def mc_test(file_name, number):
# data = np.load('/home/ws/文档/wrj/mapping_data_all/map_rgb_'+file_name+'.npz')
# train_matching_y = data['arr_0'][:]
# numbers_train = train_matching_y.shape[0] #训练集总数
BATCH_SIZE_map = 40
graph = tf.Graph()
with graph.as_default():
inputs_p1 = tf.placeholder(
tf.float32, [BATCH_SIZE_map, image_height, image_width, 1],
name='inputs_gray')
inputs_p2 = tf.placeholder(
tf.float32, [BATCH_SIZE_map, image_height, image_width, 1],
name='inputs_nir')
gen_loss, dis_loss, _, _ = model.gd_model_g2r(inputs_p1, inputs_p2)
inputs_p1_ = model.preprocess(inputs_p1)
gen = model.create_generator(inputs_p1_, 1, reuse=True)
gen = model.deprocess(gen)
# filename = '/home/ws/文档/wrj/mapping_data_gray/map_data/map_'+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_map)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
# saver.restore(sess, tf.train.latest_checkpoint('ckpt_gd_g2r_pro_all'))
saver.restore(sess,
'ckpt_gd_g2r_pro_all/model.ckpt-' + str(number))
# num = 0
try:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
step_test = 0
while not coord.should_stop():
if step_test < 1:
step_test = step_test + 1
batch, l_batch = sess.run([img_batch, label_batch])
x_batch = batch[:, :, :64, np.newaxis]
y_batch = batch[:, :, 64:, np.newaxis]
feed_dict = {inputs_p1: x_batch}
gen_out = sess.run(gen, feed_dict=feed_dict)
gen_out_dir = 'out_g2r_pro_all/epoch_' + str(number)
try:
os.makedirs(gen_out_dir)
except os.error:
pass
show_images = np.concatenate((y_batch, gen_out),
axis=1)
show_images = show_images * 255
for i in range(BATCH_SIZE_map):
cv2.imwrite(
gen_out_dir +
'/{}.png'.format(file_name + '_' + str(i + 1) +
"_gray"),
np.squeeze(x_batch[i, :, :, :]) * 255)
cv2.imwrite(
gen_out_dir +
'/{}.png'.format(file_name + '_' + str(i + 1) +
"_nir"),
np.squeeze(show_images[i, :64, :, :]))
cv2.imwrite(
gen_out_dir +
'/{}.png'.format(file_name + '_' + str(i + 1) +
"_fnir"),
np.squeeze(show_images[i, 64:, :, :]))
else:
break
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)
def gd_train(f_name):
checkpoint_dir = 'ckpt_gd_g2r_pro_' + f_name
checkpoint_dir_g = 'ckpt_g_g2r_pro_' + f_name
output_dir = 'out_g2r_pro_' + f_name
checkpoint_dir_en = 'ckpt_en_r2r_' + f_name
checkpoint_file = os.path.join(checkpoint_dir, 'model.ckpt')
checkpoint_file_g = os.path.join(checkpoint_dir_g, 'model.ckpt')
initLogging('record_gd_g2r_pro_' + f_name + '.log')
# if FLAGS.load_model is not None:
# checkpoints_dir = 'checkpoints/' + FLAGS.load_model
# else:
current_time = datetime.now().strftime('%Y%m%d-%H%M')
try:
os.makedirs(checkpoint_dir_g)
os.makedirs(checkpoint_dir)
except os.error:
pass
try:
os.makedirs(output_dir)
except os.error:
pass
#
data1 = np.load('/home/ws/文档/wrj/mapping_data_gray/map_' + f_name + '.npz')
train_matching_y = data1['arr_0'][:, 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_gray')
inputs_p2 = tf.placeholder(
tf.float32, [BATCH_SIZE_matching, image_height, image_width, 1],
name='inputs_nir')
# test_opt = tf.placeholder(tf.float32, [None, image_height, image_width, 1], name='test_opt')
# 训练 G
# 将输入图像从 （0，1）变换到 （-1，1）
inputs_p1_ = model.preprocess(inputs_p1)
inputs_p2_ = model.preprocess(inputs_p2)
gen_loss, dis_loss, _, _ = model.gd_model_g2r(inputs_p1_, inputs_p2_)
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")
]
g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(
gen_loss, var_list=gen_tvars)
nirencode_tvars = [
var for var in tf.trainable_variables()
if var.name.startswith("nir_encode")
]
# with tf.control_dependencies([g_train_opt, d_train_opt]):
# gd_train_opt = tf.no_op(name='optimizers')
#
filename = '/home/ws/文档/wrj/mapping_data_gray/map_data/map_' + f_name + '.tfrecord'
filename_queue = tf.train.string_input_producer([filename],
num_epochs=EPOCH,
shuffle=True)
img_batch, label_batch = read.batch_inputs(
filename_queue, train=True, batch_size=BATCH_SIZE_matching)
saver = tf.train.Saver(max_to_keep=20)
saver_g = tf.train.Saver(var_list=gen_tvars, max_to_keep=20)
