def demo(self):
d = tools.Data(config)
if not os.path.exists(self.demo_dir + 'depth/'):
print('Demo depth folder not present!!!')
return
filenames = glob.glob(self.demo_dir + 'depth/*')
if len(filenames) == 0:
print('No files found in depth folder!!')
return
if not os.path.exists(self.demo_dir + 'voxel/'):
os.makedirs(self.demo_dir + 'voxel/')
if len(filenames) % self.batch_size != 0:
print('Number of images should be a multiple of batch size ({})'.
format(self.batch_size))
return
for i in range(len(filenames) // self.batch_size):
X_data_files = filenames[self.batch_size * i:self.batch_size *
(i + 1)]
X_test_batch = d.load_X_Y_voxel_grids(X_data_files)
Y_pred_batch = self.sess.run(self.Y_pred,
feed_dict={self.X: X_test_batch})
for i, filename in enumerate(X_data_files):
np.save(
filename.replace('/depth/',
'/voxel/').replace('.png', '.npy'),
Y_pred_batch[i, :, :, :, :])
ae_loss_t,gan_g_loss_t,gan_d_loss_t, Y_test_pred, Y_test_pred_nosig= \
sess.run([ae_loss, gan_g_loss,gan_d_loss, Y_pred,Y_pred_nosig],feed_dict={X: X_test_batch, Y: Y_test_batch})
to_save = {
'X_test': X_test_batch,
'Y_test_pred': Y_test_pred,
'Y_test_true': Y_test_batch
}
scipy.io.savemat(self.test_results_dir + 'X_Y_pred_' +
str(epoch).zfill(2) + '_' +
str(i).zfill(4) + '.mat',
to_save,
do_compression=True)
print "epoch:", epoch, " i:", i, " test ae loss:", ae_loss_t, " gan g loss:", gan_g_loss_t, " gan d loss:", gan_d_loss_t
#### full testing
# ...
#### model saving
if i % 500 == 0 and i > 0 and epoch % 1 == 0:
saver.save(sess,
save_path=self.train_models_dir +
'model.cptk')
print "epoch:", epoch, " i:", i, " model saved!"
if __name__ == "__main__":
data = tools.Data(config)
net = Network()
net.train(data)
def train(self, configure):
data = tools.Data(configure, epoch_walked)
best_acc = 0
# X = tf.placeholder(shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]], dtype=tf.float32)
X = tf.placeholder(
shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]],
dtype=tf.float32)
# Y = tf.placeholder(shape=[batch_size, output_shape[0], output_shape[1], output_shape[2]], dtype=tf.float32)
Y = tf.placeholder(shape=[
batch_size, output_shape[0], output_shape[1], output_shape[2]
],
dtype=tf.float32)
print X.get_shape()
lr = tf.placeholder(tf.float32)
training = tf.placeholder(tf.bool)
threshold = tf.placeholder(tf.float32)
with tf.variable_scope('ae'):
Y_pred, Y_pred_modi, Y_pred_nosig = self.ae_u(
X, training, batch_size, threshold)
with tf.variable_scope('dis'):
XY_real_pair = self.dis(X, Y, training)
with tf.variable_scope('dis', reuse=True):
XY_fake_pair = self.dis(X, Y_pred, training)
with tf.device('/gpu:' + GPU0):
################################ ae loss
Y_ = tf.reshape(Y, shape=[batch_size, -1])
Y_pred_modi_ = tf.reshape(Y_pred_modi, shape=[batch_size, -1])
w = tf.placeholder(
tf.float32) # power of foreground against background
ae_loss = tf.reduce_mean(
-tf.reduce_mean(w * Y_ * tf.log(Y_pred_modi_ + 1e-8),
reduction_indices=[1]) -
tf.reduce_mean((1 - w) *
(1 - Y_) * tf.log(1 - Y_pred_modi_ + 1e-8),
reduction_indices=[1]))
sum_ae_loss = tf.summary.scalar('ae_loss', ae_loss)
################################ wgan loss
gan_g_loss = -tf.reduce_mean(XY_fake_pair)
gan_d_loss = tf.reduce_mean(XY_fake_pair) - tf.reduce_mean(
XY_real_pair)
sum_gan_g_loss = tf.summary.scalar('gan_g_loss', gan_g_loss)
sum_gan_d_loss = tf.summary.scalar('gan_d_loss', gan_d_loss)
alpha = tf.random_uniform(shape=[
batch_size, input_shape[0] * input_shape[1] * input_shape[2]
],
minval=0.0,
maxval=1.0)
Y_pred_ = tf.reshape(Y_pred, shape=[batch_size, -1])
differences_ = Y_pred_ - Y_
interpolates = Y_ + alpha * differences_
with tf.variable_scope('dis', reuse=True):
XY_fake_intep = self.dis(X, interpolates, training)
gradients = tf.gradients(XY_fake_intep, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes - 1.0)**2)
gan_d_loss += 10 * gradient_penalty
################################# ae + gan loss
gan_g_w = 5
ae_w = 100 - gan_g_w
ae_gan_g_loss = ae_w * ae_loss + gan_g_w * gan_g_loss
with tf.device('/gpu:' + GPU0):
ae_var = [
var for var in tf.trainable_variables()
if var.name.startswith('ae')
]
dis_var = [
var for var in tf.trainable_variables()
if var.name.startswith('dis')
]
ae_g_optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(
ae_gan_g_loss,
var_list=ae_var)
dis_optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(
gan_d_loss,
var_list=dis_var)
print tools.Ops.variable_count()
sum_merged = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=1)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.visible_device_list = GPU0
with tf.Session(config=config) as sess:
# if os.path.exists(self.train_models_dir):
# try:
# saver.restore(sess,self.train_models_dir+'model.cptk')
# except Exception,e:
# saver.restore(sess,'./regular/'+'model.cptk')
sum_writer_train = tf.summary.FileWriter(self.train_sum_dir,
sess.graph)
sum_write_test = tf.summary.FileWriter(self.test_sum_dir)
if os.path.isfile(self.train_models_dir +
'model.cptk.data-00000-of-00001'):
print "restoring saved model"
saver.restore(sess, self.train_models_dir + 'model.cptk')
else:
sess.run(tf.global_variables_initializer())
learning_rate_g = ori_lr * pow(power, (epoch_walked / 4))
for epoch in range(epoch_walked, 15000):
# data.shuffle_X_Y_files(label='train')
#### select data randomly each 10 epochs
if epoch % 2 == 0 and epoch > 0:
del data
gc.collect()
data = tools.Data(configure, epoch)
