def test(FLAGS):
max_steps = FLAGS.max_steps
batch_size = FLAGS.batch_size
train_dir = FLAGS.log_dir # /tmp3/first350/TensorFlow/Logs
test_dir = FLAGS.test_dir # /tmp3/first350/SegNet-Tutorial/CamVid/train.txt
test_ckpt = FLAGS.testing
image_w = FLAGS.image_w
image_h = FLAGS.image_h
image_c = FLAGS.image_c
# testing should set BATCH_SIZE = 1
batch_size = 1
image_filenames, label_filenames = get_filename_list(test_dir)
test_data_node = tf.placeholder(
tf.float32, shape=[batch_size, image_h, image_w, image_c])
test_labels_node = tf.placeholder(tf.int64,
shape=[batch_size, 360, 480, 1])
phase_train = tf.placeholder(tf.bool, name='phase_train')
loss, logits = inference(test_data_node, test_labels_node, batch_size,
phase_train)
pred = tf.argmax(logits, dimension=3)
# get moving avg
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
with tf.Session() as sess:
# Load checkpoint
saver.restore(sess, test_ckpt)
images, labels = get_all_test_data(image_filenames, label_filenames)
threads = tf.train.start_queue_runners(sess=sess)
hist = np.zeros((NUM_CLASSES, NUM_CLASSES))
for image_batch, label_batch in zip(images, labels):
feed_dict = {
test_data_node: image_batch,
test_labels_node: label_batch,
phase_train: False
}
dense_prediction, im = sess.run([logits, pred],
feed_dict=feed_dict)
# output_image to verify
if (FLAGS.save_image):
writeImage(im[0], 'testing_image.png')
hist += get_hist(dense_prediction, label_batch)
acc_total = np.diag(hist).sum() / hist.sum()
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print("acc: ", acc_total)
print("mean IU: ", np.nanmean(iu))
def test_model(two):
global max_steps, batch_size, image_w, image_h, image_c, image_dir, log_dir
count=0
#test_dir = FLAGS.test_dir # /tmp3/first350/SegNet-Tutorial/CamVid/train.txt
test_ckpt = '/home/shantams/deep-learning/tf/logs/Test/run1/model.ckpt-9000'
# testing should set BATCH_SIZE = 1
batch_size = 1
image_filenames, label_filenames = get_filename_list(image_dir)
test_data_node = tf.placeholder(
tf.float32,
shape=[batch_size, image_h, image_w, image_c])
test_labels_node = tf.placeholder(tf.int64, shape=[batch_size, 240,320,1])
phase_train = tf.placeholder(tf.bool, name='phase_train')
loss, logits = inference(test_data_node, test_labels_node, phase_train)
pred = tf.argmax(logits, axis=3)
# get moving avg
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.0001)
with tf.Session() as sess:
# Load checkpoint
saver.restore(sess, test_ckpt)
images, labels = get_all_test_data(image_filenames, label_filenames)
threads = tf.train.start_queue_runners(sess=sess)
hist = np.zeros((3, 3)) #number of classes, number of classes
for image_batch, label_batch in zip(images, labels):
#print image_batch.shape
feed_dict = {
test_data_node: image_batch,
test_labels_node: label_batch,
phase_train: False
}
dense_prediction, im = sess.run([logits, pred], feed_dict=feed_dict)
# output_image to verify
if (True):
#writeImage(im[0], 'testing_image.png')
writeImage(im[0], str(image_filenames[count]).split('/')[-1])
hist += get_hist(dense_prediction, label_batch)
count+=1
acc_total = np.diag(hist).sum() / hist.sum()
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print("acc: ", acc_total)
print("mean IU: ", np.nanmean(iu))
def test():
max_steps = config.max_steps
batch_size = config.batch_size
test_dir = config.test_dir
test_ckpt = config.testing
image_w = config.image_w
image_h = config.image_h
image_c = config.image_c
# testing should set BATCH_SIZE = 1
batch_size = 1
image_filenames, label_filenames = get_filename_list(test_dir)
test_data_node = tf.placeholder(
tf.float32, shape=[batch_size, image_h, image_w, image_c])
test_labels_node = tf.placeholder(tf.int64,
shape=[batch_size, 360, 480, 1])
phase_train = tf.placeholder(tf.bool, name='phase_train')
loss, logits = inference(test_data_node, test_labels_node, batch_size,
phase_train)
pred = tf.argmax(logits, axis=3)
# get moving avg
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.0001)
with tf.Session() as sess:
# Load checkpoint
saver.restore(sess, test_ckpt)
images, labels = get_all_test_data(image_filenames, label_filenames)
threads = tf.train.start_queue_runners(sess=sess)
hist = np.zeros((NUM_CLASSES, NUM_CLASSES))
for image_batch, label_batch in zip(images, labels):
feed_dict = {
test_data_node: image_batch,
test_labels_node: label_batch,
phase_train: False
}
dense_prediction, im = sess.run([logits, pred],
feed_dict=feed_dict)
# output_image to verify
if (config.save_image):
writeImage(im[0], '%s/testing_image.png' % config.log_dir)
print('Testing image saved.')
