def main():
# tf flag
flags = tf.flags
flags.DEFINE_string("train_data_txt", "./train.txt", "train data txt")
flags.DEFINE_string("val_data_txt", "./val.txt", "validation data txt")
flags.DEFINE_string("outdir", "./output/", "outdir")
flags.DEFINE_float("beta", 1, "hyperparameter beta")
flags.DEFINE_integer("num_of_val", 600, "number of validation data")
flags.DEFINE_integer("batch_size", 30, "batch size")
flags.DEFINE_integer("num_iteration", 500001, "number of iteration")
flags.DEFINE_integer("save_loss_step", 200, "step of save loss")
flags.DEFINE_integer("save_model_step", 500,
"step of save model and validation")
flags.DEFINE_integer("shuffle_buffer_size", 1000, "buffer size of shuffle")
flags.DEFINE_integer("latent_dim", 6, "latent dim")
flags.DEFINE_list("image_size", [9 * 9 * 9], "image size")
flags.DEFINE_string("model", './model/model_{}', "pre training model1")
flags.DEFINE_string("model2", './model/model_{}', "pre training model2")
flags.DEFINE_boolean("is_n1_opt", True, "n1_opt")
FLAGS = flags.FLAGS
# check folder
if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard',
'train'))):
os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard', 'train'))
if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard', 'val'))):
os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard', 'val'))
if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard', 'rec'))):
os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard', 'rec'))
if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard', 'kl'))):
os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard', 'kl'))
if not (os.path.exists(os.path.join(FLAGS.outdir, 'model'))):
os.makedirs(os.path.join(FLAGS.outdir, 'model'))
# read list
train_data_list = io.load_list(FLAGS.train_data_txt)
val_data_list = io.load_list(FLAGS.val_data_txt)
# shuffle list
random.shuffle(train_data_list)
# val step
val_step = FLAGS.num_of_val // FLAGS.batch_size
if FLAGS.num_of_val % FLAGS.batch_size != 0:
val_step += 1
# load train data and validation data
train_set = tf.data.Dataset.list_files(train_data_list)
train_set = train_set.apply(
tf.contrib.data.parallel_interleave(tf.data.TFRecordDataset,
cycle_length=6))
# train_set = tf.data.TFRecordDataset(train_data_list)
train_set = train_set.map(
lambda x: _parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
train_set = train_set.shuffle(buffer_size=FLAGS.shuffle_buffer_size)
train_set = train_set.repeat()
train_set = train_set.batch(FLAGS.batch_size)
train_iter = train_set.make_one_shot_iterator()
train_data = train_iter.get_next()
val_set = tf.data.Dataset.list_files(val_data_list)
val_set = val_set.apply(
tf.contrib.data.parallel_interleave(tf.data.TFRecordDataset,
cycle_length=os.cpu_count()))
# val_set = tf.data.TFRecordDataset(val_data_list)
val_set = val_set.map(
lambda x: _parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
val_set = val_set.repeat()
val_set = val_set.batch(FLAGS.batch_size)
val_iter = val_set.make_one_shot_iterator()
val_data = val_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config) as sess:
# with tf.Session() as sess:
# set network
kwargs = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_mlp,
'decoder': decoder_mlp,
'is_res': False
}
VAE = Variational_Autoencoder(**kwargs)
kwargs_2 = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': 8,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_mlp2,
'decoder': decoder_mlp_tanh,
'is_res': True,
'is_constraints': False,
# 'keep_prob': 0.5
}
VAE_2 = Variational_Autoencoder(**kwargs_2)
# print parmeters
utils.cal_parameter()
# prepare tensorboard
writer_train = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'train'), sess.graph)
writer_val = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'val'))
writer_rec = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'rec'))
writer_kl = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'kl'))
value_loss = tf.Variable(0.0)
tf.summary.scalar("loss", value_loss)
merge_op = tf.summary.merge_all()
# initialize
sess.run(init_op)
# use pre trained model
# ckpt_state = tf.train.get_checkpoint_state(FLAGS.model)
#
# if ckpt_state:
# restore_model = ckpt_state.model_checkpoint_path
# # VAE.restore_model(FLAGS.model+'model_{}'.format(FLAGS.itr))
VAE.restore_model(FLAGS.model)
if FLAGS.is_n1_opt == True:
VAE_2.restore_model(FLAGS.model2)
# training
tbar = tqdm(range(FLAGS.num_iteration), ascii=True)
for i in tbar:
train_data_batch = sess.run(train_data)
if FLAGS.is_n1_opt == True:
VAE.update(train_data_batch)
output1 = VAE.reconstruction_image(train_data_batch)
train_loss, rec_loss, kl_loss = VAE_2.update2(
train_data_batch, output1)
if i % FLAGS.save_loss_step is 0:
s = "Loss: {:.4f}, rec_loss: {:.4f}, kl_loss: {:.4f}".format(
train_loss, rec_loss, kl_loss)
tbar.set_description(s)
summary_train_loss = sess.run(merge_op,
{value_loss: train_loss})
writer_train.add_summary(summary_train_loss, i)
summary_rec_loss = sess.run(merge_op, {value_loss: rec_loss})
summary_kl_loss = sess.run(merge_op, {value_loss: kl_loss})
writer_rec.add_summary(summary_rec_loss, i)
writer_kl.add_summary(summary_kl_loss, i)
if i % FLAGS.save_model_step is 0:
# save model
VAE.save_model(i)
VAE_2.save_model2(i)
# validation
val_loss = 0.
