def main():
Exit = 0
language = input(
"Please select your language english(0),spanish(1), french(2) or a diferent language(3)\n"
)
messages, library = fn.language_menu(language)
while Exit != 1:
while True:
try:
search_or_add_input = int(input(messages[1]))
if search_or_add_input == 0:
os.system('cls')
title = input(messages[2] + '')
author = input(messages[3] + '')
year = input(messages[4] + '')
location = input(messages[5] + '')
descritpion = input(messages[6] + '')
language_input = input(messages[23] + '')
state = input(messages[24] + '')
fn.add(title, author, year, location, descritpion,
language_input, state)
elif search_or_add_input == 1:
os.system('cls')
while True:
category = input(messages[14])
try:
category = int(category)
if category not in range(7):
print(messages[0])
continue
break
except:
os.system('cls')
print(messages[0])
continue
book = input(messages[15])
fn.search(category, book, language)
else:
os.system('cls')
print(messages[0])
continue
break
except:
os.system('cls')
print(messages[0])
while True:
Exit = input(messages[18])
try:
Exit = int(Exit)
if Exit not in range(2):
print(messages[0])
continue
break
except:
os.system('cls')
print(messages[0])
os.system('cls')
print('\n' + messages[17])
time.sleep(8)
def test_search():
"""Tests that output is a dictionary that contains the message"""
assert callable(search)
assert isinstance(search("BILL IS WATCHING"), dict)
assert search("BILL IS WATCHING") == {'title': 'Gideon Rises', 'message': 'BILL IS WATCHING',
'crypt': 'ELOO LV ZDWFKLQJ',
'where': 'In pipes of Mystery Shack', 'type': 'Caesar'}
def lis():
voice=ls.recd()
if (voice=="Err-1"):
playsound("./Media/beep-10.mp3")
sp.spk("Couldn't get that")
elif (voice=='Err-2'):
playsound("./Media/beep-10.mp3")
sp.spk("Connection to Google Broken")
else:
words=voice.split(' ')
fn.search(words[0],words[1])
def main():
choice = input("(S)earch, (N)ame, (I)nfo, or Q(uit)\n").lower()
if choice == "i":
info(ask_info())
main()
elif choice == "s":
stuff = ask_value(ask_info())
search(stuff[0], stuff[1])
main()
elif choice == "n":
search_name()
main()
elif choice == "q":
sys.exit()
else:
main()
def main():
print('Welcome y\'all...\n', '-' * 15, sep='')
search_term = input('What\'s ur search term: ')
total, results = search(search_term)
print('\nThe top {} matches are:\n'.format(total))
for i, anime in enumerate(results, start=1):
print('{}.\t{}'.format(i, anime.get('title')))
anime = results[int(input('Enter ur anime number: ')) - 1]
file_path = os.path.join('links', anime['slug'] + '.txt')
print('There are {} episodes for this anime'.format(anime['episodes']))
print('Download links will be saved in {}'.format(file_path))
episodes = get_episodes(anime['id'])[1]
episodes.reverse()
with open(file_path, 'w') as f:
for i, episode in enumerate(episodes, 1):
#print('Getting link for episode', i)
embed_link = get_embed_links('https://animepahe.com/play/' +
anime['slug'] + '/' +
episode['session'])
down_link = get_down_link(embed_link)
f.write(down_link + '\n ')
#print('Got link for episode', i)
f.close()
print('That\'ll be all... Thank you :D')
def edit(book):
surname = input("Input abonent's surname >>")
name = input("Input abonent's name >>")
result = search(book, surname=surname, name=name)
if len(result) == 0:
print("I can not find this Abonent. Please, try again")
return
index = 0
for i in range(len(book)):
if book[i] == result[0]:
index = i
print("Hello! You will be promted to edit user information. Input \"-\", if you don't want to edit field")
surname = input("Input abonent's surname >>")
if not decoratorMinusFriendly(isName,surname):
print("Incorrect surname!")
return
name = input("Input abonent's name >>")
if not decoratorMinusFriendly(isName,name):
print("Incorrect name!")
return
p_number = input("Input abonent's phone number >>")
if not decoratorMinusFriendly(isNumber,p_number):
print("Incorrect phone number!")
return
birth_date = input("Input abonent's birth date >>")
if not decoratorMinusFriendly(isData,birth_date):
print("Incorrect date!")
return
editing(book, index, surname=surname, name=name, pnumb=p_number, date=birth_date)
print("Editing was successfull")
def news_detail(request, title):
"""
Gibt alle Felder einer bestimmten Nachricht;
gesucht und gefundene Tablet-Computer für die
Nachrichts-Seite zurück.
