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train.py
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train.py
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import pickle
import sys
import os
import math
import traceback
import argparse
import signal
import atexit
import time
import h5py
import random
import tensorflow as tf
import numpy as np
from pathlib import Path
seed = 1337
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
#import tensorflow.keras as keras
#import tensorflow.keras.utils
from tensorflow.keras.callbacks import ModelCheckpoint, LambdaCallback, Callback
#import tensorflow.keras.backend as K
#from model import create_model
##from myutils import prep, drop, batch_gen, seq2sent
from nltk.translate.bleu_score import corpus_bleu, sentence_bleu
import tokenizer
class HistoryCallback(Callback):
def setCatchExit(self, outdir, modeltype, timestart, mdlconfig):
self.outdir = outdir
self.modeltype = modeltype
self.history = {}
self.timestart = timestart
self.mdlconfig = mdlconfig
atexit.register(self.handle_exit)
signal.signal(signal.SIGTERM, self.handle_exit)
signal.signal(signal.SIGINT, self.handle_exit)
def handle_exit(self, *args):
if len(self.history.keys()) > 0:
try:
Path(outdir+'/histories').mkdir(parents=True, exist_ok=True)
fn = outdir+'/histories/'+self.modeltype+'_hist_'+str(self.timestart)+'.pkl'
histoutfd = open(fn, 'wb')
pickle.dump(self.history, histoutfd)
print('saved history to: ' + fn)
fn = outdir+'/histories/'+self.modeltype+'_conf_'+str(self.timestart)+'.pkl'
confoutfd = open(fn, 'wb')
pickle.dump(self.mdlconfig, confoutfd)
print('saved config to: ' + fn)
except Exception as ex:
print(ex)
traceback.print_exc(file=sys.stdout)
sys.exit()
def on_train_begin(self, logs=None):
self.epoch = []
self.history = {}
def on_epoch_end(self, epoch, logs=None):
filepath = self.outdir+'/models/'+self.modeltype+f'_E{epoch:02d}_'+str(self.timestart)+'.h5'
keras.models.save_model(model, filepath, overwrite=True, include_optimizer=False)
logs = logs or {}
self.epoch.append(epoch)
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
if __name__ == '__main__':
timestart = int(round(time.time()))
parser = argparse.ArgumentParser(description='')
parser.add_argument('--gpu', type=str, help='0 or 1', default='0')
parser.add_argument('--batch-size', dest='batch_size', type=int, default=200)
parser.add_argument('--batchgen', dest='batchgen', type=str, default='regular')
parser.add_argument('--epochs', dest='epochs', type=int, default=10)
parser.add_argument('--model-type', dest='modeltype', type=str, default='vanilla')
parser.add_argument('--with-graph', dest='withgraph', action='store_true', default=False)
parser.add_argument('--with-calls', dest='withcalls', action='store_true', default=False)
parser.add_argument('--with-biodats', dest='withbiodats', type=str , default='vanilla')
parser.add_argument('--with-simmat', dest='withsimmats', action='store_true', default=False)
parser.add_argument('--with-codevec', dest='withcodevec', action='store_true', default=False)
parser.add_argument('--simmat-file', dest='simmatfile', type=str, default='softmax_usec.pkl')
parser.add_argument('--loss-type', dest='losstype', type=str, default='cce')
parser.add_argument('--vmem-limit', dest='vmemlimit', type=int, default=0)
parser.add_argument('--data', dest='dataprep', type=str, default='/nfs/projects/smn/data/javastmt/q90')
parser.add_argument('--outdir', dest='outdir', type=str, default='outdir')
parser.add_argument('--hops', dest='hops', type=int, default= 5)
parser.add_argument('--dtype', dest='dtype', type=str, default='float32')
parser.add_argument('--tf-loglevel', dest='tf_loglevel', type=str, default='3')