for j in range(val_step):
val_data_batch = sess.run(val_data)
val_data_batch_output1 = VAE.reconstruction_image(
val_data_batch)
val_loss += VAE_2.validation2(val_data_batch,
val_data_batch_output1)
val_loss /= val_step
summary_val = sess.run(merge_op, {value_loss: val_loss})
writer_val.add_summary(summary_val, i)
def main():
# tf flag
flags = tf.flags
flags.DEFINE_string("train_data_txt",
"E:/git/beta-VAE/input/CT/shift/train.txt",
"train data txt")
flags.DEFINE_string("ground_truth_txt",
"E:/git/beta-VAE/input/CT/shift/test.txt", "i1")
flags.DEFINE_string(
"model1", 'D:/vae_result/n1/z6/beta_1/model/model_{}'.format(997500),
"i2")
flags.DEFINE_string(
"model2",
'D:/vae_result/n1+n2/all/sig/beta_1/model/model_{}'.format(197500),
"i3")
flags.DEFINE_string("outdir", "D:/vae_result/n1+n2/all/sig/beta_1/spe/",
"i4")
flags.DEFINE_float("beta", 1, "hyperparameter beta")
flags.DEFINE_integer("num_of_generate", 5000, "number of generate data")
flags.DEFINE_integer("num_of_test", 600, "number of test data")
flags.DEFINE_integer("num_of_train", 1804, "number of train data")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("latent_dim", 6, "latent dim")
flags.DEFINE_list("image_size", [9 * 9 * 9], "image size")
flags.DEFINE_boolean("const_bool", False,
"if there is sigmoid in front of last output")
FLAGS = flags.FLAGS
# check folder
if not (os.path.exists(FLAGS.outdir)):
os.makedirs(FLAGS.outdir + 'spe1/')
os.makedirs(FLAGS.outdir + 'spe2/')
os.makedirs(FLAGS.outdir + 'spe_all/')
# read list
test_data_list = io.load_list(FLAGS.ground_truth_txt)
train_data_list = io.load_list(FLAGS.train_data_txt)
# test step
test_step = FLAGS.num_of_generate // FLAGS.batch_size
if FLAGS.num_of_generate % FLAGS.batch_size != 0:
test_step += 1
# load train data
train_set = tf.data.TFRecordDataset(train_data_list)
train_set = train_set.map(
lambda x: _parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
train_set = train_set.batch(FLAGS.batch_size)
train_iter = train_set.make_one_shot_iterator()
train_data = train_iter.get_next()
# load test data
test_set = tf.data.TFRecordDataset(test_data_list)
test_set = test_set.map(
lambda x: _parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
test_set = test_set.batch(FLAGS.batch_size)
test_iter = test_set.make_one_shot_iterator()
test_data = test_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config) as sess:
# set network
# set network
kwargs = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_mlp,
'decoder': decoder_mlp,
'is_res': False
}
VAE = Variational_Autoencoder(**kwargs)
kwargs_2 = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': 8,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_mlp2,
'decoder': decoder_mlp_tanh,
'is_res': False,
'is_constraints': FLAGS.const_bool
}
VAE_2 = Variational_Autoencoder(**kwargs_2)
sess.run(init_op)
# testing
VAE.restore_model(FLAGS.model1)
VAE_2.restore_model(FLAGS.model2)
tbar = tqdm(range(FLAGS.num_of_generate), ascii=True)
specificity = []
spe_mean = []
generate_data = []
generate_data2 = []
ori = []
latent_space = []
latent_space2 = []
patch_side = 9
for i in range(FLAGS.num_of_train):
train_data_batch = sess.run(train_data)
z = VAE.plot_latent(train_data_batch)
z2 = VAE_2.plot_latent(train_data_batch)
z = z.flatten()
z2 = z2.flatten()
latent_space.append(z)
latent_space2.append(z2)
mu = np.mean(latent_space, axis=0)
var = np.var(latent_space, axis=0)
mu2 = np.mean(latent_space2, axis=0)
var2 = np.var(latent_space2, axis=0)
for i in range(FLAGS.num_of_test):
test_data_batch = sess.run(test_data)
ori_single = test_data_batch
ori_single = ori_single[0, :]
ori.append(ori_single)
file_spe1 = open(FLAGS.outdir + 'spe1/list.txt', 'w')
file_spe2 = open(FLAGS.outdir + 'spe2/list.txt', 'w')
file_spe_all = open(FLAGS.outdir + 'spe_all/list.txt', 'w')
for j in tbar:
sample_z = np.random.normal(mu, var, (1, FLAGS.latent_dim))
sample_z2 = np.random.normal(mu2, var2, (1, 8))
generate_data_single = VAE.generate_sample(sample_z)
if FLAGS.const_bool is False:
generate_data_single2 = VAE_2.generate_sample(sample_z2)
generate_data_single = generate_data_single[0, :]
generate_data_single2 = generate_data_single2[0, :]
generate_data.append(generate_data_single)
generate_data2.append(generate_data_single2)
gen = np.reshape(generate_data_single,
[patch_side, patch_side, patch_side])
gen2 = np.reshape(generate_data_single2,
[patch_side, patch_side, patch_side])
generate_data_single_all = generate_data_single + generate_data_single2
gen_all = gen + gen2
if FLAGS.const_bool is True:
generate_data_single_all = VAE_2.generate_sample2(
sample_z2, generate_data_single)
generate_data_single = generate_data_single[0, :]
generate_data_single_all = generate_data_single_all[0, :]
generate_data.append(generate_data_single)
generate_data2.append(generate_data_single_all)
gen = np.reshape(generate_data_single,
[patch_side, patch_side, patch_side])
gen_all = np.reshape(generate_data_single_all,
[patch_side, patch_side, patch_side])
generate_data_single2 = generate_data_single_all - generate_data_single
gen2 = gen_all - gen
# EUDT
gen_image = sitk.GetImageFromArray(gen)
gen_image.SetSpacing([0.885, 0.885, 1])
gen_image.SetOrigin([0, 0, 0])
gen2_image = sitk.GetImageFromArray(gen2)
gen2_image.SetSpacing([0.885, 0.885, 1])
gen2_image.SetOrigin([0, 0, 0])
gen_all_image = sitk.GetImageFromArray(gen_all)
gen_all_image.SetSpacing([0.885, 0.885, 1])
gen_all_image.SetOrigin([0, 0, 0])
# calculation
case_min_specificity = 1.0
for image_index in range(FLAGS.num_of_test):
specificity_tmp = utils.L1norm(ori[image_index],
generate_data_single_all)
if specificity_tmp < case_min_specificity:
case_min_specificity = specificity_tmp
specificity.append([case_min_specificity])
spe = np.mean(specificity)
spe_mean.append(spe)
io.write_mhd_and_raw(
gen_image, '{}.mhd'.format(
os.path.join(FLAGS.outdir, 'spe1',
'spe1_{}'.format(j + 1))))
io.write_mhd_and_raw(
gen2_image, '{}.mhd'.format(
os.path.join(FLAGS.outdir, 'spe2',
'spe2_{}'.format(j + 1))))
io.write_mhd_and_raw(
gen_all_image, '{}.mhd'.format(
os.path.join(FLAGS.outdir, 'spe_all',
'spe_all_{}'.format(j + 1))))
file_spe1.write('{}.mhd'.format(
os.path.join(FLAGS.outdir, 'spe1', 'spe1_{}'.format(j + 1))) +
"\n")
file_spe2.write('{}.mhd'.format(
