def main():
# sample = descriptors.random_sample(100000)
# print("sample obtenido")
# np.savetxt("sample100k.csv", sample, delimiter=',')
#sample = np.loadtxt("sample100k.csv", delimiter=',')
# print(sample.shape)
k = 64
#clustering
#print("Haciendo clustering...")
#codebook = generate_codebook(k, sample)
#np.savetxt("codebook60",codebook,delimiter=',')
codebook = np.loadtxt("codebook60", delimiter=',')
dataset = pickle.load(open('dataset.obj', "rb"))
#train
print("Entrenando...")
start = time.time()
svm = svm_train(codebook, dataset.get_train_set())
end = time.time()
s = "Elapsed time training {0}".format(utils.humanize_time(end - start))
print(s)
#test
print("Testeando...")
start = time.time()
svm_test(svm, codebook, dataset.get_test_set())
end = time.time()
s = "Elapsed time training {0}".format(utils.humanize_time(end - start))
print(s)
def main():
# sample = descriptors.random_sample(100000)
# print("sample obtenido")
# np.savetxt("sample100k.csv", sample, delimiter=',')
#sample = np.loadtxt("sample100k.csv", delimiter=',')
# print(sample.shape)
k = 64
#clustering
#print("Haciendo clustering...")
#codebook = generate_codebook(k, sample)
#np.savetxt("codebook60",codebook,delimiter=',')
codebook = np.loadtxt("codebook60", delimiter=',')
dataset = pickle.load(open('dataset.obj', "rb"))
#train
print("Entrenando...")
start = time.time()
svm = svm_train(codebook, dataset.get_train_set())
end = time.time()
s = "Elapsed time training {0}".format(utils.humanize_time(end - start))
print(s)
#test
print("Testeando...")
start = time.time()
svm_test(svm, codebook, dataset.get_test_set())
end = time.time()
s = "Elapsed time training {0}".format(utils.humanize_time(end - start))
print(s)
def current(self):
song = self.currentsong()
playlist = self.status()
res = self.currentsongf() + "\n"
res += "[playing] #%s/%s" % (song["pos"], playlist["playlistlength"])
if "time" in playlist.keys():
current, total = playlist["time"].split(":")
pcentage = int(100 * float(current) / float(total))
res += " %s/%s (%s%%)" % (utils.humanize_time(current), utils.humanize_time(total), pcentage)
return res
def current(self):
song = self.currentsong()
playlist = self.status()
res = self.currentsongf() + "\n"
res += "[playing] #%s/%s" % (song["pos"], playlist["playlistlength"])
if 'time' in playlist.keys():
current, total = playlist['time'].split(':')
pcentage = int(100 * float(current) / float(total))
res += " %s/%s (%s%%)" % (utils.humanize_time(current),
utils.humanize_time(total),
pcentage)
return res
def test_knn():
dataset = pickle.load(open("dataset.obj", "rb"))
n_classes = len(dataset.get_classes())
start = time.time()
predictions = knn.knn(dataset)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time using knn {0}...".format(elapsed_time))
print("predictions = \n{0}".format(predictions))
utils.write_list(predictions, "results/knn-predictions.txt")
# predictions = [
# [1, 1, 0, 2, 4, 3, 2, 0, 2, 4, 0, 3, 2, 1, 1],
# [1, 2, 4, 2, 1, 0, 4, 1, 3, 2, 2, 2, 1, 2, 1],
# [2, 3, 4, 2, 2, 0, 2, 0, 3, 3, 1, 2, 2, 2, 3],
# [0, 1, 3, 3, 3, 3, 1, 3, 3, 3, 2, 2, 3, 0, 1],
# [3, 0, 2, 1, 4, 2, 1, 0, 2, 4, 1, 1, 4, 2, 3]
# ]
hist = np.zeros((n_classes, n_classes), dtype=np.uint16)
for i in range(len(predictions)):
for j in range(len(predictions[i])):
c = predictions[i][j]
hist[i][c] += 1
print("hist = \n{0}".format(hist))
np.savetxt("results/knn-hist.csv", hist, fmt="%i", delimiter=",")
confusion_matrix = hist / 25.0
print("conf mat = \n{0}".format(confusion_matrix))
values = [confusion_matrix[i][i] for i in range(n_classes)]
precision = np.average(values)
print("precision = {0}".format(precision))
plt.matshow(confusion_matrix)
plt.title('Confusion matrix')
plt.colorbar()
plt.show()
def test_knn():
dataset = pickle.load(open("dataset.obj", "rb"))
n_classes = len(dataset.get_classes())
start = time.time()
predictions = knn.knn(dataset)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time using knn {0}...".format(elapsed_time))
print("predictions = \n{0}".format(predictions))
utils.write_list(predictions, "results/knn-predictions.txt")
