def create_label(fn_proj, fn_vol, rec, geom):
proj = dennerlein.read_noqueue(fn_proj)
volume = rec.apply(proj, geom,
fullscan=True) #MUDAR DEPOIS PARA TRUE!!!!!!
return dennerlein.write(fn_vol, volume)
def my_train():
EPOCHS = 10
BATCH_SIZE = 16
train_proj = [
"/home/davi/Documentos/ConeDeepLearningCT2/phantoms/lowdose/binary0.proj.bin",
]
train_label = [
"/home/davi/Documentos/ConeDeepLearningCT2/phantoms/lowdose/binary0.vol.bin",
]
save_path = '/home/davi/Documentos/train/model_%d/' % 0
with tf.Session() as sess:
train_list = []
label_list = []
for i in range(len(train_proj)):
train_list_ = sess.run(dennerlein.read_noqueue(train_proj[i]))
train_list.append(train_list_)
label_list_ = sess.run(dennerlein.read_noqueue(train_label[i]))
label_list.append(label_list_)
geom, angles = projtable.read(DATA_P + 'projMat.txt')
# Beleza, temos as amostras em train_proj e os labels em train_label
BATCH_SIZE = 1
features, labels = (train_list, label_list)
dataset = tf.data.Dataset.from_tensor_slices(
(np.asarray(features), np.asarray(labels))).repeat().batch(BATCH_SIZE)
iter = dataset.make_one_shot_iterator()
x, y = iter.get_next()
EPOCHS = 100000
with tf.Session(config=tf.ConfigProto(gpu_options=GPU_OPTIONS)) as sess:
global LEARNING_RATE
# Ajeita as coisas para printar um slice antes de comçar a treinar
re = ct.Reconstructor(CONF_LA,
angles[0:LIMITED_ANGLE_SIZE],
DISPLACEMENT,
name='LAReconstructor',
weights_type=WEIGHTS_TYPE)
volume_la = re.apply(x, geom)
# Esse reconstructor será utilizado para corrigir a imagem durante as EPOCHS do treinamento
re = ct.Reconstructor(CONF_LA,
angles[0:LIMITED_ANGLE_SIZE],
DISPLACEMENT,
trainable=True,
name='LAReconstructor',
weights_type=WEIGHTS_TYPE)
if not tf.train.get_global_step():
tf.train.create_global_step()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver = tf.train.Saver(max_to_keep=TRACK_LOSS)
# Printa
write_png = png.writeSlice(
volume_la[0],
'/home/davi/Documentos/train/test_model_0/slice_qualquer_antes_treinamento.png'
)
sess.run(write_png)
try:
for i in range(EPOCHS):
# if i % 30 == 0:
# print("Abaixando a LEARNING RATE")
# LEARNING_RATE /= 10
proj = train_list[0]
label = label_list[0]
train_step = tf.no_op()
volume_la = re.apply(x, geom)
volume_la = tf.expand_dims(volume_la, axis=0)
t1 = tf.math.reduce_max(y)
t2 = tf.math.reduce_max(volume_la)
label_ = y * (255 / t1)
volume_la_ = volume_la * (255 / t2)
# Add a extension to the tensor
write_png = png.writeSlice(
volume_la_[0][0],
'/home/davi/Documentos/train/test_model_0/slice_qualquer_depois_treinamento.png'
)
sess.run(write_png)
loss = tf.losses.mean_squared_error(label_, volume_la_)
with tf.control_dependencies([train_step]):
gstep = tf.train.get_global_step()
train_step = tf.train.GradientDescentOptimizer(
LEARNING_RATE).minimize(
loss,
colocate_gradients_with_ops=True,
global_step=gstep)
step = sess.run(tf.train.get_global_step())
if step % 10:
print("Salvando o modelo")
saver.save(sess, save_path + 'model', global_step=step)
# Treinando
print("Treinando")
sess.run(train_step)
except tf.errors.OutOfRangeError:
print('Done.')
finally:
coord.request_stop()