hist += get_hist(dense_prediction, label_batch)
# count+=1
acc_total = np.diag(hist).sum() / hist.sum()
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print("acc: ", acc_total)
print("mean IU: ", np.nanmean(iu))
def test(FLAGS):
max_steps = FLAGS.max_steps
batch_size = FLAGS.batch_size
train_dir = FLAGS.log_dir # /tmp3/first350/TensorFlow/Logs
test_dir = FLAGS.test_dir # /tmp3/first350/SegNet-Tutorial/CamVid/train.txt
test_ckpt = FLAGS.testing
image_w = FLAGS.image_w
image_h = FLAGS.image_h
image_c = FLAGS.image_c
# testing should set BATCH_SIZE = 1
batch_size = 1
image_filenames, label_filenames = get_filename_list(test_dir)
test_data_node = tf.placeholder(
tf.float32,
shape=[batch_size, image_h, image_w, image_c])
test_labels_node = tf.placeholder(tf.int64, shape=[batch_size, 360, 480, 1])
phase_train = tf.placeholder(tf.bool, name='phase_train')
loss, logits = inference(test_data_node, test_labels_node, batch_size, phase_train)
pred = tf.argmax(logits, axis=3)
# get moving avg
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.0001)
with tf.Session() as sess:
# Load checkpoint
saver.restore(sess, test_ckpt )
images, labels = get_all_test_data(image_filenames, label_filenames)
threads = tf.train.start_queue_runners(sess=sess)
hist = np.zeros((NUM_CLASSES, NUM_CLASSES))
for image_batch, label_batch in zip(images, labels):
feed_dict = {
test_data_node: image_batch,
test_labels_node: label_batch,
phase_train: False
}
dense_prediction, im = sess.run([logits, pred], feed_dict=feed_dict)
# output_image to verify
if (FLAGS.save_image):
writeImage(im[0], 'testing_image.png')
# writeImage(im[0], 'out_image/'+str(image_filenames[count]).split('/')[-1])
hist += get_hist(dense_prediction, label_batch)
# count+=1
acc_total = np.diag(hist).sum() / hist.sum()
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print("acc: ", acc_total)
print("mean IU: ", np.nanmean(iu))
def test(FLAGS):
max_steps = FLAGS.max_steps
batch_size = FLAGS.batch_size
train_dir = FLAGS.log_dir # /tmp3/first350/TensorFlow/Logs
test_dir = FLAGS.test_dir # /tmp3/first350/SegNet-Tutorial/CamVid/train.txt
test_ckpt = FLAGS.testing
image_w = FLAGS.image_w
image_h = FLAGS.image_h
image_c = FLAGS.image_c
# testing should set BATCH_SIZE = 1
batch_size = 1
image_filenames, label_filenames = get_filename_list(test_dir)
test_data_node = tf.placeholder(
tf.float32, shape=[batch_size, image_h, image_w, image_c])
test_labels_node = tf.placeholder(tf.int64,
shape=[batch_size, 360, 480, 1])
phase_train = tf.placeholder(tf.bool, name='phase_train')
loss, logits = inference(test_data_node, test_labels_node, batch_size,
phase_train)
pred = tf.argmax(logits, axis=3)
# get moving avg
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.05)
#config=tf.ConfigProto(gpu_options=gpu_options)
count = 0
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# Load checkpoint
saver.restore(sess, test_ckpt)
images, labels = get_all_test_data(image_filenames, label_filenames)
threads = tf.train.start_queue_runners(sess=sess)
hist = np.zeros((NUM_CLASSES, NUM_CLASSES))
for image_batch, label_batch in zip(images, labels):
#image_batch = cv2.resize(image_batch, (480, 360))
#print(image_batch.shape)
#label_batch = image_batch
feed_dict = {
test_data_node: image_batch,
test_labels_node: label_batch,
phase_train: False
}
dense_prediction, im = sess.run([logits, pred],
feed_dict=feed_dict)
# output_image to verify
if (FLAGS.save_image):
#writeImage(im[0], 'testing_image0.png')
#writeImage(im[1], 'testing_image1.png')
#writeImage(im[2], 'testing_image2.png')
writeImage(
im[0],
'out_image/' + str(image_filenames[count]).split('/')[-1])
hist += get_hist(dense_prediction, label_batch)
count += 1
def test(FLAGS):