# predictions = [
# [1, 1, 0, 2, 4, 3, 2, 0, 2, 4, 0, 3, 2, 1, 1],
# [1, 2, 4, 2, 1, 0, 4, 1, 3, 2, 2, 2, 1, 2, 1],
# [2, 3, 4, 2, 2, 0, 2, 0, 3, 3, 1, 2, 2, 2, 3],
# [0, 1, 3, 3, 3, 3, 1, 3, 3, 3, 2, 2, 3, 0, 1],
# [3, 0, 2, 1, 4, 2, 1, 0, 2, 4, 1, 1, 4, 2, 3]
# ]
hist = np.zeros((n_classes, n_classes), dtype=np.uint16)
for i in range(len(predictions)):
for j in range(len(predictions[i])):
c = predictions[i][j]
hist[i][c] += 1
print("hist = \n{0}".format(hist))
np.savetxt("results/knn-hist.csv", hist, fmt="%i", delimiter=",")
confusion_matrix = hist / 25.0
print("conf mat = \n{0}".format(confusion_matrix))
values = [confusion_matrix[i][i] for i in range(n_classes)]
precision = np.average(values)
print("precision = {0}".format(precision))
plt.matshow(confusion_matrix)
plt.title('Confusion matrix')
plt.colorbar()
plt.show()
def test_descriptors():
img = cv2.imread(constants.TESTING_IMG_PATH)
cv2.imshow("Normal Image", img)
print("Normal Image")
option = input("Enter [1] for using ORB features and other number to use SIFT.\n")
start = time.time()
if option == 1:
orb = cv2.ORB()
kp, des = orb.detectAndCompute(img, None)
else:
sift = cv2.SIFT()
kp, des = sift.detectAndCompute(img, None)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
des_name = constants.ORB_FEAT_NAME if option == ord(constants.ORB_FEAT_OPTION_KEY) else constants.SIFT_FEAT_NAME
print("Elapsed time getting descriptors {0}".format(elapsed_time))
print("Number of descriptors found {0}".format(len(des)))
if des is not None and len(des) > 0:
print("Dimension of descriptors {0}".format(len(des[0])))
print("Name of descriptors used is {0}".format(des_name))
img2 = cv2.drawKeypoints(img, kp)
# plt.imshow(img2), plt.show()
cv2.imshow("{0} descriptors".format(des_name), img2)
print("Press any key to exit ...")
cv2.waitKey()
def all_classes_descriptors(dataset, opt="sample"):
des_files = glob.glob("train/*.mat")
# train_set = dataset.get_train_set()
# Read training descriptors
print("Getting sample of the descriptors for classes")
classes_des = []
for c in range(n_classes):
# class_files = train_set[c]
path = des_files[c]
print("Reading class number {0}".format(c))
# class_des = class_descriptors(class_files)
class_des = class_des_from_file(path)
sample_size = 5000
if opt == "sample":
current_sample = utils.random_sample(class_des, sample_size)
current_sample = np.array(current_sample, dtype=np.uint16)
print("current sample shape = {0}".format(current_sample.shape))
# class_des = None
classes_des.append(current_sample)
else:
classes_des.append(class_des)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time getting training descriptors {0}".format(elapsed_time))
return classes_des
def test_store_descriptors():
start = time.time()
dataset = pickle.load(open("dataset.obj", "rb"))
main.store_test_des(dataset)
end = time.time()
s = "Elapsed time processing {0}".format(utils.humanize_time(end - start))
print(s)
def connected(self):
import urllib
import BeautifulSoup
import socket
f = urllib.urlopen("http://*****:*****@www.sleduc.fr/server-status")
soup = BeautifulSoup.BeautifulSoup(f.read())
clients = {}
res = "Liste des clients connectés sur mpd:\n"
for tr in soup.findAll("tr"):
hosts = tr.findAll("td", {"nowrap": "nowrap"})
for host in hosts:
if "mpd.sleduc.fr" in host or "mpd.leduc.42" in host:
lst = tr.findAll("td")
since = utils.humanize_time(lst[5].text)
ip = str(lst[10].text)
vhost = str(lst[11].text)
clients[ip] = (vhost, since)
for ip, couple in clients.iteritems():
vhost, since = couple
try:
reverse = socket.gethostbyaddr(ip)[0]
except socket.herror:
reverse = "Moi y'en a pas savoir résoudre"
res += "\t- %s (%s)\n\t\tSur %s, depuis %s\n" % (ip, reverse, vhost, since)
return res[0:-1]
def test_store_descriptors():
start = time.time()
dataset = pickle.load(open("dataset.obj", "rb"))
main.store_test_des(dataset)
end = time.time()
s = "Elapsed time processing {0}".format(utils.humanize_time(end - start))
print(s)
def metrics(self, rankings, train_labels, test_labels, sorted_prods):
### Calculates classification and products set and position mAP ###
###------------------------------------------------------------------###
print("Starting to calculate metrics ...")
