def mc_test(file_name, number):
data = np.load('/home/ws/文档/wrj/data_all/' + file_name + '.npz')
train_matching_y = data['arr_1'][:, np.newaxis]
numbers_train = train_matching_y.shape[0] #训练集总数
graph = tf.Graph()
with graph.as_default():
inputs_p1 = tf.placeholder(
tf.float32, [BATCH_SIZE_matching, image_height, image_width, 1],
name='inputs_p1')
inputs_p2 = tf.placeholder(
tf.float32, [BATCH_SIZE_matching, image_height, image_width, 1],
name='inputs_p2')
label_m = tf.placeholder(tf.float32, [BATCH_SIZE_matching, 1],
name='label_m')
match_loss, match_output, all_features = model.match_network(
inputs_p1, inputs_p2, label_m)
match_out = tf.round(match_output)
m_correct, m_numbers = model.evaluation(match_out, label_m)
filename = '/home/ws/文档/wrj/data_all/country/' + file_name + '.tfrecord'
filename_queue = tf.train.string_input_producer([filename],
num_epochs=1,
shuffle=False)
img_batch, label_batch = read_data.batch_inputs(
filename_queue, train=False, batch_size=BATCH_SIZE_matching)
saver = tf.train.Saver()
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=sess_config) as sess:
sess.run(tf.local_variables_initializer())
saver.restore(sess, tf.train.latest_checkpoint('ckpt_map32_diff'))
# saver.restore(sess, 'ckpt_tensor/model.ckpt-4000')
m_count = 0 # Counts the number of correct predictions.
num = numbers_train
matching_out = np.array([])
matching_label = np.array([])
try:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
step_test = 0
try:
while not coord.should_stop():
step_test = step_test + 1
batch, l_batch = sess.run([img_batch, label_batch])
l_batch = l_batch
x_batch = batch[:, :, :64, np.newaxis]
y_batch = batch[:, :, 64:, np.newaxis]
feed_dict = {
inputs_p1: x_batch,
inputs_p2: y_batch,
label_m: l_batch
}
m_correct_, m_output_ = sess.run(
[m_correct, match_output], feed_dict=feed_dict)
if step_test == 1:
matching_out = m_output_
matching_label = l_batch
elif (l_batch.size == BATCH_SIZE_matching):
matching_out = np.concatenate(
(matching_out, m_output_))
matching_label = np.concatenate(
(matching_label, l_batch))
m_count = m_count + m_correct_.astype(int)
if step_test % 100 == 0:
print(
'Step %d/%d run_test: batch_precision = %.2f '
% (step_test, num / 100,
m_correct_ / BATCH_SIZE_matching))
except Exception as e:
coord.request_stop(e)
m_precision = float(m_count) / num
print(
'Num examples: %d Num correct: %d match Precision : %0.04f '
% (num, m_count, m_precision))
save_file = open('test_map32_diff.txt', 'a')
save_file.write(file_name + ' epoch: ' + str(number) +
'\n' + 'num correct: ' + str(m_count) + '/' +
str(num) + ' match precision : ' +
str(m_precision))
save_file.write(' ')
# 绘制ROC曲线
fpr, tpr, threshold = roc_curve(matching_label,
matching_out) ###计算真正率和假正率
q = np.where(0.95 <= tpr)
q_value = q[0][0]
fpr95 = fpr[q_value]
save_file.write('match fpr95 : ' + str(fpr95 * 100))
save_file.write('\n')
save_file.write('\n')
save_file.close()
roc_dir = 'plot_curve/roc/nature_map32_diff/epoch_' + str(
number)
try:
os.makedirs(roc_dir)
except os.error:
pass
np.savez(roc_dir + '/match_map32_diff_' + file_name, fpr, tpr)
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
finally:
# When done, ask the threads to stop.
coord.request_stop()
coord.join(threads)
def mc_train():
initLogging()
current_time = datetime.now().strftime('%Y-%m-%d')
try:
os.makedirs(checkpoint_dir)
except os.error:
pass
data1 = np.load('/home/ws/文档/wrj/data_all/country.npz')
train_matching_y = data1['arr_1'][:, np.newaxis]
numbers_train = train_matching_y.shape[0] #训练集总数
epoch_steps = np.int(
numbers_train / BATCH_SIZE_matching) + 1 # 一个epoch有多少个steps
all_loss = np.array([])
graph = tf.Graph()
with graph.as_default():
inputs_p1 = tf.placeholder(
tf.float32, [BATCH_SIZE_matching, image_height, image_width, 1],
name='inputs_p1')
inputs_p2 = tf.placeholder(
tf.float32, [BATCH_SIZE_matching, image_height, image_width, 1],
name='inputs_p2')
label_m = tf.placeholder(tf.float32, [BATCH_SIZE_matching, 1],
name='label_m')
# 训练 M
match_loss, match_output, all_features = model.match_network(
inputs_p1, inputs_p2, label_m)
match_out = tf.round(match_output)
m_correct, m_numbers = model.evaluation(match_out, label_m)
m_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(
match_loss)
filename = '/home/ws/文档/wrj/data_all/country/country.tfrecord'
filename_queue = tf.train.string_input_producer([filename],
num_epochs=epoch,
shuffle=True)
img_batch, label_batch = read_data.batch_inputs(
filename_queue, train=True, batch_size=BATCH_SIZE_matching)
tf.summary.scalar('mathing_loss', match_loss)
summary = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=20)
init = tf.global_variables_initializer()
gpu_options = tf.GPUOptions(allow_growth=True)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=sess_config) as sess:
summary_writer = tf.summary.FileWriter(train_dir, sess.graph)
sess.run(tf.local_variables_initializer())
sess.run(init)
try:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
step = 0
while not coord.should_stop():
start_time = time.time()
step = step + 1
batch, l_batch = sess.run([img_batch, label_batch])
l_batch = l_batch
x_batch = batch[:, :, :64, np.newaxis]
y_batch = batch[:, :, 64:, np.newaxis]
feed_dict = {
inputs_p1: x_batch,
inputs_p2: y_batch,
label_m: l_batch
}
_, m_loss, m_output_ = sess.run(
[m_train_opt, match_loss, match_output],
feed_dict=feed_dict)
if step % 10 == 0:
loss_write = np.array([[step, m_loss]])
if step == 10:
all_loss = loss_write
else:
all_loss = np.concatenate((all_loss, loss_write))
if step % 100 == 0:
duration = time.time() - start_time
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
logging.info(
'>> Step %d run_train: matching_loss = %.3f (%.3f sec)'
% (step, m_loss, duration))
if (step % epoch_steps == 0) and ((step / epoch_steps) % 3
== 0):
current_epoch = int(step / epoch_steps)
if current_epoch > 25:
logging.info('>> %s Saving in %s' %
(datetime.now(), checkpoint_dir))
saver.save(sess,
checkpoint_file,
global_step=current_epoch)
mc_test_all(current_epoch)
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
except Exception as e:
coord.request_stop(e)
finally:
saver.save(sess, checkpoint_file, global_step=step)
np.save(os.path.join(checkpoint_dir, 'ckpt_map32_diff'),
all_loss)
print('Model saved in file :%s' % checkpoint_dir)