"""
title = title.replace('-', ' ')
news = News.objects.get(title=title)
news_title = news.title
news_intro = news.intro
news_text = news.text
news_date = news.date
news_pic = [news.pic1, news.pic2]
news_brand = Brand.objects.filter(news__title=title)
news_tablet = TabletPC.objects.filter(news__title=title)
query_string, found_entries = search(request)
return render_to_response(
'news.html', {'query_string': query_string,
'found_entries': found_entries,
'news_title': news_title,
'news_intro': news_intro,
'news_text': news_text,
'news_date': news_date,
'news_pic': news_pic,
'news_brand': news_brand,
'news_tablet': news_tablet},
context_instance=RequestContext(request))
def main_loop():
while True:
system('clear')
driver = init_driver(URL)
header(TITLE)
key_word = input("[+] Search: ")
exit_check(key_word, driver)
driver = search(driver, key_word)
titles, links = get_search_results(driver)
choice = browse_loop(titles, links, 'browse')
driver = init_driver(links[int(choice) - 1])
episode_titles, episode_links = get_episodes(driver)
episode_choice = browse_loop(episode_titles, episode_links, 'episodes')
driver = init_driver(episode_links[int(episode_choice) - 1])
video_url = get_video_url(driver)
driver = init_driver(video_url)
dl_url, status = get_video(driver)
if status:
download_video(dl_url, titles[int(choice) - 1],
episode_titles[int(episode_choice) - 1])
else:
input("[+] Press any key to continue...")
continue
def show_brand(request, name):
"""
Gibt den Markennamen, die Information, das Logo,
die entsprechenden Nachrichten und Tablet-Computer;
gesucht und gefundene Tablet-Computer für die
Marken-Seite zurück.
"""
try:
name = name.replace('-', ' ').title()
brand = Brand.objects.get(name=name)
brand_name = brand.name
brand_info = brand.info
brand_logo = brand.logo
brand_tablet = TabletPC.objects.filter(brand__name=name)
brand_news = News.objects.filter(brand__name=name)
except Brand.DoesNotExist:
brand = brand_name = brand_info = None
brand_logo = brand_tablet = brand_news = None
query_string, found_entries = search(request)
return render_to_response(
'brand.html', {'name': name,
'brand_name': brand_name,
'brand_info': brand_info,
'brand_news': brand_news,
'brand_logo': brand_logo,
'brand_tablet': brand_tablet,
'query_string': query_string,
'found_entries': found_entries},
context_instance=RequestContext(request))
def tablet_detail(request, name):
"""
Gibt alle Felder eines bestimmten Tablet-Computers;
gesucht und gefundene Tablet-Computer für die
Tablet-Seite zurück.
"""
name = name.replace('-', ' ')
tablet = TabletPC.objects.get(name=name)
tablet_name = tablet.name
tablet_intro = tablet.intro
tablet_info = tablet.info
tablet_os = tablet.os
tablet_link = tablet.link
tablet_pic = [tablet.pic1, tablet.pic2, tablet.pic3, tablet.pic4]
tablet_brand = tablet.brand
tablet_news = News.objects.filter(tablet__name=name)
query_string, found_entries = search(request)
return render_to_response(
'tablet.html', {'query_string': query_string,
'found_entries': found_entries,
'tabletpc': tablet,
'tablet_name': tablet_name,
'tablet_intro': tablet_intro,
'tablet_info': tablet_info,
'tablet_os': tablet_os,
'tablet_pic': tablet_pic,
'tablet_link': tablet_link,
'tablet_news': tablet_news,
'tablet_brand': tablet_brand},
context_instance=RequestContext(request))
def show(request):
"""
Gibt die Marken, die neuesten zwanzig Tablet-Computer Namen;
wenn verfügbar, die letzte Nachrichts-Titel, Einführungstext;
gesucht und gefundene Tablet-Computer für die Index-Seite zurück.