start = time.time()
rel_ranks = []
for i in range(len(rankings)):
rel_ranks.append(
utils.relevance_ranking(rankings[i], train_labels,
test_labels[i]))
# Classification mAP
#-----------------------------------------------------------------------
class_ap = [utils.class_ap(rel_rk) for rel_rk in rel_ranks]
class_ap_filename = "results/class_avg_precs_{0}.txt".format(
self.n_classes)
utils.write_list(class_ap, class_ap_filename)
class_map = np.mean(class_ap)
self.log += "ranking size = {0}".format(len(rankings[0])) + "\n"
s = "classification mean average precision = {0}".format(class_map)
self.log += s + "\n"
print(s)
# Dot products average precision
#-----------------------------------------------------------------------
# Set
set_prec = []
for i in range(len(rankings)):
indices = [prods[0] for prods in sorted_prods[i]]
precision = utils.prod_set_prec(indices, rankings[i])
set_prec.append(precision)
set_ap_filename = "results/set_avg_precs_{0}.txt".format(
self.n_classes)
utils.write_list(set_prec, set_ap_filename)
set_map = np.mean(set_prec)
s = "set mean average precision = {0}".format(set_map)
self.log += s + "\n"
print(s)
# Position
pos_prec = []
for i in range(len(rankings)):
indices = [prods[0] for prods in sorted_prods[i]]
precision = utils.prod_pos_prec(indices, rankings[i])
pos_prec.append(precision)
pos_ap_filename = "results/pos_avg_precs_{0}.txt".format(
self.n_classes)
utils.write_list(pos_prec, pos_ap_filename)
pos_map = np.mean(pos_prec)
s = "position mean average precision = {0}".format(pos_map)
self.log += s + "\n"
print(s)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time calculating metrics: {0}".format(elapsed_time)
self.log += s + "\n"
print(s)
def solve_game_specialized_ab_memo(board,
depth,
memo,
at_least_draw=False,
at_most_draw=False):
"""
Computes the theoretical result of the current position.
The return value is:
1 when the current player wins
0 when the game ends in a draw with optimal play
-1 when the other player wins
"""
h = board.myhash
if h not in memo:
Stats.total += 1
if Stats.total % 1000000 == 0:
Stats.end = time()
time_diff = Stats.end - Stats.start
amount, suffix = humanize_time(time_diff)
percentage = (100 * Stats.total) / 668607278
print(
f"visited {Stats.total // 1000000}M nodes [{percentage:.2f}%]"
f" in {amount:.2f}{suffix}")
# this will never be -1, because we don't go that far
evaluation = board.evaluate
if evaluation is not None:
if evaluation == 1:
# this means we have just lost
return -1
else:
return 0
if depth == 0:
raise RecursionError
result = -1
for move in board.legal_moves:
value = -solve_game_specialized_ab_memo(
board.apply_move(move), depth - 1, memo, at_most_draw,
at_least_draw)
result = max(value, result)
# if we found a winning move, there is no need to check other moves
if result == 1:
break
if result == 0:
if at_most_draw:
# we don't want to save this result in memo - it may not be accurate!
# break
return 0
else:
at_least_draw = True
memo[h] = result
return memo[h]
def test_codebook():
dataset = pickle.load(open(constants.DATASET_OBJ_FILENAME, "rb"))
option = input("Enter [1] for using ORB features or [2] to use SIFT features.\n")
start = time.time()
des = descriptors.all_descriptors(dataset, dataset.get_train_set(), option)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time getting all the descriptors is {0}".format(elapsed_time))
k = 64
des_name = constants.ORB_FEAT_NAME if option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
codebook_filename = "codebook_{0}_{1}.csv".format(k, des_name)
start = time.time()
codebook = descriptors.gen_codebook(dataset, des, k)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time calculating the k means for the codebook is {0}".format(elapsed_time))
np.savetxt(codebook_filename, codebook, delimiter=constants.NUMPY_DELIMITER)
print("Codebook loaded in {0}, press any key to exit ...".format(constants.CODEBOOK_FILE_NAME))
cv2.waitKey()
def store_hash(self, wta_hash):
## Store the hash in a binary file
print("Storing the hash in a file ...")
start = time.time()
hash_filename = "results/wtahash_{0}.obj".format(self.n_classes)
pickle.dump(wta_hash, open(hash_filename, "wb"), protocol=2)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time storing the hash {0}".format(elapsed_time)
self.log += s + "\n"
print(s)
def store_hash(self, wta_hash):
## Store the hash in a binary file
print("Storing the hash in a file ...")
start = time.time()
hash_filename = "results/wtahash_{0}.obj".format(self.n_classes)
pickle.dump(wta_hash, open(hash_filename, "wb"), protocol=2)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time storing the hash {0}".format(elapsed_time)
self.log += s + "\n"
print(s)
def best_classifiers(self, images, ranking_size):
start = time.time()
ConvWTAImage = wta.ConvertirenWTA(images, self.permutations, self.n,
self.k, self.w)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time converting test set to WTA binary {0}.".format(
elapsed_time))
BW1 = wta.GetinBinaryMayor(ConvWTAImage)
values = wta.ObtenerValoresTotalesWTA(self.classifiersBW, BW1,
self.whash, ranking_size)
return values
def solve_game_main():
try:
# -result = solve_game_alphabeta_top(Board(), Config.COLS * Config.ROWS)
result = solve_game_specialized_ab_top_memo(Board(),
Config.COLS * Config.ROWS)
except RecursionError:
result = "Not deep enough"
print(f"Game solved. Result = {result}")
print(f"[1 = first player wins, -1 = second player wins, 0 = draw]")
print(f"Total visited nodes = {Stats.total:,}")
Stats.end = time()
amount, suffix = humanize_time(Stats.end - Stats.start)
print(f"Total time: {amount:.2f}{suffix}")
def get_rankings(self, test_data, wta_hash, ranking_size):
### Get the rankings for the test set ###
###------------------------------------------------------------------###
print("Generating ranking matrix for the test set ...")