#### full testing
# ...
train_amount = len(data.train_numbers)
test_amount = len(data.test_numbers)
if train_amount >= test_amount and train_amount > 0 and test_amount > 0 and data.total_train_batch_num > 0 and data.total_test_seq_batch > 0:
weight_for = 0.35 * (1 - epoch * 1.0 / 15000) + 0.5
if epoch % 4 == 0:
print '********************** FULL TESTING ********************************'
time_begin = time.time()
lung_img = ST.ReadImage('./WANG_REN/lung_img.vtk')
mask_dir = "./WANG_REN/airway"
test_batch_size = batch_size
# test_data = tools.Test_data(dicom_dir,input_shape)
test_data = tools.Test_data(lung_img, input_shape,
'vtk_data')
test_data.organize_blocks()
test_mask = read_dicoms(mask_dir)
array_mask = ST.GetArrayFromImage(test_mask)
array_mask = np.transpose(array_mask, (2, 1, 0))
print "mask shape: ", np.shape(array_mask)
time1 = time.time()
block_numbers = test_data.blocks.keys()
for i in range(0, len(block_numbers), test_batch_size):
batch_numbers = []
if i + test_batch_size < len(block_numbers):
temp_input = np.zeros([
test_batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
for j in range(test_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0],
0:block_shape[1],
0:block_shape[2]] += block_array
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[Y_pred, Y_pred_modi, Y_pred_nosig],
feed_dict={
X: temp_input,
training: False,
w: weight_for,
threshold: upper_threshold
})
for j in range(test_batch_size):
test_data.upload_result(
batch_numbers[j],
Y_temp_modi[j, :, :, :])
else:
temp_batch_size = len(block_numbers) - i
temp_input = np.zeros([
temp_batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
for j in range(temp_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0],
0:block_shape[1],
0:block_shape[2]] += block_array
X_temp = tf.placeholder(shape=[
temp_batch_size, input_shape[0],
input_shape[1], input_shape[2]
],
dtype=tf.float32)
with tf.variable_scope('ae', reuse=True):
Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp = self.ae_u(
X_temp, training, temp_batch_size,
threshold)
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[
Y_pred_temp, Y_pred_modi_temp,
Y_pred_nosig_temp
],
feed_dict={
X_temp: temp_input,
training: False,
w: weight_for,
threshold: upper_threshold
})
for j in range(temp_batch_size):
test_data.upload_result(
batch_numbers[j],
Y_temp_modi[j, :, :, :])
test_result_array = test_data.get_result()
print "result shape: ", np.shape(test_result_array)
r_s = np.shape(test_result_array) # result shape
e_t = 10 # edge thickness
to_be_transformed = np.zeros(r_s, np.float32)
to_be_transformed[
e_t:r_s[0] - e_t, e_t:r_s[1] - e_t, e_t:r_s[2] -
e_t] += test_result_array[e_t:r_s[0] - e_t,
e_t:r_s[1] - e_t,
e_t:r_s[2] - e_t]
print np.max(to_be_transformed)
print np.min(to_be_transformed)
final_img = ST.GetImageFromArray(
np.transpose(to_be_transformed, [2, 1, 0]))
final_img.SetSpacing(test_data.space)
print "writing full testing result"
print '/usr/analyse_airway/test_result/test_result' + str(
epoch) + '.vtk'
ST.WriteImage(
final_img,
'/usr/analyse_airway/test_result/test_result' +
str(epoch) + '.vtk')
if epoch == 0:
mask_img = ST.GetImageFromArray(
np.transpose(array_mask, [2, 1, 0]))
mask_img.SetSpacing(test_data.space)
ST.WriteImage(
mask_img,
'/usr/analyse_airway/test_result/test_mask.vtk'
)
test_IOU = 2 * np.sum(
to_be_transformed * array_mask) / (
np.sum(to_be_transformed) + np.sum(array_mask))
print "IOU accuracy: ", test_IOU
time_end = time.time()
print '******************** time of full testing: ' + str(
time_end - time_begin) + 's ********************'
data.shuffle_X_Y_pairs()
total_train_batch_num = data.total_train_batch_num
# train_files=data.X_train_files
# test_files=data.X_test_files
# total_train_batch_num = 500
print "total_train_batch_num:", total_train_batch_num
for i in range(total_train_batch_num):
#### training
X_train_batch, Y_train_batch = data.load_X_Y_voxel_train_next_batch(
)
# X_train_batch, Y_train_batch = data.load_X_Y_voxel_grids_train_next_batch()
# Y_train_batch=np.reshape(Y_train_batch,[batch_size, output_shape[0], output_shape[1], output_shape[2], 1])
gan_d_loss_c, = sess.run(
[gan_d_loss],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
ae_loss_c, gan_g_loss_c, sum_train = sess.run(
[ae_loss, gan_g_loss, sum_merged],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
if epoch % 4 == 0 and epoch > 0 and i == 0:
learning_rate_g = learning_rate_g * power
sess.run(
[ae_g_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
if epoch <= 5:
sess.run(
[dis_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
elif epoch <= 20:
sess.run(
[dis_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
else:
sess.run(
[dis_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
sum_writer_train.add_summary(
sum_train, epoch * total_train_batch_num + i)
if i % 2 == 0:
print "epoch:", epoch, " i:", i, " train ae loss:", ae_loss_c, " gan g loss:", gan_g_loss_c, " gan d loss:", gan_d_loss_c, " learning rate: ", learning_rate_g
#### testing
if i % 20 == 0 and epoch % 1 == 0:
try:
X_test_batch, Y_test_batch = data.load_X_Y_voxel_test_next_batch(
fix_sample=False)
# Y_test_batch = np.reshape(Y_test_batch,[batch_size, output_shape[0], output_shape[1], output_shape[2], 1])
ae_loss_t,gan_g_loss_t,gan_d_loss_t, Y_test_pred,Y_test_modi, Y_test_pred_nosig= \
sess.run([ae_loss, gan_g_loss,gan_d_loss, Y_pred,Y_pred_modi,Y_pred_nosig],feed_dict={X: X_test_batch, threshold:upper_threshold, Y: Y_test_batch,training:False, w: weight_for})
predict_result = np.float32(Y_test_modi > 0.01)
predict_result = np.reshape(
predict_result, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
# Foreground
# if np.sum(Y_test_batch)>0:
# accuracy_for = np.sum(predict_result*Y_test_batch)/np.sum(Y_test_batch)
# Background
# accuracy_bac = np.sum((1-predict_result)*(1-Y_test_batch))/(np.sum(1-Y_test_batch))
# IOU
predict_probablity = np.float32(
(Y_test_modi - 0.01) > 0)
predict_probablity = np.reshape(
predict_probablity, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
accuracy = 2 * np.sum(
np.abs(predict_probablity *
Y_test_batch)) / np.sum(
np.abs(predict_result) +
np.abs(Y_test_batch))
# if epoch%30==0 and epoch>0:
# to_save = {'X_test': X_test_batch, 'Y_test_pred': Y_test_pred,'Y_test_true': Y_test_batch}
# scipy.io.savemat(self.test_results_dir + 'X_Y_pred_' + str(epoch).zfill(2) + '_' + str(i).zfill(4) + '.mat', to_save, do_compression=True)
print "epoch:", epoch, " i:", "\nIOU accuracy: ", accuracy, "\ntest ae loss:", ae_loss_t, " gan g loss:", gan_g_loss_t, " gan d loss:", gan_d_loss_t
if accuracy > best_acc:
saver.save(
sess,
save_path=self.train_models_dir +
'model.cptk')
print "epoch:", epoch, " i:", i, "best model saved!"