os.path.join(FLAGS.outdir, 'spe2', 'spe2_{}'.format(j + 1))) +
"\n")
file_spe_all.write('{}.mhd'.format(
os.path.join(FLAGS.outdir, 'spe_all', 'spe_all_{}'.format(
j + 1))) + "\n")
file_spe1.close()
file_spe2.close()
file_spe_all.close()
print('specificity = %f' % np.mean(specificity))
np.savetxt(os.path.join(FLAGS.outdir, 'specificity.csv'),
specificity,
delimiter=",")
# spe graph
plt.plot(spe_mean)
plt.grid()
# plt.show()
plt.savefig(FLAGS.outdir + "Specificity.png")
def main():
# tf flag
flags = tf.flags
flags.DEFINE_string(
"model",
'G:/experiment_result/liver/VAE/set_4/down_64/RBF/alpha_0.1/4/beta_10/model/model_{}'
.format(1350), "model")
flags.DEFINE_string(
"outdir",
'G:/experiment_result/liver/VAE/set_4/down_64/RBF/alpha_0.1/4/beta_10/random',
"outdir")
flags.DEFINE_string("gpu_index", "0", "GPU-index")
flags.DEFINE_float("beta", 1.0, "hyperparameter beta")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("latent_dim", 2, "latent dim")
flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size")
FLAGS = flags.FLAGS
# check folder
if not (os.path.exists(FLAGS.outdir)):
os.makedirs(FLAGS.outdir)
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config(index=FLAGS.gpu_index)) as sess:
# set network
kwargs = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_resblock_bn,
'decoder': decoder_resblock_bn,
'downsampling': down_sampling,
'upsampling': up_sampling,
'is_training': False,
'is_down': False
}
VAE = Variational_Autoencoder(**kwargs)
sess.run(init_op)
# testing
VAE.restore_model(FLAGS.model)
# 2 dim vis
for j in range(-2, 3):
for i in range(-2, 3):
mean = [0.37555057, 0.8882291]
var = [32.121346, 24.540127]
sample_z = [[i, j]]
sample_z = np.asarray(mean) + np.sqrt(
np.asarray(var)) * sample_z
generate_data = VAE.generate_sample(sample_z)
generate_data = generate_data[0, :, :, :, 0]
# EUDT
generate_data = generate_data.astype(np.float32)
eudt_image = sitk.GetImageFromArray(generate_data)
eudt_image.SetSpacing([1, 1, 1])
eudt_image.SetOrigin([0, 0, 0])
# label
label = np.where(generate_data > 0.5, 0, 1)
label = label.astype(np.int16)
label_image = sitk.GetImageFromArray(label)
label_image.SetSpacing([1, 1, 1])
label_image.SetOrigin([0, 0, 0])
io.write_mhd_and_raw(
label_image, '{}.mhd'.format(
os.path.join(FLAGS.outdir, '2_dim',
str(i) + '_' + str(j))))
def main():
parser = argparse.ArgumentParser(
description='py, test_data_txt, ground_truth_txt, outdir')
parser.add_argument('--ground_truth_txt', '-i1', default='')
parser.add_argument('--model', '-i2', default='./model_{}'.format(50000))
parser.add_argument('--outdir', '-i3', default='')
args = parser.parse_args()
# check folder
if not (os.path.exists(args.outdir)):
os.makedirs(args.outdir)
# tf flag
flags = tf.flags
flags.DEFINE_float("beta", 0.1, "hyperparameter beta")
flags.DEFINE_integer("num_of_generate", 1000, "number of generate data")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("latent_dim", 2, "latent dim")
flags.DEFINE_list("image_size", [512, 512, 1], "image size")
FLAGS = flags.FLAGS
# load ground truth
ground_truth = io.load_matrix_data(args.ground_truth_txt, 'int32')
print(ground_truth.shape)
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config) as sess:
# set network
kwargs = {
'sess': sess,
'outdir': args.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': cnn_encoder,
'decoder': cnn_decoder
}
VAE = Variational_Autoencoder(**kwargs)
sess.run(init_op)
# testing
VAE.restore_model(args.model)
tbar = tqdm(range(FLAGS.num_of_generate), ascii=True)
specificity = []
for i in tbar:
sample_z = np.random.normal(0, 1.0, (1, FLAGS.latent_dim))
generate_data = VAE.generate_sample(sample_z)
generate_data = generate_data[0, :, :, 0]
# EUDT
eudt_image = sitk.GetImageFromArray(generate_data)
eudt_image.SetSpacing([1, 1])
eudt_image.SetOrigin([0, 0])
# label
label = np.where(generate_data > 0, 0, 1)
label_image = sitk.GetImageFromArray(label)
label_image.SetSpacing([1, 1])
label_image.SetOrigin([0, 0])
# calculate ji
case_max_ji = 0.
for image_index in range(ground_truth.shape[0]):
ji = utils.jaccard(label, ground_truth[image_index])
if ji > case_max_ji:
case_max_ji = ji
specificity.append([case_max_ji])
# output image
io.write_mhd_and_raw(
eudt_image,
'{}.mhd'.format(os.path.join(args.outdir, 'EUDT', str(i + 1))))
io.write_mhd_and_raw(
label_image,
'{}.mhd'.format(os.path.join(args.outdir, 'label',
str(i + 1))))
print('specificity = %f' % np.mean(specificity))
# output csv file
with open(os.path.join(args.outdir, 'specificity.csv'), 'w',
newline='') as file:
writer = csv.writer(file)
writer.writerows(specificity)
writer.writerow(['specificity:', np.mean(specificity)])
def main():
# tf flag
flags = tf.flags
flags.DEFINE_string(
"train_data_txt",
'F:/data_info/VAE_liver/set_5/TFrecord/fold_1/train.txt',
"train data txt")
flags.DEFINE_string(
"outdir",
'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/beta_1',
"outdir")
flags.DEFINE_string("gpu_index", "0", "GPU-index")
flags.DEFINE_float("beta", 1, "hyperparameter beta")
flags.DEFINE_integer("batch_size", 12, "batch size")
flags.DEFINE_integer("num_iteration", 12001, "number of iteration")
flags.DEFINE_integer("save_loss_step", 150, "step of save loss")
flags.DEFINE_integer("save_model_step", 150,
"step of save model and validation")
flags.DEFINE_integer("shuffle_buffer_size", 200, "buffer size of shuffle")
flags.DEFINE_integer("latent_dim", 4, "latent dim")
flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size")
FLAGS = flags.FLAGS
# check folder
if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard'))):
os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard'))
if not (os.path.exists(os.path.join(FLAGS.outdir, 'model'))):
os.makedirs(os.path.join(FLAGS.outdir, 'model'))
# read list
train_data_list = io.load_list(FLAGS.train_data_txt)
# shuffle list
random.shuffle(train_data_list)
# load train data
train_set = tf.data.Dataset.list_files(train_data_list)
train_set = train_set.apply(
tf.contrib.data.parallel_interleave(
lambda x: tf.data.TFRecordDataset(x, compression_type='GZIP'),
cycle_length=os.cpu_count()))
train_set = train_set.map(
lambda x: utils._parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
# train_set = train_set.cache()
train_set = train_set.shuffle(buffer_size=FLAGS.shuffle_buffer_size)
train_set = train_set.repeat()
