import torch import argparse import os import sys # this is some massive kludge that only works if it's called from a shell and not an import/PIP package # it's smart-yet-irritating module-model loader breaks when trying to load something specifically when not from a shell sys.path.insert(0, './dlas/codes/') # this is also because DLAS is not written as a package in mind # it'll gripe when it wants to import from train.py sys.path.insert(0, './dlas/') # for PIP, replace it with: # sys.path.insert(0, os.path.dirname(os.path.realpath(dlas.__file__))) # sys.path.insert(0, f"{os.path.dirname(os.path.realpath(dlas.__file__))}/../") # don't even really bother trying to get DLAS PIP'd # without kludge, it'll have to be accessible as `codes` and not `dlas` from codes import train as tr from utils import util, options as option # this is effectively just copy pasted and cleaned up from the __main__ section of training.py # I'll clean it up better parser = argparse.ArgumentParser() parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml') parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher') args = parser.parse_args() opt = option.parse(args.opt, is_train=True) if args.launcher != 'none': # export CUDA_VISIBLE_DEVICES for running in distributed mode. if 'gpu_ids' in opt.keys(): gpu_list = ','.join(str(x) for x in opt['gpu_ids']) os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list print('export CUDA_VISIBLE_DEVICES=' + gpu_list) trainer = tr.Trainer() #### distributed training settings if args.launcher == 'none': # disabled distributed training opt['dist'] = False trainer.rank = -1 if len(opt['gpu_ids']) == 1: torch.cuda.set_device(opt['gpu_ids'][0]) print('Disabled distributed training.') else: opt['dist'] = True init_dist('nccl') trainer.world_size = torch.distributed.get_world_size() trainer.rank = torch.distributed.get_rank() torch.cuda.set_device(torch.distributed.get_rank()) trainer.init(args.opt, opt, args.launcher) trainer.do_training()