from __future__ import division
import math
import random
import pprint
import scipy.misc
import numpy as np
from time import gmtime, strftime
from six.moves import xrange
import matplotlib.pyplot as plt
import re
import os
import shutil
import argparse
import time
import gzip
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from utils.dataset import Dataset
from scipy.io import loadmat
''' ------------------------------------------------------------------------------
DATA METHODS
------------------------------------------------------------------------------ '''
def get_data(data_file):
data_path = '../data/'
if(data_file == 'MNIST'):
data_path +='MNIST_data'
if(data_file == 'FREY'):
data_path +='frey_rawface.mat'
if(data_file == 'CIFAR10'):
data_path +='MNIST_data'
return
def load_data(dataset_name):
flat = False
if(dataset_name == 'MNIST'):
return load_MNIST()
elif(dataset_name == 'FREY'):
return load_FREY()
return None
def load_FREY():
data_path = '../data/frey_rawface.mat'
mat = loadmat(data_path)
data = mat['ff']
data = np.transpose(data) # [num_images, dimension]
data = np.array(data, dtype=np.float32)
for i in range(data.shape[0]):
min_value = np.min(data[i,:])
max_value = np.max(data[i,:])
num = (data[i,:] - min_value)
den = (max_value - min_value)
data[i,:] = num/den
data_dim = data.shape[1]
num_images = data.shape[0]
train_size = int(num_images*0.8)
valid_size = int(num_images*0.1)
test_size = num_images - train_size - valid_size
x_train = data[:train_size]
x_valid = data[train_size:(train_size+valid_size)]
x_test = data[(train_size+valid_size):]
x_train = np.reshape(x_train, [-1, 28, 20, 1])
x_valid = np.reshape(x_valid, [-1, 28, 20, 1])
x_test = np.reshape(x_test, [-1, 28, 20, 1])
x_train_labels = np.zeros(x_train.shape[0])
x_valid_labels = np.zeros(x_valid.shape[0])
x_test_labels = np.zeros(x_test.shape[0])
train_dataset = Dataset(x_train, x_train_labels)
valid_dataset = Dataset(x_valid, x_valid_labels)
test_dataset = Dataset(x_test, x_test_labels)
print('Train Data: ', train_dataset.x.shape)
print('Valid Data: ', valid_dataset.x.shape)
print('Test Data: ', test_dataset.x.shape)
return train_dataset, valid_dataset, test_dataset
def load_MNIST():
data_path = '../data/MNIST_data'
data = input_data.read_data_sets(data_path, one_hot=False)
x_train_aux = data.train.images
x_test = data.test.images
data_dim = data.train.images.shape[1]
n_train = data.train.images.shape[0]
train_size = int(n_train * 0.8)
valid_size = n_train - train_size
x_valid, x_train = merge_datasets(x_train_aux, data_dim, train_size, valid_size)
print('Data loaded. ', time.localtime().tm_hour,
':', time.localtime().tm_min, 'h')
# logs.write('\tData loaded ' + str(time.localtime().tm_hour) +':' + str(time.localtime().tm_min) + 'h\n')
x_train = np.reshape(x_train, [-1, 28, 28, 1])
x_valid = np.reshape(x_valid, [-1, 28, 28, 1])
x_test = np.reshape(x_test, [-1, 28, 28, 1])
train_dataset = Dataset(x_train, data.train.labels)
valid_dataset = Dataset(x_valid, data.train.labels)
test_dataset = Dataset(x_test, data.test.labels)
print('Train Data: ', train_dataset.x.shape)
print('Valid Data: ', valid_dataset.x.shape)
print('Test Data: ', test_dataset.x.shape)
return train_dataset, valid_dataset, test_dataset
def merge_datasets(data, data_dim, train_size, valid_size=0):
valid_dataset = np.ndarray((valid_size, data_dim), dtype=np.float32)
train_dataset = np.ndarray((train_size, data_dim), dtype=np.float32)
np.random.shuffle(data)
if valid_dataset is not None:
valid_dataset = data[:valid_size, :]
train_dataset = data[valid_size:, :]
return valid_dataset, train_dataset
''' ------------------------------------------------------------------------------
FILES & DIRS
------------------------------------------------------------------------------ '''