best_acc = accuracy
except Exception, e:
print e
#### model saving
if i % 30 == 0 and epoch % 1 == 0:
# regular_train_dir = "./regular/"
# if not os.path.exists(regular_train_dir):
# os.makedirs(regular_train_dir)
saver.save(sess,
save_path=self.train_models_dir +
'model.cptk')
print "epoch:", epoch, " i:", i, "regular model saved!"
else:
print "bad data , next epoch", epoch
def train(self, configure):
# data
data = tools.Data(configure, epoch_walked / re_example_epoch)
# network
X = tf.placeholder(
shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]],
dtype=tf.float32)
Y = tf.placeholder(shape=[
batch_size, output_shape[0], output_shape[1], output_shape[2]
],
dtype=tf.float32)
lr = tf.placeholder(tf.float32)
training = tf.placeholder(tf.bool)
threshold = tf.placeholder(tf.float32)
with tf.variable_scope('generator'):
Y_pred, Y_pred_modi, Y_pred_nosig = self.ae_u(
X, training, batch_size, threshold)
with tf.variable_scope('discriminator'):
XY_real_pair = self.dis(X, Y, training)
with tf.variable_scope('discriminator', reuse=True):
XY_fake_pair = self.dis(X, Y_pred, training)
# loss function
# generator loss
Y_ = tf.reshape(Y, shape=[batch_size, -1])
Y_pred_modi_ = tf.reshape(Y_pred_modi, shape=[batch_size, -1])
w = tf.placeholder(tf.float32) # foreground weight
g_loss = tf.reduce_mean(-tf.reduce_mean(
w * Y_ * tf.log(Y_pred_modi_ + 1e-8), reduction_indices=[1]) -
tf.reduce_mean((1 - w) * (1 - Y_) *
tf.log(1 - Y_pred_modi_ + 1e-8),
reduction_indices=[1]))
g_loss_sum = tf.summary.scalar("generator cross entropy", g_loss)
# discriminator loss
gan_d_loss = tf.reduce_mean(XY_fake_pair) - tf.reduce_mean(
XY_real_pair)
alpha = tf.random_uniform(shape=[
batch_size, input_shape[0] * input_shape[1] * input_shape[2]
],
minval=0.0,
maxval=1.0)
Y_pred_ = tf.reshape(Y_pred, shape=[batch_size, -1])
differences_ = Y_pred_ - Y_
interpolates = Y_ + alpha * differences_
with tf.variable_scope('discriminator', reuse=True):
XY_fake_intep = self.dis(X, interpolates, training)
gradients = tf.gradients(XY_fake_intep, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes - 1.0)**2)
gan_d_loss += 10 * gradient_penalty
gan_d_loss_sum = tf.summary.scalar("total loss of discriminator",
gan_d_loss)
# generator loss with gan loss
gan_g_loss = -tf.reduce_mean(XY_fake_pair)
gan_g_w = 5
ae_w = 100 - gan_g_w
ae_gan_g_loss = ae_w * g_loss + gan_g_w * gan_g_loss
ae_g_loss_sum = tf.summary.scalar("total loss of generator",
ae_gan_g_loss)
# trainers
ae_var = [
var for var in tf.trainable_variables()
if var.name.startswith('generator')
]
dis_var = [
var for var in tf.trainable_variables()
if var.name.startswith('discriminator')
]
ae_g_optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(
ae_gan_g_loss, var_list=ae_var)
dis_optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(
gan_d_loss, var_list=dis_var)
# accuracy
block_acc = tf.placeholder(tf.float32)
total_acc = tf.placeholder(tf.float32)
train_sum = tf.summary.scalar("train_block_accuracy", block_acc)
test_sum = tf.summary.scalar("total_test_accuracy", total_acc)
train_merge_op = tf.summary.merge(
[train_sum, ae_g_loss_sum, gan_d_loss_sum, g_loss_sum])
test_merge_op = tf.summary.merge([test_sum])
saver = tf.train.Saver(max_to_keep=1)
# config = tf.ConfigProto(allow_soft_placement=True)
# config.gpu_options.visible_device_list = GPU0
with tf.Session() as sess:
# define tensorboard writer
sum_writer_train = tf.summary.FileWriter(self.train_sum_dir,
sess.graph)
sum_write_test = tf.summary.FileWriter(self.test_sum_dir,
sess.graph)
# load model data if pre-trained
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
if os.path.isfile(self.train_models_dir +
'model.cptk.data-00000-of-00001'):
print "restoring saved model"
saver.restore(sess, self.train_models_dir + 'model.cptk')
learning_rate_g = ori_lr * pow(power, (epoch_walked / decay_step))
# start training loop
global_step = step_walked
for epoch in range(epoch_walked, MAX_EPOCH):
if epoch % re_example_epoch == 0 and epoch > 0:
del data
gc.collect()
data = tools.Data(configure, epoch / re_example_epoch)
train_amount = len(data.train_numbers)
test_amount = len(data.test_numbers)
if train_amount >= test_amount and train_amount > 0 and test_amount > 0 and data.total_train_batch_num > 0 and data.total_test_seq_batch > 0:
# actual foreground weight
weight_for = 0.5 + (1 - 1.0 * epoch / MAX_EPOCH) * 0.35
if epoch % total_test_epoch == 0:
self.full_testing(sess, X, w, threshold, test_merge_op,
sum_write_test, training, weight_for,
total_acc, Y_pred, Y_pred_modi,
Y_pred_nosig, epoch)
# print '********************** FULL TESTING ********************************'
# time_begin = time.time()
# origin_dir = read_dicoms(test_dir + "original1")
# mask_dir = test_dir + "airway"
# test_batch_size = batch_size
# # test_data = tools.Test_data(dicom_dir,input_shape)
# test_data = tools.Test_data(origin_dir, input_shape, 'vtk_data')
# test_data.organize_blocks()
# test_mask = read_dicoms(mask_dir)
# array_mask = ST.GetArrayFromImage(test_mask)
# array_mask = np.transpose(array_mask, (2, 1, 0))
# print "mask shape: ", np.shape(array_mask)
# block_numbers = test_data.blocks.keys()
# for i in range(0, len(block_numbers), test_batch_size):
# batch_numbers = []
# if i + test_batch_size < len(block_numbers):
# temp_input = np.zeros(
# [test_batch_size, input_shape[0], input_shape[1], input_shape[2]])
# for j in range(test_batch_size):
# temp_num = block_numbers[i + j]
# temp_block = test_data.blocks[temp_num]
# batch_numbers.append(temp_num)
# block_array = temp_block.load_data()
# block_shape = np.shape(block_array)
# temp_input[j, 0:block_shape[0], 0:block_shape[1],
# 0:block_shape[2]] += block_array
# Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
