#from TensorMol import *
from TensorMol import MSet, JOULEPERHARTREE
import argparse as arg
from multiprocessing import Queue, Process, Manager, Pool
from matplotlib import pyplot as plt
parser = arg.ArgumentParser(
description=
'Grep qm area from an Amber MDcrd trajory to make training dataset!')
parser.add_argument('-i', '--input')
args = parser.parse_args()
jsonfile = args.input
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
if __name__ == "__main__":
UpdateGPARAMS(jsonfile)
LoadModel()
if not os.path.exists("./results"):
os.system("mkdir ./results")
for i in range(len(GPARAMS.Dataset_setting.Inputdatasetlist)):
TMPSet = MSet(GPARAMS.Dataset_setting.Inputdatasetlist[i])
TMPSet.Load()
f1 = open(
'./results/' + GPARAMS.Dataset_setting.Inputdatasetlist[i] +
'.result', 'w')
f2 = open(
'./results/' + GPARAMS.Dataset_setting.Inputdatasetlist[i] +
'_e.csv', 'w')
f3 = open(
'./results/' + GPARAMS.Dataset_setting.Inputdatasetlist[i] +
'_f.csv', 'w')
f4 = open(
if __name__ == "__main__":
manager = Manager()
QMQueue = manager.Queue()
DataQueue = manager.Queue()
GPUQueue = manager.Queue()
NetstrucQueue = manager.Queue()
if os.path.exists('./networks/lastsave'):
os.system("rm ./networks/lastsave/* -r")
os.system("cp *.ESOINN Sfactor.in ./networks/lastsave ")
UpdateGPARAMS(jsonfile)
for i in GPARAMS.Compute_setting.Gpulist:
GPUQueue.put(i)
for stage in range(GPARAMS.Train_setting.Trainstage,\
GPARAMS.Train_setting.Stagenum+GPARAMS.Train_setting.Trainstage):
LoadModel()
#==Main MD process with productor and Consumer model==
ProductPool = Pool(len(GPARAMS.Compute_setting.Gpulist))
Resultlist = []
for i in range(len(GPARAMS.System_setting)):
result = ProductPool.apply_async(productor, (i, QMQueue, GPUQueue))
Resultlist.append(result)
ProductPool.close()
for i in range(len(GPARAMS.System_setting)):
tmp = Resultlist[i].get()
print(tmp)
Consumer_Process = Process(target=consumer, args=(QMQueue, ))
Consumer_Process.start()
ProductPool.terminate()
ProductPool.join()