start = time.time()
rankings = wta_hash.best_classifiers(test_data, ranking_size)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time generating ranking matrix: {0}".format(elapsed_time)
self.log += s + "\n"
print(s)
return rankings
def print_time_estimate(depth, times):
if times:
if len(times) > 1:
den = max(0.1, times[-2])
estimate = (times[-1]**2) / den
else:
estimate = times[-1] * Config.COLS
est_time, suffix = humanize_time(estimate)
print(
f"Current depth: {depth} "
f"estimated time: {est_time:.2f}{suffix}",
flush=True,
end="")
def create_hash(self, train_data, n, k, w):
### Use WTAHash on it ###
###------------------------------------------------------------------###
print ("Starting to generate hash table ...")
start = time.time()
wta_hash = wh.WTAHash(train_data, n, k, w)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time on generation of hash table: {0}".format(elapsed_time)
self.log += s + "\n"
print (s)
return wta_hash
def get_rankings(self, test_data, wta_hash, ranking_size):
### Get the rankings for the test set ###
###------------------------------------------------------------------###
print ("Generating ranking matrix for the test set ...")
start = time.time()
rankings = wta_hash.best_classifiers(test_data, ranking_size)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time generating ranking matrix: {0}".format(elapsed_time)
self.log += s + "\n"
print (s)
return rankings
def create_hash(self, train_data, n, k, w):
### Use WTAHash on it ###
###------------------------------------------------------------------###
print("Starting to generate hash table ...")
start = time.time()
wta_hash = wh.WTAHash(train_data, n, k, w)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time on generation of hash table: {0}".format(
elapsed_time)
self.log += s + "\n"
print(s)
return wta_hash
def solve_game_memo(board, depth, memo, moves_made):
"""
Computes the theoretical result of the current position.
The return value is:
1 when the current player wins
0 when the game ends in a draw with optimal play
-1 when the other player wins
"""
h = board.myhash
SOLVE_CUT_OFF = 18
if h not in memo:
Stats.total += 1
if Stats.total % 2500000 == 0:
Stats.end = time()
time_diff = Stats.end - Stats.start
amount, suffix = humanize_time(time_diff)
#percentage = (100 * Stats.total) / TOTAL55_20
print(
f"visited {Stats.total // 1000}k nodes" # [{percentage:.2f}%]"
f" in {amount:.2f}{suffix} [{len(memo):,}]")
# this will never be -1, because we don't go that far
evaluation = board.evaluate
if evaluation is not None:
if evaluation == 1:
# this means we have just lost
result = -1
else:
result = 0
elif depth == 0:
raise RecursionError
else:
# we use the negamax schema
# our best result is the worst result of the opponent
result = -1 * min([
solve_game_memo(board.apply_move(move), depth - 1, memo,
moves_made + 1) for move in board.legal_moves
])
if moves_made < SOLVE_CUT_OFF:
memo[h] = result
else:
return result
return memo[h]
def test(self, session, test_eval):
self.logger.info("testing started")
test_handle = session.run(self.iterators[0].string_handle())
tStart = time.time()
session.run(self.inits[0])
test_metrics, _ = self.evaluate_batch(
0, self.num_batches[0], test_eval, session, "test", test_handle)
running_time = time.time() - tStart
self.logger.info(
"TEST: em_score: {0:.4f}, em_score_start: {1:.4f}, "
"em_score_end: {2:.4f}, f1_score: {3:.4f}, loss: {4:.4f} running_time: {5:s}".format(
test_metrics["exact_match"], test_metrics["em_start"],
test_metrics["em_end"], test_metrics["f1"], test_metrics["loss"],
humanize_time(running_time)))
def best_classifiers(self, images, ranking_size):
start = time.time()
ConvWTAImage = wta.ConvertirenWTA(
images, self.permutations, self.n, self.k, self.w
)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time converting test set to WTA binary {0}.".format(
elapsed_time
)
)
BW1 = wta.GetinBinaryMayor(ConvWTAImage)
values = wta.ObtenerValoresTotalesWTA(
self.classifiersBW, BW1, self.whash, ranking_size
)
return values
def read_descriptors(self, training_percentage, set_name):
### Load training information matrix ###
###------------------------------------------------------------------###
print("Reading {0} instances ...".format(set_name))
start = time.time()
data, labels = cluster.load_classes(training_percentage, self.dataset,
set_name, self.n_classes)
end = time.time()
self.log += "{0} matrix of shape {1}\n".format(set_name, data.shape)
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time reading the {0} files: {1}".format(
set_name, elapsed_time)
self.log += s + "\n"
print(s)
return data, labels
def knn(dataset, des_classes):
start = time.time()
n_classes = len(dataset.get_classes())
classes_sample = all_classes_descriptors(dataset)
# Read testing descriptors
start = time.time()
test_folders = glob.glob("test/*")
predictions = []
counter = 0
for folder in test_folders:
print("Starting to predict the test set of class {0}.".format(counter))
predictions.append([])
# The i-th element of this list has the descriptors for the i-th image;
# all the images are in the same class-folder
test_files = glob.glob(folder + "/*.mat")
# for i in range(len(test_files)):
for i in range(25):
distances = np.zeros(n_classes)
# percentage = (i * 100) / len(test_files)
percentage = (i * 100) / 25
print("Loading SIFT from file {0} of {1} ({2}%) class={3} ...".