# [Y_pred, Y_pred_modi, Y_pred_nosig],
# feed_dict={X: temp_input,
# training: False,
# w: weight_for,
# threshold: upper_threshold + test_extra_threshold})
# for j in range(test_batch_size):
# test_data.upload_result(batch_numbers[j], Y_temp_modi[j, :, :, :])
# else:
# temp_batch_size = len(block_numbers) - i
# temp_input = np.zeros(
# [temp_batch_size, input_shape[0], input_shape[1], input_shape[2]])
# for j in range(temp_batch_size):
# temp_num = block_numbers[i + j]
# temp_block = test_data.blocks[temp_num]
# batch_numbers.append(temp_num)
# block_array = temp_block.load_data()
# block_shape = np.shape(block_array)
# temp_input[j, 0:block_shape[0], 0:block_shape[1],
# 0:block_shape[2]] += block_array
# X_temp = tf.placeholder(
# shape=[temp_batch_size, input_shape[0], input_shape[1], input_shape[2]],
# dtype=tf.float32)
# with tf.variable_scope('generator', reuse=True):
# Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp = self.ae_u(X_temp, training,
# temp_batch_size,
# threshold)
# Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
# [Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp],
# feed_dict={X_temp: temp_input,
# training: False,
# w: weight_for,
# threshold: upper_threshold + test_extra_threshold})
# for j in range(temp_batch_size):
# test_data.upload_result(batch_numbers[j], Y_temp_modi[j, :, :, :])
# test_result_array = test_data.get_result()
# print "result shape: ", np.shape(test_result_array)
# to_be_transformed = self.post_process(test_result_array)
# if epoch % output_epoch == 0:
# self.output_img(to_be_transformed, test_data.space, epoch)
# if epoch == 0:
# mask_img = ST.GetImageFromArray(np.transpose(array_mask, [2, 1, 0]))
# mask_img.SetSpacing(test_data.space)
# ST.WriteImage(mask_img, './test_result/test_mask.vtk')
# test_IOU = 2 * np.sum(to_be_transformed * array_mask) / (
# np.sum(to_be_transformed) + np.sum(array_mask))
# test_summary = sess.run(test_merge_op, feed_dict={total_acc: test_IOU})
# sum_write_test.add_summary(test_summary, global_step=epoch)
# print "IOU accuracy: ", test_IOU
# time_end = time.time()
# print '******************** time of full testing: ' + str(time_end - time_begin) + 's ********************'
data.shuffle_X_Y_pairs()
total_train_batch_num = data.total_train_batch_num
print "total_train_batch_num:", total_train_batch_num
for i in range(total_train_batch_num):
X_train_batch, Y_train_batch = data.load_X_Y_voxel_train_next_batch(
)
# calculate loss value
# print "calculate begin"
gan_d_loss_c, = sess.run(
[gan_d_loss],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
g_loss_c, gan_g_loss_c = sess.run(
[g_loss, ae_gan_g_loss],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
# print "calculate ended"
if epoch % decay_step == 0 and epoch > epoch_walked and i == 0:
learning_rate_g = learning_rate_g * power
sess.run(
[ae_g_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
sess.run(
[dis_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
# print "training ended"
global_step += 1
# output some results
if i % show_step == 0:
print "epoch:", epoch, " i:", i, " train ae loss:", g_loss_c, " gan g loss:", gan_g_loss_c, " gan d loss:", gan_d_loss_c, " learning rate: ", learning_rate_g
if i % block_test_step == 0 and epoch % 1 == 0:
try:
X_test_batch, Y_test_batch = data.load_X_Y_voxel_test_next_batch(
fix_sample=False)
g_loss_t, gan_g_loss_t, gan_d_loss_t, Y_test_pred, Y_test_modi, Y_test_pred_nosig = \
sess.run([g_loss, ae_gan_g_loss, gan_d_loss, Y_pred, Y_pred_modi, Y_pred_nosig],
feed_dict={X: X_test_batch,
threshold: upper_threshold + test_extra_threshold,
Y: Y_test_batch, training: False, w: weight_for})
predict_result = np.float32(Y_test_modi > 0.01)
predict_result = np.reshape(
predict_result, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
print np.max(Y_test_pred)
print np.min(Y_test_pred)
# IOU
predict_probablity = np.float32(
(Y_test_modi - 0.01) > 0)
predict_probablity = np.reshape(
predict_probablity, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
accuracy = 2 * np.sum(
np.abs(predict_probablity *
Y_test_batch)) / np.sum(
np.abs(predict_result) +
np.abs(Y_test_batch))
print "epoch:", epoch, " global step: ", global_step, "\nIOU accuracy: ", accuracy, "\ntest ae loss:", g_loss_t, " gan g loss:", gan_g_loss_t, " gan d loss:", gan_d_loss_t
print "weight of foreground : ", weight_for
print "upper threshold of testing", (
upper_threshold + test_extra_threshold)
train_summary = sess.run(
train_merge_op,
feed_dict={
block_acc: accuracy,
X: X_test_batch,
threshold:
upper_threshold + test_extra_threshold,
Y: Y_test_batch,
training: False,
w: weight_for
})
sum_writer_train.add_summary(
train_summary, global_step=global_step)
except Exception, e:
print e
#### model saving
if i % model_save_step == 0 and epoch % 1 == 0:
saver.save(sess,
save_path=self.train_models_dir +
'model.cptk')
print "epoch:", epoch, " i:", i, "regular model saved!"
