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jblitzar
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github-python

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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)