train_set = train_set.batch(FLAGS.batch_size)
train_iter = train_set.make_one_shot_iterator()
train_data = train_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config(index=FLAGS.gpu_index)) as sess:
# # set network
kwargs = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_resblock_bn,
'decoder': decoder_resblock_bn,
'downsampling': down_sampling,
'upsampling': up_sampling,
'learning_rate': 1e-4,
'is_training': True,
'is_down': False
}
VAE = Variational_Autoencoder(**kwargs)
# print parmeters
utils.cal_parameter()
# prepare tensorboard
writer_train = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'train'), sess.graph)
writer_rec = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'train_rec'))
writer_kl = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'train_kl'))
value_loss = tf.Variable(0.0)
tf.summary.scalar("loss", value_loss)
merge_op = tf.summary.merge_all()
# initialize
sess.run(init_op)
# # training
tbar = tqdm(range(FLAGS.num_iteration), ascii=True)
epoch_train_loss = []
epoch_kl_loss = []
epoch_rec_loss = []
for i in tbar:
train_data_batch = sess.run(train_data)
train_loss, rec_loss, kl_loss = VAE.update(train_data_batch)
epoch_train_loss.append(train_loss)
epoch_kl_loss.append(kl_loss)
epoch_rec_loss.append(rec_loss)
if i % FLAGS.save_loss_step is 0:
s = "Loss: {:.4f}, kl_loss: {:.4f}, rec_loss: {:.4f}"\
.format(np.mean(epoch_train_loss), np.mean(epoch_kl_loss), np.mean(epoch_rec_loss))
tbar.set_description(s)
summary_train_loss = sess.run(merge_op,
{value_loss: train_loss})
summary_rec_loss = sess.run(merge_op, {value_loss: rec_loss})
summary_kl_loss = sess.run(merge_op, {value_loss: kl_loss})
writer_train.add_summary(summary_train_loss, i)
writer_rec.add_summary(summary_rec_loss, i)
writer_kl.add_summary(summary_kl_loss, i)
epoch_train_loss.clear()
epoch_kl_loss.clear()
epoch_rec_loss.clear()
if i % FLAGS.save_model_step is 0:
# save model
VAE.save_model(i)
def main():
# tf flag
flags = tf.flags
flags.DEFINE_string(
"val_data_txt", 'F:/data_info/VAE_liver/set_5/TFrecord/fold_1/val.txt',
"validation data txt")
flags.DEFINE_string(
"model_dir",
'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/beta_10/model',
"dir of model")
flags.DEFINE_string(
"outdir",
'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/beta_10',
"outdir")
flags.DEFINE_string("gpu_index", "0", "GPU-index")
flags.DEFINE_float("beta", 1, "hyperparameter beta")
flags.DEFINE_integer("num_of_val", 76, "number of validation data")
flags.DEFINE_integer("train_iteration", 12001,
"number of training iteration")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer(
"num_per_val", 150,
"number per each validation(equal step of saving model)")
flags.DEFINE_integer("latent_dim", 4, "latent dim")
flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size")
FLAGS = flags.FLAGS
# check folder
if not (os.path.exists(os.path.join(FLAGS.outdir, 'tensorboard'))):
os.makedirs(os.path.join(FLAGS.outdir, 'tensorboard'))
# read list
val_data_list = io.load_list(FLAGS.val_data_txt)
# number of model
num_of_model = FLAGS.train_iteration // FLAGS.num_per_val
if FLAGS.train_iteration % FLAGS.num_per_val != 0:
num_of_model += 1
if FLAGS.train_iteration % FLAGS.num_per_val == 0:
num_of_model -= 1
# val_iter
num_val_iter = FLAGS.num_of_val // FLAGS.batch_size
if FLAGS.num_of_val % FLAGS.batch_size != 0:
num_val_iter += 1
# load validation data
val_set = tf.data.TFRecordDataset(val_data_list, compression_type='GZIP')
val_set = val_set.map(
lambda x: utils._parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
val_set = val_set.repeat()
val_set = val_set.batch(FLAGS.batch_size)
val_iter = val_set.make_one_shot_iterator()
val_data = val_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config(index=FLAGS.gpu_index)) as sess:
# # set network
kwargs = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_resblock_bn,
'decoder': decoder_resblock_bn,
'downsampling': down_sampling,
'upsampling': up_sampling,
'is_training': False,
'is_down': False
}
VAE = Variational_Autoencoder(**kwargs)
# print parmeters
utils.cal_parameter()
# prepare tensorboard
writer_val = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'val'))
writer_val_rec = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'val_rec'))
writer_val_kl = tf.summary.FileWriter(
os.path.join(FLAGS.outdir, 'tensorboard', 'val_kl'))
value_loss = tf.Variable(0.0)
tf.summary.scalar("loss", value_loss)
merge_op = tf.summary.merge_all()
# initialize
sess.run(init_op)
# # validation
tbar = tqdm(range(num_of_model), ascii=True)
for i in tbar:
VAE.restore_model(FLAGS.model_dir +
'/model_{}'.format(i * FLAGS.num_per_val))
val_loss_all = []
val_rec_all = []
val_kl_all = []
for j in range(num_val_iter):
val_data_batch = sess.run(val_data)
val_loss, val_rec, val_kl = VAE.validation(val_data_batch)
val_loss_all.append(val_loss)
val_rec_all.append(val_rec)
val_kl_all.append(val_kl)
val_loss, val_rec, val_kl = np.mean(val_loss_all), np.mean(
val_rec_all), np.mean(val_kl_all)
s = "val: {:.4f}, val_rec: {:.4f}, val_kl: {:.4f} ".format(
val_loss, val_rec, val_kl)
tbar.set_description(s)
summary_val = sess.run(merge_op, {value_loss: val_loss})
summary_val_rec = sess.run(merge_op, {value_loss: val_rec})
summary_val_kl = sess.run(merge_op, {value_loss: val_kl})
writer_val.add_summary(summary_val, i * FLAGS.num_per_val)
writer_val_rec.add_summary(summary_val_rec, i * FLAGS.num_per_val)
writer_val_kl.add_summary(summary_val_kl, i * FLAGS.num_per_val)
val_loss_all.clear()
val_rec_all.clear()
val_kl_all.clear()
def main():
parser = argparse.ArgumentParser(
description='py, test_data_txt, model, outdir')
parser.add_argument('--test_data_txt', '-i1', default='')
parser.add_argument('--model', '-i2', default='./model_{}'.format(50000))
parser.add_argument('--outdir', '-i3', default='')
args = parser.parse_args()
# check folder
if not (os.path.exists(args.outdir)):
os.makedirs(args.outdir)
# tf flag
flags = tf.flags
flags.DEFINE_float("beta", 0.1, "hyperparameter beta")
flags.DEFINE_integer("num_of_test", 100, "number of test data")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("latent_dim", 2, "latent dim")
flags.DEFINE_list("image_size", [512, 512, 1], "image size")
FLAGS = flags.FLAGS
# read list
test_data_list = io.load_list(args.test_data_txt)