format(
# i, len(test_files), percentage, counter
i,
25,
percentage,
counter))
fname = test_files[i]
data = sio.loadmat(fname)
des = data["stored"]
# Find the nearest class
for c in range(n_classes):
s = "Dist for img number {0} to class {1} (real = {2})".format(
i, c, counter)
print(s)
class_des = classes_sample[c]
distances[c] = dist_nn_class(des, class_des)
predictions[-1].append(np.argmin(distances))
counter += 1
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time testing with KNN {0}".format(elapsed_time))
return predictions
def read_descriptors(self, training_percentage, set_name):
### Load training information matrix ###
###------------------------------------------------------------------###
print ("Reading {0} instances ...".format(set_name))
start = time.time()
data, labels = cluster.load_classes(
training_percentage, self.dataset, set_name, self.n_classes
)
end = time.time()
self.log += "{0} matrix of shape {1}\n".format(set_name, data.shape)
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time reading the {0} files: {1}".format(
set_name, elapsed_time
)
self.log += s + "\n"
print (s)
return data, labels
def test_create_hash():
n = 32
w = 2
k = 16
train_file = sio.loadmat("train/des_bear.mat")
train_data = train_file["stored"]
print("Starting to generate hash table ...")
start = time.time()
wta_hash = wh.WTAHash(train_data, n, k, w)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time on generation of hash table: {0}".format(elapsed_time))
print("Starting to store the hash ...")
start = time.time()
pickle.dump(wta_hash,
open("bear_hash.obj", "wb"),
protocol=pickle.HIGHEST_PROTOCOL)
end = time.time()
print("Elapsed time storing the hash {0} seconds".format(end - start))
def test_create_hash():
n = 32
w = 2
k = 16
train_file = sio.loadmat("train/des_bear.mat")
train_data = train_file["stored"]
print ("Starting to generate hash table ...")
start = time.time()
wta_hash = wh.WTAHash(train_data, n, k, w)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
print("Elapsed time on generation of hash table: {0}".format(elapsed_time))
print("Starting to store the hash ...")
start = time.time()
pickle.dump(
wta_hash, open("bear_hash.obj", "wb"), protocol=pickle.HIGHEST_PROTOCOL
)
end = time.time()
print("Elapsed time storing the hash {0} seconds".format(end - start))
def main():
start = time.time()
# Define the training and testing sets
# path = "dataset"
# dataset = Dataset(path)
# dataset.generate_sets()
# pickle.dump(dataset, open("dataset.obj", "wb"), protocol=2)
dataset = pickle.load(open("dataset.obj", "rb"))
# Get SIFT descriptors per class
classes = dataset.get_classes()
train_set = dataset.get_train_set()
for i in range(5, len(classes)):
class_name = classes[i]
class_files = train_set[i]
print("Getting descriptors for class {0} of length {1}".format(
class_name, len(class_files)))
store_train_des(class_files, class_name)
end = time.time()
s = "Elapsed time processing {0}".format(utils.humanize_time(end - start))
print(s)
def evaluate_batch(self, epoch, num_batches, eval_file, sess, data_type, str_handle):
answer_dict = {}
remapped_dict = {}
losses = []
tStart = time.time()
for nb in range(1, num_batches + 1):
if self.summaries:
qa_id, loss, yp1, yp2, grad_sums = sess.run(
[self.model.qa_ids, self.model.loss, self.model.yp1, self.model.yp2,
self.grad_summaries_merged],
feed_dict={self.model.handle: str_handle, self.model.is_train: False})
else:
qa_id, loss, yp1, yp2 = sess.run(
[self.model.qa_ids, self.model.loss, self.model.yp1, self.model.yp2],
feed_dict={self.model.handle: str_handle, self.model.is_train: False})
grad_sums = None
answer_dict_, remapped_dict_ = convert_tokens(
eval_file, qa_id.tolist(), yp1.tolist(), yp2.tolist())
answer_dict.update(answer_dict_)
remapped_dict.update(remapped_dict_)
losses.append(loss)
running_time = time.time() - tStart
rt = humanize_time(running_time / float(nb + 1) * num_batches - running_time)
print('Epoch {0:d} {1:s}: {2:d}/{3:d} batches; ETA: {4:s}'.format(
epoch+1+self.epoch_start, data_type, nb, num_batches, rt), end='\r')
if data_type == 'test':
with open(os.path.join(self.out_dir, 'answer.json'), "w") as fh:
json.dump(remapped_dict, fh)
loss = np.mean(losses)
metrics = evaluate(eval_file, answer_dict)
metrics["loss"] = loss
loss_sum = tf.Summary(value=[tf.Summary.Value(
tag="{}/loss".format(data_type), simple_value=metrics["loss"]), ])
f1_sum = tf.Summary(value=[tf.Summary.Value(
tag="{}/f1".format(data_type), simple_value=metrics["f1"]), ])
em_sum = tf.Summary(value=[tf.Summary.Value(
tag="{}/em".format(data_type), simple_value=metrics["exact_match"]), ])
return metrics, [loss_sum, f1_sum, em_sum, grad_sums]
def solve_game_quick_jump(board, depth):
"""
Computes the theoretical result of the current position.