"""
brands = Brand.objects.order_by('name')
tablets = TabletPC.objects.order_by('-id')[:20]
try:
news = News.objects.order_by('-id')[:1].get()
news_title = news.title
news_intro = news.intro
except News.DoesNotExist:
news = news_title = news_intro = None
query_string, found_entries = search(request)
return render_to_response(
'index.html', {'query_string': query_string,
'found_entries': found_entries,
'brands': brands,
'tablets': tablets,
'news_title': news_title,
'news_intro': news_intro},
context_instance=RequestContext(request))
def choose_menupoint():
inputs = ui.get_inputs("Please enter a number: ", "")
option = inputs[0]
if option == "1":
my_events.start()
elif option == "2":
join_events.start()
elif option == "3":
functions.search()
elif option == "4":
functions.show_random_events()
elif option == "5":
functions.join_event()
elif option == "0":
sys.exit(0)
else:
raise KeyError("There is no such option.")
def delete(book):
response = input("What way are you choose?\n input \"1\" ---> deletion by name+surname\n input \"2\" ---> deletion by the phone number\n>>>")
if response.strip() == '1':
surname = input("Input abonent's surname >>")
name = input("Input abonent's name >>")
if not isNameAndSurname(surname, name):
return
ab_arr = search(book, surname=surname, name=name)
if len(ab_arr) == 0:
print("I can not find this Abonent. Please, try again")
return
book.remove(ab_arr[0])
print("Abonent was deleted successfully")
elif response.strip() == '2':
p_number = input("Input abonent's phone number >>")
if not isNumber(p_number):
print("Incorrect number!")
return
ab_arr = search(book, pnumb=p_number)
if len(ab_arr) == 0:
print("I can not find this Abonent. Please, try again")
return
elif len(ab_arr) == 1:
book.remove(ab_arr[0])
print("Abonent was deleted successfully")
return
else:
print(
"I found several Abonents. Which one of them are you want to delete? Specify the numbers (start from 1)(splitting by space)")
printBook(ab_arr)
response = input()
response.strip()
response = response.split(' ')
N = len(ab_arr)
for i in range(len(response)):
if not response[i].isdigit():
print("I don't understand(((((")
return
response[i] = int(response[i])
for index in response:
if index <= N:
book.remove(ab_arr[index - 1])
print("Abonent was deleted successfully")
else:
print("I don't understand(((((")
def clientthread(conn, addr):
conn.send(
'Service Started. Choose an option Useing Uppercase letter\n #(R)egister\n #(U)pload files\n #(S)earch for a file\n #(E)xit'
)
onlinePeers.append(addr[0])
while True:
data = conn.recv(1024)
if not data:
continue
elif data.split('\n')[0] == 'REGISTER':
functions.register(conn, addr, data.split('\n')[1], str(addr[1]))
# functions.register(conn, addr, "client" + str(addr[1]))
elif data.split('\n')[0] == 'SHARE_FILES':
functions.share(conn, addr, data.split('\n')[1], str(addr[1]))
elif data.split('\n')[0] == 'SEARCH':
functions.search(conn, addr, data.split('\n')[1], onlinePeers)
onlinePeers.remove(addr[0])
conn.close()
def clientthread(conn,addr):
conn.send('Welcome to the server. Select an option\n 1. (R)egister\n 2. (U)pload files\n 3. (S)earch for a file\n 4. (E)xit')
activePeers.append(addr[0])
#print activePeers
while 1:
data = conn.recv(1024)
#reply = 'OK...' + data
if not data:
break
if data.split('\n')[0] == 'REGISTER':
functions.register(conn, addr, data.split('\n')[1])
elif data.split('\n')[0] == 'SHARE_FILES':
functions.share(conn,addr,data.split('\n')[1])
elif data.split('\n')[0] == 'SEARCH':
functions.search(conn,addr,data.split('\n')[1],activePeers)
elif data == 'TEST':
functions.checkDB(conn)
activePeers.remove(addr[0])
conn.close()
def find(book):
print(
"Hello! You will be promted to fill in user information. Input \"-\", if you don't want to specify field for search")
surname = input("Input abonent's surname >>")
name = input("Input abonent's name >>")
p_number = input("Input abonent's phone number >>")
birth_date = input("Input abonent's birth date >>")
result = search(book, surname=surname, name=name, pnumb=p_number, date=birth_date)
print("I found...")