# test step
test_step = FLAGS.num_of_test // FLAGS.batch_size
if FLAGS.num_of_test % FLAGS.batch_size != 0:
test_step += 1
# load test data
test_set = tf.data.TFRecordDataset(test_data_list)
test_set = test_set.map(
lambda x: _parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
test_set = test_set.batch(FLAGS.batch_size)
test_iter = test_set.make_one_shot_iterator()
test_data = test_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config) as sess:
# set network
kwargs = {
'sess': sess,
'outdir': args.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': cnn_encoder,
'decoder': cnn_decoder
}
VAE = Variational_Autoencoder(**kwargs)
sess.run(init_op)
# testing
VAE.restore_model(args.model)
tbar = tqdm(range(test_step), ascii=True)
preds = []
ori = []
for k in tbar:
test_data_batch = sess.run(test_data)
ori_single = test_data_batch
preds_single = VAE.reconstruction_image(ori_single)
preds_single = preds_single[0, :, :, 0]
ori_single = ori_single[0, :, :, 0]
preds.append(preds_single)
ori.append(ori_single)
# # label
ji = []
for j in range(len(preds)):
# EUDT
eudt_image = sitk.GetImageFromArray(preds[j])
eudt_image.SetSpacing([1, 1])
eudt_image.SetOrigin([0, 0])
label = np.where(preds[j] > 0, 0, 1)
label_image = sitk.GetImageFromArray(label)
label_image.SetSpacing([1, 1])
label_image.SetOrigin([0, 0])
ori_label = np.where(ori[j] > 0, 0, 1)
ori_label_image = sitk.GetImageFromArray(ori_label)
ori_label_image.SetSpacing([1, 1])
ori_label_image.SetOrigin([0, 0])
# # calculate ji
ji.append(utils.jaccard(label, ori_label))
# output image
io.write_mhd_and_raw(
eudt_image, '{}.mhd'.format(
os.path.join(args.outdir, 'EUDT', 'recon_{}'.format(j))))
io.write_mhd_and_raw(
label_image, '{}.mhd'.format(
os.path.join(args.outdir, 'label', 'recon_{}'.format(j))))
generalization = np.mean(ji)
print('generalization = %f' % generalization)
# output csv file
with open(os.path.join(args.outdir, 'generalization.csv'), 'w',
newline='') as file:
writer = csv.writer(file)
writer.writerows(ji)
writer.writerow(['generalization= ', generalization])
def main():
parser = argparse.ArgumentParser(description='py, train_data_txt, val_data_txt, outdir')
parser.add_argument('--train_data_txt', '-i1', default='', help='train data txt')
parser.add_argument('--val_data_txt', '-i2', default='', help='validation data txt')
parser.add_argument('--outdir', '-i3', default='./beta_0.1', help='outdir')
args = parser.parse_args()
# check folder
if not (os.path.exists(os.path.join(args.outdir, 'tensorboard', 'train'))):
os.makedirs(os.path.join(args.outdir, 'tensorboard', 'train'))
if not (os.path.exists(os.path.join(args.outdir, 'tensorboard', 'val'))):
os.makedirs(os.path.join(args.outdir, 'tensorboard', 'val'))
if not (os.path.exists(os.path.join(args.outdir, 'tensorboard', 'rec'))):
os.makedirs(os.path.join(args.outdir, 'tensorboard', 'rec'))
if not (os.path.exists(os.path.join(args.outdir, 'tensorboard', 'kl'))):
os.makedirs(os.path.join(args.outdir, 'tensorboard', 'kl'))
if not (os.path.exists(os.path.join(args.outdir, 'model'))):
os.makedirs(os.path.join(args.outdir, 'model'))
# tf flag
flags = tf.flags
flags.DEFINE_float("beta", 0.1, "hyperparameter beta")
flags.DEFINE_integer("num_of_val", 1000, "number of validation data")
flags.DEFINE_integer("batch_size", 30, "batch size")
flags.DEFINE_integer("num_iteration", 50001, "number of iteration")
flags.DEFINE_integer("save_loss_step", 50, "step of save loss")
flags.DEFINE_integer("save_model_step", 500, "step of save model and validation")
flags.DEFINE_integer("shuffle_buffer_size", 10000, "buffer size of shuffle")
flags.DEFINE_integer("latent_dim", 2, "latent dim")
flags.DEFINE_list("image_size", [512, 512, 1], "image size")
FLAGS = flags.FLAGS
# read list
train_data_list = io.load_list(args.train_data_txt)
val_data_list = io.load_list(args.val_data_txt)
# shuffle list
random.shuffle(train_data_list)
# val step
val_step = FLAGS.num_of_val // FLAGS.batch_size
if FLAGS.num_of_val % FLAGS.batch_size != 0:
val_step += 1
# load train data and validation data
train_set = tf.data.TFRecordDataset(train_data_list)
train_set = train_set.map(lambda x: _parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
train_set = train_set.shuffle(buffer_size=FLAGS.shuffle_buffer_size)
train_set = train_set.repeat()
train_set = train_set.batch(FLAGS.batch_size)
train_iter = train_set.make_one_shot_iterator()
train_data = train_iter.get_next()
val_set = tf.data.TFRecordDataset(val_data_list)
val_set = val_set.map(lambda x: _parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
val_set = val_set.repeat()
val_set = val_set.batch(FLAGS.batch_size)
val_iter = val_set.make_one_shot_iterator()
val_data = val_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config = utils.config) as sess:
# set network
kwargs = {
'sess': sess,
'outdir': args.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': cnn_encoder,
'decoder': cnn_decoder
}
VAE = Variational_Autoencoder(**kwargs)
# print parmeters
utils.cal_parameter()
# prepare tensorboard
writer_train = tf.summary.FileWriter(os.path.join(args.outdir, 'tensorboard', 'train'), sess.graph)
writer_val = tf.summary.FileWriter(os.path.join(args.outdir, 'tensorboard', 'val'))
writer_rec = tf.summary.FileWriter(os.path.join(args.outdir, 'tensorboard', 'rec'))
writer_kl = tf.summary.FileWriter(os.path.join(args.outdir, 'tensorboard', 'kl'))
value_loss = tf.Variable(0.0)
tf.summary.scalar("loss", value_loss)
merge_op = tf.summary.merge_all()
# initialize
sess.run(init_op)
# training
tbar = tqdm(range(FLAGS.num_iteration), ascii=True)
for i in tbar:
train_data_batch = sess.run(train_data)
train_loss, rec_loss, kl_loss = VAE.update(train_data_batch)
if i % FLAGS.save_loss_step is 0:
s = "Loss: {:.4f}, rec_loss: {:.4f}, kl_loss: {:.4f}".format(train_loss, rec_loss, kl_loss)
tbar.set_description(s)
summary_train_loss = sess.run(merge_op, {value_loss: train_loss})
writer_train.add_summary(summary_train_loss, i)
summary_rec_loss = sess.run(merge_op, {value_loss: rec_loss})
summary_kl_loss = sess.run(merge_op, {value_loss: kl_loss})
writer_rec.add_summary(summary_rec_loss, i)
writer_kl.add_summary(summary_kl_loss, i)
if i % FLAGS.save_model_step is 0:
# save model
VAE.save_model(i)
# validation
val_loss = 0.