The return value is:
1 when the current player wins
0 when the game ends in a draw with optimal play
-1 when the other player wins
"""
Stats.total += 1
if Stats.total % 500000 == 0:
Stats.end = time()
time_diff = Stats.end - Stats.start
amount, suffix = humanize_time(time_diff)
percentage = (100 * Stats.total) / TOTAL44
print(f"visited {Stats.total // 1000000}M nodes [{percentage:.2f}%]"
f" in {amount:.2f}{suffix}")
# this will never be -1, because we don't go that far
evaluation = board.evaluate
if evaluation is not None:
if evaluation == 1:
# this means we have just lost
return -1
else:
return 0
if depth == 0:
raise RecursionError
result = -1
for move in board.legal_moves:
value = -solve_game_quick_jump(board.apply_move(move), depth - 1)
result = max(value, result)
# if we found a winning move, there is no need to check other moves
if result == 1:
break
return result
def use_normal_map(normal_img, normal_opt):
# Create an array from the image
normal_im_array = np.asarray(normal_img)
# TODO move this up
normals_filename = (NORMAL_VECTORS_FILENAME + normal_opt +
NORMAL_VECTORS_FILE_EXT)
if os.path.exists(normals_filename):
# Load normals from file
print("Loading normal vectors from file {}".format(normals_filename))
normals = np.load(normals_filename)
w, h, _ = normals.shape
return normals, w, h
# Create the normals vector map
start_normals = time.time()
normals = adjust_normal_map(normal_im_array)
np.save(normals_filename, normals)
print("Normal vectors stored inside {} file".format(normals_filename))
end_normals = time.time()
elapsed_time = utils.humanize_time(end_normals - start_normals)
print("Time adjusting normals was: {}".format(elapsed_time))
# Create output image vector
w, h, _ = normals.shape
return normals, w, h
def search_all_main(start=0, end=Config.COLS * Config.ROWS):
prev_times = []
print(f"Max depth = {end}")
for depth in range(start, end + 1):
Stats.reset()
print_time_estimate(depth, prev_times)
start = time()
memo_size = 0
#search_all(Board(), depth)
memo_size = search_all_memo_top(Board(), depth)
end = time()
time_diff = end - start
prev_times.append(time_diff)
elapsed, suffix = humanize_time(time_diff)
if Stats.terminal > 0:
percentage = 100 * Stats.finished // Stats.terminal
else:
percentage = 100
print(f"\rDepth {depth:2}: {Stats.total:13,} "
f"states in {elapsed:2.2f}{suffix} "
f"[{percentage}%] {Stats.finished:,} / {Stats.terminal:,} "
f"[memo size: {memo_size:,}]")
def main():
start = time.time()
# Define the training and testing sets
# path = "dataset"
# dataset = Dataset(path)
# dataset.generate_sets()
# pickle.dump(dataset, open("dataset.obj", "wb"), protocol=2)
dataset = pickle.load(open("dataset.obj", "rb"))
# Get SIFT descriptors per class
classes = dataset.get_classes()
train_set = dataset.get_train_set()
for i in range(5, len(classes)):
class_name = classes[i]
class_files = train_set[i]
print("Getting descriptors for class {0} of length {1}".format(
class_name, len(class_files)
)
)
store_train_des(class_files, class_name)
end = time.time()
s = "Elapsed time processing {0}".format(utils.humanize_time(end - start))
print(s)
def main(is_interactive=True,
k=64,
des_option=constants.ORB_FEAT_OPTION,
svm_kernel=cv2.SVM_LINEAR):
if not is_interactive:
experiment_start = time.time()
# Check for the dataset of images
if not os.path.exists(constants.DATASET_PATH):
print("Dataset not found, please copy one.")
return
dataset = Dataset(constants.DATASET_PATH)
dataset.generate_sets()
# Check for the directory where stores generated files
if not os.path.exists(constants.FILES_DIR_NAME):
os.makedirs(constants.FILES_DIR_NAME)
if is_interactive:
des_option = input(
"Enter [1] for using ORB features or [2] to use SIFT features.\n")
k = input(
"Enter the number of cluster centers you want for the codebook.\n")
svm_option = input(
"Enter [1] for using SVM kernel Linear or [2] to use RBF.\n")
svm_kernel = cv2.SVM_LINEAR if svm_option == 1 else cv2.SVM_RBF
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
log = Log(k, des_name, svm_kernel)
codebook_filename = filenames.codebook(k, des_name)
if is_interactive:
codebook_option = input(
"Enter [1] for generating a new codebook or [2] to load one.\n")
else:
codebook_option = constants.GENERATE_OPTION
if codebook_option == constants.GENERATE_OPTION:
# Calculate all the training descriptors to generate the codebook
start = time.time()
des = descriptors.all_descriptors(dataset, dataset.get_train_set(),
des_option)
end = time.time()
log.train_des_time(end - start)
# Generates the codebook using K Means
print("Generating a codebook using K-Means with k={0}".format(k))
start = time.time()
codebook = descriptors.gen_codebook(dataset, des, k)
end = time.time()
log.codebook_time(end - start)
# Stores the codebook in a file
utils.save(codebook_filename, codebook)
print("Codebook saved in {0}".format(codebook_filename))
else:
# Load a codebook from a file
print("Loading codebook ...")
