def test(self):
image_filename, label_filename = get_filename_list(
self.test_file, self.config)
with self.graph.as_default():
with self.sess as sess:
loss, accuracy, prediction = normal_loss(
self.logits, self.labels_pl, self.num_classes)
images, labels = get_all_test_data(image_filename,
label_filename)
#acc_final = []
#iu_final = []
#iu_mean_final = []
loss_tot = []
acc_tot = []
pred_tot = []
#hist = np.zeros((self.num_classes, self.num_classes))
step = 0
for image_batch, label_batch in zip(images, labels):
image_batch = np.reshape(
image_batch,
[1, self.input_h, self.input_w, self.input_c])
label_batch = np.reshape(
label_batch, [1, self.input_h, self.input_w, 1])
feed_dict = {
self.inputs_pl: image_batch,
self.labels_pl: label_batch,
self.batch_size_pl: 1
}
fetches = [loss, accuracy, self.logits, prediction]
loss_per, acc_per, logit, pred = sess.run(
fetches=fetches, feed_dict=feed_dict)
loss_tot.append(loss_per)
acc_tot.append(acc_per)
pred_tot.append(pred)
print(
"Image Index {}: TEST Loss{:6.3f}, TEST Accu {:6.3f}".
format(step, loss_tot[-1], acc_tot[-1]))
step = step + 1
#per_class_acc(logit, label_batch, self.num_classes)
#hist += get_hist(logit, label_batch)
#acc_tot = np.diag(hist).sum() / hist.sum()
#iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
#print("Total Accuracy for test image: ", acc_tot)
#print("Total MoI for test images: ", iu)
#print("mean MoI for test images: ", np.nanmean(iu))
#acc_final.append(acc_tot)
#iu_final.append(iu)
#iu_mean_final.append(np.nanmean(iu))
return pred_tot
def test(self):
image_filename, label_filename = get_filename_list(
self.test_file, self.config)
with self.graph.as_default():
with self.sess as sess:
saver = tf.train.Saver()
saver.restore(self.sess,
os.path.join(self.saved_dir, 'model.ckpt-19'))
loss, accuracy, prediction = normal_loss(
self.logits, self.labels_pl, self.num_classes)
images, labels = get_all_test_data(image_filename,
label_filename)
loss_tot = []
acc_tot = []
pred_tot = []
step = 0
for image_batch, label_batch in zip(images, labels):
image_batch = np.reshape(
image_batch,
[1, self.input_h, self.input_w, self.input_c])
label_batch = np.reshape(
label_batch, [1, self.input_h, self.input_w, 1])
feed_dict = {
self.inputs_pl: image_batch,
self.labels_pl: label_batch,
self.batch_size_pl: 1
}
fetches = [loss, accuracy, self.logits, prediction]
loss_per, acc_per, logit, pred = sess.run(
fetches=fetches, feed_dict=feed_dict)
loss_tot.append(loss_per)
acc_tot.append(acc_per)
pred_tot.append(pred)
print(
"Image Index {}: TEST Loss{:6.3f}, TEST Accu {:6.3f}".
format(step, loss_tot[-1], acc_tot[-1]))
step = step + 1
def test(self):
image_filename, label_filename = get_filename_list(self.test_file, self.config)
with self.graph.as_default():
with self.sess as sess:
loss, accuracy, prediction = normal_loss(self.logits, self.labels_pl, self.num_classes)
prob = tf.nn.softmax(self.logits, dim=-1)
prob = tf.reshape(prob, [self.input_h, self.input_w, self.num_classes])
images, labels = get_all_test_data(image_filename, label_filename)
NUM_SAMPLE = []
for i in range(30):
NUM_SAMPLE.append(2 * i + 1)
acc_final = []
iu_final = []
iu_mean_final = []
