def write_test_volumes(test_proj, test_label):
with tf.Session(config=tf.ConfigProto(gpu_options=GPU_OPTIONS)) as sess:
m = Model([test_proj], [test_label], [test_proj], [test_label], sess)
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)
# compute volumes before training and export dennerlein
proj_filename = os.path.splitext(
os.path.splitext(os.path.basename(test_proj))[0])[0]
out_fa = LOG_DIR + ('test_%s_%s_fa.bin' %
(proj_filename, WEIGHTS_TYPE))
out_la = LOG_DIR + ('test_%s_%s_la.bin' %
(proj_filename, WEIGHTS_TYPE))
vol_before = m.test_vol
write_dennerlein = dennerlein.write(out_la, vol_before)
write_dennerlein_label = dennerlein.write(out_fa, m.test_label)
sess.run([write_dennerlein, write_dennerlein_label])
coord.request_stop()
coord.join(threads)
sess.close()
tf.reset_default_graph()
def test_model(validation_proj, validation_label, test_proj, test_label,
save_path, log_dir, i):
step = 0
losses = []
print("********* Iteracao: " + str(i) + "\n\n")
# find checkpoint files
checkpoints = []
with open(save_path + 'checkpoint') as f:
f = f.readlines()
pattern = re.compile('all_model_checkpoint_paths:\s\"(.+)\"')
for line in f:
for match in re.finditer(pattern, line):
checkpoints.append(match.groups()[0])
with tf.Session(config=tf.ConfigProto(gpu_options=GPU_OPTIONS)) as sess:
m = Model([test_proj], [test_label], [test_proj], [test_label], sess)
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)
# write_png = png.writeSlice( m.test_label[0], log_dir + 'slice_label_'+str(i)+'.png' )
# sess.run(write_png)
# compute volume before training and export central slice
print('Computing volume ' + str(i) + ' without trained parameters')
vol_before = m.test_vol
# write_png = png.writeSlice( vol_before[CONF.proj_shape.N // 2], log_dir + 'slice_before_training.png' )
write_png = png.writeSlice(
vol_before[31],
log_dir + 'slice_before_training_' + str(i) + '.png')
# write_png_label = png.writeSlice( m.test_label[CONF.proj_shape.N // 2], log_dir + 'slice_label.png' )
# write_png_label = png.writeSlice( m.test_label[0], log_dir + 'slice_label.png' )
# vol_before_np, _, _, vol_label_np = sess.run( [vol_before, write_png, write_png_label, m.test_label] )
vol_before_np, _ = sess.run([vol_before, write_png])
# find best checkpoint
# print( 'Searching best checkpoint' )
saver = tf.train.Saver()
m.setTest(validation_proj, validation_label, sess)
best_cp_i = 51
best_cp_loss = math.inf
#print("Hard Code for the best checkpoint 1102")
print("\n\n**********************************")
#print(checkpoints)
print(checkpoints[best_cp_i])
"""
for i, cp in enumerate( checkpoints ):
# for i in range(300, 400):
saver.restore( sess, checkpoints[i] )
loss = sess.run( m.test_loss )
print("Modelo: ")
print(i)
print("Loss: ")
print(loss)
if loss < best_cp_loss:
best_cp_i = i
best_cp_loss = loss
print( '.', end = '', flush = True )
"""
print('')
print("Terminando de testar os modelos ********\n\n")
print("Melhor loss: ")
print(best_cp_i)
saver.restore(sess, checkpoints[best_cp_i])
#print("Loss do modelo " + str(best_cp_i))
#loss = sess.run( m.test_loss )
#print(loss)
# load best model and set test volume
print('Computing volume ' + str(i) + ' with trained parameters')
m.setTest([test_proj], [test_label], sess)
saver.restore(sess, checkpoints[best_cp_i])
# compute volume after training and export central slice + dennerlein
vol_after = m.test_vol
#write_png = png.writeSlice( vol_after[CONF.proj_shape.N // 2], log_dir + 'slice_after_training.png' )
write_png = png.writeSlice(
vol_after[31], log_dir + 'slice_after_training_' + str(i) + '.png')
write_dennerlein = dennerlein.write(
log_dir + 'after_training_' + str(i) + '.bin', vol_after)
vol_after_np, _, _ = sess.run([vol_after, write_png, write_dennerlein])
coord.request_stop()
coord.join(threads)
sess.close()
tf.reset_default_graph()
return losses, step
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 test_model(validation_proj, validation_label, test_proj, test_label,
save_path, log_dir):
step = 0
losses = []
# find checkpoint files
checkpoints = []
with open(save_path + 'checkpoint') as f:
f = f.readlines()
pattern = re.compile('all_model_checkpoint_paths:\s\"(.+)\"')
for line in f:
for match in re.finditer(pattern, line):
checkpoints.append(match.groups()[0])
with tf.Session(config=tf.ConfigProto(gpu_options=GPU_OPTIONS)) as sess:
m = Model([test_proj], [test_label], [test_proj], [test_label], sess)
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)
# compute volume before training and export central slice
print('Computing volume without trained parameters')
vol_before = m.test_vol
write_png = png.writeSlice(vol_before[CONF.proj_shape.N // 2],
log_dir + 'slice_before_training.png')
write_png_label = png.writeSlice(m.test_label[CONF.proj_shape.N // 2],
log_dir + 'slice_label.png')
vol_before_np, _, _, vol_label_np = sess.run(
[vol_before, write_png, write_png_label, m.test_label])
# find best checkpoint
print('Searching best checkpoint')
saver = tf.train.Saver()
m.setTest(validation_proj, validation_label, sess)
best_cp_i = 0
best_cp_loss = math.inf
for i, cp in enumerate(checkpoints):
saver.restore(sess, cp)
loss = sess.run(m.test_loss)
if loss < best_cp_loss:
best_cp_i = i
best_cp_loss = loss
print('.', end='', flush=True)
print('')
# load best model and set test volume
print('Computing volume with trained parameters')
m.setTest([test_proj], [test_label], sess)
saver.restore(sess, checkpoints[best_cp_i])
# compute volume after training and export central slice + dennerlein
vol_after = m.test_vol
write_png = png.writeSlice(vol_after[CONF.proj_shape.N // 2],
log_dir + 'slice_after_training.png')
write_dennerlein = dennerlein.write(log_dir + 'after_training.bin',
vol_after)
vol_after_np, _, _ = sess.run([vol_after, write_png, write_dennerlein])
coord.request_stop()
coord.join(threads)
sess.close()
tf.reset_default_graph()
return losses, step