codebook = utils.load(codebook_filename)
print("Codebook with shape = {0} loaded.".format(codebook.shape))
# Train and test the dataset
classifier = Classifier(dataset, log)
svm = classifier.train(svm_kernel,
codebook,
des_option=des_option,
is_interactive=is_interactive)
print("Training ready. Now beginning with testing")
result, labels = classifier.test(codebook,
svm,
des_option=des_option,
is_interactive=is_interactive)
# Store the results from the test
classes = dataset.get_classes()
log.classes(classes)
log.classes_counts(dataset.get_classes_counts())
result_filename = filenames.result(k, des_name, svm_kernel)
test_count = len(dataset.get_test_set()[0])
result_matrix = np.reshape(result, (len(classes), test_count))
utils.save_csv(result_filename, result_matrix)
# Create a confusion matrix
confusion_matrix = np.zeros((len(classes), len(classes)), dtype=np.uint32)
for i in range(len(result)):
predicted_id = int(result[i])
real_id = int(labels[i])
confusion_matrix[real_id][predicted_id] += 1
print("Confusion Matrix =\n{0}".format(confusion_matrix))
log.confusion_matrix(confusion_matrix)
log.save()
print("Log saved on {0}.".format(filenames.log(k, des_name, svm_kernel)))
if not is_interactive:
experiment_end = time.time()
elapsed_time = utils.humanize_time(experiment_end - experiment_start)
print("Total time during the experiment was {0}".format(elapsed_time))
else:
# Show a plot of the confusion matrix on interactive mode
utils.show_conf_mat(confusion_matrix)
raw_input("Press [Enter] to exit ...")
def main(is_interactive=True, k=64, des_option=constants.ORB_FEAT_OPTION, svm_kernel=cv2.SVM_LINEAR):
if not is_interactive:
experiment_start = time.time()
# Check for the dataset of images
if not os.path.exists(constants.DATASET_PATH):
print("Dataset not found, please copy one.")
return
dataset = Dataset(constants.DATASET_PATH)
dataset.generate_sets()
# Check for the directory where stores generated files
if not os.path.exists(constants.FILES_DIR_NAME):
os.makedirs(constants.FILES_DIR_NAME)
if is_interactive:
des_option = input("Enter [1] for using ORB features or [2] to use SIFT features.\n")
k = input("Enter the number of cluster centers you want for the codebook.\n")
svm_option = input("Enter [1] for using SVM kernel Linear or [2] to use RBF.\n")
svm_kernel = cv2.SVM_LINEAR if svm_option == 1 else cv2.SVM_RBF
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
log = Log(k, des_name, svm_kernel)
codebook_filename = filenames.codebook(k, des_name)
if is_interactive:
codebook_option = input("Enter [1] for generating a new codebook or [2] to load one.\n")
else:
codebook_option = constants.GENERATE_OPTION
if codebook_option == constants.GENERATE_OPTION:
# Calculate all the training descriptors to generate the codebook
start = time.time()
des = descriptors.all_descriptors(dataset, dataset.get_train_set(), des_option)
end = time.time()
log.train_des_time(end - start)
# Generates the codebook using K Means
print("Generating a codebook using K-Means with k={0}".format(k))
start = time.time()
codebook = descriptors.gen_codebook(dataset, des, k)
end = time.time()
log.codebook_time(end - start)
# Stores the codebook in a file
utils.save(codebook_filename, codebook)
print("Codebook saved in {0}".format(codebook_filename))
else:
# Load a codebook from a file
print("Loading codebook ...")
codebook = utils.load(codebook_filename)
print("Codebook with shape = {0} loaded.".format(codebook.shape))
# Train and test the dataset
classifier = Classifier(dataset, log)
svm = classifier.train(svm_kernel, codebook, des_option=des_option, is_interactive=is_interactive)
print("Training ready. Now beginning with testing")
result, labels = classifier.test(codebook, svm, des_option=des_option, is_interactive=is_interactive)
# Store the results from the test
classes = dataset.get_classes()
log.classes(classes)
log.classes_counts(dataset.get_classes_counts())
result_filename = filenames.result(k, des_name, svm_kernel)
test_count = len(dataset.get_test_set()[0])
result_matrix = np.reshape(result, (len(classes), test_count))
utils.save_csv(result_filename, result_matrix)
# Create a confusion matrix
confusion_matrix = np.zeros((len(classes), len(classes)), dtype=np.uint32)
for i in range(len(result)):
predicted_id = int(result[i])
real_id = int(labels[i])
confusion_matrix[real_id][predicted_id] += 1
print("Confusion Matrix =\n{0}".format(confusion_matrix))
log.confusion_matrix(confusion_matrix)
log.save()
print("Log saved on {0}.".format(filenames.log(k, des_name, svm_kernel)))
if not is_interactive:
experiment_end = time.time()
elapsed_time = utils.humanize_time(experiment_end - experiment_start)
print("Total time during the experiment was {0}".format(elapsed_time))
else:
# Show a plot of the confusion matrix on interactive mode
utils.show_conf_mat(confusion_matrix)
raw_input("Press [Enter] to exit ...")
