text stringlengths 1 93.6k |
|---|
help="Total number of training updates to perform.")
|
parser.add_argument("--optim", default='adamw',
|
choices=['adam', 'adamax', 'adamw'],
|
help="optimizer")
|
parser.add_argument("--betas", default=[0.9, 0.98], nargs='+',
|
help="beta for adam optimizer")
|
parser.add_argument("--dropout", default=0.1, type=float,
|
help="tune dropout regularization")
|
parser.add_argument("--weight_decay", default=0.01, type=float,
|
help="weight decay (L2) regularization")
|
parser.add_argument("--grad_norm", default=2.0, type=float,
|
help="gradient clipping (-1 for no clipping)")
|
parser.add_argument("--warmup_steps", default=10000, type=int,
|
help="Number of training steps to perform linear "
|
"learning rate warmup for.")
|
# device parameters
|
parser.add_argument('--seed', type=int, default=42,
|
help="random seed for initialization")
|
parser.add_argument('--fp16', action='store_true',
|
help="Whether to use 16-bit float precision instead "
|
"of 32-bit")
|
parser.add_argument('--n_workers', type=int, default=4,
|
help="number of data workers")
|
parser.add_argument('--pin_mem', action='store_true', help="pin memory")
|
# can use config files
|
parser.add_argument('--config', required=True, help='JSON config files')
|
args = parse_with_config(parser)
|
if exists(args.output_dir) and os.listdir(args.output_dir):
|
raise ValueError("Output directory ({}) already exists and is not "
|
"empty.".format(args.output_dir))
|
# options safe guard
|
if args.conf_th == -1:
|
assert args.max_bb + args.max_txt_len + 2 <= 512
|
else:
|
assert args.num_bb + args.max_txt_len + 2 <= 512
|
main(args)
|
# <FILESEP>
|
from __future__ import print_function
|
import yaml
|
import easydict
|
import os
|
import numpy as np
|
import torch.optim as optim
|
import torch.nn.functional as F
|
from torch.autograd import Variable
|
import torchvision.transforms as transforms
|
from apex import amp, optimizers
|
from data_loader.get_loader import get_loader
|
from utils.utils import *
|
from utils.lr_schedule import inv_lr_scheduler
|
from utils.loss import *
|
from models.LinearAverage import LinearAverage
|
from eval import test
|
# Training settings
|
import argparse
|
parser = argparse.ArgumentParser(description='Pytorch DA',
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
parser.add_argument('--config', type=str, default='config.yaml', help='/path/to/config/file')
|
parser.add_argument('--source_path', type=str, default='./utils/source_list.txt', metavar='B',
|
help='path to source list')
|
parser.add_argument('--target_path', type=str, default='./utils/target_list.txt', metavar='B',
|
help='path to target list')
|
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
|
help='how many batches to wait before logging training status')
|
parser.add_argument('--exp_name', type=str, default='office_close', help='/path/to/config/file')
|
parser.add_argument("--gpu_devices", type=int, nargs='+', default=None, help="")
|
# args = parser.parse_args()
|
args = parser.parse_args()
|
config_file = args.config
|
conf = yaml.load(open(config_file))
|
save_config = yaml.load(open(config_file))
|
conf = easydict.EasyDict(conf)
|
gpu_devices = ','.join([str(id) for id in args.gpu_devices])
|
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_devices
|
args.cuda = torch.cuda.is_available()
|
source_data = args.source_path
|
target_data = args.target_path
|
evaluation_data = args.target_path
|
batch_size = conf.data.dataloader.batch_size
|
filename = source_data.split("_")[1] + "2" + target_data.split("_")[1]
|
filename = os.path.join("record", args.exp_name,
|
config_file.replace(".yaml", ""), filename)
|
if not os.path.exists(os.path.dirname(filename)):
|
os.makedirs(os.path.dirname(filename))
|
print("record in %s " % filename)
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.