for j in range(val_step):
val_data_batch = sess.run(val_data)
val_loss += VAE.validation(val_data_batch)
val_loss /= val_step
summary_val = sess.run(merge_op, {value_loss: val_loss})
writer_val.add_summary(summary_val, i)
def main():
# tf flag
flags = tf.flags
flags.DEFINE_string(
"test_data_txt",
'F:/data_info/VAE_liver/set_5/TFrecord/fold_1/test.txt',
"test data txt")
flags.DEFINE_string(
"indir",
'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_5/beta_7',
"input dir")
flags.DEFINE_string(
"outdir",
'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_5/beta_7/rec',
"outdir")
flags.DEFINE_integer("model_index", 3300, "index of model")
flags.DEFINE_string("gpu_index", "0", "GPU-index")
flags.DEFINE_float("beta", 1.0, "hyperparameter beta")
flags.DEFINE_integer("num_of_test", 75, "number of test data")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("latent_dim", 5, "latent dim")
flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size")
FLAGS = flags.FLAGS
# check folder
if not (os.path.exists(FLAGS.outdir)):
os.makedirs(FLAGS.outdir)
# read list
test_data_list = io.load_list(FLAGS.test_data_txt)
# test step
test_step = FLAGS.num_of_test // FLAGS.batch_size
if FLAGS.num_of_test % FLAGS.batch_size != 0:
test_step += 1
# load test data
test_set = tf.data.TFRecordDataset(test_data_list, compression_type='GZIP')
test_set = test_set.map(
lambda x: utils._parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
test_set = test_set.batch(FLAGS.batch_size)
test_iter = test_set.make_one_shot_iterator()
test_data = test_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config(index=FLAGS.gpu_index)) as sess:
# set network
kwargs = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_resblock_bn,
'decoder': decoder_resblock_bn,
'downsampling': down_sampling,
'upsampling': up_sampling,
'is_training': False,
'is_down': False
}
VAE = Variational_Autoencoder(**kwargs)
sess.run(init_op)
# testing
VAE.restore_model(
os.path.join(FLAGS.indir, 'model',
'model_{}'.format(FLAGS.model_index)))
tbar = tqdm(range(test_step), ascii=True)
preds = []
ori = []
ji = []
for k in tbar:
test_data_batch = sess.run(test_data)
ori_single = test_data_batch
preds_single = VAE.reconstruction_image(ori_single)
preds_single = preds_single[0, :, :, :, 0]
ori_single = ori_single[0, :, :, :, 0]
preds.append(preds_single)
ori.append(ori_single)
# # label
ji = []
for j in range(len(preds)):
# EUDT
eudt_image = sitk.GetImageFromArray(preds[j])
eudt_image.SetSpacing([1, 1, 1])
eudt_image.SetOrigin([0, 0, 0])
label = np.where(preds[j] > 0.5, 0, 1)
# label = np.where(preds[j] > 0.5, 1, 0.5)
label = label.astype(np.int16)
label_image = sitk.GetImageFromArray(label)
label_image.SetSpacing([1, 1, 1])
label_image.SetOrigin([0, 0, 0])
ori_label = np.where(ori[j] > 0.5, 0, 1)
ori_label_image = sitk.GetImageFromArray(ori_label)
ori_label_image.SetSpacing([1, 1, 1])
ori_label_image.SetOrigin([0, 0, 0])
# # calculate ji
ji.append([utils.jaccard(label, ori_label)])
# output image
io.write_mhd_and_raw(
label_image, '{}.mhd'.format(
os.path.join(FLAGS.outdir, 'label',
'recon_{}'.format(j))))
generalization = np.mean(ji)
print('generalization = %f' % generalization)
# # output csv file
with open(os.path.join(
FLAGS.outdir,
'generalization_{}.csv'.format(FLAGS.model_index)),
'w',
newline='') as file:
writer = csv.writer(file)
writer.writerows(ji)
writer.writerow(['generalization= ', generalization])
def main():
# tf flag
flags = tf.flags
# flags.DEFINE_string("test_data_txt", "./input/CT/patch/test.txt", "i1")
flags.DEFINE_string("test_data_txt", "./input/axis2/noise/test.txt", "i1")
# flags.DEFINE_string("model", './output/CT/patch/model2/z24/alpha_1e-5/beta_0.1/fine/model/model_{}'.format(244000), "i2")
# flags.DEFINE_string("outdir", "./output/CT/patch/model2/z24/alpha_1e-5/beta_0.1/fine/latent/", "i3")
flags.DEFINE_string(
"model",
'./output/axis2/noise/model2/z24/alpha_1e-5/model/model_{}'.format(
9072000), "i2")
flags.DEFINE_string("outdir",
"./output/axis2/noise/model2/z24/alpha_1e-5/latent/",
"i3")
flags.DEFINE_float("beta", 1, "hyperparameter beta")
# flags.DEFINE_integer("num_of_test", 607, "number of test data")
flags.DEFINE_integer("num_of_test", 3000, "number of test data")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("latent_dim", 24, "latent dim")
flags.DEFINE_list("image_size", [9 * 9 * 9], "image size")
FLAGS = flags.FLAGS
# check folder
if not (os.path.exists(FLAGS.outdir)):
os.makedirs(FLAGS.outdir + 'morphing/')
# read list
test_data_list = io.load_list(FLAGS.test_data_txt)
# test step
test_step = FLAGS.num_of_test // FLAGS.batch_size
if FLAGS.num_of_test % FLAGS.batch_size != 0:
test_step += 1
# load test data
test_set = tf.data.TFRecordDataset(test_data_list)
test_set = test_set.map(
lambda x: _parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
test_set = test_set.batch(FLAGS.batch_size)
test_iter = test_set.make_one_shot_iterator()
test_data = test_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config) as sess:
# set network
kwargs = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_mlp,
'decoder': decoder_mlp
}
VAE = Variational_Autoencoder(**kwargs)
sess.run(init_op)
patch_side = 9
patch_center = int(patch_side / 2)
# testing
VAE.restore_model(FLAGS.model)
tbar = tqdm(range(test_step), ascii=True)
preds = []
ori = []
latent_space = []
for k in tbar:
test_data_batch = sess.run(test_data)
ori_single = test_data_batch
z = VAE.plot_latent(ori_single)
z = z.flatten()
latent_space.append(z)
latent_space = np.asarray(latent_space)
# print("latent_space =",latent_space.shape)
# print(latent_space[0])
# print(latent_space[1])
# print(latent_space[2])
# print(latent_space[3])
# print(latent_space[4])
mu = np.mean(latent_space, axis=0)
var = np.var(latent_space, axis=0)
sigma = np.sqrt(var)
plt.figure(figsize=(8, 6))
fig = plt.scatter(latent_space[:, 0], latent_space[:, 1])
plt.xlabel('dim_1')
plt.ylabel('dim_2')
