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 visual_results_external_image(self, images, FLAG_MAX_VOTE = False):
#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"
i_width = 64
i_height =64
images = [misc.imresize(image, (i_height, i_width)) for image in images]
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 = cal_loss(logits=self.logits,
labels=self.labels_pl)
prob = tf.nn.softmax(self.logits,dim = -1)
num_sample_generate = 30
pred_tot = []
var_tot = []
labels = []
for i in range(len(images)):
labels.append(np.array([[1 for x in range(64)] for y in range(64)]))
inference_time = []
start_time = time.time()
for image_batch, label_batch in zip(images,labels):
#for image_batch in zip(images):
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)
inference_time.append(time.time() - start_time)
start_time = time.time()
try:
draw_plots_bayes_external(images, pred_tot, var_tot)
return pred_tot, var_tot, inference_time
except:
return pred_tot, var_tot, inference_time
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)
