def MakeModels(model_file, output_path):
model = util.ReadModel(model_file)
for l in model.layer:
for p in l.param:
EditPretrainedModels(p, output_path)
for e in model.edge:
for p in e.param:
EditPretrainedModels(p, output_path)
util.WritePbtxt(model_file, model)
from deepnet import util
import numpy as np
import os
if __name__ == '__main__':
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("--pfamid")
args = parser.parse_args()
pfamid = args.pfamid
data_pbtxt_file = os.path.join(pfamid, 'data.pbtxt')
data_pbtxt = util.ReadData(data_pbtxt_file)
data_pbtxt.name = pfamid
data_pbtxt.prefix = os.path.join(
os.path.split(data_pbtxt.prefix)[0], pfamid)
for data in data_pbtxt.data:
fname = os.path.basename(data.file_pattern)
for t in ('train', 'valid', 'test'):
if t in data.name:
X = np.load(os.path.join(pfamid, pfamid + "_" + t + ".npy"))
data.size = X.shape[0]
data.dimensions[0] = X.shape[1]
data.file_pattern = os.path.abspath(
os.path.join(pfamid, pfamid + "_" + t + ".npy"))
util.WritePbtxt(data_pbtxt_file, data_pbtxt)
def SetupDataPbtxt(data_pbtxt_file, data_path):
data_pbtxt = util.ReadData(data_pbtxt_file)
for data in data_pbtxt.data:
fname = os.path.basename(data.file_pattern)
data.file_pattern = os.path.join(data_path, fname)
util.WritePbtxt(data_pbtxt_file, data_pbtxt)
def MakeTrainers(trainer_file, data_pbtxt_file, output_path):
trainer = util.ReadOperation(trainer_file)
trainer.data_proto = data_pbtxt_file
trainer.checkpoint_directory = output_path
util.WritePbtxt(trainer_file, trainer)
def MakeDataPbtxt(data_pbtxt_file, data_path):
data_pbtxt = util.ReadData('mnist.pbtxt')
for data in data_pbtxt.data:
fname = os.path.basename(data.file_pattern)
data.file_pattern = os.path.join(data_path, fname)
util.WritePbtxt(data_pbtxt_file, data_pbtxt)