if len(result)==0:
print("Nothing(((")
else:
printBook(result)
def age(book):
surname = input("Input abonent's surname >>")
name = input("Input abonent's name >>")
if not isNameAndSurname(surname,name):
return
result = search(book, surname=surname, name=name)
if len(result) == 0:
print("I can not find this Abonent. Please, try again")
return
if result[0].age() == -1:
print("Age is not setted")
else:
print(result[0].age())
def clientthread(conn, addr):
conn.send(
'Welcome to the server. Select an option\n 1. (R)egister\n 2. (U)pload files\n 3. (S)earch for a file\n 4. (E)xit'
)
activePeers.append(addr[0])
#print activePeers
while 1:
data = conn.recv(1024)
#reply = 'OK...' + data
if not data:
break
if data.split('\n')[0] == 'REGISTER':
functions.register(conn, addr, data.split('\n')[1])
elif data.split('\n')[0] == 'SHARE_FILES':
functions.share(conn, addr, data.split('\n')[1])
elif data.split('\n')[0] == 'SEARCH':
functions.search(conn, addr, data.split('\n')[1], activePeers)
elif data == 'TEST':
functions.checkDB(conn)
activePeers.remove(addr[0])
conn.close()
def news_all(request):
"""
Gibt alle Nachrichteneingaben;
gesucht und gefundene Tablet-Computer
für die Nachrichten-Seite zurück.
"""
news = News.objects.all().order_by('-id')
query_string, found_entries = search(request)
return render_to_response(
'newsall.html', {'query_string': query_string,
'found_entries': found_entries,
'news': news},
context_instance=RequestContext(request))
def show_android(request):
"""
Gibt alle Tablet-Computer mit Android-Betriebssystem,
Nachrichten die Android-Betriebssystem enthalten;
gesucht und gefundene Tablet-Computer für die
Android-Seite zurück.
"""
tablets = TabletPC.objects.filter(os__icontains="android")
tablet_news = News.objects.filter(
Q(intro__icontains="android") | Q(text__icontains="android"))
query_string, found_entries = search(request)
return render_to_response(
'android.html', {'query_string': query_string,
'found_entries': found_entries,
'tablet_news': tablet_news,
'tablets': tablets},
context_instance=RequestContext(request))
def add(book):
surname = input("Input abonent's surname >>")
name = input("Input abonent's name >>")
if not isNameAndSurname(surname,name):
return
p_number = input("Input abonent's phone number >>")
if not isNumber(p_number):
print("Incorrect number!")
return
birth_date = ""
response = input(
"Are you want to add Abonent's birth date? Input \"Y\" if you want or input anything else if no >>")
if response.strip() == "Y":
birth_date = input("Input abonent's birth date >>")
if not isData(birth_date):
print("Incorrect date!")
return
ab = search(book, surname=surname, name=name)
if len(ab) > 0:
print("I found Abonent with same name and surname. What should I do? Input the number")
response = input(
"\"1\"---> delete old record and add new record\n\"2\"---> change name and surname of new record\n\"3\"---> adort addition\n>>>")
if response == '1':
book.remove(ab[0])
book.append(Abonent(surname + ';' + name + ';' + p_number + ';' + birth_date))
print("Success")
elif response == '2':
surname = input("Input new abonent's surname >>")
name = input("Input new abonent's name >>")
book.append(Abonent(surname + ';' + name + ';' + p_number + ';' + birth_date))
print("Success")
elif response == '3':
print("Addition aborted")
return
else:
print("Incorrect number!")