parser.add_argument('--datfile', dest='datfile', type=str, default='dataset.pkl')
parser.add_argument('--only-print-summary', dest='onlyprintsummary', action='store_true', default=False)
parser.add_argument('--memory-network-input', dest='memory_network_input', type=str, default='positional-encoding',
help='the input module for memory networks. default: positional-encoding (from the '
'"End-To-End Memory Networks"). The other option: eos-embedding (from the "Ask Me Anything: '
'Dynamic Memory Networks for Natural Language Processing")')
parser.add_argument('--max-sent-len', dest='maxsentlen', type=int, default=50,
help='for the memory networks, set the maximum length of the sentences/lines. default: 50')
parser.add_argument('--max-sent-cnt', dest='maxsentcnt', type=int, default=50,
help='for the memory networks, set the maximum number of lines. default: 50')
args = parser.parse_args()
outdir = args.outdir
dataprep = args.dataprep
gpu = args.gpu
hops = args.hops
batch_size = args.batch_size
batchgen = args.batchgen
epochs = args.epochs
modeltype = args.modeltype
withgraph = args.withgraph
withcalls = args.withcalls
withcodevec = args.withcodevec
withbiodats = False
withsimmat = args.withsimmats
simmatfile = args.simmatfile
losstype = args.losstype
vmemlimit = args.vmemlimit
onlyprintsummary = args.onlyprintsummary
memorynetwork_input = args.memory_network_input
max_sentence_len = args.maxsentlen
max_sentence_cnt = args.maxsentcnt
if memorynetwork_input != "positional-encoding" and memorynetwork_input != "eos-embedding":
print('memory-network-input: {} is not a valid option. use deafult: positional-encoding'.format(memorynetwork_input))
memorynetwork_input = "positional-encoding"
#datfile = args.datfile
if args.withbiodats != 'vanilla':
withbiodats = True
biodatfile = args.withbiodats
os.environ['TF_CPP_MIN_LOG_LEVEL'] = args.tf_loglevel
os.environ['CUDA_VISIBLE_DEVICES'] = gpu
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
if(vmemlimit > 0):
tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=vmemlimit)])
except RuntimeError as e:
print(e)
#if(vmemlimit > 0):
# if gpus:
# try:
# tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=vmemlimit)])
# except RuntimeError as e:
# print(e)
import tensorflow.keras as keras
import tensorflow.keras.utils
#from tensorflow.keras.callbacks import ModelCheckpoint, LambdaCallback, Callback
import tensorflow.keras.backend as K
from model import create_model
K.set_floatx(args.dtype)
if batchgen == 'qs':
from qs_myutils import prep, drop, batch_gen, seq2sent
else:
from myutils import prep, drop, batch_gen, seq2sent
print(dataprep)
prep('loading sequences... ')
sqlfile = '{}/rawdats.sqlite'.format(dataprep)
extradata = pickle.load(open('%s/dataset_short.pkl' % (dataprep), 'rb'))
seqdata = h5py.File('%s/dataset_seqs.h5' % (dataprep), 'r')
drop()
if withgraph:
prep('loading graph data... ')
graphdata = pickle.load(open('%s/dataset_graph.pkl' % (dataprep), 'rb'))
for k, v in extradata.items():
graphdata[k] = v
extradata = graphdata
drop()
if withcalls:
prep('loading call data... ')
callnodes = pickle.load(open('%s/callsnodes.pkl' % (dataprep), 'rb'))
calledges = pickle.load(open('%s/callsedges.pkl' % (dataprep), 'rb'))
callnodesdata = pickle.load(open('%s/callsnodedata.pkl' % (dataprep), 'rb'))
extradata['callnodes'] = callnodes
extradata['calledges'] = calledges
extradata['callnodedata'] = callnodesdata
drop()
if withbiodats:
prep('loading biomodel results... ')
biodats = pickle.load(open(biodatfile, 'rb'))
extradata['biodats'] = biodats
drop()
if withcodevec:
prep('loading codevec... ')
codevecfile = h5py.File("%s/q90codebert.h5" % (dataprep), 'r')
extradata['codevec'] = codevecfile