# uncomment the line below to only run for two times.
# NUM_SAMPLE = [1, 30]
NUM_SAMPLE = [1]
for num_sample_generate in NUM_SAMPLE:
loss_tot = []
acc_tot = []
pred_tot = []
var_tot = []
hist = np.zeros((self.num_classes, self.num_classes))
step = 0
for image_batch, label_batch in zip(images, labels):
image_batch = np.reshape(image_batch, [1, self.input_h, self.input_w, self.input_c])
label_batch = np.reshape(label_batch, [1, self.input_h, self.input_w, 1])
# comment the code below to apply the dropout for all the samples
if num_sample_generate == 1:
feed_dict = {self.inputs_pl: image_batch, self.labels_pl: label_batch,
self.is_training_pl: False,
self.keep_prob_pl: 0.5, self.with_dropout_pl: False,
self.batch_size_pl: 1}
else:
feed_dict = {self.inputs_pl: image_batch, self.labels_pl: label_batch,
self.is_training_pl: False,
self.keep_prob_pl: 0.5, self.with_dropout_pl: True,
self.batch_size_pl: 1}
# uncomment this code below to run the dropout for all the samples
# feed_dict = {test_data_tensor: image_batch, test_label_tensor:label_batch, phase_train: False, keep_prob:0.5, phase_train_dropout:True}
fetches = [loss, accuracy, self.logits, prediction]
if self.bayes is False:
loss_per, acc_per, logit, pred = sess.run(fetches=fetches, feed_dict=feed_dict)
var_one = []
else:
logit_iter_tot = []
loss_iter_tot = []
acc_iter_tot = []
prob_iter_tot = []
logit_iter_temp = []
for iter_step in range(num_sample_generate):
loss_iter_step, acc_iter_step, logit_iter_step, prob_iter_step = sess.run(
fetches=[loss, accuracy, self.logits, prob], feed_dict=feed_dict)
loss_iter_tot.append(loss_iter_step)
acc_iter_tot.append(acc_iter_step)
logit_iter_tot.append(logit_iter_step)
prob_iter_tot.append(prob_iter_step)
logit_iter_temp.append(
np.reshape(logit_iter_step, [self.input_h, self.input_w, self.num_classes]))
loss_per = np.nanmean(loss_iter_tot)
acc_per = np.nanmean(acc_iter_tot)
logit = np.nanmean(logit_iter_tot, axis=0)
print(np.shape(prob_iter_tot))
prob_mean = np.nanmean(prob_iter_tot, axis=0)
prob_variance = np.var(prob_iter_tot, axis=0)
logit_variance = np.var(logit_iter_temp, axis=0)
# THIS TIME I DIDN'T INCLUDE TAU
pred = np.reshape(np.argmax(prob_mean, axis=-1), [-1]) # pred is the predicted label
var_sep = [] # var_sep is the corresponding variance if this pixel choose label k
length_cur = 0 # length_cur represent how many pixels has been read for one images
for row in np.reshape(prob_variance, [self.input_h * self.input_w, self.num_classes]):
temp = row[pred[length_cur]]
length_cur += 1
var_sep.append(temp)
var_one = np.reshape(var_sep, [self.input_h,
self.input_w]) # var_one is the corresponding variance in terms of the "optimal" label
pred = np.reshape(pred, [self.input_h, self.input_w])
loss_tot.append(loss_per)
acc_tot.append(acc_per)
pred_tot.append(pred)
var_tot.append(var_one)
print("Image Index {}: TEST Loss{:6.3f}, TEST Accu {:6.3f}".format(step, loss_tot[-1], acc_tot[-1]))
step = step + 1
per_class_acc(logit, label_batch, self.num_classes)
hist += get_hist(logit, label_batch)
acc_tot = np.diag(hist).sum() / hist.sum()
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
print("Total Accuracy for test image: ", acc_tot)
print("Total MoI for test images: ", iu)
print("mean MoI for test images: ", np.nanmean(iu))
acc_final.append(acc_tot)
iu_final.append(iu)
iu_mean_final.append(np.nanmean(iu))
return acc_final, iu_final, iu_mean_final, prob_variance, logit_variance, pred_tot, var_tot
def visual_results(self, dataset_type="TRAIN", NUM_IMAGES=3):
#train_dir = "./saved_models/segnet_vgg_bayes/segnet_vgg_bayes_30000/model.ckpt-30000"
#train_dir = "./saved_models/segnet_scratch/segnet_scratch_30000/model.ckpt-30000"
image_w = self.config["INPUT_WIDTH"]
image_h = self.config["INPUT_HEIGHT"]
image_c = self.config["INPUT_CHANNELS"]
train_dir = self.config["SAVE_MODEL_DIR"]
FLAG_BAYES = self.config["BAYES"]
with self.sess as sess:
# Restore saved session
saver = tf.train.Saver()
saver.restore(sess, train_dir)
_, _, prediction = normal_loss(logits=self.logits,
labels=self.labels_pl,
number_class=self.num_classes)
prob = tf.nn.softmax(self.logits, dim=-1)
if (dataset_type == 'TRAIN'):
test_type_path = self.config["TRAIN_FILE"]
indexes = random.sample(range(367), NUM_IMAGES)
#indexes = [0,75,150,225,300]