return
else:
book.append(Abonent(surname + ';' + name + ';' + p_number + ';' + birth_date))
print("New abonent was added succesfully")
def code_tokenify(arity_list: [(int, TokenLib.Token)]) -> [Code_Token]:
code_tokens = []
exprs = []
expr = []
patterns = [
"0", "1", "2", "020", "021", "022", "02", "10", "11", "12", "20", "21",
"22", "102", "110", "111", "112", "120", "121", "122", "202", "210",
"211", "212", "220", "221", "222"
]
pattern = ""
while len(arity_list):
if (pattern in patterns
and pattern + str(arity_list[-1][0]) not in patterns):
exprs.append([pattern, expr])
expr = []
pattern = ""
pattern += str(arity_list[-1][0])
expr.append(arity_list[-1][1])
arity_list.pop()
if expr and pattern in patterns:
exprs.append([pattern, expr])
expr = []
pattern = ""
for expr in exprs:
arity_pattern, data = expr
ctkn = None
#print(arity_pattern, [m.get_data() for m in data])
if arity_pattern == "0":
# Nilad
ctkn = Code_Token(self, None, data[0].get_value())
elif arity_pattern == "1":
# Monad
ctkn = Code_Token(functions.search(data[0]), None,
Relative_Argument)
elif arity_pattern == "2":
# Dyad
ctkn = Code_Token(functions.search(data[0]), Relative_Argument,
Relative_Argument)
elif arity_pattern == "020":
# Nilad-Dyad-Nilad
ctkn = Code_Token(functions.search(data[1]), data[0].get_value(),
data[2].get_value())
elif arity_pattern == "021":
# Nilad-Dyad-Monad
right = Code_Token(functions.search(data[2]), None,
Relative_Argument)
ctkn = Code_Token(functions.search(data[1]), data[0].get_value(),
right)
elif arity_pattern == "02":
# Nilad-Dyad
ctkn = Code_Token(functions.search(data[1]), data[0].get_value(),
Relative_Argument)
elif arity_pattern == "10":
# Monad-Nilad
ctkn = Code_Token(functions.search(data[0]), None,
data[1].get_value())
elif arity_pattern == "11":
# Monad-Monad
right = Code_Token(functions.search(data[1]), None,
Relative_Argument)
ctkn = Code_Token(functions.search(data[0]), None, right)
elif arity_pattern == "12":
# Monad-Dyad
left = Code_Token(functions.search(data[0]), None,
Relative_Argument)
ctkn = Code_Token(functions.search(data[1]), left,
Relative_Argument)
elif arity_pattern == "20":
# Dyad-Nilad
ctkn = Code_Token(functions.search(data[0]), Relative_Argument,
data[1].get_value())
elif arity_pattern == "21":
# Dyad-Monad
right = Code_Token(functions.search(data[1]), None,
Relative_Argument)
ctkn = Code_Token(functions.search(data[0]), Relative_Argument,
right)
elif arity_pattern == "22":
# Dyad-Dyad
left = Code_Token(functions.search(data[0]), Relative_Argument,
Relative_Argument)
ctkn = Code_Token(functions.search(data[1]), left,
Relative_Argument)
elif arity_pattern == "102":
# Monad-Nilad-Dyad
left = Code_Token(functions.search(data[0]), None,
data[1].get_value())
ctkn = Code_Token(functions.search(data[2]), left,
Relative_Argument)
elif arity_pattern == "110":
# Monad-Monad-Nilad
right = Code_Token(functions.search(data[1]), None,
data[2].get_value())
ctkn = Code_Token(functions.search(data[0]), None, right)
elif arity_pattern == "111":
# Monad-Monad-Monad
right_1 = Code_Token(functions.search(data[2]), None,
Relative_Argument)
right = Code_Token(functions.search(data[1]), None, right_1)
ctkn = Code_Token(functions.search(data[0]), None, right)
elif arity_pattern == "120":
# Monad-Dyad-Nilad
left = Code_Token(functions.search(data[0]), None,
Relative_Argument)
ctkn = Code_Token(functions.search(data[1]), left,
data[2].get_value())
elif arity_pattern == "121":
# Monad-Dyad-Monad
left = Code_Token(functions.search(data[0]), None,
Relative_Argument)
right = Code_Token(functions.search(data[2]), None,
Relative_Argument)
ctkn = Code_Token(functions.search(data[1]), left, right)
elif arity_pattern == "122":
# Monad-Dyad-Dyad
left = Code_Token(functions.search(data[0]), None,
Relative_Argument)
right = Code_Token(functions.search(data[2]), Relative_Argument,
Relative_Argument)
ctkn = Code_Token(functions.search(data[1]), left, right)
elif arity_pattern == "202":
# Dyad-Nilad-Dyad
left = Code_Token(functions.search(data[0]), Relative_Argument,
data[1].get_value())