saver_en = tf.train.Saver(var_list=nirencode_tvars)
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:
sess.run(tf.local_variables_initializer())
sess.run(init)
saver_en.restore(sess,
tf.train.latest_checkpoint(checkpoint_dir_en))
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])
x_batch = batch[:, :, :64, np.newaxis]
y_batch = batch[:, :, 64:, np.newaxis]
feed_dict = {inputs_p1: x_batch, inputs_p2: y_batch}
_, _, g_loss, d_loss = sess.run(
[d_train_opt, g_train_opt, gen_loss, dis_loss],
feed_dict=feed_dict)
if step % 10 == 0:
loss_write = np.array([[step, g_loss, d_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
logging.info(
'>> Step %d run_train: g_loss = %.2f d_loss = %.2f (%.3f sec)'
% (step, g_loss, d_loss, duration))
if (step % epoch_steps == 0) and ((step / epoch_steps) % 10
== 0):
current_epoch = int(step / epoch_steps)
if current_epoch >= 40:
logging.info('>> %s Saving in %s' %
(datetime.now(), checkpoint_dir))
saver.save(sess,
checkpoint_file,
global_step=current_epoch)
saver_g.save(sess,
checkpoint_file_g,
global_step=current_epoch)
# mc_test_all(current_epoch)
if current_epoch >= 40:
mc_test(f_name, 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)
saver_g.save(sess, checkpoint_file_g, global_step=step)
np.save(
os.path.join(checkpoint_dir, 'ckpt_map_gd_pro_' + f_name),
all_loss)
np.save(
os.path.join(checkpoint_dir_g,
'ckpt_map_gd_pro_' + f_name), 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_draw(file_name, number):
# data = np.load('/media/ws/98E62262E622413C/zzzz_wrj/wrj/mapping_data_all/map_rgb_'+file_name+'.npz')
# train_matching_y = data['arr_0'][:]
# numbers_train = train_matching_y.shape[0] #训练集总数
BATCH_SIZE_map = 40
graph = tf.Graph()
with graph.as_default():
inputs_p1 = tf.placeholder(
tf.float32, [BATCH_SIZE_map, image_height, image_width, 1],
name='inputs_gray')
inputs_p2 = tf.placeholder(
tf.float32, [BATCH_SIZE_map, image_height, image_width, 1],
name='inputs_nir')
inputs_p2_ = model.preprocess(inputs_p2)
# gen_loss, all_layers = model.gd_model_r2r(inputs_p2_)
gen, all_layers = model.create_generator(inputs_p2_, 1, reuse=True)
gen = model.deprocess(gen)
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_map)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.local_variables_initializer())
saver.restore(sess, tf.train.latest_checkpoint('ckpt_gd_r2r_all'))
# saver.restore(sess, 'ckpt_tensor/model.ckpt-4000')
# num = 0
try:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
step_test = 0
while not coord.should_stop():
if step_test < 1:
step_test = step_test + 1
batch, l_batch = sess.run([img_batch, label_batch])
y_batch = batch[:, :, 64:, np.newaxis]
feed_dict = {inputs_p2: y_batch}
gen_out, layers_out = sess.run([gen, all_layers],
feed_dict=feed_dict)
gen_out_dir = 'out_r2r_country/epoch_' + str(number)
try:
os.makedirs('feature_map')
os.makedirs(gen_out_dir)
except os.error:
pass
show_images = np.concatenate((y_batch, gen_out),
axis=1)
show_images = show_images * 255
for i in range(BATCH_SIZE_map):
# cv2.imwrite(gen_out_dir+'/{}.png'.format(file_name+'_'+str(i+1)+"_opt"), np.squeeze(x_batch[i,:,:,:]*255))
cv2.imwrite(
gen_out_dir +
'/{}.png'.format(file_name + '_' + str(i + 1) +
"_nir"),
np.squeeze(show_images[i, :64, :, :]))
cv2.imwrite(
gen_out_dir +
'/{}.png'.format(file_name + '_' + str(i + 1) +
"_fnir"),
np.squeeze(show_images[i, 64:, :, :]))
layers = np.squeeze(layers_out[2][i, :, :, :])
m = 0
n = 0
s = 8
max_ = np.max(layers)
min_ = np.min(layers)
layers_ = (layers - min_) / (max_ - min_)
features = np.ones(
(11 * s, 12 * s), np.uint8) * 255
for j in range(128):
feature = np.squeeze(
np.uint8(layers_[:, :, j] * 255))
features[m * s:m * s + s,
n * s:n * s + s] = feature
n += 1
if (n >= 12):
n = 0
m += 1
cv2.imwrite(
'feature_map/{}.png'.format(file_name + '_' +
str(i + 1)),
features)
else:
break
except KeyboardInterrupt:
print('INTERRUPTED')
coord.request_stop()
except Exception as e:
coord.request_stop(e)
finally:
coord.request_stop()
coord.join(threads)