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train_gd_6_opt2sar.py
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train_gd_6_opt2sar.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Nov 14 16:55:26 2017
@author: DELL
"""
'''
用于生成器的训练
opt --> sar
'''
import numpy as np
import math
import time
import model_patent as model
import tensorflow as tf
import os
from datetime import datetime
import logging
from scipy import misc
batch_size = 200
epoch = 100
learning_rate = 2e-4
image_width = 32
image_height = 32
checkpoint_dir = 'ckpt_gd6_o2s'
checkpoint_dir_g = 'ckpt_g6_o2s'
output_dir = 'out6_o2s'
checkpoint_file = os.path.join(checkpoint_dir, 'model.ckpt')
checkpoint_file_g = os.path.join(checkpoint_dir_g, 'model.ckpt')
train_dir='summary_gd'
def initLogging(logFilename='record_gd6_o2s.log'):
"""Init for logging
"""
logging.basicConfig(
level = logging.DEBUG,
format='%(asctime)s-%(levelname)s-%(message)s',
datefmt = '%y-%m-%d %H:%M',
filename = logFilename,
filemode = 'w');
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s-%(levelname)s-%(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
initLogging()
def gd_shuffle(epoch,batch,x_data,y_data):
for i in range(epoch):
shuffle_index=np.random.permutation(y_data.shape[0])
x_data1, y_data1 = x_data[shuffle_index], y_data[shuffle_index]
batch_per_epoch = math.ceil(y_data.shape[0] / batch)
for b in range(batch_per_epoch):
if (b*batch+batch)>y_data.shape[0]:
m,n = b*batch, y_data.shape[0]
else:
m,n = b*batch, b*batch+batch
x_batch, y_batch = x_data1[m:n,:], y_data1[m:n,:]
yield x_batch, y_batch
def combine_images(generated_images):
num = generated_images.shape[0]
width = int(math.sqrt(num))
height = int(math.ceil(float(num)/width))
shape = generated_images.shape[1:3]
image = np.zeros((height*shape[0], width*shape[1]),
dtype=generated_images.dtype)
for index, img in enumerate(generated_images):
i = int(index/width)
j = index % width
image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = img[:,:,0]
# image = image[:,:,np.newaxis]
return image
def gd_train():
# if FLAGS.load_model is not None:
# checkpoints_dir = 'checkpoints/' + FLAGS.load_model
# else:
current_time = datetime.now().strftime('%Y%m%d-%H%M')
checkpoints_dir = 'checkpoints/{}'.format(current_time)
try:
os.makedirs(checkpoint_dir_g)
os.makedirs(checkpoint_dir)
except os.error:
pass
try:
os.makedirs(checkpoints_dir)
except os.error:
pass
try:
os.makedirs(output_dir)
except os.error:
pass
data2 = np.load('6_up_sift_harris_mapping_data.npy')
X_train = data2[:70000,:,:32,:] # sar
Y_train = data2[:70000,:,32:,:] # opt
X_test = data2[70000:70100,:,:32,:]
Y_test = data2[70000:70100,:,32:,:]
graph = tf.Graph()
with graph.as_default():
inputs_sar = tf.placeholder(tf.float32, [batch_size, image_height, image_width, 1], name='inputs_sar')
inputs_opt = tf.placeholder(tf.float32, [batch_size, image_height, image_width, 1], name='inputs_opt')
test_sar = tf.placeholder(tf.float32, [None, image_height, image_width, 1], name='test_sar')
# 训练 G
gen_loss, dis_loss, _ = model.gd_model_o2s(inputs_sar, inputs_opt)
discrim_tvars = [var for var in tf.trainable_variables() if var.name.startswith("discriminator")]
d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(dis_loss,var_list=discrim_tvars)
gen_tvars = [var for var in tf.trainable_variables() if var.name.startswith("generator_2")]
g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(gen_loss,var_list=gen_tvars)
# with tf.control_dependencies([g_train_opt, d_train_opt]):
# gd_train_opt = tf.no_op(name='optimizers')
tf.summary.scalar('gen_loss', gen_loss)
tf.summary.scalar('dis_loss', dis_loss)
summary = tf.summary.merge_all()
saver = tf.train.Saver()
saver_g = tf.train.Saver(var_list=gen_tvars)
init = tf.global_variables_initializer()
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(train_dir, sess.graph)
sess.run(init)
# saver.restore(sess, tf.train.latest_checkpoint('ckpt_gd2'))
# saver.restore(sess, 'ckpt_gd2/model.ckpt-4000')
try:
shuffle1= gd_shuffle(epoch,batch_size,X_train,Y_train)
for step, (x_batch, y_batch) in enumerate(shuffle1):
start_time = time.time()
step = step + 1
feed_dict = {inputs_sar:x_batch, inputs_opt:y_batch}
_, _, g_loss,d_loss = sess.run([d_train_opt,g_train_opt, gen_loss, dis_loss], feed_dict = feed_dict)
duration = time.time() - start_time
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
#
if step % 100 == 0:
logging.info('>> Step %d run_train: g_loss = %.2f d_loss = %.2f (%.3f sec)'
% (step, g_loss, d_loss, duration))
if step % 2000 == 0 :
logging.info('>> %s Saving in %s' % (datetime.now(), checkpoint_dir))
saver.save(sess, checkpoint_file, global_step=step)
saver_g.save(sess, checkpoint_file_g, global_step=step)
gen = model.create_generator_2(test_sar, 1, reuse=True)
gen_out = sess.run(gen, feed_dict={test_sar:Y_test} )
show_images=np.concatenate((X_test,gen_out,Y_test),axis=1)
result = combine_images(show_images)
result = result*255
misc.imsave('out6_o2s/{}.png'.format(str(epoch)+"_"+str(step)), result)
except KeyboardInterrupt:
print('INTERRUPTED')
finally:
saver.save(sess, checkpoint_file, global_step=step)
saver_g.save(sess, checkpoint_file_g, global_step=step)
print('Model saved in file :%s'%checkpoint_dir)
if __name__ == '__main__':
gd_train()