def main():
#import data
training_data = GetData(TRAINING_DIR)
test_data = GetData(TEST_DIR)
with tf.name_scope('inputs'):
#create the model
x = tf.placeholder(tf.float32,
[Batch_SIZE, Img_depth, Img_rows, Img_cols, 1],
name='x_input')
# Define loss and optimizer
y_ = tf.placeholder(
tf.int16, [Batch_SIZE, Img_depth, Img_rows, Img_cols, n_class],
name='y__input')
#define a global step
global_step = tf.Variable(0, name="global_step")
# Build the graph for the deep net
network, outputs = network(x)
dice_loss = dice_coef_loss(outputs, y_)
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(1e-5).minimize(dice_loss)
#add ops to save and restore all the variables
saver = tf.train.Saver()
training_summary = tf.summary.scalar("training_loss", dice_loss)
validation_summary = tf.summary.scalar("validation_loss", dice_loss)
#use only single CPU
m_config = tf.ConfigProto()
m_config.gpu_options.allow_growth = True
with tf.Session(config=m_config) as sess:
summary_writer = tf.summary.FileWriter("log/", sess.graph)
sess.run(tf.global_variables_initializer(
)) #when continue training this model, should comment this line
#first start to train the model, should comment these lines
# check_points_list = tf.train.latest_checkpoint(LOG_DIR) #return the filename of the lastest checkpoint
# print(len(check_points_list))
# print(check_points_list) #is the name of this checkpoint
# saver.restore(sess,check_points_list)
#
global_step_value = sess.run(global_step)
print("Last iteration:", global_step_value)
for i in range(global_step_value + 1, 150000 + 1):
images, labels = training_data.next_batch(Batch_SIZE)
feed_dict_train = {x: images, y_: labels}
feed_dict_train.update(network.all_drop) #enable noise layers
train_step.run(feed_dict=feed_dict_train)
if i % 50 == 0:
print("iteration now:", i)
train_loss, train_summ = sess.run(
[dice_loss, training_summary], feed_dict=feed_dict_train)
summary_writer.add_summary(train_summ, i)
print('train loss %g' % train_loss)
images_test, labels_test = test_data.next_batch(Batch_SIZE)
dp_dict = tl.utils.dict_to_one(
network.all_drop) #disable nosie layers when testing
feed_dict_test = {x: images_test, y_: labels_test}
feed_dict_test.update(dp_dict)
# loss = dice_loss.eval(feed_dict=feed_dict)
valid_loss, valid_summ = sess.run(
[dice_loss, validation_summary], feed_dict=feed_dict_test)
summary_writer.add_summary(valid_summ, i)
print('test loss %g' % valid_loss)
print('----------------------------------')
if i % 5000 == 0:
print("iteration now:", i)
output_image = sess.run(
outputs,
feed_dict=feed_dict_test) #use the test next_batch
# output_image = outputs.eval(feed_dict=feed_dict_test)
print(type(output_image))
print(np.shape(output_image))
# output_image = np.asarray(output_image)
# output_image= outputs.eval(feed_dict={x:images})
for j in range(Batch_SIZE):
labels_test_union = labels_test[
...,
0] * 500 + labels_test[..., 1] * 600 + labels_test[
...,
2] * 420 + labels_test[..., 3] * 550 + labels_test[
..., 4] * 205 + labels_test[
..., 5] * 820 + labels_test[..., 6] * 850
input_Image = images_test[..., 0]
LVB = output_image[..., 0]
out_LVB = LVB[j, ...]
RVB = output_image[..., 1]
out_RVB = RVB[j, ...]
LAB = output_image[..., 2]
out_LAB = LAB[j, ...]
RAB = output_image[..., 3]
out_RAB = RAB[j, ...]
MLV = output_image[..., 4]
out_MLV = MLV[j, ...]
AA = output_image[..., 5]
out_AA = AA[j, ...]
PA = output_image[..., 6]
out_PA = PA[j, ...]
BACK = output_image[..., 7]
out_BACK = BACK[j, ...]
#将heart单独的label存储下来，查看效果
CreatNii_save(
out_LVB, save_dir,
"out_LVB" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
CreatNii_save(
out_RVB, save_dir,
"out_RVB" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
CreatNii_save(
out_LAB, save_dir,
"out_LAB" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
CreatNii_save(
out_RAB, save_dir,
"out_RAB" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
CreatNii_save(
out_MLV, save_dir,
"out_MLV" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
CreatNii_save(out_AA, save_dir,
"out_AA" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
CreatNii_save(out_PA, save_dir,
"out_PA" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
CreatNii_save(
out_BACK, save_dir,
"out_BACK" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
CreatNii_save(
input_Image[j, ...], save_dir,
"Input_Test_Image" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
CreatNii_save(
(labels_test_union[j,
...]).astype(np.float32), save_dir,
"Test_Label" + str(i) + "_" + str(j) + ".nii.gz",
np.eye(4))
if i % 1000 == 0:
print("iteration now:", i)
#注意global_step.assign()并不会改变global_step的值，只是创造了这么一个操作，只有运行它之后，global_step才会真正被赋值
global_step_op = global_step.assign(
i
) #this line is necessary, if not the iteration number is always 0
print("global_step_value:", sess.run(global_step_op))
saver.save(
sess, CHECKPOINT_FL, global_step=i
) #the "global_step" here is different from the one above
print("================================")
print("model is saved")