iterations = FLAGS.iterations
batch_size = FLAGS.batch_size
train_dir = FLAGS.log_dir # /tmp3/first350/TensorFlow/Logs
test_image_list = FLAGS.test_image_list # /tmp3/first350/SegNet-Tutorial/CamVid/train.txt
test_ckpt = FLAGS.ckpt_for_test
image_w = FLAGS.image_w
image_h = FLAGS.image_h
image_c = FLAGS.image_c
# test should set batch_size = 1
batch_size = 1
image_filenames, label_filenames = get_filename_list(test_image_list)
test_data_node = tf.placeholder(
tf.float32, shape=[batch_size, image_h, image_w, image_c])
test_labels_node = tf.placeholder(tf.int64,
shape=[batch_size, image_h, image_w, 1])
phase_train = tf.placeholder(tf.bool, name='phase_train')
loss, logits = inference(FLAGS, test_data_node, test_labels_node,
batch_size, phase_train)
pred = tf.argmax(logits, axis=3)
# get moving avg
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.0001)
with tf.Session() as sess:
# Load checkpoint
saver.restore(sess, test_ckpt)
images, labels = get_all_test_data(image_filenames, label_filenames)
threads = tf.train.start_queue_runners(sess=sess)
hist = np.zeros((FLAGS.num_classes, FLAGS.num_classes))
i = -1
for image_batch, label_batch in zip(images, labels):
i += 1
feed_dict = {
test_data_node: image_batch,
test_labels_node: label_batch,
phase_train: False
}
dense_prediction, im = sess.run([logits, pred],
feed_dict=feed_dict)
# output_image to verify
if (FLAGS.save_image):
writeImage(
FLAGS, im[0],
str(image_filenames[i]).rstrip('.png') +
'_hanteikekka.png')
hist += get_hist(dense_prediction, label_batch)
# count+=1
acc_total = np.diag(hist).sum() / hist.sum()
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print("acc: ", acc_total)
print("mean IU: ", np.nanmean(iu))
def test(FLAGS):
max_steps = FLAGS.max_steps
batch_size = FLAGS.batch_size
train_dir = FLAGS.log_dir # /tmp3/first350/TensorFlow/Logs
test_dir = FLAGS.test_dir # /tmp3/first350/SegNet-Tutorial/CamVid/train.txt
test_ckpt = FLAGS.testing
image_w = FLAGS.image_w
image_h = FLAGS.image_h
image_c = FLAGS.image_c
# testing should set BATCH_SIZE = 1
batch_size = 1
image_filenames, label_filenames = get_filename_list(test_dir)
test_data_node = tf.placeholder(
tf.float32,
shape=[batch_size, image_h, image_w, image_c], name = 'input')
test_labels_node = tf.placeholder(tf.int64, shape=[batch_size, 90, 120, 1])
phase_train = tf.placeholder(tf.bool, name='phase_train')
loss, logits = inference(test_data_node, test_labels_node, batch_size, phase_train)
logits = tf.identity(logits, name = 'output')
pred = tf.argmax(logits, axis=3)
# get moving avg
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.0001)
with tf.Session() as sess:
# Load checkpoint
count = 0
saver.restore(sess, test_ckpt )
images, labels = get_all_test_data(image_filenames, label_filenames)
threads = tf.train.start_queue_runners(sess=sess)
hist = np.zeros((NUM_CLASSES, NUM_CLASSES))
for image_batch, label_batch in zip(images, labels):
feed_dict = {
test_data_node: image_batch,
test_labels_node: label_batch,
phase_train: False
}
dense_prediction, im = sess.run([logits, pred], feed_dict=feed_dict)
# output_image to verify
if (FLAGS.save_image):
writeImage(im[0], 'testing_image.png')
writeImage(im[0], 'out_image/'+str(image_filenames[count]).split('/')[-1])
hist += get_hist(dense_prediction, label_batch)
count+=1
acc_total = np.diag(hist).sum() / hist.sum()
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print("acc: ", acc_total)
print("mean IU: ", np.nanmean(iu))
graph_def = sess.graph.as_graph_def()
for node in graph_def.node:
p#rint(node)
if node.op == 'RefSwitch':
node.op = 'Switch'
for index in range(len(node.input)):
if 'moving_' in node.input[index]:
node.input[index] = node.input[index] + '/read'
elif node.op == 'AssignSub':
