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train_6_sia_map.py
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train_6_sia_map.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 4 19:37:06 2017
@author: wrj
"""
'''
直接输入光和SAR，对比实验
双分支特征向图网络
'''
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
batch_size = 200
epoch = 40
learning_rate = 2e-4
image_width = 32
image_height = 32
checkpoint_dir = 'ckpt_6_sia_map'
checkpoint_dir_g = 'ckpt_g6'
checkpoint_file = os.path.join(checkpoint_dir, 'model.ckpt')
checkpoint_file_g = os.path.join(checkpoint_dir_g, 'model.ckpt')
train_dir='summary_fm3_sia_map'
def initLogging(logFilename='record_sia_map.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 gfm_shuffle(epoch,batch,x_data,y_data,label):
for i in range(epoch):
shuffle_index=np.random.permutation(y_data.shape[0])
x_data1, y_data1, label1 = x_data[shuffle_index], y_data[shuffle_index], label[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, label_batch = x_data1[m:n,:], y_data1[m:n,:], label1[m:n,:]
yield x_batch, y_batch, label_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 gfm_train():
current_time = datetime.now().strftime('%Y%m%d-%H%M')
checkpoints_dir = 'checkpoints/{}'.format(current_time)
try:
os.makedirs(checkpoint_dir)
os.makedirs(checkpoints_dir)
except os.error:
pass
data1 = np.load('6_up_sift_harris_transform_train_test_data.npz')
patch_train = data1['arr_0']
patch_1_train = patch_train[:200000,:,:32,:] # sar
patch_2_train = patch_train[:200000,:,32:,:] # opt
y_train = data1['arr_2'][:200000,:]
patch_test = data1['arr_1']
patch_1_test = patch_test[:3000,:,:32,:] # sar
patch_2_test = patch_test[:3000,:,32:,:] # opt
y_test = data1['arr_3'][:3000,:]
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')
inputs_lab = tf.placeholder(tf.float32, [batch_size, 1], name='inputs_lab')
# 训练 M
match_loss,m_output = model.gfm_sia_map(inputs_sar, inputs_opt, inputs_lab)
out = tf.round(m_output)
correct,ram = model.evaluation(out, inputs_lab)
m_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(match_loss)
tf.summary.scalar('mathing_loss', match_loss)
summary = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=10)
init = tf.global_variables_initializer()
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(train_dir, sess.graph)
sess.run(init)
try:
shuffle1= gfm_shuffle(epoch,batch_size,patch_1_train,patch_2_train,y_train)
for step, (x_batch, y_batch, l_batch) in enumerate(shuffle1):
start_time = time.time()
step = step + 1
feed_dict = {inputs_sar:x_batch, inputs_opt:y_batch, inputs_lab:l_batch}
_, m_loss,m_output_, m_out_ = sess.run([m_train_opt, match_loss, m_output, out ], 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: matching_loss = %.2f (%.3f sec)'
% (step, m_loss, duration))
if step % 3000 == 0 :
logging.info('>> %s Saving in %s' % (datetime.now(), checkpoint_dir))
saver.save(sess, checkpoint_file, global_step=step)
#
if step % 500 == 0 :
# test
true_count = 0 # Counts the number of correct predictions.
num = np.size(y_test)
shuffle_test= gfm_shuffle(1,batch_size,patch_1_test,patch_2_test,y_test)
for step_test, (x_batch, y_batch, l_batch) in enumerate(shuffle_test):
feed_dict = {inputs_sar:x_batch, inputs_opt:y_batch, inputs_lab:l_batch}
result, p_out, p_r = sess.run([correct,out,ram], feed_dict=feed_dict)
true_count = true_count + result
precision = float(true_count) / num
logging.info('Num examples: %d Num correct: %d Precision : %0.04f' %
(num, true_count, precision))
except KeyboardInterrupt:
print('INTERRUPTED')
finally:
saver.save(sess, checkpoint_file, global_step=step)
print('Model saved in file :%s'%checkpoint_dir)
def gfm_test():
data1 = np.load('6_up_sift_harris_transform_train_test_data.npz')
patch_test = data1['arr_1']
patch_1_test = patch_test[:30000,:,:32,:] # sar
patch_2_test = patch_test[:30000,:,32:,:] # opt
y_test = data1['arr_3'][:30000,:]
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')
inputs_lab = tf.placeholder(tf.float32, [batch_size, 1], name='inputs_lab')
match_loss,m_output = model.gfm_sia_map(inputs_sar, inputs_opt, inputs_lab)
out = tf.round(m_output)
correct,ram = model.evaluation(out, inputs_lab)
saver = tf.train.Saver()
with tf.Session() as sess:
# saver.restore(sess, tf.train.latest_checkpoint('ckpt_fm3'))
saver.restore(sess, 'ckpt_6_sia_map/model.ckpt-4000')
true_count = 0 # Counts the number of correct predictions.
num = np.size(y_test)
shuffle_test= gfm_shuffle(1,batch_size,patch_1_test,patch_2_test,y_test)
for step1, (x_batch, y_batch, l_batch) in enumerate(shuffle_test):
feed_dict = {inputs_sar:x_batch, inputs_opt:y_batch, inputs_lab:l_batch}
result, p_out, p_ram, p_m = sess.run([correct,out,ram,m_output], feed_dict=feed_dict)
true_count = true_count + result
if step1 % 10 == 0:
print('Step %d run_test: batch_precision = %.2f '
% (step1, result/batch_size))
precision = float(true_count) / num
print(' Num examples: %d Num correct: %d Precision : %0.04f' %
(num, true_count, precision))
if __name__ == '__main__':
gfm_train()
# gfm_test()