def run(*argv):
import argparse
import time
from utils import Storage
parser = argparse.ArgumentParser(
description='A language model with GRU. 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", by default 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('--dh_size',
type=int,
default=200,
help='Size of decoder GRU')
parser.add_argument(
'--droprate',
type=float,
default=0,
help=
'The probability to be zeroed in dropout. 0 indicates for don\'t use dropout'
)
parser.add_argument(
'--decode_mode',
type=str,
choices=['max', 'sample', 'gumbel', 'samplek', 'beam'],
default='samplek',
help=
'The decode strategy when freerun. Choices: max, sample, gumbel(=sample), \
samplek(sample from topk), beam(beamsearch). Default: samplek')
parser.add_argument('--batchnorm',
action='store_true',
help='Use bathnorm')
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('--temperature',
type=float,
default=1,
help='Temperature. Default: 1')
parser.add_argument(
'--dataid',
type=str,
default='resources://MSCOCO',
help='Resources/path for data set. Default: resources://MSCOCO')
parser.add_argument('--epoch',
type=int,
default=100,
help="Epoch for training. Default: 100")
parser.add_argument('--batch_per_epoch',
type=int,
default=500,
help="Batches per epoch. Default: 1500")
parser.add_argument(
'--wvid',
type=str,
default="resources://Glove300d",
help=
"Resources/path for pretrained wordvector. Default: resources://Glove300d"
)
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.)'
)
parser.add_argument('--seed',
type=int,
default=0,
help='Specify random seed. Default: 0')
parser.add_argument('--lr',
type=float,
default=1e-3,
help='Learning rate. Default: 0.001')
cargs = parser.parse_args(argv)
# general setting
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.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
## dataset settings
args.dataid = cargs.dataid
args.tokenizer = "space"
args.max_sent_length = 50
args.convert_to_lower_letter = False
args.min_frequent_vocab_times = 10
args.min_rare_vocab_times = 0
args.wvid = cargs.wvid
## training settings
args.epochs = cargs.epoch
args.lr = cargs.lr
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.checkpoint_steps = 20
args.checkpoint_max_to_keep = 5
## arguments for restoring checkpoints
args.restore_optimizer = True
load_exclude_set = []
restoreCallback = None
## architecture settings
args.batch_per_epoch = cargs.batch_per_epoch
args.embedding_size = 300
args.dh_size = cargs.dh_size
args.droprate = cargs.droprate
args.batchnorm = cargs.batchnorm
## decoding settings
args.decode_mode = cargs.decode_mode
args.top_k = cargs.top_k
args.length_penalty = cargs.length_penalty
args.temperature = cargs.temperature
## random seed
args.seed = cargs.seed
import random
random.seed(cargs.seed)
import torch
torch.manual_seed(cargs.seed)
import numpy as np
np.random.seed(cargs.seed)
from main import main
main(args, load_exclude_set, restoreCallback)
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)
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. It can train using RAML, Scheduled Sampling or Policy Gradient algorithms.')
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('--model', type=str, default="basic",choices=["basic","raml","scheduled-sampling","policy-gradient"],
help='The type of algorithm. Choices: basic, raml, schedule-sampling, policy-gradient. Default: basic Seq2seq')
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", by default use last model you run. \
It can also be an url started with "http". \
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('--lr', type=float, default=1e-3,
help='Learning rate. Default: 0.001')
parser.add_argument('--eh_size', type=int, default=200,
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('--dataid', type=str, default='resources://OpenSubtitles#OpenSubtitles',
help='Resource id for data set. It can be a resource name or a local path. Default: resources://OpenSubtitles#OpenSubtitles')
parser.add_argument('--epoch', type=int, default=100,
help="Epoch for training. Default: 100")
parser.add_argument('--batch_per_epoch', type=int, default=1500,
help="Batches per epoch. Default: 1500")
parser.add_argument('--wvclass', type=str, default='Glove',
help="Wordvector class, none for not using pretrained wordvec. Default: Glove")
parser.add_argument('--wvid', type=str, default="resources://Glove300d",
help="Resource id for pretrained wordvector. Default: resources://Glove300d")
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('--device', type=int, default=0,
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=0,
help='Specify random seed. Default: 0')
# RAML parameters
parser.add_argument('--raml_file', type=str, default='samples_iwslt14.txt',
help='the samples and rewards described in RAML')
parser.add_argument('--n_samples', type=int, default=10,
help='number of samples for every target sentence')
parser.add_argument('--tau', type=float, default=0.4,
help='the temperature in RAML algorithm')
# Scheduled sampling parameters
parser.add_argument('--decay_factor', type=float, default=500.,
help='The hyperparameter controling the speed of increasing '
'the probability of sampling from model. Default: 500.')
# Policy Gradient parameters
parser.add_argument('--epoch_teacherForcing', type=int, default=10,
help='How long to run teacherForcing before running policy gradient. Default: 10')
parser.add_argument('--nb_sample_training', type=int, default=20,
help='How many samples we take for each batch during policy gradient. Default: 20')
parser.add_argument('--policy_gradient_reward_mode', type=str, default='mean',
help='How the policy gradient is applied. Default: mean')
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.model = cargs.model
args.restore = cargs.restore
args.mode = cargs.mode
args.dataset = cargs.dataset
args.datapath = cargs.dataid
args.epochs = cargs.epoch
args.wvclass = cargs.wvclass
args.wvpath = cargs.wvid
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.device = cargs.device
# RAML parameters
args.raml_file = cargs.raml_file
args.n_samples = cargs.n_samples
args.tau = cargs.tau
# Scheduled sampling parameters
args.decay_factor = cargs.decay_factor
# Policy Gradient parameters
args.epoch_teacherForcing = cargs.epoch_teacherForcing # How long to run teacherForcing before running policy gradient
args.nb_sample_training = cargs.nb_sample_training # How many samples we take for each batch during policy gradient
args.policy_gradient_reward_mode = cargs.policy_gradient_reward_mode # How many samples we take for each batch during policy gradient
# The following arguments are not controlled by command line.
args.restore_optimizer = True
load_exclude_set = []
restoreCallback = None
args.batch_per_epoch = cargs.batch_per_epoch
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 = cargs.lr
args.batch_size = 3*args.n_samples if args.model=="raml" else 32
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
args.seed = cargs.seed
import random
random.seed(cargs.seed)
import torch
torch.manual_seed(cargs.seed)
import numpy as np
np.random.seed(cargs.seed)
from main import main
main(args, load_exclude_set, restoreCallback)
