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train.py
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train.py
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#!/usr/bin/python3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
torch.autograd.set_detect_anomaly(True)
import argparse
import logging
import os
from model_train import ProcrustEs
from experiment_impact_tracker.compute_tracker import ImpactTracker
def parse_args(args=None):
parser = argparse.ArgumentParser(
description='ProcrustEs',
usage='train.py [<args>] [-h | --help]'
)
parser.add_argument('--cuda', action='store_true', help='use GPU')
parser.add_argument('--debug', action='store_true')
parser.add_argument('--data_path', type=str, default=None)
parser.add_argument('-lr', '--learning_rate', default=0.001, type=float)
parser.add_argument('-td', '--total_dim', default=2000, type=int)
parser.add_argument('-sd', '--sub_dim', default=20, type=int)
parser.add_argument('--init_embedding', default=None, type=str)
parser.add_argument('--use_scale', type=bool, default=True)
parser.add_argument('--max_step', default=1000, type=int)
parser.add_argument('--save_step', default=200, type=int)
# parser.add_argument('--theta', default=0.005, type=float)
parser.add_argument('-save', '--save_path', default="", type=str)
parser.add_argument('--seed', default=999, type=int) # fixed
parser.add_argument('--eps', default=1e-7, type=float)
parser.add_argument('--reg', default=0., type=float)
parser.add_argument('--gamma', default=1, type=float)
args = parser.parse_args(args)
return args
def read_triple(file_path, entity2id, relation2id):
'''
Read triples and map them into ids.
'''
rel_ent_dict = {}
with open(file_path) as fin:
for line in fin:
h, r, t = line.strip().split('\t')
rel_ent_dict.setdefault(relation2id[r], [])
rel_ent_dict[relation2id[r]].append((entity2id[h], entity2id[t]))
for rel_id in rel_ent_dict:
rel_ent_dict[rel_id] = torch.LongTensor(rel_ent_dict[rel_id])
return rel_ent_dict
def set_logger(args):
'''
Write logs to checkpoint and console
'''
log_file = os.path.join(args.save_path, 'train.log')
logging.basicConfig(
format='%(asctime)s %(levelname)-8s %(message)s',
level=logging.INFO if not args.debug else logging.DEBUG,
datefmt='%Y-%m-%d %H:%M:%S',
filename=log_file,
filemode='w'
)
console = logging.StreamHandler()
console.setLevel(logging.INFO if not args.debug else logging.DEBUG)
formatter = logging.Formatter('%(asctime)s %(levelname)-8s %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
def main(args):
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
set_logger(args)
tracker = ImpactTracker(args.save_path)
tracker.launch_impact_monitor()
with open(os.path.join(args.data_path, 'entities.dict')) as fin:
entity2id = dict()
for line in fin:
eid, entity = line.strip().split('\t')
entity2id[entity] = int(eid)
with open(os.path.join(args.data_path, 'relations.dict')) as fin:
relation2id = dict()
for line in fin:
rid, relation = line.strip().split('\t')
relation2id[relation] = int(rid)
nentity = len(entity2id)
nrelation = len(relation2id)
logging.info('Data Path: %s' % args.data_path)
logging.info('#entity: %d' % nentity)
logging.info('#relation: %d' % nrelation)
rel_ent_dict = read_triple(os.path.join(args.data_path, 'train.txt'), entity2id, relation2id)
logging.info('#train: %d' % len(rel_ent_dict))
model = ProcrustEs(rel_ent_dict, nentity, nrelation, args.total_dim, args.sub_dim, args.cuda, args.save_path, args.eps)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
eps=args.eps,
weight_decay=args.reg,
)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.save_step, gamma=args.gamma, last_epoch=-1)
old_loss = torch.tensor(float("Inf"))
if args.cuda:
model = model.cuda()
old_loss = old_loss.cuda()
# training loop
for epoch in range(args.max_step):
info = tracker.get_latest_info_and_check_for_errors()
model.normalise()
save_flag = not ((epoch + 1) % args.save_step)
loss = model(save=save_flag)
logging.info(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
old_loss = loss
model(save=True)
if __name__ == '__main__':
main(parse_args())