# Path: dlearn/utils/serialize.py
# def load_data(fpath):
#     with open(fpath, 'rb') as f:
#         data = cPickle.load(f)
#     return data
# 
# def save_data(data, fpath):
#     with open(fpath, 'wb') as f:
#         cPickle.dump(data, f, cPickle.HIGHEST_PROTOCOL)


import os
import sys
import argparse
import numpy as np
import theano
import dlearn.stats as stats
    import matplotlib.pyplot as plt
from dlearn.utils.serialize import load_data, save_data

homepath = os.path.join('..', '..')

if not homepath in sys.path:
    sys.path.insert(0, homepath)



# Program arguments parser
desctxt = """
Train latent network. Use learned attribute and segmentation network.
"""

dataset_txt = """
The input dataset data_name.pkl.
"""

model_txt = """

# Path: dlearn/utils/serialize.py
# def load_data(fpath):
#     with open(fpath, 'rb') as f:
#         data = cPickle.load(f)
#     return data
# 
# def save_data(data, fpath):
#     with open(fpath, 'wb') as f:
#         cPickle.dump(data, f, cPickle.HIGHEST_PROTOCOL)


# Path: examples/human_sar/evaluate.py
import os
import sys
import argparse
import numpy as np
import theano
import dlearn.stats as stats
    import matplotlib.pyplot as plt
from dlearn.utils.serialize import load_data, save_data


homepath = os.path.join('..', '..')

if not homepath in sys.path:
    sys.path.insert(0, homepath)



# Program arguments parser
desctxt = """
Train latent network. Use learned attribute and segmentation network.
"""

dataset_txt = """
The input dataset data_name.pkl.
"""

model_txt = """

The model model_name.pkl.