def main(args, load_exclude_set, restoreCallback):
logging.basicConfig(\
filename=0,\
level=logging.DEBUG,\
format='%(asctime)s %(filename)s[line:%(lineno)d] %(message)s',\
datefmt='%H:%M:%S')
if args.debug:
debug()
logging.info(json.dumps(args, indent=2))
cuda_init(0, args.cuda)
volatile = Storage()
volatile.load_exclude_set = load_exclude_set
volatile.restoreCallback = restoreCallback
data_class = BERTLanguageProcessingBase.load_class('BERT' + args.dataset)
data_arg = Storage()
data_arg.file_id = args.datapath
data_arg.bert_vocab = args.bert_vocab
wordvec_class = WordVector.load_class(args.wvclass)
if wordvec_class is None:
wordvec_class = Glove
def load_dataset(data_arg, wvpath, embedding_size):
wv = wordvec_class(wvpath)
dm = data_class(**data_arg)
return dm, wv.load(embedding_size, dm.vocab_list)
if args.cache:
dm, volatile.wordvec = try_cache(
load_dataset, (data_arg, args.wvpath, args.embedding_size),
args.cache_dir, data_class.__name__ + "_" + wordvec_class.__name__)
else:
dm, volatile.wordvec = load_dataset(data_arg, args.wvpath,
args.embedding_size)
volatile.dm = dm
param = Storage()
param.args = args
param.volatile = volatile
model = Seq2seq(param)
if args.mode == "train":
model.train_process()
elif args.mode == "test":
model.test_process()
else:
raise ValueError("Unknown mode")
def run(*argv):
import argparse
import time
from utils import Storage
parser = argparse.ArgumentParser(
description=
'A seq2seq model with GRU encoder and decoder. Attention, beamsearch,\
dropout and batchnorm is supported.')
args = Storage()
parser.add_argument(
'--name',
type=str,
default=None,
help=
'The name of your model, used for tensorboard, etc. Default: runXXXXXX_XXXXXX (initialized by current time)'
)
parser.add_argument('--restore',
type=str,
default=None,
help='Checkpoints name to load. \
"NAME_last" for the last checkpoint of model named NAME. "NAME_best" means the best checkpoint. \
You can also use "last" and "best", defaultly use last model you run. \
Attention: "NAME_last" and "NAME_best" are not guaranteed to work when 2 models with same name run in the same time. \
"last" and "best" are not guaranteed to work when 2 models run in the same time.\
Default: None (don\'t load anything)')
parser.add_argument('--mode',
type=str,
default="train",
help='"train" or "test". Default: train')
parser.add_argument('--eh_size',
type=int,
default=384,
help='Size of encoder GRU')
parser.add_argument('--dh_size',
type=int,
default=200,
help='Size of decoder GRU')
parser.add_argument(
'--droprate',
type=float,
default=0,
help=
'The probability to be zerod in dropout. 0 indicates for don\'t use dropout'
)
parser.add_argument('--batchnorm',
action='store_true',
help='Use bathnorm')
parser.add_argument(
'--decode_mode',
type=str,
choices=['max', 'sample', 'gumbel', 'samplek', 'beam'],
default='beam',
help=
'The decode strategy when freerun. Choices: max, sample, gumbel(=sample), \
samplek(sample from topk), beam(beamsearch). Default: beam')
parser.add_argument(
'--top_k',
type=int,
default=10,
help='The top_k when decode_mode == "beam" or "samplek"')
parser.add_argument(
'--length_penalty',
type=float,
default=0.7,
help=
'The beamsearch penalty for short sentences. The penalty will get larger when this becomes smaller.'
)
parser.add_argument('--dataset',
type=str,
default='OpenSubtitles',
help='Dataloader class. Default: OpenSubtitles')
parser.add_argument(
'--datapath',
type=str,
default='resources://OpenSubtitles',
help='Directory for data set. Default: resources://OpenSubtitles')
parser.add_argument('--epoch',
type=int,
default=100,
help="Epoch for training. Default: 100")
parser.add_argument(
'--wvclass',
type=str,
default='Glove',
help=
"Wordvector class, none for not using pretrained wordvec. Default: Glove"
)
parser.add_argument(
'--wvpath',
type=str,
default="resources://Glove300d",
help=
"Resources of pretrained wordvector. Default: resources://Glove300d")
parser.add_argument('--bert_model',
type=str,
default="bert-base-uncased",
help="Name of bert model. Default: bert-base-uncased")
parser.add_argument('--bert_vocab',
type=str,
default="bert-base-uncased",
help="Name of bert vocab. Default: bert-base-uncased")
parser.add_argument(
'--out_dir',
type=str,
default="./output",
help='Output directory for test output. Default: ./output')
parser.add_argument(
'--log_dir',
type=str,
default="./tensorboard",
help='Log directory for tensorboard. Default: ./tensorboard')
parser.add_argument(
'--model_dir',
type=str,
default="./model",
help='Checkpoints directory for model. Default: ./model')
parser.add_argument(
'--cache_dir',
type=str,
default="./cache",
help='Checkpoints directory for cache. Default: ./cache')
parser.add_argument('--cpu', action="store_true", help='Use cpu.')
parser.add_argument('--debug',
action='store_true',
help='Enter debug mode (using ptvsd).')
parser.add_argument(
'--cache',
action='store_true',
help=
'Use cache for speeding up load data and wordvec. (It may cause problems when you switch dataset.)'
)
cargs = parser.parse_args(argv)
# Editing following arguments to bypass command line.
args.name = cargs.name or time.strftime("run%Y%m%d_%H%M%S",
time.localtime())
args.restore = cargs.restore
args.mode = cargs.mode
args.dataset = cargs.dataset
args.datapath = cargs.datapath
args.epochs = cargs.epoch
args.wvclass = cargs.wvclass
args.wvpath = cargs.wvpath
args.bert_model = cargs.bert_model
args.bert_vocab = cargs.bert_vocab
args.out_dir = cargs.out_dir
args.log_dir = cargs.log_dir
args.model_dir = cargs.model_dir
args.cache_dir = cargs.cache_dir
args.debug = cargs.debug
args.cache = cargs.cache
args.cuda = not cargs.cpu
# The following arguments are not controlled by command line.
args.restore_optimizer = True
load_exclude_set = []
restoreCallback = None
args.batch_per_epoch = 500
args.embedding_size = 300
args.eh_size = cargs.eh_size
args.dh_size = cargs.dh_size
args.decode_mode = cargs.decode_mode
args.top_k = cargs.top_k
args.length_penalty = cargs.length_penalty
args.droprate = cargs.droprate
args.batchnorm = cargs.batchnorm
args.lr = 1e-3
args.batch_size = 64
args.batch_num_per_gradient = 4
args.grad_clip = 5
args.show_sample = [0] # show which batch when evaluating at tensorboard
args.max_sent_length = 50
args.checkpoint_steps = 20
args.checkpoint_max_to_keep = 5
import random
random.seed(0)
from main import main
main(args, load_exclude_set, restoreCallback)