else:
print "bad data , next epoch", epoch
def train(self, configure):
# data
data = tools.Data(configure, epoch_walked / re_example_epoch)
# network
X = tf.placeholder(
shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]],
dtype=tf.float32)
Y = tf.placeholder(shape=[
batch_size, output_shape[0], output_shape[1], output_shape[2]
],
dtype=tf.float32)
lr = tf.placeholder(tf.float32)
training = tf.placeholder(tf.bool)
threshold = tf.placeholder(tf.float32)
with tf.variable_scope('segmenter'):
Y_pred, Y_pred_modi, Y_pred_nosig = self.ae_u(
X, training, batch_size, threshold)
# loss function
Y_ = tf.reshape(Y, shape=[batch_size, -1])
Y_pred_modi_ = tf.reshape(Y_pred_modi, shape=[batch_size, -1])
w = tf.placeholder(tf.float32) # foreground weight
cross_loss = tf.reduce_mean(
-tf.reduce_mean(w * Y_ * tf.log(Y_pred_modi_ + 1e-8),
reduction_indices=[1]) -
tf.reduce_mean((1 - w) *
(1 - Y_) * tf.log(1 - Y_pred_modi_ + 1e-8),
reduction_indices=[1]))
loss_sum = tf.summary.scalar("cross entropy", cross_loss)
# trainers
optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(cross_loss)
# accuracy
block_acc = tf.placeholder(tf.float32)
total_acc = tf.placeholder(tf.float32)
train_sum = tf.summary.scalar("train_block_accuracy", block_acc)
test_sum = tf.summary.scalar("total_test_accuracy", total_acc)
train_merge_op = tf.summary.merge([train_sum, loss_sum])
test_merge_op = tf.summary.merge([test_sum])
saver = tf.train.Saver(max_to_keep=1)
# config = tf.ConfigProto(allow_soft_placement=True)
# config.gpu_options.visible_device_list = GPU0
with tf.Session() as sess:
# define tensorboard writer
sum_writer_train = tf.summary.FileWriter(self.train_sum_dir,
sess.graph)
sum_write_test = tf.summary.FileWriter(self.test_sum_dir,
sess.graph)
# load model data if pre-trained
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
if os.path.isfile(self.train_models_dir +
'model.cptk.data-00000-of-00001'):
print "restoring saved model"
saver.restore(sess, self.train_models_dir + 'model.cptk')
learning_rate_g = ori_lr * pow(power, (epoch_walked / decay_step))
# start training loop
global_step = step_walked
for epoch in range(epoch_walked, MAX_EPOCH):
if epoch % re_example_epoch == 0 and epoch > 0:
del data
gc.collect()
data = tools.Data(configure, epoch / re_example_epoch)
train_amount = len(data.train_numbers)
test_amount = len(data.test_numbers)
if train_amount >= test_amount and train_amount > 0 and test_amount > 0 and data.total_train_batch_num > 0 and data.total_test_seq_batch > 0:
# actual foreground weight
weight_for = 0.5 + (1 - 1.0 * epoch / MAX_EPOCH) * 0.35
if epoch % total_test_epoch == 0 and epoch > 0:
print '********************** FULL TESTING ********************************'
time_begin = time.time()
origin_dir = read_dicoms(test_dir + "original1")
mask_dir = test_dir + "artery"
test_batch_size = batch_size
# test_data = tools.Test_data(dicom_dir,input_shape)
test_data = tools.Test_data(origin_dir, input_shape,
'vtk_data')
test_data.organize_blocks()
test_mask = read_dicoms(mask_dir)
array_mask = ST.GetArrayFromImage(test_mask)
array_mask = np.transpose(array_mask, (2, 1, 0))
print "mask shape: ", np.shape(array_mask)
block_numbers = test_data.blocks.keys()
for i in range(0, len(block_numbers), test_batch_size):
batch_numbers = []
if i + test_batch_size < len(block_numbers):
temp_input = np.zeros([
test_batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
for j in range(test_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0],
0:block_shape[1],
0:block_shape[2]] += block_array
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[Y_pred, Y_pred_modi, Y_pred_nosig],
feed_dict={
X:
temp_input,
training:
False,
w:
weight_for,
threshold:
upper_threshold + test_extra_threshold
})
for j in range(test_batch_size):
test_data.upload_result(
batch_numbers[j],
Y_temp_modi[j, :, :, :])
else:
temp_batch_size = len(block_numbers) - i
temp_input = np.zeros([
temp_batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
for j in range(temp_batch_size):
temp_num = block_numbers[i + j]
temp_block = test_data.blocks[temp_num]
batch_numbers.append(temp_num)
block_array = temp_block.load_data()
block_shape = np.shape(block_array)
temp_input[j, 0:block_shape[0],
0:block_shape[1],
0:block_shape[2]] += block_array
X_temp = tf.placeholder(shape=[
temp_batch_size, input_shape[0],
input_shape[1], input_shape[2]
],
dtype=tf.float32)
with tf.variable_scope('segmenter',
reuse=True):
Y_pred_temp, Y_pred_modi_temp, Y_pred_nosig_temp = self.ae_u(
X_temp, training, temp_batch_size,
threshold)
Y_temp_pred, Y_temp_modi, Y_temp_pred_nosig = sess.run(
[
Y_pred_temp, Y_pred_modi_temp,
Y_pred_nosig_temp
],
feed_dict={
X_temp:
temp_input,
training:
False,
w:
weight_for,
threshold:
upper_threshold + test_extra_threshold
})
for j in range(temp_batch_size):
test_data.upload_result(
batch_numbers[j],
Y_temp_modi[j, :, :, :])
test_result_array = test_data.get_result()
print "result shape: ", np.shape(test_result_array)
to_be_transformed = self.post_process(
test_result_array)
if epoch % output_epoch == 0:
self.output_img(to_be_transformed, test_data.space,
epoch)
if epoch == 0:
mask_img = ST.GetImageFromArray(
np.transpose(array_mask, [2, 1, 0]))
mask_img.SetSpacing(test_data.space)
ST.WriteImage(mask_img,
'./test_result/test_mask.vtk')
test_IOU = 2 * np.sum(
to_be_transformed * array_mask) / (
np.sum(to_be_transformed) + np.sum(array_mask))
test_summary = sess.run(
test_merge_op, feed_dict={total_acc: test_IOU})
sum_write_test.add_summary(test_summary,
global_step=epoch)
print "IOU accuracy: ", test_IOU
time_end = time.time()
print '******************** time of full testing: ' + str(
time_end - time_begin) + 's ********************'
data.shuffle_X_Y_pairs()
total_train_batch_num = data.total_train_batch_num
print "total_train_batch_num:", total_train_batch_num
for i in range(total_train_batch_num):
X_train_batch, Y_train_batch = data.load_X_Y_voxel_train_next_batch(
)
# calculate loss value
# print "calculate begin"
loss_c = sess.run(
[cross_loss],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
# print "calculate ended"
if epoch % decay_step == 0 and epoch > epoch_walked and i == 0:
learning_rate_g = learning_rate_g * power
sess.run(
[optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
# print "training ended"
global_step += 1
# output some results
if i % show_step == 0:
print "epoch:", epoch, " i:", i, " train loss:", loss_c, " gan g loss:", learning_rate_g
if i % block_test_step == 0 and epoch % 1 == 0:
try:
X_test_batch, Y_test_batch = data.load_X_Y_voxel_test_next_batch(
fix_sample=False)
Y_test_pred, Y_test_modi, Y_test_pred_nosig ,loss_t= \
sess.run([ Y_pred, Y_pred_modi, Y_pred_nosig,cross_loss],
feed_dict={X: X_test_batch,
threshold: upper_threshold + test_extra_threshold,
Y: Y_test_batch, training: False, w: weight_for})
predict_result = np.float32(Y_test_modi > 0.01)
predict_result = np.reshape(
predict_result, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
print np.max(Y_test_pred)
print np.min(Y_test_pred)
# IOU
predict_probablity = np.float32(
(Y_test_modi - 0.01) > 0)
predict_probablity = np.reshape(
predict_probablity, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
accuracy = 2 * np.sum(
np.abs(predict_probablity *
Y_test_batch)) / np.sum(
np.abs(predict_result) +
np.abs(Y_test_batch))
print "epoch:", epoch, " global step: ", global_step, "\nIOU accuracy: ", accuracy, "\ntest ae loss:", loss_t
print "weight of foreground : ", weight_for
print "upper threshold of testing", (
upper_threshold + test_extra_threshold)
train_summary = sess.run(
train_merge_op,
feed_dict={
block_acc: accuracy,
X: X_test_batch,
threshold:
upper_threshold + test_extra_threshold,
Y: Y_test_batch,
training: False,
w: weight_for
})
sum_writer_train.add_summary(
train_summary, global_step=global_step)
except Exception, e:
print e
#### model saving
if i % model_save_step == 0 and epoch % 1 == 0:
saver.save(sess,
save_path=self.train_models_dir +
'model.cptk')
print "epoch:", epoch, " i:", i, "regular model saved!"
else:
print "bad data , next epoch", epoch
def train(self, configure):
# data
data = tools.Data(configure, epoch_walked / re_example_epoch)
# network
X = tf.placeholder(
shape=[batch_size, input_shape[0], input_shape[1], input_shape[2]],
dtype=tf.float32)
Y = tf.placeholder(shape=[
batch_size, output_shape[0], output_shape[1], output_shape[2]
],
dtype=tf.float32)
lr = tf.placeholder(tf.float32)
training = tf.placeholder(tf.bool)
threshold = tf.placeholder(tf.float32)
with tf.variable_scope('generator'):
Y_pred, Y_pred_modi, Y_pred_nosig = self.ae_u(
X, training, batch_size, threshold)
with tf.variable_scope('discriminator'):
XY_real_pair = self.dis(X, Y, training)
with tf.variable_scope('discriminator', reuse=True):
XY_fake_pair = self.dis(X, Y_pred, training)
# loss function
# generator loss
Y_ = tf.reshape(Y, shape=[batch_size, -1])
Y_pred_modi_ = tf.reshape(Y_pred_modi, shape=[batch_size, -1])
w = tf.placeholder(tf.float32) # foreground weight
g_loss = tf.reduce_mean(-tf.reduce_mean(
w * Y_ * tf.log(Y_pred_modi_ + 1e-8), reduction_indices=[1]) -
tf.reduce_mean((1 - w) * (1 - Y_) *
tf.log(1 - Y_pred_modi_ + 1e-8),
reduction_indices=[1]))
g_loss_sum = tf.summary.scalar("generator cross entropy", g_loss)
# discriminator loss
gan_d_loss = tf.reduce_mean(XY_fake_pair) - tf.reduce_mean(
XY_real_pair)
alpha = tf.random_uniform(shape=[
batch_size, input_shape[0] * input_shape[1] * input_shape[2]
],
minval=0.0,
maxval=1.0)
Y_pred_ = tf.reshape(Y_pred, shape=[batch_size, -1])
differences_ = Y_pred_ - Y_
interpolates = Y_ + alpha * differences_
with tf.variable_scope('discriminator', reuse=True):
XY_fake_intep = self.dis(X, interpolates, training)
gradients = tf.gradients(XY_fake_intep, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1]))
gradient_penalty = tf.reduce_mean((slopes - 1.0)**2)
gan_d_loss += 10 * gradient_penalty
gan_d_loss_sum = tf.summary.scalar("total loss of discriminator",
gan_d_loss)
# generator loss with gan loss
gan_g_loss = -tf.reduce_mean(XY_fake_pair)
gan_g_w = 5
ae_w = 100 - gan_g_w
ae_gan_g_loss = ae_w * g_loss + gan_g_w * gan_g_loss
