def gfm_test(Data_file_name):
Data_file_name = Data_file_name
Data_file = h5py.File(Data_file_name +'.mat')
Data_file.keys()
patch_1_test = Data_file['test_sar_patch'] #sar
patch_2_test= Data_file['test_opt_patch'] #opt
patch_1_test = np.array(patch_1_test) #sar
patch_2_test= np.array(patch_2_test) #opt
num1 = int(patch_1_test.shape[0]/batch_size)*batch_size
num2 = int(patch_2_test.shape[0]/batch_size)*batch_size
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')
# g_outputs = model.create_generator(inputs_sar, 1)
match_loss,m_output= model.gfm_modelb(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_fm1b_' + Data_file_name.split('_')[1])))
saver.restore(sess, tf.train.latest_checkpoint('ckpt_fm3br_6'))
# saver.restore(sess, 'ckpt_fm1b/model.ckpt-4000')
all_mout = np.array([])
for i in range(num2): #opt
opt1 = patch_2_test[i,:,:,:]
opt = np.tile(opt1,(num1,1,1,1))
sar = patch_1_test[:num1,:,:,:]
print(i)
patch_1_sar = sar #sar
patch_2_opt = opt #opt
y_test = np.zeros((num1,1))
# all_lab = np.array([])
# num = np.size(y_test)
shuffle_test= gfm_shuffle(1,batch_size,patch_1_sar,patch_2_opt,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_m = sess.run([correct, m_output], feed_dict=feed_dict)
if step1 == 0:
all_mout = p_m
else:
all_mout = np.concatenate((all_mout, p_m), axis=0)
# true_count = true_count + result
if step1 % 100 == 0:
print('Step %d run_test: batch_precision = %.2f '
% (step1, result/batch_size))
all_mout = all_mout.T
if i == 0:
results = all_mout
else:
results = np.concatenate((results, all_mout), axis=0)
return results
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[:67000, :, :32, :] # sar
patch_2_test = patch_test[:67000, :, 32:, :] # opt
y_test = data1['arr_3'][:67000, :]
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_map+g_6'))
# saver.restore(sess, 'ckpt_map+g_6/model.ckpt-12000')
true_count = 0 # Counts the number of correct predictions.
all_mout = np.array([])
all_lab = np.array([])
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)
if step1 == 0:
all_mout = p_m
all_lab = l_batch
else:
all_mout = np.concatenate((all_mout, p_m), axis=0)
all_lab = np.concatenate((all_lab, l_batch), axis=0)
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))
def gfm_train():
current_time = datetime.now().strftime('%Y%m%d-%H%M')
checkpoints_dir = 'checkpoints/{}'.format(current_time)
try:
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
fake_opt = model.create_generator_1(inputs_sar, 1)
gen_1 = [
var for var in tf.trainable_variables()
if var.name.startswith("generator_1")
]
fake_sar = model.create_generator_2(inputs_opt, 1)
gen_2 = [
var for var in tf.trainable_variables()
if var.name.startswith("generator_2")
]
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_g_1 = tf.train.Saver(var_list=gen_1)
saver_g_2 = tf.train.Saver(var_list=gen_2)
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)
saver_g_1.restore(sess, tf.train.latest_checkpoint('ckpt_g6_s2o'))
saver_g_2.restore(sess, tf.train.latest_checkpoint('ckpt_g6_o2s'))
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
}
_, loss, m_output_ = sess.run(
[m_train_opt, match_loss, m_output],
feed_dict=feed_dict)
fake_opt_ = sess.run([fake_opt],
feed_dict={inputs_sar: x_batch})
fake_sar_ = sess.run([fake_sar],
feed_dict={inputs_opt: y_batch})
fake_opt_ = np.array(fake_opt_, np.float64)[0, :]
fake_sar_ = np.array(fake_sar_, np.float64)[0, :]
shuffle_index = np.random.permutation(batch_size)
shuffle_index = np.array(shuffle_index, np.int32)
fake_opt0 = fake_opt_[shuffle_index]
fake_sar0 = fake_sar_[shuffle_index]
X1 = np.concatenate(
(x_batch, x_batch, fake_sar_, fake_sar0), axis=0)
Y1 = np.concatenate(
(fake_opt_, fake_opt0, y_batch, y_batch), axis=0)
L1 = [1] * batch_size + [0] * batch_size + [
1
] * batch_size + [0] * batch_size
L1 = np.array(L1, np.float64)[:, np.newaxis]
shuffle0 = gfm_shuffle(1, batch_size, X1, Y1, L1)
for step1, (x_batch, y_batch,
l_batch) in enumerate(shuffle0):
feed_dict0 = {
inputs_sar: x_batch,
inputs_opt: y_batch,
inputs_lab: l_batch
}
_, G_loss, m_output_ = sess.run(
[m_train_opt, match_loss, m_output],
feed_dict=feed_dict0)
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: loss = %.2f G_loss = %.2f (%.3f sec)'
% (step, loss, G_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_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_tensor(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_train():
current_time = datetime.now().strftime('%Y%m%d-%H%M')
checkpoints_dir = 'checkpoints/{}'.format(current_time)
try:
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, :]
data2 = np.load('6_up_sift_harris_mapping_data.npy')
X_test = data2[30000:30100, :, :32, :]
Y_test = data2[30000:30100, :, 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')
inputs_lab = tf.placeholder(tf.float32, [batch_size, 1],
name='inputs_lab')
g_outputs = model.create_generator(inputs_sar, 1)
gen_tvars = [
var for var in tf.trainable_variables()
if var.name.startswith("generator")
]
# 训练 M
match_loss, m_output = model.gfm_sia_tensor(g_outputs, 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_g = tf.train.Saver(var_list=gen_tvars)
saver = tf.train.Saver()
init = tf.global_variables_initializer()
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(train_dir, sess.graph)
sess.run(init)
saver_g.restore(sess, tf.train.latest_checkpoint(checkpoint_dir_g))
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()
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 % 1000 == 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))
inputs_sar_test = tf.placeholder(
tf.float32, [100, image_height, image_width, 1],
name='inputs_sar')
g_out_test = model.create_generator(inputs_sar_test,
1,
reuse=True)
feed_dict = {inputs_sar_test: X_test}
g_out_test_result = sess.run(g_out_test,
feed_dict=feed_dict)
show_images = np.concatenate(
(X_test, g_out_test_result, Y_test), axis=1)
result = combine_images(show_images)
result = result * 255
misc.imsave(
'out6/_sia_tensorg_{}.png'.format(
str(epoch) + "_" + str(step)), result)
except KeyboardInterrupt:
print('INTERRUPTED')
finally:
saver.save(sess, checkpoint_file, global_step=step)
print('Model saved in file :%s' % checkpoint_dir)