def visual_results(self, dataset_type="TEST", images_index=3):
image_w = self.config["INPUT_WIDTH"]
image_h = self.config["INPUT_HEIGHT"]
image_c = self.config["INPUT_CHANNELS"]
with self.sess as sess:
# Restore saved session
saver = tf.train.Saver()
saver.restore(self.sess,
os.path.join(self.saved_dir, 'model.ckpt-19'))
_, _, prediction = cal_loss(logits=self.logits,
labels=self.labels_pl)
if (dataset_type == 'TRAIN'):
test_type_path = self.config["TRAIN_FILE"]
if type(images_index) == list:
indexes = images_index
else:
indexes = random.sample(range(367), images_index)
elif (dataset_type == 'VAL'):
test_type_path = self.config["VAL_FILE"]
if type(images_index) == list:
indexes = images_index
else:
indexes = random.sample(range(101), images_index)
elif (dataset_type == 'TEST'):
test_type_path = self.config["TEST_FILE"]
if type(images_index) == list:
indexes = images_index
else:
indexes = random.sample(range(233), images_index)
# 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 images_index
images = [images[i] for i in indexes]
labels = [labels[i] for i in indexes]
pred_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])
fetches = [prediction]
feed_dict = {
self.inputs_pl: image_batch,
self.labels_pl: label_batch,
self.batch_size_pl: 1
}
pred = sess.run(fetches=fetches, feed_dict=feed_dict)
pred = np.reshape(pred, [image_h, image_w])
pred_tot.append(pred)
draw_plots(images, labels, 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:
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 load_data(manifest, indices=None, n=None):
image_filenames, label_filenames = get_filename_list(manifest)
if indices is None:
indices = range(
0, len(label_filenames)) if n is None else np.random.choice(
range(0, len(label_filenames)), n)
image_filenames = [image_filenames[i] for i in indices]
label_filenames = [label_filenames[i] for i in indices]
images, labels = get_all_test_data(image_filenames, label_filenames)
return indices, np.array(images), np.array(labels)
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 = "TEST", images_index = 3, FLAG_MAX_VOTE = False):
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"]
print(FLAG_BAYES)
with self.sess as sess:
# Restore saved session
saver = tf.train.Saver()
saver.restore(sess, train_dir)
_, _, prediction = cal_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"]
if type(images_index) == list:
indexes = images_index
else:
'''CHANGE IT BACK'''
#indexes = random.sample(range(367),images_index)
indexes = random.sample(range(6),images_index)
#indexes = [0,75,150,225,300]
elif (dataset_type=='VAL'):
test_type_path = self.config["VAL_FILE"]
if type(images_index) == list:
indexes = images_index
else:
#indexes = random.sample(range(101),images_index)
indexes = random.sample(range(10),images_index)
#indexes = [0,25,50,75,100]
elif (dataset_type=='TEST'):
test_type_path = self.config["TEST_FILE"]
if type(images_index) == list:
indexes = images_index
else:
indexes = random.sample(range(5),images_index)
#indexes = random.sample(range(233),images_index)
#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 images_index
images = [images[i] for i in indexes]
labels = [labels[i] for i in indexes]
num_sample_generate = 30
pred_tot = []
var_tot = []
print(image_c)
for image_batch, label_batch in zip(images,labels):
print(image_batch.shape)
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:
print("NON BAYES")
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,
self.batch_size_pl: 1}
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: True,
self.batch_size_pl: 1}
prob_iter_tot = []
pred_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)
pred_iter_tot.append(np.reshape(np.argmax(prob_iter_step,axis = -1),[-1]))
if FLAG_MAX_VOTE is True:
prob_variance,pred = MAX_VOTE(pred_iter_tot,prob_iter_tot,self.config["NUM_CLASSES"])
#acc_per = np.mean(np.equal(pred,np.reshape(label_batch,[-1])))
var_one = var_calculate(pred,prob_variance)
pred = np.reshape(pred,[image_h,image_w])
else:
prob_mean = np.nanmean(prob_iter_tot,axis = 0)
prob_variance = np.var(prob_iter_tot, axis = 0)
pred = np.reshape(np.argmax(prob_mean,axis = -1),[-1]) #pred is the predicted label with the mean of generated samples
#THIS TIME I DIDN'T INCLUDE TAU
var_one = var_calculate(pred,prob_variance)
pred = np.reshape(pred,[image_h,image_w])
pred_tot.append(pred)
var_tot.append(var_one)
draw_plots_bayes(images, labels, pred_tot, var_tot)
return (images,labels,pred_tot,var_tot)
def transfer_output(self,rotation=[0],dataset_type = "TRAIN"):
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"]
datagen = ImageDataGenerator()
with self.sess as sess:
saver=tf.train.Saver()
saver.restore(sess, train_dir)
#self.logits=tf.nn.bias_add(self.conv, self.biases, name=scope.name)
#_, _, prediction = cal_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"]
else:
test_type_path = self.config["TEST_FILE"]
image_filename,label_filename = get_filename_list(test_type_path, self.config)
images, labels = get_all_test_data(image_filename,label_filename)
print(len(images))
image_batch=np.reshape(images,[len(images),image_h,image_w,image_c])