ctkn = Code_Token(functions.search(data[2]), left,
Relative_Argument)
elif arity_pattern == "210":
# Dyad-Monad-Nilad
right = Code_Token(functions.search(data[1]), None,
data[2].get_value())
ctkn = Code_Token(functions.search(data[0]), Relative_Argument,
right)
elif arity_pattern == "211":
# Dyad-Monad-Monad
right_1 = Code_Token(functions.search(data[2]), None,
Relative_Argument)
right = Code_Token(functions.search(data[1]), None, right_1)
ctkn = Code_Token(functions.search(data[0]), Relative_Argument,
right)
elif arity_pattern == "212":
# Dyad-Monad-Dyad
right_1 = Code_Token(functions.search(data[1]), None,
Relative_Argument)
left = Code_Token(functions.search(data[0]), Relative_Argument,
right_1)
ctkn = Code_Token(functions.search(data[2]), left,
Relative_Argument)
elif arity_pattern == "220":
# Dyad-Dyad-Nilad
left = Code_Token(functions.search(data[0]), Relative_Argument,
Relative_Argument)
ctkn = Code_Token(functions.search(data[1]), left,
data[2].get_value())
elif arity_pattern == "221":
# Dyad-Dyad-Monad
left = Code_Token(functions.search(data[0]), Relative_Argument,
Relative_Argument)
right = Code_Token(functions.search(data[2]), None,
Relative_Argument)
ctkn = Code_Token(functions.search(data[1]), left, right)
elif arity_pattern == "222":
# Triple Dyad
left_1 = Code_Token(functions.search(data[0]), Relative_Argument,
Relative_Argument)
left = Code_Token(functions.search(data[1]), left_1,
Relative_Argument)
ctkn = Code_Token(functions.search(data[2]), left,
Relative_Argument)
code_tokens.append(ctkn)
return code_tokens
def answer_process():
if not request.args:
return redirect(url_for('index'))
some_id = request.args.get('some_id')
if some_id:
owner_list = []
owner_stats = db.session.query(Posts.wall_owner).all()
for item in owner_stats:
owner_list.append(str(item[0]))
if str(some_id) in owner_list:
w = 'Информация об этом пользователе уже есть.'
return render_template('search.html', owner_id=some_id, w=w)
else:
info = search(some_id)
if len(info) == 2:
return render_template(info[0], message=info[1])
else:
all_comments, all_posts, all_authors = info[0], info[1], info[
2]
com_df = pd.DataFrame(all_comments)
post_df = pd.DataFrame(all_posts)
auth_df = pd.DataFrame(all_authors)
# posts
for index, row in post_df.iterrows():
id = row['id']
author_id = row['user_id']
text = row['text']
lem_text = row['lem_text']
likes = row['likes']
date_time = row['date_time']
weekday = row['weekday']
wall_owner = row['wall_owner']
post = Posts(id=id,
author_id=author_id,
text=text,
lem_text=lem_text,
likes=likes,
date_time=date_time,
weekday=weekday,
wall_owner=wall_owner)
try:
db.session.add(post)
db.session.commit()
db.session.refresh(post)
except sqlalchemy.exc.InvalidRequestError:
db.session.rollback()
# comments
for index, row in com_df.iterrows():
id = row['id']
post_id = row['post_id']
author_id = row['author_id']
text = row['text']
lem_text = row['lem_text']
likes = row['likes']
date_time = row['date_time']
weekday = row['weekday']
wall_owner = row['wall_owner']
comment = Comments(id=id,
post_id=post_id,
author_id=author_id,
text=text,
lem_text=lem_text,
likes=likes,
date_time=date_time,
weekday=weekday,
wall_owner=wall_owner)
try:
db.session.add(comment)
db.session.commit()
db.session.refresh(comment)
except:
pass
# authors
author_base = []
author_stats = db.session.query(Authors.id).all()
for item in author_stats:
author_base.append(str(item[0]))
for index, row in auth_df.iterrows():
if str(row['id']) not in author_base:
id = row['id']
sex = row['sex']
bdate = row['bdate']
day = row['day']
month = row['month']
year = row['year']
city = row['city']
faculty = row['faculty']
books = row['books']
interests = row['interests']
home_town = row['home_town']
career = row['career']
author = Authors(id=id,
sex=sex,
bdate=bdate,
day=day,
month=month,
year=year,
city=city,
faculty=faculty,
books=books,
interests=interests,
home_town=home_town,
career=career)
db.session.add(author)
db.session.commit()
db.session.refresh(author)
# db.session.commit()
w = 'Информация скачана!'
