Datasets:
jblitzar
/
github-python-corpus

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cfg = get_cfg()
# for poly lr schedule
add_deeplab_config(cfg)
add_mask_former_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
# Setup logger for "mask_former" module
if not args.eval_only:
setup_wandb(cfg, args)
setup_logger(
output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="mask_former"
)
return cfg
def main(args):
cfg = setup(args)
if args.eval_only:
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume
)
if model._region_clip_adapter is not None:
model._region_clip_adapter.load_state_dict(model.clip_adapter.state_dict())
if cfg.TEST.AUG.ENABLED:
res = Trainer.test_with_TTA(cfg, model)
else:
res = Trainer.test(cfg, model)
if comm.is_main_process():
verify_results(cfg, res)
return res
trainer = Trainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
if __name__ == "__main__":
args = default_argument_parser().parse_args()
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args,),
)
# <FILESEP>
import torch
torch.backends.cudnn.benchmark = True
import torch.optim as optim
import os
from dataset.imgdata import getloader
from utils.model_utils import get_arch
from utils.common import print_network
from torch.optim.lr_scheduler import MultiStepLR
import time
import argparse
from loss import SSIM
parser = argparse.ArgumentParser()
parser.add_argument('--arch', type=str, default="IDT")
parser.add_argument('--in_chans', type=int, default=3)
parser.add_argument('--embed_dim', type=int, default=32)
parser.add_argument('--win_size', type=int, default=8)
parser.add_argument('--depths', type=int, nargs='+', default=[3, 3, 2, 2, 1, 1, 2, 2, 3])
parser.add_argument('--num_heads', type=int, nargs='+', default=[1, 2, 4, 8, 16, 16, 8, 4, 2])
parser.add_argument('--mlp_ratio', type=float, default=4.0)
parser.add_argument('--qkv_bias', type=bool, default=True)
parser.add_argument('--downtype', type=str, default='Downsample', help="Downsample|Shufflesample")
parser.add_argument('--uptype', type=str, default='Upsample', help="Upsample|Unshufflesample")
parser.add_argument('--batch_size', type=int, default=4)
parser.add_argument('--shuffle', action='store_true', help='shuffle for dataloader')
parser.add_argument('--crop_size', type=int, default=128, help='crop size for network')
parser.add_argument('--num_workers', type=int, default=1)
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. -1 for CPU')
parser.add_argument('--nepochs', type=int, default=400)
parser.add_argument('--lr', type=float, default=1e-4, help='initial learning rate')
parser.add_argument('--channel', type=int, default=32)
parser.add_argument('--data_path', type=str, default="E:\\rain_data_train_Heavy")
parser.add_argument('--save_path', type=str, default="E:\\transformer_model")
parser.add_argument('--save_freq', type=int, default=10)
parser.add_argument('--dis_freq', type=int, default=40)
parser.add_argument('--milestone', type=int, nargs='+', default=[100, 250, 350], help="When to decay learning rate")
parser.add_argument('--embed', type=int, default=32)
opt = parser.parse_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_ids
if __name__ == '__main__':
savepath = opt.save_path
if not os.path.exists(savepath):
os.makedirs(savepath)