def main(args):
highres = utils.matrix_extract(args.chromosome, 10000, args.inputfile)
logging.debug('dividing, filtering and downsampling files...')
highres_sub, index = utils.divide(highres)
logging.debug(str(highres_sub.shape))
#np.save(infile+"highres",highres_sub)
lowres = utils.genDownsample(highres, 1 / float(args.scalerate))
lowres_sub, index = utils.divide(lowres)
logging.debug(str(lowres_sub.shape))
#np.save(infile+"lowres",lowres_sub)
logging.debug('start training...')
trainConvNet.train(lowres_sub, highres_sub, args.outmodel, args.checkpoint)
logging.debug('finished...')
def main(args):
highres = utils.matrix_extract(args.chromosome, args.chromosome, 10000,
args.inputfile)
print('dividing, filtering and downsampling files...', flush=True)
highres_sub, index = utils.train_divide(highres)
print(highres_sub.shape)
#np.save(infile+"highres",highres_sub)
lowres = utils.genDownsample(highres, 1 / float(args.scalerate))
lowres_sub, index = utils.train_divide(lowres)
print(lowres_sub.shape)
#np.save(infile+"lowres",lowres_sub)
print('start training...', flush=True)
trainConvNet.train(lowres_sub, highres_sub, args.outmodel)
print('finished...', flush=True)