def run(*argv):
import argparse
import time
from utils import Storage
parser = argparse.ArgumentParser(description='A classification model with GRU encoder and MLP for prediction. \
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=200,
help='Size of encoder GRU')
parser.add_argument('--class_num', type=int, default=5,
help='Number of classes')
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('--dataset', type=str, default='SST',
help='Dataloader class. Default: SST')
parser.add_argument('--datapath', type=str, default='resources://SST',
help='Directory for data set. Default: resources://SST')
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://Glove300d",
help="Directory 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.)')
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
# 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.class_num = cargs.class_num
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)