def save_img(fig, model_name, image_name, result_dir):
complete_name = result_dir + '/' + model_name + '_' + image_name + '.png'
idx = 1
while(os.path.exists(complete_name)):
complete_name = result_dir + '/' + model_name + '_' + image_name + '_'+str(idx)+'.png'
idx+=1
fig.savefig(complete_name)
def save_args(args, summary_dir):
my_file = summary_dir + '/' + 'my_args.txt'
args_string = str(args).replace(', ', ' --')
with open(my_file, 'a+') as file_:
file_.write(args_string)
def create_dirs(dirs):
"""
dirs - a list of directories to create if these directories are not found
:param dirs:
:return exit_code: 0:success -1:failed
"""
try:
for dir_ in dirs:
if not os.path.exists(dir_):
os.makedirs(dir_)
return 0
except Exception as err:
print("Creating directories error: {0}".format(err))
exit(-1)
''' ------------------------------------------------------------------------------
FOLDER/FILE METHODS
------------------------------------------------------------------------------ '''
def check_folder(log_dir):
if not os.path.exists(log_dir):
os.makedirs(log_dir)
return log_dir
def clean_folder(folder):
if os.path.exists(folder):
shutil.rmtree(folder, ignore_errors=True)
return
def open_log_file(filename, args):
'''
Open a file and writes the first line if it does not exists
'''
if(os.path.isfile(filename) ):
return
with open(filename, 'w+') as logfile:
my_string = ''
for arg in args[:-1]:
my_string+= arg +';'
my_string+= args[-1] + '\n'
logfile.write(my_string)
return
def write_log_file(filename, args):
'''
Write a line to a file with elements separated by commas.
'''
if(not os.path.isfile(filename) ):
return
with open(filename, 'a+') as logfile:
my_string = ''
for arg in args[:-1]:
my_string+= arg +';'
my_string+= args[-1] + '\n'
logfile.write(my_string)
return
''' ------------------------------------------------------------------------------
PRINT METHODS
------------------------------------------------------------------------------ '''
def printt(string, log):
if(log):
print(string)
def print_loss(epoch, start_time, avg_loss, avg_loss_recons, avg_loss_KL, diff=0):
retval = "Epoch: [%2d] time: %4.4f, loss: %.8f, rec: %.8f, kl: %.8f, diff: %.8f" % (epoch, time.time() - start_time, avg_loss, avg_loss_recons, avg_loss_KL, diff)
print(retval)
return retval
def print_loss_GMVAE(epoch, start_time, avg_loss, avg_loss_recons, avg_loss_cp, avg_loss_wp, avg_loss_yp, diff=0):
retval = "Epoch: [%2d] time: %4.4f, loss: %.8f, rec: %.8f, cond_prior: %.8f,w_prior: %.8f,y_prior: %.8f, diff: %.8f" % (epoch, time.time() - start_time, avg_loss, avg_loss_recons, avg_loss_cp,avg_loss_wp, avg_loss_yp, diff)
print(retval)
return retval
def get_time():
return strftime("%Y-%m-%d %H:%M:%S", time.gmtime()) + '\n'
def get_params(args):
retval = ''
for key in args:
retval += '\t' + str(key) + ':' + str(args[key]) + '\n'
return retval
''' ------------------------------------------------------------------------------
TF METHODS
------------------------------------------------------------------------------ '''
def lrelu(x, leak=0.2, name="lrelu"):
with tf.variable_scope(name):
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
return f1 * x + f2 * abs(x)
def get_variable(dim, name, init_value=0.54):
out = tf.get_variable(name,
initializer=tf.constant_initializer(init_value),
shape=[1, dim],
trainable=True,
dtype=tf.float32)
out = tf.nn.softplus(out)
return out
def variable_summary(var, name='summaries'):
with tf.name_scope(name):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)
# tf.summary.scalar('max', tf.reduce_max(var))
# tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
return
def softplus_bias(tensor):
out = tf.add(tf.nn.softplus(tensor), 0.1)
return out
def sigmoid(x):
return 1 / (1 + math.exp(-x))