node.op = 'Sub'
if 'use_locking' in node.attr: del node.attr['use_locking']
elif node.op == 'AssignAdd':
node.op = 'Add'
if 'use_locking' in node.attr: del node.attr['use_locking']
constant_graph = graph_util.convert_variables_to_constants(sess, graph_def,
["output"])
graph_io.write_graph(constant_graph, './', "FoosballBallTrackerHeatmap.pb", as_text=False)
def test(FLAGS):
max_steps = FLAGS.max_steps
batch_size = FLAGS.batch_size
train_dir = FLAGS.log_dir # /tmp3/first350/TensorFlow/Logs
test_dir = FLAGS.test_dir # /tmp3/first350/SegNet-Tutorial/CamVid/train.txt
test_ckpt = FLAGS.testing
image_w = FLAGS.image_w
image_h = FLAGS.image_h
image_c = FLAGS.image_c
datadir = FLAGS.datadir
outdir = FLAGS.output_dir
dataset = FLAGS.dataset
use_weights = FLAGS.use_weights
# testing should set BATCH_SIZE = 1
batch_size = 1
# select weights
# class weight calculation used in segnet
# weights for dataset de_top14
# "detop15", "eutop25", "worldtiny2k", "kaggle_dstl", "vaihingen",
# "detop15_nores", "eutop25_nores", "worldtiny2k_nores"
if not use_weights:
print('dont use weights during evaluation')
use_weights = tf.constant([1.0 for i in range(FLAGS.num_class)])
image_filenames, label_filenames = get_filename_list(test_dir)
test_data_node = tf.placeholder(
tf.float32, shape=[batch_size, image_h, image_w, image_c])
test_labels_node = tf.placeholder(tf.int64,
shape=[batch_size, image_h, image_w, 1])
phase_train = tf.placeholder(tf.bool, name='phase_train')
loss, logits = inference(FLAGS.num_class, test_data_node, test_labels_node,
batch_size, phase_train, use_weights)
pred = tf.argmax(logits, axis=3)
# get moving avg
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.0001)
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(test_ckpt)
# Load checkpoint
vars_to_restore = get_tensors_in_checkpoint_file(
file_name=ckpt.model_checkpoint_path)
# print(vars_to_restore)
# print([v.name for v in tf.global_variables()])
vars_to_restore = [
v for v in tf.global_variables()
if (not 'conv_classifier' in v.name or not FLAGS.not_restore_last)
and v.name[:-2] in vars_to_restore
]
# saver = tf.train.Saver(variables_to_restore)
saver = tf.train.Saver(vars_to_restore)
saver.restore(sess, ckpt.model_checkpoint_path)
images, labels = get_all_test_data(image_filenames, label_filenames,
datadir)
threads = tf.train.start_queue_runners(sess=sess)
hist = np.zeros((FLAGS.num_class, FLAGS.num_class))
eval_file = open(FLAGS.dataset, 'wt+')
for image_batch, label_batch in tqdm(zip(images, labels),
desc="Testing",
leave=True,
total=len(images)):
feed_dict = {
test_data_node: image_batch,
test_labels_node: label_batch,
phase_train: False
}
dense_prediction, im = sess.run([logits, pred],
feed_dict=feed_dict)
# output_image to verify
if (FLAGS.save_image):
if not os.path.exists(outdir):
os.makedirs(outdir)
writeImage(im[0],
outdir + '/testing_image.png',
dataset=dataset)
# writeImage(im[0], 'out_image/'+str(image_filenames[count]).split('/')[-1])
# calc iou and acc values for each image (for significance test)
lhist = get_hist(dense_prediction, label_batch)
lacc = np.diag(lhist).sum() / lhist.sum()
liu = np.diag(lhist) / (lhist.sum(1) + lhist.sum(0) -
np.diag(lhist))
acc_line = "acc"
iou_line = "iou"
for cls in range(lhist.shape[0]):
iou = liu[cls]
if float(lhist.sum(1)[cls]) == 0:
local_acc = 0.0
else:
local_acc = np.diag(lhist)[cls] / float(lhist.sum(1)[cls])
acc_line = acc_line + ", " + str(local_acc)
iou_line = iou_line + ", " + str(iou)
eval_file.write(acc_line + "\n")
eval_file.write(iou_line + "\n")
eval_file.flush()
hist += lhist
# count+=1
acc_total = np.diag(hist).sum() / hist.sum()
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
eval_file.close()
print("acc: ", acc_total)
