def main():
global args, logger, v_id
args = parser.parse_args()
cfg = load_config(args)
init_log('global', logging.INFO)
if args.log != "":
add_file_handler('global', args.log, logging.INFO)
logger = logging.getLogger('global')
logger.info(args)
# setup model
if args.arch == 'Custom':
from custom import Custom
model = Custom(anchors=cfg['anchors'])
else:
parser.error('invalid architecture: {}'.format(args.arch))
if args.resume:
assert isfile(args.resume), '{} is not a valid file'.format(args.resume)
model = load_pretrain(model, args.resume)
model.eval()
device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
model = model.to(device)
# setup dataset
dataset = load_dataset(args.dataset)
# VOS or VOT?
if args.dataset in ['DAVIS2016', 'DAVIS2017', 'ytb_vos'] and args.mask:
vos_enable = True # enable Mask output
else:
vos_enable = False
total_lost = 0 # VOT
iou_lists = [] # VOS
speed_list = []
for v_id, video in enumerate(dataset.keys(), start=1):
if args.video != '' and video != args.video:
continue
if vos_enable:
iou_list, speed = track_vos(model, dataset[video], cfg['hp'] if 'hp' in cfg.keys() else None,
args.mask, args.refine, args.dataset in ['DAVIS2017', 'ytb_vos'], device=device)
iou_lists.append(iou_list)
else:
lost, speed = track_vot(model, dataset[video], cfg['hp'] if 'hp' in cfg.keys() else None,
args.mask, args.refine, device=device)
total_lost += lost
speed_list.append(speed)
# report final result
if vos_enable:
for thr, iou in zip(thrs, np.mean(np.concatenate(iou_lists), axis=0)):
logger.info('Segmentation Threshold {:.2f} mIoU: {:.3f}'.format(thr, iou))
else:
logger.info('Total Lost: {:d}'.format(total_lost))
logger.info('Mean Speed: {:.2f} FPS'.format(np.mean(speed_list)))
"""
import os
#import importlib
import sys
#importlib.reload(sys)
#sys.setdefaultencoding('utf8')
if sys.version[0] == '2':
reload(sys)
sys.setdefaultencoding("utf-8")
sys.path.append('..')
sys.path.append('../utils')
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import log_helper
log_helper.init_log('brc')
import pickle
import argparse
import logging
from dataset import BRCDataset
from vocab import Vocab
from rc_model import RCModel
from model_helper_v1 import ModelHelper
import gensim
logger = logging.getLogger("brc")
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
#encoding: utf-8
import logging
from log_helper import init_log
init_log('brc')
logger = logging.getLogger('brc')
import pickle
import torch
import torch.utils.data
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import os
#logger = logging.getLogger('global')
class BRCDataLoader(torch.utils.data.DataLoader):#torch.utils.data.DataLoader这个似乎是一个经常被使用的类，有必要细看一哈
def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
num_workers=0, pin_memory=False, drop_last=False):
super(BRCDataLoader, self).__init__(dataset, batch_size, shuffle, sampler, batch_sampler,
num_workers, self._collate_fn, pin_memory, drop_last)
def _collate_fn(self, batch):
'''
Args:
batch: list of dict like
one_piece = { 'question_token_ids': sample['question_token_ids'],
'question_length': len(sample['question_token_ids']),
'start_id': sample['answer_spans'][0][0] if 'answer_spans' in sample else None,
'end_id': sample['answer_spans'][0][1] if 'answer_spans' in sample else None,
'answer_passage_id': sample['answer_passages'][0] if 'answer_passages' in sample else None,
'passage_token_ids': [[],[],[]],
'passage_length': [0,0,0]}
def main():
global args, best_acc, tb_writer, logger
args = parser.parse_args()
init_log('global', logging.INFO)
if args.log != "":
add_file_handler('global', args.log, logging.INFO)
logger = logging.getLogger('global')
logger.info("\n" + collect_env_info())
logger.info(args)
cfg = load_config(args)
logger.info("config \n{}".format(json.dumps(cfg, indent=4)))
if args.log_dir:
tb_writer = SummaryWriter(args.log_dir)
else:
tb_writer = Dummy()
# build dataset
train_loader, val_loader = build_data_loader(cfg)
if args.arch == 'Custom':
model = Custom(anchors=cfg['anchors'])
elif args.arch == 'Custom_Sky':
model = Custom_Sky(anchors=cfg['anchors'])
else:
exit()
logger.info(model)
if args.pretrained:
model = load_pretrain(model, args.pretrained)
# print(summary(model=model, input_size=(3, 511, 511), batch_size=1))
model = model.cuda()
dist_model = torch.nn.DataParallel(model,
list(range(
torch.cuda.device_count()))).cuda()
if args.resume and args.start_epoch != 0:
model.features.unfix((args.start_epoch - 1) / args.epochs)
optimizer, lr_scheduler = build_opt_lr(model, cfg, args, args.start_epoch)
# optionally resume from a checkpoint
if args.resume:
print(args.resume)
assert os.path.isfile(args.resume), '{} is not a valid file'.format(
args.resume)
model, optimizer, args.start_epoch, best_acc, arch = restore_from(
model, optimizer, args.resume)
dist_model = torch.nn.DataParallel(
model, list(range(torch.cuda.device_count()))).cuda()
logger.info(lr_scheduler)
logger.info('model prepare done')
train(train_loader, dist_model, optimizer, lr_scheduler, args.start_epoch,
cfg)
def init_logs():
log.init_log('main')
log.init_log('database')
log.init_log('logging')
log.init_log('sql')
log.init_log('gui')