# When done, ask the threads to stop.
coord.request_stop()
coord.join(threads)
def mc_test(file_name, number):
data = np.load('/home/ws/文档/wrj/data_all_test/test_' + file_name + '.npz')
train_matching_y = data['arr_1'][:, np.newaxis]
numbers_train = train_matching_y.shape[0] #训练集总数
graph = tf.Graph()
with graph.as_default():
inputs_p1 = tf.placeholder(
tf.float32, [BATCH_SIZE_matching, image_height, image_width, 1],
name='inputs_gray')
inputs_p2 = tf.placeholder(
tf.float32, [BATCH_SIZE_matching, image_height, image_width, 1],
name='inputs_nir')
label_m = tf.placeholder(tf.float32, [BATCH_SIZE_matching, 1],
name='label_m')
inputs_p1_ = model_g.preprocess(inputs_p1)
g_outputs = model_g.create_generator(inputs_p1_, 1)
g_outputs_ = model_g.deprocess(g_outputs)
# 训练 M
match_loss, match_output, all_features = model.match_network(
g_outputs_, inputs_p2, label_m)
match_out = tf.round(match_output)
m_correct, m_numbers = model.evaluation(match_out, label_m)
# /home/ws/文档/wrj/data_all_test/test_data/test_
filename = '/home/ws/文档/wrj/data_all_test/test_data/test_' + file_name + '.tfrecord'
# filename = '/home/ws/文档/wrj/data_all/country/'+file_name+'.tfrecord'
filename_queue = tf.train.string_input_producer([filename],
num_epochs=1,
shuffle=False)
img_batch, label_batch = read.batch_inputs(
filename_queue, train=False, batch_size=BATCH_SIZE_matching)
gen_tvars = [
var for var in tf.trainable_variables()
if var.name.startswith("generator")
]
saver_g = tf.train.Saver(var_list=gen_tvars)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
# saver.restore(sess, tf.train.latest_checkpoint('ckpt_map_gd'))
saver.restore(sess, 'ckpt_map_gd/model.ckpt-27')
saver_g.restore(
sess, tf.train.latest_checkpoint(checkpoint_dir_g + '_all'))
# saver.restore(sess, 'ckpt_tensor/model.ckpt-4000')
m_count = 0 # Counts the number of correct predictions.
num = numbers_train
matching_out = np.array([])
matching_label = np.array([])
try:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
step_test = 0
try:
while not coord.should_stop():
step_test = step_test + 1
batch, l_batch = sess.run([img_batch, label_batch])
l_batch = l_batch
x_batch = batch[:, :, :64, np.newaxis]
y_batch = batch[:, :, 64:, np.newaxis]
feed_dict = {
inputs_p1: x_batch,
inputs_p2: y_batch,
label_m: l_batch
}
m_correct_, m_output_ = sess.run(
[m_correct, match_output], feed_dict=feed_dict)
if step_test == 1:
matching_out = m_output_
matching_label = l_batch
elif (l_batch.size == BATCH_SIZE_matching):
matching_out = np.concatenate(
(matching_out, m_output_))
matching_label = np.concatenate(
(matching_label, l_batch))
m_count = m_count + m_correct_.astype(int)
if step_test % 100 == 0:
print(
'Step %d/%d run_test: batch_precision = %.2f '
% (step_test, num / 100,
m_correct_ / BATCH_SIZE_matching))
except Exception as e:
coord.request_stop(e)
m_precision = float(m_count) / num
print(
'Num examples: %d Num correct: %d match Precision : %0.04f '
% (num, m_count, m_precision))
save_file = open('test_map_gd.txt', 'a')
save_file.write(file_name + ' epoch: ' + str(number) +
'\n' + 'num correct: ' + str(m_count) + '/' +
str(num) + ' match precision : ' +
str(m_precision))
save_file.write(' ')
# 计算 tp,tn,fp,fn
pre_out = np.round(matching_out)
pre_out = pre_out.astype(int)
lab_out = matching_label.astype(int)
tp = np.sum(pre_out & lab_out)
tn = m_count - tp
fp = np.sum(pre_out) - tp
fn = np.sum(lab_out) - tp
save_file.write(file_name + ' tp: ' + str(tp)+ ' tn: ' + str(tn) \
+ ' fp: ' + str(fp)+ ' fn: ' + str(fn))
# 绘制ROC曲线
fpr, tpr, threshold = roc_curve(matching_label,
matching_out) ###计算真正率和假正率
q = np.where(0.95 <= tpr)
q_value = q[0][0]
fpr95 = fpr[q_value]
save_file.write('match fpr95 : ' + str(fpr95 * 100))
save_file.write('\n')
save_file.write('\n')
save_file.close()
roc_dir = 'plot_curve/epoch_' + str(number)
try:
os.makedirs(roc_dir)
except os.error:
pass
np.savez(roc_dir + '/match_map_gd_' + file_name, fpr, tpr)
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
finally:
# When done, ask the threads to stop.
coord.request_stop()
coord.join(threads)