print("mean IU: ", np.nanmean(iu))
cl_name = get_dataset_classnames(dataset)
for cls in range(hist.shape[0]):
iou = iu[cls]
if float(hist.sum(1)[cls]) == 0:
acc = 0.0
else:
acc = np.diag(hist)[cls] / float(hist.sum(1)[cls])
print(" class %s accuracy = %f, IoU = %f" %
(cl_name[cls].ljust(12), acc, iou))
def infere(FLAGS):
test_dir = FLAGS.test_dir # /tmp3/first350/SegNet-Tutorial/CamVid/train.txt
test_ckpt = FLAGS.testing
image_w = FLAGS.image_w
image_h = FLAGS.image_h
image_c = FLAGS.image_c
datadir = FLAGS.datadir
outdir = FLAGS.output_dir
max_infere = FLAGS.max_infere
dataset = FLAGS.dataset
batch_size = 1
use_weights = FLAGS.use_weights
image_filenames, label_filenames = get_filename_list(test_dir)
test_data_node = tf.placeholder(
tf.float32, shape=[batch_size, image_h, image_w, image_c])
phase_train = tf.placeholder(tf.bool, name='phase_train')
# select weights
# class weight calculation used in segnet
# weights for dataset de_top14
# "detop15", "eutop25", "worldtiny2k", "kaggle_dstl", "vaihingen",
# "detop15_nores", "eutop25_nores", "worldtiny2k_nores"
print('dont use weights during inference')
use_weights = tf.constant([1.0 for i in range(FLAGS.num_class)])
# feed only sat images to the net
logits = inference(FLAGS.num_class, test_data_node, None, batch_size,
phase_train, use_weights)
# calc certainity grey scale
certainity = get_certainity(logits, [image_h, image_w])
pred = tf.argmax(logits, axis=3)
# get moving avg
# variable_averages = tf.train.ExponentialMovingAverage(
# MOVING_AVERAGE_DECAY)
# variables_to_restore = variable_averages.variables_to_restore()
ckpt = tf.train.get_checkpoint_state(test_ckpt)
vars_to_restore = get_tensors_in_checkpoint_file(
file_name=ckpt.model_checkpoint_path)
# print(vars_to_restore)
# print([v.name for v in tf.global_variables()])
vars_to_restore = [
v for v in tf.global_variables()
if (not 'conv_classifier' in v.name or not FLAGS.not_restore_last)
and v.name[:-2] in vars_to_restore
]
# loader = tf.train.Saver(variables_to_restore)
loader = tf.train.Saver(vars_to_restore)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.0001)
with tf.Session() as sess:
# Load checkpoint
loader.restore(sess, ckpt.model_checkpoint_path)
images, labels = get_all_test_data(image_filenames, label_filenames,
datadir, max_infere)
hist = np.zeros((FLAGS.num_class, FLAGS.num_class))
# create out dir if not exist
if not os.path.exists(outdir):
os.makedirs(outdir)
print(
"Warning! No Softmax Output before Argmax in Segnet! Certainity does not display probability! \n"
"(Neither does a ConvNet with Softmax Output by the way ^^)")
for i in trange(0,
min(len(images), max_infere),
desc="Infering",
leave=True):
image_fname = image_filenames[i]
image = images[i]
label = labels[
i] # not used currently, but will later be needed to save gt data along with pred
print(image[0].shape)
print(label[0].shape)
feed_dict = {test_data_node: image, phase_train: False}
dense_prediction, im, cert_img = sess.run(
[logits, pred, certainity], feed_dict=feed_dict)
# output_image to verify
if not os.path.exists(outdir):
os.makedirs(outdir)
save_dir_name = os.path.dirname(outdir + '/' + image_fname)
base_name = os.path.basename(outdir + '/' + image_fname)
subdir = os.path.join(save_dir_name, str(i))
if not os.path.exists(subdir):
os.makedirs(subdir)
print(subdir)
# writeImage(image, subdir + '/' + 'sat' + base_name)
# deprec function TODO replace imsave with imageio.imwrite!
misc.imsave(subdir + '/' + 'cert_' + base_name, cert_img[0])
misc.imsave(subdir + '/' + 'sat_' + base_name,
image[0].astype(int))
writeImage(im[0],
subdir + '/' + 'pred_' + base_name,
dataset=dataset)
writeImage(np.squeeze(label[0]),
subdir + '/' + 'gt_' + base_name,
dataset=dataset)
