pytorch-image-models/data/__init__.py

from data.constants import *
from data.config import resolve_data_config
from data.dataset import Dataset
from data.transforms import *
from data.loader import create_loader
from data.mixup import mixup_target, FastCollateMixup
Some transform/data/loader refactoring, hopefully didn't break things * factor out data related constants to own file * move data related config helpers to own file * add a variant of RandomResizeCrop that randomizes interpolation method * remove old Numpy version of RandomErasing * cleanup torch version of RandomErasing and use it in either GPU loader batch mode or single image cpu Transform 2019-05-16 22:52:17 -07:00			`from data.constants import *`
			`from data.config import resolve_data_config`
Add some Nvidia performance enhancements (prefetch loader, fast collate), and refactor some of training and model fact/transforms 2019-03-10 14:23:16 -07:00			`from data.dataset import Dataset`
Uniform pretrained model handling. * All models have 'default_cfgs' dict * load/resume/pretrained helpers factored out * pretrained load operates on state_dict based on default_cfg * test all models in validate * schedule, optim factor factored out * test time pool wrapper applied based on default_cfg 2019-04-11 21:32:16 -07:00			`from data.transforms import *`
			`from data.loader import create_loader`
Mixup and prefetcher improvements * Do mixup in custom collate fn if prefetcher enabled, reduces performance impact * Move mixup code to own file * Add arg to disable prefetcher * Fix no cuda transfer when prefetcher off * Random erasing when prefetcher off wasn't changed to match new args, fixed * Default random erasing to off (prob = 0.) for train 2019-05-18 22:17:51 -07:00			`from data.mixup import mixup_target, FastCollateMixup`