def predict_time(self, time):
elapsed_time = utils.humanize_time(time)
self.text += "Elapsed time predicting the testing set is {0}\n".format(elapsed_time)
def vlad_time(self, time, set):
str = "Time for getting VLAD global descriptors of the {0} images was {1}.\n"
elapsed_time = utils.humanize_time(time)
self.text += str.format(set, elapsed_time)
def svm_time(self, time):
str = "Time for calculating the SVM was {0}.\n"
elapsed_time = utils.humanize_time(time)
self.text += str.format(elapsed_time)
def codebook_time(self, time):
str = "Time for generating the codebook with k-means was {0}.\n"
elapsed_time = utils.humanize_time(time)
self.text += str.format(elapsed_time)
def train_des_time(self, time):
str = "Time for getting all the local descriptors of the training images was {0}.\n"
elapsed_time = utils.humanize_time(time)
self.text += str.format(elapsed_time)
def dot_products(self, train_data, test_data, rankings):
''' Calculates the dot product for each element in the test set with
every element of the train set. Returns a matrix with two columns
matrix. The first column is the index of the object in the train set
and the second column is the value of the dot product of that object
with the test object with index equal to the number of the row. Then
the number of rows is the number of objects in the test set.
Args:
train_data (np matrix of floats): Each row is the vector of an
object in the train set.
test_data (np matrix of floats): Each row is the vector of an object
in the test set.
rankings (list of lists int): The ranking created for each object
in the test set.
Returns:
list of list of tuples {
e.g.:
0 ranking_size
| |
0 - [[(21, 0.91), (3, 0.87), ...],
[(10, 0.83), (0, 0.72), ...],
...
len(test_data) - [ ... ]]
int: Index of the object in the train set that should be ranked
in the i-th position where i is the number of the row,
float: The value of the dot product between the object in the
train set and the object in the test set in the i-th
position where i is the number of the row.
},
numpy array of arrays of floats: Dot products where the [i-th, j-th]
element is the product between the i-th object of the testing
set and the j-th object of the training set.
'''
### Calculate dot product on the variables ###
###------------------------------------------------------------------###
print ("Calculating dot products on the rankings ...")
start = time.time()
# products is the matrix that stores the dot product of each testing
# vector with each training vector
sorted_prods = []
products = []
ranking_size = len(rankings[0])
step = (len(test_data) * 5) / 100
train_norm = [utils.normalize(train_vec) for train_vec in train_data]
train_norm = np.array(train_norm)
for i in range(len(test_data)):
# y is the current testing vector
y = test_data[i]
y_norm = utils.normalize(y)
current_tuples = []
products.append([])
for j in range(len(train_data)):
# vector is the training object ranked in the current position
vector_norm = train_norm[j]
prod = np.dot(y_norm, vector_norm)
if j < ranking_size:
products[i].append(prod)
current_tuples.append( (j, prod) )
current_tuples.sort(key=lambda x: x[1], reverse=True)
sorted_prods.append(current_tuples[:ranking_size])
if i % step == 0:
percentage = (i * 100) / len(test_data)
print (
"Vector number {0} of {1} ({2}%) multiplied".format(
i, len(test_data), percentage
)
)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time calculating dot products: {0}".format(elapsed_time)
self.log += s + "\n"
print (s)
return sorted_prods, np.array(products)
def metrics(self, rankings, train_labels, test_labels, sorted_prods):
### Calculates classification and products set and position mAP ###
###------------------------------------------------------------------###
print("Starting to calculate metrics ...")
start = time.time()
rel_ranks = []
for i in range(len(rankings)):
rel_ranks.append(
utils.relevance_ranking(
rankings[i], train_labels, test_labels[i]
)
)
# Classification mAP
#-----------------------------------------------------------------------
class_ap = [utils.class_ap(rel_rk) for rel_rk in rel_ranks]
class_ap_filename = "results/class_avg_precs_{0}.txt".format(
self.n_classes
)
utils.write_list(class_ap, class_ap_filename)
class_map = np.mean(class_ap)
self.log += "ranking size = {0}".format(len(rankings[0])) + "\n"
s = "classification mean average precision = {0}".format(class_map)
self.log += s + "\n"
print(s)
# Dot products average precision
#-----------------------------------------------------------------------
# Set
set_prec = []
for i in range(len(rankings)):
indices = [prods[0] for prods in sorted_prods[i]]
precision = utils.prod_set_prec(indices, rankings[i])
set_prec.append(precision)
set_ap_filename = "results/set_avg_precs_{0}.txt".format(
self.n_classes
)
utils.write_list(set_prec, set_ap_filename)
set_map = np.mean(set_prec)
s = "set mean average precision = {0}".format(set_map)
self.log += s + "\n"
print(s)
# Position
pos_prec = []
for i in range(len(rankings)):
indices = [prods[0] for prods in sorted_prods[i]]
precision = utils.prod_pos_prec(indices, rankings[i])
pos_prec.append(precision)
pos_ap_filename = "results/pos_avg_precs_{0}.txt".format(
self.n_classes
)
utils.write_list(pos_prec, pos_ap_filename)
pos_map = np.mean(pos_prec)
s = "position mean average precision = {0}".format(pos_map)
self.log += s + "\n"
print(s)
end = time.time()
elapsed_time = utils.humanize_time(end - start)
s = "Elapsed time calculating metrics: {0}".format(elapsed_time)
self.log += s + "\n"
print (s)