elif (dataset_type == 'VAL'):
test_type_path = self.config["VAL_FILE"]
indexes = random.sample(range(101), NUM_IMAGES)
#indexes = [0,25,50,75,100]
elif (dataset_type == 'TEST'):
test_type_path = self.config["TEST_FILE"]
indexes = random.sample(range(233), NUM_IMAGES)
#indexes = [0,50,100,150,200]
# Load images
image_filename, label_filename = get_filename_list(
test_type_path, self.config)
images, labels = get_all_test_data(image_filename, label_filename)
# Keep images subset of length NUM_IMAGES
images = [images[i] for i in indexes[0:NUM_IMAGES]]
labels = [labels[i] for i in indexes[0:NUM_IMAGES]]
num_sample_generate = 30
pred_tot = []
var_tot = []
for image_batch, label_batch in zip(images, labels):
image_batch = np.reshape(image_batch,
[1, image_h, image_w, image_c])
label_batch = np.reshape(label_batch, [1, image_h, image_w, 1])
if FLAG_BAYES is False:
fetches = [prediction]
feed_dict = {
self.inputs_pl: image_batch,
self.labels_pl: label_batch,
self.is_training_pl: False,
self.keep_prob_pl: 0.5
}
pred = sess.run(fetches=fetches, feed_dict=feed_dict)
pred = np.reshape(pred, [image_h, image_w])
var_one = []
else:
feed_dict = {
self.inputs_pl: image_batch,
self.labels_pl: label_batch,
self.is_training_pl: False,
self.keep_prob_pl: 0.5,
self.with_dropout_pl: False
}
prob_iter_tot = []
for iter_step in range(num_sample_generate):
prob_iter_step = sess.run(fetches=[prob],
feed_dict=feed_dict)
prob_iter_tot.append(prob_iter_step)
prob_mean = np.nanmean(prob_iter_tot, axis=0)
prob_variance = np.var(prob_iter_tot, axis=0)
#THIS TIME I DIDN'T INCLUDE TAU
pred = np.reshape(np.argmax(prob_mean, axis=-1),
[-1]) #pred is the predicted label
var_sep = [
] #var_sep is the corresponding variance if this pixel choose label k
length_cur = 0 #length_cur represent how many pixels has been read for one images
for row in np.reshape(prob_variance,
[image_h * image_w, 12]):
temp = row[pred[length_cur]]
length_cur += 1
var_sep.append(temp)
var_one = np.reshape(
var_sep, [image_h, image_w]
) #var_one is the corresponding variance in terms of the "optimal" label
pred = np.reshape(pred, [image_h, image_w])
pred_tot.append(pred)
var_tot.append(var_one)
draw_plots(images, labels, pred_tot)
def train(self, max_steps=30001, batch_size=3):
# For train the bayes, the FLAG_OPT SHOULD BE SGD, BUT FOR TRAIN THE NORMAL SEGNET,
# THE FLAG_OPT SHOULD BE ADAM!!!
image_filename, label_filename = get_filename_list(
self.test_file, self.config)
val_image_filename, val_label_filename = get_filename_list(
self.val_file, self.config)
with self.graph.as_default():
if self.images_tr is None:
self.images_tr, self.labels_tr = dataset_inputs(
image_filename, label_filename, batch_size, self.config)
self.images_val, self.labels_val = dataset_inputs(
val_image_filename, val_label_filename, batch_size,
self.config)
loss, accuracy, prediction = normal_loss(
logits=self.logits,
labels=self.labels_pl,
number_class=self.num_classes)
train, global_step = train_op(total_loss=loss, opt=self.opt)
summary_op = tf.summary.merge_all()
self.saver = tf.train.Saver(tf.global_variables())
with self.sess.as_default():
self.sess.run(tf.local_variables_initializer())
self.sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# The queue runners basic reference:
# https://www.tensorflow.org/versions/r0.12/how_tos/threading_and_queues
train_writer = tf.summary.FileWriter(self.tb_logs,
self.sess.graph)
for step in range(max_steps):
image_batch, label_batch = self.sess.run(
[self.images_tr, self.labels_tr])
feed_dict = {
self.inputs_pl: image_batch,
self.labels_pl: label_batch,
self.is_training_pl: True,
self.keep_prob_pl: 0.5,
self.with_dropout_pl: True
}
_, _loss, _accuracy, summary = self.sess.run(
[train, loss, accuracy, summary_op],
feed_dict=feed_dict)
self.train_loss.append(_loss)
self.train_accuracy.append(_accuracy)
print(
"Iteration {}: Train Loss{:6.3f}, Train Accu {:6.3f}".
format(step, self.train_loss[-1],
self.train_accuracy[-1]))
if step % 100 == 0:
conv_classifier = self.sess.run(self.logits,
feed_dict=feed_dict)
print(
'per_class accuracy by logits in training time',
per_class_acc(conv_classifier, label_batch,
self.num_classes))
# per_class_acc is a function from utils
train_writer.add_summary(summary, step)
if step % 1000 == 0:
print("start validating.......")