ae_g_loss_sum = tf.summary.scalar("total loss of generator",
ae_gan_g_loss)
# trainers
ae_var = [
var for var in tf.trainable_variables()
if var.name.startswith('generator')
]
dis_var = [
var for var in tf.trainable_variables()
if var.name.startswith('discriminator')
]
ae_g_optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(
ae_gan_g_loss, var_list=ae_var)
dis_optim = tf.train.AdamOptimizer(learning_rate=lr,
beta1=0.9,
beta2=0.999,
epsilon=1e-8).minimize(
gan_d_loss, var_list=dis_var)
# accuracy
block_acc = tf.placeholder(tf.float32)
total_acc = tf.placeholder(tf.float32)
train_sum = tf.summary.scalar("train_block_accuracy", block_acc)
test_sum = tf.summary.scalar("total_test_accuracy", total_acc)
train_merge_op = tf.summary.merge(
[train_sum, ae_g_loss_sum, gan_d_loss_sum, g_loss_sum])
test_merge_op = tf.summary.merge([test_sum])
saver = tf.train.Saver(max_to_keep=1)
# config = tf.ConfigProto(allow_soft_placement=True)
# config.gpu_options.visible_device_list = GPU0
with tf.Session() as sess:
# define tensorboard writer
sum_writer_train = tf.summary.FileWriter(self.train_sum_dir,
sess.graph)
sum_write_test = tf.summary.FileWriter(self.test_sum_dir,
sess.graph)
# load model data if pre-trained
sess.run(
tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer()))
if os.path.isfile(self.train_models_dir +
'model.cptk.data-00000-of-00001'):
print "restoring saved model"
saver.restore(sess, self.train_models_dir + 'model.cptk')
learning_rate_g = ori_lr * pow(power, (epoch_walked / decay_step))
# start training loop
global_step = step_walked
for epoch in range(epoch_walked, MAX_EPOCH):
if epoch % re_example_epoch == 0 and epoch > 0:
del data
gc.collect()
data = tools.Data(configure, epoch / re_example_epoch)
train_amount = len(data.train_numbers)
test_amount = len(data.test_numbers)
if train_amount >= test_amount and train_amount > 0 and test_amount > 0 and data.total_train_batch_num > 0 and data.total_test_seq_batch > 0:
# actual foreground weight
weight_for = 0.5 + (1 - 1.0 * epoch / MAX_EPOCH) * 0.35
if epoch % total_test_epoch == 0:
self.full_testing(sess, X, w, threshold, test_merge_op,
sum_write_test, training, weight_for,
total_acc, Y_pred, Y_pred_modi,
Y_pred_nosig, epoch)
data.shuffle_X_Y_pairs()
total_train_batch_num = data.total_train_batch_num
print "total_train_batch_num:", total_train_batch_num
for i in range(total_train_batch_num):
X_train_batch, Y_train_batch = data.load_X_Y_voxel_train_next_batch(
)
# calculate loss value
# print "calculate begin"
gan_d_loss_c, = sess.run(
[gan_d_loss],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
g_loss_c, gan_g_loss_c = sess.run(
[g_loss, ae_gan_g_loss],
feed_dict={
X: X_train_batch,
Y: Y_train_batch,
training: False,
w: weight_for,
threshold: upper_threshold
})
# print "calculate ended"
if epoch % decay_step == 0 and epoch > epoch_walked and i == 0:
learning_rate_g = learning_rate_g * power
sess.run(
[ae_g_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
sess.run(
[dis_optim],
feed_dict={
X: X_train_batch,
threshold: upper_threshold,
Y: Y_train_batch,
lr: learning_rate_g,
training: True,
w: weight_for
})
# print "training ended"
global_step += 1
# output some results
if i % show_step == 0:
print "epoch:", epoch, " i:", i, " train ae loss:", g_loss_c, " gan g loss:", gan_g_loss_c, " gan d loss:", gan_d_loss_c, " learning rate: ", learning_rate_g
if i % block_test_step == 0 and epoch % 1 == 0:
try:
X_test_batch, Y_test_batch = data.load_X_Y_voxel_test_next_batch(
fix_sample=False)
g_loss_t, gan_g_loss_t, gan_d_loss_t, Y_test_pred, Y_test_modi, Y_test_pred_nosig = \
sess.run([g_loss, ae_gan_g_loss, gan_d_loss, Y_pred, Y_pred_modi, Y_pred_nosig],
feed_dict={X: X_test_batch,
threshold: upper_threshold + test_extra_threshold,
Y: Y_test_batch, training: False, w: weight_for})
predict_result = np.float32(Y_test_modi > 0.01)
predict_result = np.reshape(
predict_result, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
print np.max(Y_test_pred)
print np.min(Y_test_pred)
# IOU
predict_probablity = np.float32(
(Y_test_modi - 0.01) > 0)
predict_probablity = np.reshape(
predict_probablity, [
batch_size, input_shape[0],
input_shape[1], input_shape[2]
])
accuracy = 2 * np.sum(
np.abs(predict_probablity *
Y_test_batch)) / np.sum(
np.abs(predict_result) +
np.abs(Y_test_batch))
print "epoch:", epoch, " global step: ", global_step, "\nIOU accuracy: ", accuracy, "\ntest ae loss:", g_loss_t, " gan g loss:", gan_g_loss_t, " gan d loss:", gan_d_loss_t
print "weight of foreground : ", weight_for
print "upper threshold of testing", (
upper_threshold + test_extra_threshold)
train_summary = sess.run(
train_merge_op,
feed_dict={
block_acc: accuracy,
X: X_test_batch,
threshold:
upper_threshold + test_extra_threshold,
Y: Y_test_batch,
training: False,
w: weight_for
})
sum_writer_train.add_summary(
train_summary, global_step=global_step)
except Exception, e:
print e
#### model saving
if i % model_save_step == 0 and epoch % 1 == 0:
saver.save(sess,
save_path=self.train_models_dir +
'model.cptk')
print "epoch:", epoch, " i:", i, "regular model saved!"
