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main.py
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main.py
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#!/usr/bin/env python3
'''
Main file
Author: Oyesh Mann Singh
How to run:
python main.py -k 1 -d cpu
'''
import os
import argparse
import shutil
import warnings
from utils.dataloader import Dataloader
import utils.utilities as utilities
import utils.splitter as splitter
from tqdm import tqdm
from config.config import Configuration
from models.models import LSTMTagger, CharLSTMTagger
from train import Trainer
tqdm.pandas(desc='Progress')
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.simplefilter(action='ignore', category=UserWarning)
def parse_args():
"""
Argument Parser
"""
parser = argparse.ArgumentParser(description="NER Main Parser")
parser.add_argument("-c", "--config", dest="config_file", type=str, metavar="PATH", default="./config/config.ini",
help="Configuration file path")
parser.add_argument("-l", "--log_dir", dest="log_dir", type=str, metavar="PATH", default="./logs",
help="Log file path")
parser.add_argument("-d", "--device", dest="device", type=str, default="cuda:3",
help="device[‘cpu’,‘cuda:0’,‘cuda:1’,..]")
parser.add_argument("-v", "--verbose", action='store_true', default=False, help="Print data description")
parser.add_argument("-e", "--eval", action='store_true', default=False, help="For evaluation purpose only")
parser.add_argument("-p", "--pos", action='store_true', default=False, help="Use POS one-hot-encoding")
parser.add_argument("-r", "--char", action='store_true', default=False, help="Use character-level CNN")
parser.add_argument("-g", "--grapheme", action='store_true', default=False, help="Use grapheme-level CNN")
parser.add_argument("-k", "--kfold", dest="kfold", type=int, default=5, metavar="INT",
help="K-fold cross validation [default:1]")
parser.add_argument("-i", "--infer", action='store_true',
default=False, help="For inference purpose only")
parser.add_argument("--txt", dest="txt", type=str,
default="रबि लामिछाने नेपालि जन्ता को हिरो हुन", help="Input text (For inference purpose only)")
args = parser.parse_args()
if os.path.exists(args.log_dir):
shutil.rmtree(args.log_dir)
os.mkdir(args.log_dir)
# Init Logger
log_file = os.path.join(args.log_dir, 'complete.log')
data_log = os.path.join(args.log_dir, 'data_log.log')
logger = utilities.get_logger(log_file)
config = Configuration(config_file=args.config_file, logger=logger)
config.device = args.device
config.verbose = args.verbose
config.eval = args.eval
config.kfold = args.kfold
config.log_dir = args.log_dir
config.log_file = log_file
config.data_log = data_log
config.use_pos = args.pos
config.use_char = args.char
config.use_graph = args.grapheme
config.vocab_file = 'vocab/vocab.pkl'
config.label_file = 'vocab/labels.pkl'
config.infer = args.infer
config.txt = args.txt
logger.info("***************************************")
logger.info("Data file : {}".format(config.data_file))
logger.info("Device : {}".format(config.device))
logger.info("Verbose : {}".format(config.verbose))
logger.info("Eval mode : {}".format(config.eval))
logger.info("K-fold : {}".format(config.kfold))
logger.info("Log directory: {}".format(config.log_dir))
logger.info("Data log file: {}".format(config.data_log))
logger.info("Use POS one-hot-encoding: {}".format(config.use_pos))
logger.info("Use character-level CNN: {}".format(config.use_char))
logger.info("Use grapheme-level CNN: {}".format(config.use_graph))
logger.info("Inference mode: {}".format(config.infer))
if config.infer:
logger.info("Text: {}".format(config.txt))
logger.info("***************************************")
# if not config.eval:
# if os.path.exists(config.output_dir):
# shutil.rmtree(config.output_dir)
# os.mkdir(config.output_dir)
# if os.path.exists(config.results_dir):
# shutil.rmtree(config.results_dir)
# os.mkdir(config.results_dir)
return config, logger
# Inference section
def infer(config, logger):
k = str(1)
dataloader = Dataloader(config, k)
# Load model
arch = LSTMTagger(config, dataloader).to(config.device)
# Print network configuration
logger.info(arch)
# Trainer
model = Trainer(config, logger, dataloader, arch, k)
model.load_checkpoint()
logger.info("Inferred results")
pred_tag = model.infer(config.txt)
for s, p in zip(config.txt.split(), pred_tag):
print(s + '\t' + p + '\n')
return pred_tag
def train_test(config, logger):
"""
Main File
"""
if config.kfold > 0 and not config.eval:
logger.info("Splitting dataset into {0}-fold".format(config.kfold))
splitter.main(input_file=config.data_file,
output_dir=config.root_path,
verbose=config.verbose,
kfold=config.kfold,
pos=config.use_pos,
log_file=config.data_log)
tot_acc = 0
tot_prec = 0
tot_rec = 0
tot_f1 = 0
for i in range(0, config.kfold):
# To match the output filenames
k = str(i + 1)
if not config.eval:
logger.info("Starting training on {0}th-fold".format(k))
# Load data iterator
dataloader = Dataloader(config, k)
# Debugging purpose. Don't delete
# sample = next(iter(train_iter))
# print(sample.TEXT)
# Load model
if config.use_char or config.use_graph:
assert config.use_char ^ config.use_graph, "Either use Character-Level or Grapheme-Level. Not both!!!"
lstm = CharLSTMTagger(config, dataloader).to(config.device)
else:
lstm = LSTMTagger(config, dataloader).to(config.device)
# Print network configuration
logger.info(lstm)
model = Trainer(config, logger, dataloader, lstm, k)
if not config.eval:
# Train
logger.info("Training started !!!")
model.fit()
# Test
model.load_checkpoint()
logger.info("Testing Started !!!")
acc, prec, rec, f1 = model.predict()
logger.info("Accuracy: %6.2f%%; Precision: %6.2f%%; Recall: %6.2f%%; FB1: %6.2f " % (acc, prec, rec, f1))
tot_acc += acc
tot_prec += prec
tot_rec += rec
tot_f1 += f1
logger.info("Final Accuracy: %6.2f%%; Final Precision: %6.2f%%; Final Recall: %6.2f%%; Final FB1: %6.2f " % (
tot_acc / config.kfold, tot_prec / config.kfold, tot_rec / config.kfold, tot_f1 / config.kfold))
def main():
"""
Main File
"""
# Parse argument
config, logger = parse_args()
if config.infer:
infer(config, logger)
else:
train_test(config, logger)
if __name__ == "__main__":
main()