_val_loss = []
_val_acc = []
hist = np.zeros((self.num_classes, self.num_classes))
for test_step in range(int(20)):
fetches_valid = [loss, accuracy, self.logits]
image_batch_val, label_batch_val = self.sess.run(
[self.images_val, self.labels_val])
feed_dict_valid = {
self.inputs_pl: image_batch_val,
self.labels_pl: label_batch_val,
self.is_training_pl: True,
self.keep_prob_pl: 1.0,
self.with_dropout_pl: False
}
# since we still using mini-batch, so in the batch norm we set phase_train to be
# true, and because we didin't run the trainop process, so it will not update
# the weight!
_loss, _acc, _val_pred = self.sess.run(
fetches_valid, feed_dict_valid)
_val_loss.append(_loss)
_val_acc.append(_acc)
hist += get_hist(_val_pred, label_batch_val)
print_hist_summary(hist)
self.val_loss.append(np.mean(_val_loss))
self.val_acc.append(np.mean(_val_acc))
print(
"Iteration {}: Train Loss {:6.3f}, Train Acc {:6.3f}, Val Loss {:6.3f}, Val Acc {:6.3f}"
.format(step, self.train_loss[-1],
self.train_accuracy[-1], self.val_loss[-1],
self.val_acc[-1]))
coord.request_stop()
coord.join(threads)
def test(self):
image_filename, label_filename = get_filename_list(self.test_file)
with tf.Session() as sess:
# Restore saved session
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.runtime_dir))
loss, accuracy, prediction = normal_loss(self.logits,
self.labels_pl,
self.n_classes)
images, labels = get_all_test_data(image_filename, label_filename)
NUM_SAMPLE = []
for i in range(30):
NUM_SAMPLE.append(2 * i + 1)
acc_final = []
iu_final = []
iu_mean_final = []
# uncomment the line below to only run for two times.
# NUM_SAMPLE = [1, 30]
NUM_SAMPLE = [1]
for num_sample_generate in NUM_SAMPLE:
loss_tot = []
acc_tot = []
hist = np.zeros((self.n_classes, self.n_classes))
step = 0
for image_batch, label_batch in zip(images, labels):
image_batch = np.reshape(
image_batch,
[1, self.input_h, self.input_w, self.input_c])
label_batch = np.reshape(
label_batch, [1, self.input_h, self.input_w, 1])
# comment the code below to apply the dropout for all the samples
if num_sample_generate == 1:
feed_dict = {
self.inputs_pl: image_batch,
self.labels_pl: label_batch,
self.is_training_pl: False,
self.keep_prob_pl: 0.5,
self.with_dropout_pl: False,
self.batch_size_pl: 1
}
else:
feed_dict = {
self.inputs_pl: image_batch,
self.labels_pl: label_batch,
self.is_training_pl: False,
self.keep_prob_pl: 0.5,
self.with_dropout_pl: True,
self.batch_size_pl: 1
}
loss_per, acc_per, logit, pred = sess.run(
[loss, accuracy, self.logits, prediction],
feed_dict=feed_dict)
loss_tot.append(loss_per)
acc_tot.append(acc_per)
print(
"Image Index {}: TEST Loss{:6.3f}, TEST Accu {:6.3f}".
format(step, loss_tot[-1], acc_tot[-1]))
step = step + 1
per_class_acc(logit, label_batch, self.n_classes)
hist += get_hist(logit, label_batch)
acc_tot = np.diag(hist).sum() / hist.sum()
iu = np.diag(hist) / (hist.sum(1) + hist.sum(0) -
np.diag(hist))
print("Total Accuracy for test image: ", acc_tot)
print("Total MoI for test images: ", iu)
print("mean MoI for test images: ", np.nanmean(iu))
acc_final.append(acc_tot)
iu_final.append(iu)
iu_mean_final.append(np.nanmean(iu))
return acc_final, iu_final, iu_mean_final