else:
print "bad data , next epoch", epoch
import tools as t
if __name__ == '__main__':
ds_v = t.Data('../data/sets/r1/', 'v.npy')
ds_v.load_dataset()
ds_v.prepare_dataset()
# ds_v.prepare_dataset()
# t.display_data([ds_v], 0, 5000)
# ds_p = t.Data('../data/sets/r0/', 'p1.npy')
# ds_t = t.Data('../data/sets/r0/', 't1.npy')
#
# ds_p.load_dataset()
# ds_p.prepare_dataset(type='sfa-p')
#
# ds_t.load_dataset()
# ds_t.prepare_dataset()
cut_position = 0
time_series_length = 500
time_series_count = 1
row_length = time_series_length * time_series_count
sfa_p = t.SFAAlg(ds_v.pd)
sfa_p.transform(cut_position, row_length, time_series_length,
time_series_count)
# print(sfa_p.transformed.todense())
import tools as t
import classifier as c
import configuration as conf
import analitics as a
import numpy as np
if __name__ == '__main__':
normal = t.Data(conf.path, conf.data_set_type)
normal.add_dataset_from_file('r2/normal/', 'normal', 'csv')
timeout = t.Data(conf.path, conf.data_set_type)
timeout.add_dataset_from_file('r2/normal/', 'timeout', 'csv')
ds_sizes = 5000
ds_pos = 0
ds_power = []
ds_power = np.append(
ds_power,
t.prepare_dataset(normal.ods['Power'], ds_pos, ds_sizes, 'cut'))
ds_power = np.append(
ds_power,
t.prepare_dataset(timeout.ods['Power'], ds_pos, ds_sizes, 'cut'))
ds_temperature = []
ds_temperature = np.append(
ds_temperature,
t.prepare_dataset(normal.ods['Temp'], ds_pos, ds_sizes, 'diff'))
ds_temperature = np.append(
ds_temperature,
t.prepare_dataset(timeout.ods['Temp'], ds_pos, ds_sizes, 'diff'))
def execute(self):
"""
Make the plot!
return the Figure object to the user (they can edit it if they please)
"""
fig = plt.figure()
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], hspace=0.0)
self.ax1 = fig.add_subplot(gs[0])
self.ax2 = fig.add_subplot(gs[1], sharex=self.ax1)
plt.setp(self.ax1.get_xticklabels(), visible=False)
# organize data for plotting
data2plot = None # data points (only support one source of data in data/mc plot)
bckg2plot = [] # backgrounds
signal2plot = [] # signal distribution
for e in self.data2plot:
d2p = self.data2plot[e]
sample_type = d2p.draw_type
if sample_type == 'data': data2plot = d2p
elif sample_type == 'background': bckg2plot.append(d2p)
elif sample_type == 'signal': signal2plot.append(d2p)
## Data points
if data2plot is not None:
if self.asimov:
data2plot = self.make_asimov(bckg2plot)
if self.blind_data is not None:
data2plot = self.make_blind(data2plot)
data2plot.draw_type = 'errorbar'
data2plot.kwargs["zorder"] = 125
tmp_data = self.plotErrorbar(data2plot)
data2plot = tmp_data
elif data2plot is None:
data2plot = PlotterData('data')
data2plot.data = tools.Data()
data2plot.data.content = np.array(
[np.nan for _ in bckg2plot[0].data.content])
if self.asimov:
# Use the total bckg to plot 'asimov' data
data2plot = self.make_asimov(bckg2plot)
data2plot.draw_type = 'errorbar'
data2plot.kwargs["zorder"] = 125
tmp_data = self.plotErrorbar(data2plot)
data2plot = tmp_data
self.data2plot[data2plot.name] = data2plot # update the dictionary
## Background samples
bottom = None # 'bottom' for stacking histograms
bckg_unc = None
for n, hist2plot in enumerate(bckg2plot):
hist2plot.draw_type = 'stepfilled'
hist2plot.kwargs["zorder"] = 100 + n
hist2plot.kwargs[
"bottom"] = bottom # stack the background contributions
tmp_hist2plot = self.plotHistogram(
hist2plot, uncertainty=hist2plot.uncertainty)
bckg2plot[n] = tmp_hist2plot # update data
try:
bottom += hist2plot.plotData.copy() # modify bottom
except:
bottom = hist2plot.plotData.copy()
if bckg_unc is None:
bckg_unc = np.square(hist2plot.data.error.copy())
else:
bckg_unc += np.square(hist2plot.data.error.copy())
# store the total background prediction and uncertainty
prediction = deepcopy(bckg2plot[0]) # copy attributes from bckg2plot
prediction.plotData = bottom.copy() # copy data from sum of all bckgs
prediction.data.content = bottom.copy()
prediction.data.error = np.sqrt(bckg_unc)
self.data2plot['total_bckg'] = prediction
for i in bckg2plot:
self.data2plot[i.name] = i # update the dictionary
## Signal distributions (designed for BSM, but could support a SM signal)
for n, hist2plot in enumerate(signal2plot):
hist2plot.draw_type = 'stepfilled' if self.stack_signal else 'step'
hist2plot.kwargs["zorder"] = 150 + n
hist2plot.kwargs["bottom"] = bottom if self.stack_signal else None
tmp_hist2plot = self.plotHistogram(hist2plot,
uncertainty=self.uncertainty)
signal2plot[n] = tmp_hist2plot # update data
try:
bottom += hist2plot.plotData.copy(
) # stack signal on top of background
except:
bottom = hist2plot.plotData.copy()
for i in signal2plot:
self.data2plot[i.name] = i # update the dictionary
## ratio plot [data/mc (mc=total background)]
self.ratio.Add(numerator=data2plot.name,
denominator=self.datamc_denominator)
# ratio plot uncertainty band (uncertainty on the prediction)
# there are no data points to draw, we just want the uncertainty (centered on 1.)
self.drawPredictionUncertainty()
self.plotRatio()
## Axis ticks/labels
self.set_xaxis(self.ax2)
self.set_yaxis()
## CMS label
self.text_labels()
## Legend
self.drawLegend()
return fig