plt.title('latent distribution')
plt.savefig(FLAGS.outdir + "latent_space.png")
if FLAGS.latent_dim == 3:
if not (os.path.exists(FLAGS.outdir + "3D/")):
os.makedirs(FLAGS.outdir + "3D/")
utils.matplotlib_plt(latent_space, FLAGS.outdir)
# check folder
# fig = plt.figure()
# ax = fig.add_subplot(111, projection="3d")
# ax.scatter(latent_space[:, 0], latent_space[:, 1], latent_space[:, 2], marker="x")
# ax.scatter(latent_space[:5, 0], latent_space[:5, 1], latent_space[:5, 2], marker="o", color='orange')
plt.figure(figsize=(8, 6))
plt.scatter(latent_space[:, 0], latent_space[:, 1])
plt.scatter(latent_space[:5, 0], latent_space[:5, 1], color='orange')
plt.title('latent distribution')
plt.xlabel('dim_1')
plt.ylabel('dim_2')
plt.savefig(FLAGS.outdir + "back_projection.png")
# plt.show()
#### display a 2D manifold of digits
plt.figure()
n = 13
digit_size = patch_side
figure1 = np.zeros((digit_size * n, digit_size * n))
figure2 = np.zeros((digit_size * n, digit_size * n))
figure3 = np.zeros((digit_size * n, digit_size * n))
# linearly spaced coordinates corresponding to the 2D plot
# of digit classes in the latent space
grid_x = np.linspace(-3 * sigma[0], 3 * sigma[0], n)
grid_y = np.linspace(-3 * sigma[1], 3 * sigma[1], n)[::-1]
for i, yi in enumerate(grid_y):
for j, xi in enumerate(grid_x):
z_sample = []
if FLAGS.latent_dim == 2:
z_sample = np.array([[xi, yi]])
if FLAGS.latent_dim == 3:
z_sample = np.array([[xi, yi, 0]])
if FLAGS.latent_dim == 4:
z_sample = np.array([[xi, yi, 0, 0]])
if FLAGS.latent_dim == 6:
z_sample = np.array([[xi, yi, 0, 0, 0, 0]])
if FLAGS.latent_dim == 8:
z_sample = np.array([[xi, yi, 0, 0, 0, 0, 0, 0]])
if FLAGS.latent_dim == 24:
z_sample = np.array([[
xi, yi, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
]])
if FLAGS.latent_dim == 25:
z_sample = np.array([[
xi, yi, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
]])
x_decoded = VAE.generate_sample(z_sample)
generate_data = x_decoded[0].reshape(digit_size, digit_size,
digit_size)
digit_axial = generate_data[patch_center, :, :]
digit_coronal = generate_data[:, patch_center, :]
digit_sagital = generate_data[:, :, patch_center]
digit1 = np.reshape(digit_axial, [patch_side, patch_side])
digit2 = np.reshape(digit_coronal, [patch_side, patch_side])
digit3 = np.reshape(digit_sagital, [patch_side, patch_side])
fig2 = plt.imshow(digit_axial,
cmap='Greys_r',
vmin=0,
vmax=1,
interpolation='none')
plt.savefig(FLAGS.outdir + 'morphing/' + str(i) + '@' +
str(j) + 'fig.png')
figure1[i * digit_size:(i + 1) * digit_size,
j * digit_size:(j + 1) * digit_size] = digit1
figure2[i * digit_size:(i + 1) * digit_size,
j * digit_size:(j + 1) * digit_size] = digit2
figure3[i * digit_size:(i + 1) * digit_size,
j * digit_size:(j + 1) * digit_size] = digit3
# set graph
start_range = digit_size // 2
end_range = n * digit_size + start_range + 1
pixel_range = np.arange(start_range, end_range, digit_size)
sample_range_x = np.round(grid_x, 1)
sample_range_y = np.round(grid_y, 1)
# axial
plt.figure(figsize=(10, 10))
plt.xticks(pixel_range, sample_range_x)
plt.yticks(pixel_range, sample_range_y)
plt.xlabel("z[0]")
plt.ylabel("z[1]")
plt.imshow(figure1,
cmap='Greys_r',
vmin=0,
vmax=1,
interpolation='none')
plt.savefig(FLAGS.outdir + "digit_axial.png")
# plt.show()
# coronal
plt.figure(figsize=(10, 10))
plt.xticks(pixel_range, sample_range_x)
plt.yticks(pixel_range, sample_range_y)
plt.xlabel("z[0]")
plt.ylabel("z[1]")
plt.imshow(figure2,
cmap='Greys_r',
vmin=0,
vmax=1,
interpolation='none')
plt.savefig(FLAGS.outdir + "digit_coronal.png")
# plt.show()
# sagital
plt.figure(figsize=(10, 10))
plt.xticks(pixel_range, sample_range_x)
plt.yticks(pixel_range, sample_range_y)
plt.xlabel("z[0]")
plt.ylabel("z[1]")
plt.imshow(figure3,
cmap='Greys_r',
vmin=0,
vmax=1,
interpolation='none')
plt.savefig(FLAGS.outdir + "digit_sagital.png")
def main():
# tf flag
flags = tf.flags
flags.DEFINE_string("test_data_txt", 'F:/data_info/VAE_liver/set_5/TFrecord/fold_1/train.txt', "test data txt")
flags.DEFINE_string("dir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_4/beta_6', "input dir")
flags.DEFINE_integer("model_index", 3450 ,"index of model")
flags.DEFINE_string("gpu_index", "0", "GPU-index")
flags.DEFINE_float("beta", 1.0, "hyperparameter beta")
flags.DEFINE_integer("num_of_test", 4681, "number of test data")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("latent_dim", 4, "latent dim")
flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size")
FLAGS = flags.FLAGS
# check folder
if not (os.path.exists(FLAGS.dir)):
os.makedirs(FLAGS.dir)
# read list
test_data_list = io.load_list(FLAGS.test_data_txt)
# test step
test_step = FLAGS.num_of_test // FLAGS.batch_size
if FLAGS.num_of_test % FLAGS.batch_size != 0:
test_step += 1
# load test data
test_set = tf.data.TFRecordDataset(test_data_list, compression_type = 'GZIP')
test_set = test_set.map(lambda x: utils._parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
test_set = test_set.batch(FLAGS.batch_size)
test_iter = test_set.make_one_shot_iterator()
test_data = test_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config = utils.config(index=FLAGS.gpu_index)) as sess:
# set network
kwargs = {
'sess': sess,
'outdir': FLAGS.dir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_resblock_bn,
'decoder': decoder_resblock_bn,
'downsampling': down_sampling,
'upsampling': up_sampling,
'is_training': False,
'is_down': False
}
VAE = Variational_Autoencoder(**kwargs)
sess.run(init_op)
# testing
VAE.restore_model(os.path.join(FLAGS.dir,'model','model_{}'.format(FLAGS.model_index)))
tbar = tqdm(range(test_step), ascii=True)
latent_space = []
for k in tbar:
test_data_batch = sess.run(test_data)
ori_single = test_data_batch
z = VAE.plot_latent(ori_single)
z = z.flatten()
if FLAGS.latent_dim == 1:
z = [z[0], 0]
latent_space.append(z)
latent_space = np.asarray(latent_space)
plt.figure(figsize=(8, 6))
fig = plt.scatter(latent_space[:, 0], latent_space[:, 1], alpha=0.2)
