def run(argv):
import argparse
import time
from utils import Storage
parser = argparse.ArgumentParser(description='A CVAE model')
args = Storage()
parser.add_argument(
'--name',
type=str,
default='CVAE',
help=
'The name of your model, used for variable scope and tensorboard, etc. Default: runXXXXXX_XXXXXX (initialized by current time)'
)
parser.add_argument(
'--restore',
type=str,
default='last',
help=
'Checkpoints name to load. "last" for last checkpoints, "best" for best checkpoints on dev. Attention: "last" and "best" wiil cause unexpected behaviour when run 2 models in the same dir at 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('--dataset',
type=str,
default='SwitchboardCorpus',
help='Dataloader class. Default: SwitchboardCorpus')
parser.add_argument(
'--datapath',
type=str,
default='resources://SwitchboardCorpus',
help='Directory for data set. Default: SwitchboardCorpus')
parser.add_argument('--epoch',
type=int,
default=100,
help="Epoch for trainning. 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://Glove200d",
help=
"Directory for pretrained wordvector. Default: resources://Glove200d")
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.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
args.softmax_samples = 512
args.use_hcf = True
args.full_kl_step = 10000
args.latent_size = 200
args.topic_embedding_size = 30
args.da_embedding_size = 30
args.word_embedding_size = 200
args.session_window = 10
args.repeat_N = 2
args.eh_size = 300
args.ch_size = 600
args.dh_size = 400
args.lr = 1e-3
args.lr_decay = 0.995
args.batch_size = 3
args.grad_clip = 5.0
args.show_sample = [0]
args.min_vocab_times = 5
args.max_sen_length = 50
args.max_turn_length = 1000
args.checkpoint_steps = 1
args.checkpoint_max_to_keep = 5
import random
random.seed(0)
from main import main
main(args)
if args.mode == 'test':
import os
import json
res = {'working_dir': './', 'entry': 'run', 'args': argv}
if os.path.exists("./result.json"):
res.update(json.load(open("./result.json")))
json.dump(res, open("result.json", "w"))
def run():
import argparse
import time
from utils import Storage
parser = argparse.ArgumentParser(description='A hred model')
args = Storage()
parser.add_argument(
'--name',
type=str,
default='hred',
help=
'The name of your model, used for variable scope and tensorboard, etc. Default: runXXXXXX_XXXXXX (initialized by current time)'
)
parser.add_argument(
'--restore',
type=str,
default='last',
help=
'Checkpoints name to load. "last" for last checkpoints, "best" for best checkpoints on dev. Attention: "last" and "best" wiil cause unexpected behaviour when run 2 models in the same dir at 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('--dataset',
type=str,
default='UbuntuCorpus',
help='Dataloader class. Default: UbuntuCorpus')
parser.add_argument('--datapath',
type=str,
default='./data',
help='Directory for data set. Default: ./data')
parser.add_argument('--epoch',
type=int,
default=100,
help="Epoch for trainning. Default: 100")
parser.add_argument(
'--wvclass',
type=str,
default=None,
help=
"Wordvector class, none for not using pretrained wordvec. Default: None"
)
parser.add_argument(
'--wvpath',
type=str,
default=None,
help="Directory for pretrained wordvector. Default: None")
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()
# 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.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
args.softmax_samples = 512
args.embedding_size = 300
args.eh_size = 200
args.ch_size = 200
args.dh_size = 200
args.lr = 1e-3
args.lr_decay = 0.995
args.batch_size = 128
args.grad_clip = 5.0
args.show_sample = [0]
args.min_vocab_times = 50
args.max_sen_length = 50
args.max_turn_length = 11
args.checkpoint_steps = 1000
args.checkpoint_max_to_keep = 5
import random
random.seed(0)
from main import main
main(args)
def run(*argv):
parser = argparse.ArgumentParser(description='A hred model')
args = Storage()
parser.add_argument(
'--name',
type=str,
default='hred',
help=
'The name of your model, used for variable scope and tensorboard, etc. Default: runXXXXXX_XXXXXX (initialized by current time)'
)
parser.add_argument(
'--restore',
type=str,
default='best',
help=
'Checkpoints name to load. "last" for last checkpoints, "best" for best checkpoints on dev. Attention: "last" and "best" wiil cause unexpected behaviour when run 2 models in the same dir at 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('--dataset',
type=str,
default='MyHRED',
help='Dataloader class. Default: UbuntuCorpus')
parser.add_argument('--datapath',
type=str,
default='../data/film',
help='Directory for data set. Default: UbuntuCorpus')
parser.add_argument('--epoch',
type=int,
default=20,
help="Epoch for trainning. Default: 100")
parser.add_argument('--batch_size',
type=int,
default=32,
help="The batch size of data when train or test.")
parser.add_argument('--max_sent_length',
type=int,
default=512,
help="The max encoded sent length when train.")
parser.add_argument('--max_decoder_length',
type=int,
default=50,
help="The max decoded sent length when inference.")
parser.add_argument('--num_turns',
type=int,
default=8,
help="The max number of turns of the post field.")
parser.add_argument(
'--wv_class',
type=str,
default='TencentChinese',
help=
"Wordvector class, none for not using pretrained wordvec. Default: Glove"
)
parser.add_argument(
'--wv_path',
type=str,
default='wordvector/chinese',
help=
"Directory for pretrained wordvector. Default: resources://Glove300d")
parser.add_argument(
'--output_dir',
type=str,
default="./output/film",
help='Output directory for test output. Default: ./output')
parser.add_argument(
'--log_dir',
type=str,
default="./tensorboard/film",
help='Log directory for tensorboard. Default: ./tensorboard')
parser.add_argument(
'--model_dir',
type=str,
default="./model/film",
help='Checkpoints directory for model. Default: ./model')
parser.add_argument(
'--cache_dir',
type=str,
default="./cache/film",
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.)'
)
parser.add_argument('--seed',
type=int,
default=42,
help="The random seed in the train process.")
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.batch_size = cargs.batch_size
args.wv_class = cargs.wv_class
args.wv_path = cargs.wv_path
args.output_dir = cargs.output_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
args.seed = cargs.seed
args.max_sent_length = cargs.max_sent_length
args.max_decoder_length = cargs.max_decoder_length
args.num_turns = cargs.num_turns
args.softmax_samples = 512
args.embedding_size = 200
args.eh_size = 200
args.ch_size = 200
args.dh_size = 200
args.lr = 1e-3
args.lr_decay = 0.99
args.grad_clip = 5.0
args.show_sample = [0]
args.checkpoint_steps = 100
args.checkpoint_max_to_keep = 5
random.seed(args.seed)
main(args)
args.datapath = cargs.datapath
args.epochs = cargs.epoch
args.wvclass = cargs.wvclass
args.wvpath = cargs.wvpath
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
args.softmax_samples = 512
args.embedding_size = 300
args.eh_size = 200
args.ch_size = 200
args.dh_size = 200
args.lr = 1e-3
args.lr_decay = 0.995
args.batch_size = 128
args.grad_clip = 5.0
args.show_sample = [0]
args.min_vocab_times = 50
args.max_sen_length = 50
args.max_turn_length = 11
args.checkpoint_steps = 1000
args.checkpoint_max_to_keep = 5
import random
random.seed(0)