return render_template('search.html', owner_id=some_id, w=w)
else:
return redirect(url_for('answer_process'))
import functions
" Reading an unsolved Sudoku from a CSV file called input.csv consisting of an unsolved Sudoku with 0’s representing blanks"
Test = open("input.csv", 'r')
text = Test.read()
grid = text.replace(',', '')
grid1 = grid.replace('\n', '')
Test.close()
# Solving Sudoku puzzle using Constraint Propagation algorithm
output = functions.display(functions.search(functions.parse_grid(grid1)))
# Convert the final result to CSV format
j = 1
list1 = ''
for i in output:
if j % 9 != 0:
list1 = list1 + i + ","
elif j % 9 == 0:
list1 = list1 + i + "\n"
j += 1
# Saving the result in a CSV file called Result.csv
out = open("Result.csv", 'w')
out.write(list1)
out.close()
def main():
op = 0
while op != 14:
print('---' * 40)
print('\t********Library Management System***********')
print('---' * 40)
print('\tpress 1 for Add Student')
print('\tpress 2 for Add Faculty')
print('\tpress 3 for Add Book')
print('\tpress 4 for issue book for Student')
print('\tpress 5 for issue book for faculty')
print('\tpress 6 for search book')
print('\tpress 7 for search student')
print('\tpress 8 for search faculty')
print('\tpress 9 for return book from Student')
print('\tpress 10 for return book from faculty')
print('\tpress 11 for display information of student')
print('\tpress 12 for display information of faculty')
print('\tpress 13 for display information of books')
print('\tpress 14 for Exit')
print('---' * 40)
op = int(input('Enter choice :: '))
print('---' * 40)
print(
"[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[ Please carefully filled the entry ]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]"
)
if (op == 1):
f.add_student()
elif (op == 2):
f.add_faculty()
elif (op == 3):
f.add_books()
elif (op == 4):
f.book_issue()
elif (op == 5):
f.book_issue_fac()
elif (op == 6):
if os.path.exists('Books.pkl') is True:
with open('Books.pkl', 'rb') as b:
isbn = input('Enter book ISBN :: ').upper()
f.search(b, isbn, 3)
else:
print('No any directory')
elif (op == 7):
if os.path.exists('Student.pkl') is True:
with open('Student.pkl', 'rb') as b:
stu = input('Enter Student ID :: ').upper()
f.search(b, stu, 1)
else:
print('No any directory')
elif (op == 8):
if os.path.exists('Faulty.pkl') is True:
with open('Faculty.pkl', 'rb') as b:
fac = input('Enter Faculty ID :: ').upper()
f.search(b, fac, 2)
else:
print('No any directory')
elif (op == 9):
f.return_student()
elif (op == 10):
f.return_faculty()
elif (op == 11):
f.display_student()
elif (op == 12):
f.display_faculty()
elif (op == 13):
f.display_books()
elif (op == 14):
print("*************** Thankyou ********************")
break
else:
print("Invalid choice")
def main():
command_list = {
'1. Stop': 'If you want to stop using our Phone book, you should use this command',
'2. Add persons': 'You can add persons to Phone book',
'3. Visualisation': 'You can see all elements of Phone book and some information about it',
'4. Search': 'Searching over phone book by one or several fields',
'5. Get the age of the person': 'You can get the actual age of the person',
'6. Change data': 'You can change the information about existing person',
'7. Get number': 'You can get the phone number of the person',
'8. Delete person': 'You can delete a record about the person',
'9. Compare age of persons': 'You can find out how many people are younger/older/equal than/to N years old.'
'You should enter the parameter N',
'10. Clear screen': 'You can clear screen if you want',
'11. Search by month and day': 'You can search records in Phone book by month and day at the same time',
'12. Delete person by number': 'You can delete person by number'
}
phone_book = {}
with open("surname_numbers.txt") as file:
for line in file:
key, *value = line.replace('\n', ':').split(':')
phone_book[key] = value[:-1]
while True:
print('Welcome to our phone book!')
func.visualisation_of_commands(command_list)
print("Enter the number of the command, please:", end=' ')
command = input()
if command == '1':
break
elif command == '2':
print('Enter your data about person.The format of data is ' + color.BOLD +
'Name Surname:Number:Date of birth(if you want)' + color.END)
print('Date of birth format: XX/XX/XXXX')
print('Example: Alex Bystrov:89100000000:01/04/1999', end=' ')
data = input().replace('\n', ':').split(':')
while len(data) < 2:
print('Wrong, format. You should enter at least full name and phone number.')
