def run_default_validation_server(datadir='../dataset', prefix='val_'):
'''
run a validation server with mostly default values.
'''
try:
datapath = glob.glob(os.path.join(datadir, 'camera', prefix) + "*")
if len(datapath) == 0:
print 'no files found to validate with'
else:
print 'validating with', len(datapath), 'files'
gen = datagen(datapath,
time_len=1,
batch_size=256,
ignore_goods=False,
show_time=False)
start_server(gen, port=5556, hwm=20)
except KeyboardInterrupt:
pass
'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-02-02--10-16-58.h5'
% (str_data),
'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-02-08--14-56-28.h5'
% (str_data),
'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-02-11--21-32-47.h5'
% (str_data),
'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-03-29--10-50-20.h5'
% (str_data),
'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-04-21--14-48-08.h5'
% (str_data),
'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-05-12--22-20-00.h5'
% (str_data),
]
# 2 for validation
validation_path = [
'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-06-02--21-39-29.h5'
% (str_data),
'D:/DR/Dataset/comma-dataset/comma_dataset/%s/2016-06-08--11-46-01.h5'
% (str_data)
]
if args.validation: datapath = validation_path
else: datapath = train_path
gen = datagen(datapath,
time_len=args.time,
batch_size=args.batch,
ignore_goods=args.nogood)
start_server(gen, port=args.port, hwm=args.buffer)
help='Serve validation dataset instead.')
parser.add_argument('--small',
dest='small',
action='store_true',
default=False)
args, more = parser.parse_known_args()
if config.UseFeat: str_data = "feat"
else: str_data = "cam"
if args.small: # 10% of dataset
if args.validation:
filenames = os.path.join(config.h5path, 'val_small.txt')
else:
filenames = os.path.join(config.h5path, 'train_small.txt')
else:
if args.validation: filenames = os.path.join(config.h5path, 'val.txt')
else: filenames = os.path.join(config.h5path, 'train.txt')
with open(filenames, 'r') as f:
file_paths = [
'%s%s/%s.h5' % (config.h5path, str_data, x.strip())
for x in f.readlines()
]
gen = datagen(file_paths,
time_len=config.timelen,
batch_size=config.batch_size,
ignore_goods=args.nogood)
start_server(gen, port=args.port, hwm=args.buffer)
# 9 for training
train_path = [
'./extract-bagfiles/retas_1.h5',
'./extract-bagfiles/retas_2.h5',
'./extract-bagfiles/curvas_suaves_1.h5',
'./extract-bagfiles/curvas_suaves_2.h5',
'./extract-bagfiles/curvas_em_T_1.h5',
]
# 2 for validation
validation_path = [
'./extract-bagfiles/retas_1.h5',
'./extract-bagfiles/retas_2.h5',
'./extract-bagfiles/curvas_suaves_1.h5',
'./extract-bagfiles/curvas_suaves_2.h5',
'./extract-bagfiles/curvas_em_T_1.h5',
]
if args.validation:
datapath = validation_path
else:
datapath = train_path
gen = datagen(datapath,
time_len=args.time,
batch_size=args.batch,
ignore_goods=args.nogood,
data_set=args.DataTrainAndTest)
start_server(gen, port=args.port, hwm=args.buffer)
# 2 for validation
validation_path = [
'./camera/2016-06-02--21-39-29.h5', './camera/2016-06-08--11-46-01.h5'
]
# 2 for test
test_path = [
'./camera/2016-01-30--13-46-00.h5',
'./camera/2016-05-12--22-20-00.h5',
]
datapath = test_path
time_length = 30
pack_size = 256 # 每个tfrecords文件中有256个视频序列
gen = datagen(datapath,
time_len=time_length,
batch_size=pack_size,
ignore_goods=False)
data = next(gen)
dataset_num = data[3]
count = 0
while count * 30 < dataset_num / 25: #dataset_num :
try:
data = next(gen) # 取出256*30张图片
starts = count
ends = starts + pack_size - 1
count = count + pack_size
tfrecords_filename = '../video_prediction/data/comma/test/traj_%d_to_%d.tfrecords' % (
starts, ends)
writer = python_io.TFRecordWriter(
parser.add_argument('--validation', dest='validation', action='store_true', default=False, help='Serve validation dataset instead.')
args, more = parser.parse_known_args()
# 9 for training
train_path = [
'./dataset/camera/2016-01-30--11-24-51.h5',
'./dataset/camera/2016-01-30--13-46-00.h5',
'./dataset/camera/2016-01-31--19-19-25.h5',
'./dataset/camera/2016-02-02--10-16-58.h5',
'./dataset/camera/2016-02-08--14-56-28.h5',
'./dataset/camera/2016-02-11--21-32-47.h5',
'./dataset/camera/2016-03-29--10-50-20.h5',
'./dataset/camera/2016-04-21--14-48-08.h5',
'./dataset/camera/2016-05-12--22-20-00.h5',
]
# 2 for validation
validation_path = [
'./dataset/camera/2016-06-02--21-39-29.h5',
'./dataset/camera/2016-06-08--11-46-01.h5'
]
if args.validation:
datapath = validation_path
else:
datapath = train_path
gen = datagen(datapath, time_len=args.time, batch_size=args.batch, ignore_goods=args.nogood)
start_server(gen, port=args.port, hwm=args.buffer)