label_batch=np.reshape(labels,[len(labels),image_h,image_w,1])
#pred_tot = []
#var_tot = []
logit_aug=[]
image_aug=[]
label_aug=[]
for deg in rotation:
logit_tot=[]
image_tot=[]
label_tot=[]
for image_batch, label_batch in zip(images,labels):
#print(image_batch.shape)
#to just convert the image into the standard format
#for example: 1(batch size),64,64,3
image_batch=datagen.apply_transform(image_batch,{'theta':deg})
label_batch=datagen.apply_transform(label_batch,{'theta':deg})
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])
image_tot.append(image_batch)
label_tot.append(label_batch)
#non baysien model
#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,
self.with_dropout_pl: True,
self.batch_size_pl: 1}
#pred = sess.run(fetches = fetches, feed_dict = feed_dict)
logit= sess.run([self.logits],feed_dict = feed_dict)
#原本是images_h* images_w, with each element the label of it
#pred = np.reshape(pred,[image_h,image_w])
#pred_tot.append(pred)
logit_tot.append(logit)
logit_aug.append(logit_tot)
image_aug.append(image_tot)
label_aug.append(label_tot)
return logit_aug,label_aug,image_aug#logit_tot,label_tot,image_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 visual_results(self,
dataset_type="TEST",
images_index=3,
FLAG_MAX_VOTE=False):
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)
kernel = variable_with_weight_decay('weights',
initializer=initialization(
1, 64),
shape=[1, 1, 64, 3],
wd=False)
conv = tf.nn.conv2d(self.deconv1_3,
kernel, [1, 1, 1, 1],
padding='SAME')
biases = variable_with_weight_decay('biases',
tf.constant_initializer(0.0),
shape=[3],
wd=False)
logits = tf.nn.bias_add(conv, biases, name="scope.name")
#exit()
sess.run(tf.global_variables_initializer())
#sess.run(logits)
_, _, prediction = cal_loss(logits=logits, labels=self.labels_pl)
prob = tf.nn.softmax(logits, dim=-1)
print(
"==================================================================================="
)
print(prediction)
#exit()
if (dataset_type == 'TRAIN'):
test_type_path = self.config["TRAIN_FILE"]
if type(images_index) == list:
indexes = images_index
else:
indexes = random.sample(range(367), images_index)
#indexes = [0,75,150,225,300]
elif (dataset_type == 'VAL'):
test_type_path = self.config["VAL_FILE"]
if type(images_index) == list:
indexes = images_index
else:
indexes = random.sample(range(101), images_index)
#indexes = [0,25,50,75,100]
elif (dataset_type == 'TEST'):
test_type_path = self.config["TEST_FILE"]
if type(images_index) == list:
indexes = images_index
else:
indexes = random.sample(range(233), images_index)
#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 images_index
images = [images[i] for i in indexes]
labels = [labels[i] for i in indexes]
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,
self.batch_size_pl: 1
}
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: True,
self.batch_size_pl: 1
}
prob_iter_tot = []
pred_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)
pred_iter_tot.append(
np.reshape(np.argmax(prob_iter_step, axis=-1),
[-1]))
if FLAG_MAX_VOTE is True:
prob_variance, pred = MAX_VOTE(
pred_iter_tot, prob_iter_tot,
self.config["NUM_CLASSES"])
#acc_per = np.mean(np.equal(pred,np.reshape(label_batch,[-1])))
var_one = var_calculate(pred, prob_variance)
pred = np.reshape(pred, [image_h, image_w])
else:
prob_mean = np.nanmean(prob_iter_tot, axis=0)
prob_variance = np.var(prob_iter_tot, axis=0)
pred = np.reshape(
np.argmax(prob_mean, axis=-1), [-1]
) #pred is the predicted label with the mean of generated samples
#THIS TIME I DIDN'T INCLUDE TAU
var_one = var_calculate(pred, prob_variance)
pred = np.reshape(pred, [image_h, image_w])
pred_tot.append(pred)
var_tot.append(var_one)
draw_plots_bayes(images, labels, pred_tot, var_tot)
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
def visual_results(self,
dataset_type="TEST",
indices=None,
n_samples=3,
model_file=None):
with tf.Session() as sess:
# Restore saved session
saver = tf.train.Saver()
if model_file is None:
saver.restore(sess,
tf.train.latest_checkpoint(FLAGS.runtime_dir))
else:
saver.restore(sess, os.path.join(FLAGS.runtime_dir,
model_file))
_, _, prediction = cal_loss(logits=self.logits,
labels=self.labels_pl,
n_classes=self.n_classes)
test_type_path = None
if dataset_type == 'TRAIN':
test_type_path = self.train_file
elif dataset_type == 'VAL':
test_type_path = self.val_file
elif dataset_type == 'TEST':
test_type_path = self.test_file
# Load images
image_filenames, label_filenames = get_filename_list(
test_type_path)
images, labels = get_all_test_data(image_filenames,
label_filenames)
if not indices:
indices = random.sample(range(len(images)), n_samples)
# Keep images subset of length images_index
images = [images[i] for i in indices]
labels = [labels[i] for i in indices]
pred_tot = []
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])
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,
self.batch_size_pl: 1
}
pred = sess.run(fetches=fetches, feed_dict=feed_dict)
pred = np.reshape(pred, [self.input_h, self.input_w])
pred_tot.append(pred)
draw_plots_bayes(images, labels, pred_tot)