drop()
if withsimmat:
prep('loading target comwords distribution... ')
softmax_usemat = pickle.load(open('%s/%s' % (dataprep, simmatfile), 'rb'))
extradata['target_dist'] = softmax_usemat
drop()
prep('loading tokenizers... ')
comstok = extradata['comstok']
tdatstok = extradata['tdatstok']
sdatstok = tdatstok
smlstok = extradata['smlstok']
if withgraph:
graphtok = extradata['graphtok']
drop()
if batchgen == 'qs':
steps = int(np.array(seqdata.get('/ctrain').shape[0])/batch_size)*int(np.array(seqdata.get('/ctrain')).shape[1])
valsteps = int(np.array(seqdata.get('/cval').shape[0])/batch_size)*int(np.array(seqdata.get('/ctrain')).shape[1])
else:
steps = int(np.array(seqdata.get('/ctrain').shape[0])/batch_size)
valsteps = int(np.array(seqdata.get('/cval').shape[0])/batch_size)
tdatvocabsize = tdatstok.vocab_size
comvocabsize = comstok.vocab_size
smlvocabsize = smlstok.vocab_size
print('tdatvocabsize %s' % (tdatvocabsize))
print('comvocabsize %s' % (comvocabsize))
print('smlvocabsize %s' % (smlvocabsize))
print('batch size {}'.format(batch_size))
print('steps {}'.format(steps))
print('training data size {}'.format(steps*batch_size))
print('vaidation data size {}'.format(valsteps*batch_size))
print('------------------------------------------')
print('for memory networks:')
print('input module type: %s' % (memorynetwork_input))
print('------------------------------------------')
config = dict()
config['hops'] = hops
config['tdatvocabsize'] = tdatvocabsize
config['comvocabsize'] = comvocabsize
config['smlvocabsize'] = smlvocabsize
try:
config['fidloc'] = extradata['fidloc']
config['locfid'] = extradata['locfid']
config['comstok'] = extradata['comstok']
config['comlen'] = int(np.array(seqdata.get('/ctrain')).shape[1])
config['tdatlen'] = int(np.array(seqdata.get('/dttrain')).shape[1])
config['sdatlen'] = extradata['config']['sdatlen']
config['smllen'] = int(np.array(seqdata.get('/strain')).shape[1])
config['batchgen'] = batchgen
config['target_dist'] = extradata['target_dist']
except KeyError:
pass # some configurations do not have all data, which is fine
config['batch_size'] = batch_size
config['memorynetwork_input'] = memorynetwork_input
config['max_sentence_len'] = max_sentence_len
config['max_sentence_cnt'] = max_sentence_cnt
config['loss_type'] = losstype
print(config.keys())
prep('creating model... ')
config, model = create_model(modeltype, config)
drop()
print(model.summary())
if onlyprintsummary:
sys.exit()
gen = batch_gen(seqdata, extradata, 'train', config)
Path(outdir+'/models').mkdir(parents=True, exist_ok=True)
#checkpoint = ModelCheckpoint(outdir+'/'+modeltype+'_E{epoch:02d}_TA{acc:.2f}_VA{val_acc:.2f}_VB{val_bleu:}.h5', monitor='val_loss')
#checkpoint = ModelCheckpoint(outdir+'/models/'+modeltype+'_E{epoch:02d}_'+str(timestart)+'.h5')
savehist = HistoryCallback()
savehist.setCatchExit(outdir, modeltype, timestart, config)
valgen = batch_gen(seqdata, extradata, 'val', config)
# If you want it to calculate BLEU Score after each epoch use callback_valgen and test_cb
#####
#callback_valgen = batch_gen_train_bleu(seqdata, comvocabsize, 'val', modeltype, batch_size=batch_size)
#test_cb = mycallback(callback_valgen, steps)
#####
callbacks = [ savehist ]
try:
history = model.fit(x=gen, steps_per_epoch=steps, epochs=epochs, verbose=1, max_queue_size=8, workers=1, use_multiprocessing=False, callbacks=callbacks, validation_data=valgen, validation_steps=valsteps)
last_model_filepath = outdir + '/models/' + modeltype + '_last-epoch_' + str(timestart) + '.h5'
keras.models.save_model(model, last_model_filepath, overwrite=True, include_optimizer=False)
except Exception as ex:
print(ex)
traceback.print_exc(file=sys.stdout)