plt.title('latent distribution')
plt.xlabel('dim_1')
plt.ylabel('dim_2')
plt.savefig(os.path.join(FLAGS.dir, 'latent_distribution_{}.PNG'.format(FLAGS.model_index)))
# filename = open(os.path.join(FLAGS.outdir, 'latent_distribution.pickle'), 'wb')
# pickle.dump(fig, filename)
# plt.show()
latent_space = np.asarray(latent_space)
mean = np.average(latent_space, axis=0)
var = np.var(latent_space, axis=0, ddof=1)
print(mean)
print(var)
print(np.cov(latent_space.transpose()))
print('skew, kurtosis')
print(skew(latent_space, axis=0))
print(kurtosis(latent_space, axis=0))
# output mean and var
np.savetxt(os.path.join(FLAGS.dir, 'mean_{}.txt'.format(FLAGS.model_index)), mean)
np.savetxt(os.path.join(FLAGS.dir, 'var_{}.txt'.format(FLAGS.model_index)), var)
def main():
# tf flag
flags = tf.flags
flags.DEFINE_string("ground_truth_txt", 'F:/data_info/VAE_liver/set_5/PCA/alpha_0.1/fold_1/test_label.txt', "ground truth txt")
flags.DEFINE_string("indir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_4/beta_6', "input dir")
flags.DEFINE_string("outdir", 'G:/experiment_result/liver/VAE/set_5/down/64/alpha_0.1/fold_1/VAE/axis_4/beta_6/random', "outdir")
flags.DEFINE_integer("model_index", 3450 ,"index of model")
flags.DEFINE_string("gpu_index", "0", "GPU-index")
flags.DEFINE_float("beta", 1.0, "hyperparameter beta")
flags.DEFINE_integer("num_of_generate", 1000, "number of generate data")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("latent_dim", 4, "latent dim")
flags.DEFINE_list("image_size", [56, 72, 88, 1], "image size")
FLAGS = flags.FLAGS
np.random.seed(1)
# check folder
if not (os.path.exists(FLAGS.outdir)):
os.makedirs(FLAGS.outdir)
# load ground truth
ground_truth = io.load_matrix_data(FLAGS.ground_truth_txt, 'int32')
print(ground_truth.shape)
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config = utils.config(index=FLAGS.gpu_index)) as sess:
# set network
kwargs = {
'sess': sess,
'outdir': FLAGS.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': encoder_resblock_bn,
'decoder': decoder_resblock_bn,
'downsampling': down_sampling,
'upsampling': up_sampling,
'is_training': False,
'is_down': False
}
VAE = Variational_Autoencoder(**kwargs)
sess.run(init_op)
# testing
VAE.restore_model(os.path.join(FLAGS.indir,'model','model_{}'.format(FLAGS.model_index)))
mean = np.loadtxt(os.path.join(FLAGS.indir, 'mean_{}.txt'.format(FLAGS.model_index)))
var = np.loadtxt(os.path.join(FLAGS.indir, 'var_{}.txt'.format(FLAGS.model_index)))
specificity = []
tbar = tqdm(range(FLAGS.num_of_generate), ascii=True)
for i in tbar:
sample_z = np.random.normal(0, 1.0, (1, FLAGS.latent_dim))
sample_z = np.asarray(mean) + np.sqrt(np.asarray(var)) * sample_z
generate_data = VAE.generate_sample(sample_z)
generate_data = generate_data[0, :, :, :, 0]
# EUDT
eudt_image = sitk.GetImageFromArray(generate_data)
eudt_image.SetSpacing([1, 1, 1])
eudt_image.SetOrigin([0, 0, 0])
# label
label = np.where(generate_data > 0.5, 0, 1)
label = label.astype(np.int8)
label_image = sitk.GetImageFromArray(label)
label_image.SetSpacing([1, 1, 1])
label_image.SetOrigin([0, 0, 0])
# # calculate ji
case_max_ji = 0.
for image_index in range(ground_truth.shape[0]):
ji = utils.jaccard(label, ground_truth[image_index])
if ji > case_max_ji:
case_max_ji = ji
specificity.append([case_max_ji])
# # output image
# io.write_mhd_and_raw(eudt_image, '{}.mhd'.format(os.path.join(FLAGS.outdir, 'EUDT', str(i+1))))
# io.write_mhd_and_raw(label_image, '{}.mhd'.format(os.path.join(FLAGS.outdir, 'label', str(i + 1))))
print('specificity = %f' % np.mean(specificity))
# # output csv file
with open(os.path.join(FLAGS.outdir, 'specificity_{}.csv'.format(FLAGS.model_index)), 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(specificity)
writer.writerow(['specificity:', np.mean(specificity)])
def main():
parser = argparse.ArgumentParser(
description='py, test_data_txt, model, outdir')
parser.add_argument('--test_data_txt', '-i1', default='')
parser.add_argument('--model', '-i2', default='./model_{}'.format(50000))
parser.add_argument('--outdir', '-i3', default='')
args = parser.parse_args()
# check folder
if not (os.path.exists(args.outdir)):
os.makedirs(args.outdir)
# tf flag
flags = tf.flags
flags.DEFINE_float("beta", 0.1, "hyperparameter beta")
flags.DEFINE_integer("num_of_test", 100, "number of test data")
flags.DEFINE_integer("batch_size", 1, "batch size")
flags.DEFINE_integer("latent_dim", 2, "latent dim")
flags.DEFINE_list("image_size", [512, 512, 1], "image size")
FLAGS = flags.FLAGS
# read list
test_data_list = io.load_list(args.test_data_txt)
# test step
test_step = FLAGS.num_of_test // FLAGS.batch_size
if FLAGS.num_of_test % FLAGS.batch_size != 0:
test_step += 1
# load test data
test_set = tf.data.TFRecordDataset(test_data_list)
test_set = test_set.map(
lambda x: _parse_function(x, image_size=FLAGS.image_size),
num_parallel_calls=os.cpu_count())
test_set = test_set.batch(FLAGS.batch_size)
test_iter = test_set.make_one_shot_iterator()
test_data = test_iter.get_next()
# initializer
init_op = tf.group(tf.initializers.global_variables(),
tf.initializers.local_variables())
with tf.Session(config=utils.config) as sess:
# set network
kwargs = {
'sess': sess,
'outdir': args.outdir,
'beta': FLAGS.beta,
'latent_dim': FLAGS.latent_dim,
'batch_size': FLAGS.batch_size,
'image_size': FLAGS.image_size,
'encoder': cnn_encoder,
'decoder': cnn_decoder
}
VAE = Variational_Autoencoder(**kwargs)
sess.run(init_op)
# testing
VAE.restore_model(args.model)
tbar = tqdm(range(test_step), ascii=True)
preds = []
ori = []
latent_space = []
for k in tbar:
test_data_batch = sess.run(test_data)
ori_single = test_data_batch
z = VAE.plot_latent(ori_single)
z = z.flatten()
latent_space.append(z)
latent_space = np.asarray(latent_space)
plt.figure(figsize=(8, 6))
fig = plt.scatter(latent_space[:, 0], latent_space[:, 1])
plt.title('latent distribution')
plt.xlabel('dim_1')
plt.ylabel('dim_2')
plt.show()