync the directory:

```python
import os
import argparse

def dir_dict(dir: str) -> dict:
dd = {}

for subdir, dirs, files in os.walk(dir):
for file in files:
f = '/'.join(os.path.join(subdir, file).split("/")[-3:])
dd[f] = os.path.join(subdir, file)

return dd

def main():
parser = argparse.ArgumentParser()
parser.add_argument('dir1', type=str)
parser.add_argument('dir2', type=str)
args = parser.parse_args()

removed = []

dir1_dict = dir_dict(args.dir1)
dir2_dict = dir_dict(args.dir2)

for k, v in dir1_dict.items():
if not dir2_dict.get(k):
removed.append(v)

for k, v in dir2_dict.items():
if not dir1_dict.get(k):
removed.append(v)

for file in removed:
os.remove(file)

return len(removed)

if __name__ == "__main__":
print ( f"removed {main()} un-synced files" )
```

Start by locating the rs_trainer data-loading entry point and inspect how training, validation, and evaluation directories are matched. Decide whether the scope is a standalone preprocessing utility, documentation, or an option to ignore or remove un-synced masks and tiles; done means the chosen behavior is defined and verified for mismatched directories.

Written by the indexing model from the issue text.