print('Example: Alex Bystov:89100000000:01/04/1999')
print('Please, try again here:', end=' ')
data = input().replace('\n', ':').split(':')
name, *value = data
if len(value) > 1:
number = value[0]
date = value[1]
else:
number = value[0]
date = ''
while func.number_check(number) == 0:
print("Please try again, enter only number:", end=' ')
number = input()
while func.date_check(date) == 0:
print("Please try again,enter only date:", end=' ')
date = input()
while func.name_check(name) == 0:
print("Please try again,enter name and surname:", end=' ')
name = input()
name = name.title()
if number[0] == '+' and number[1] == '7':
number = number.replace('+7', '8', 1)
func.add_persons(phone_book, name, number, date)
elif command == '3':
func.visualisation(phone_book)
elif command == '4':
print("Hy! Let's search for someone. You have 4 parameters to search \n"
"Name Surname Number Date \n"
"1. You can search by Name, you should enter: Name _ _ _ \n"
"2. You can search by Surname, you should enter: _ Surname _ _\n"
"3. You can search by Number, you should enter: _ _ Number _\n"
"4. You can search by Birth Date, you should enter: _ _ _ Date\n"
"5. You can search by Birth Date and Surname at the same time, you should enter: _ Surname _ Date\n"
"6. You can search by Name and Surname at the same time, you should enter: Name Surname _ _\n")
print('Example: Petr _ _ _')
print('Enter your data:', end=' ')
data = input().split()
while len(data) != 4:
print('Wrong, format. You should search by 4 parameters.')
print('Example: Petr _ _ _ or _ Slavutin _ _ _')
print('Please, try again here:', end=' ')
data = input().split()
ob1, ob2, ob3, ob4 = data[0], data[1], data[2], data[3]
func.search(phone_book, ob1, ob2, ob3, ob4)
elif command == '5':
print('Choose the name and surname please.')
print('Example: Alex Bystov', end=' ')
name = input()
while func.name_check(name) == 0:
print("Please try again, enter name and surname:", end=' ')
name = input()
name = name.title()
func.age_of_the_person(phone_book, name, 0)
elif command == '6':
print('What data do you want to change?')
print('1. Name and surname')
print('2. Number')
print('3. Birth date')
print('Enter the number of the command here:', end=' ')
choice = input()
if choice == '1':
print('Choose the name and surname, which you want to change.')
print('Example: Alex Bystov.')
print('Enter your data here:', end=' ')
name = input()
while func.name_check(name) == 0:
print("Please try again,enter name and surname:", end=' ')
name = input()
name = name.title()
func.change_name(phone_book, name)
if choice == '2':
print('Choose the name and surname, which number you want to change.')
print('Example: Alex Bystov.')
print('Enter your data here:', end=' ')
name = input()
while func.name_check(name) == 0:
print("Please try again,enter name and surname:", end=' ')
name = input()
name = name.title()
func.change_number(phone_book, name)
if choice == '3':
print('Choose the name and surname, which birth date you want to change.')
print('Example: Alex Bystov.')
print('Enter your data here:', end=' ')
name = input()
while func.name_check(name) == 0:
print("Please try again,enter name and surname:", end=' ')
name = input()
name = name.title()
func.change_date(phone_book, name)
elif command == '7':
print('Choose the name and surname, which number you want to know please.')
print('Example: Alex Bystov.')
print('Enter your data here:', end=' ')
name = input()
while func.name_check(name) == 0:
print("Please try again,enter name and surname:", end=' ')
name = input()
name = name.title()
func.get_ph_number(phone_book, name)
elif command == '8':
print("Choose the name and surname, which you want to delete please.")
print('Example: Alex Bystov.')
print('Enter your data here:', end=' ')
name = input()
while func.name_check(name) == 0:
print("Please try again,enter name and surname:", end=' ')
name = input()
name = name.title()
func.del_person(phone_book, name)
elif command == '9':
print('Please enter the age with that you want compare persons age in Phone book (N):', end=' ')
comparison_number = input()
while func.comparison_number_check(comparison_number) == 0:
print('Wrong input format, please try again:', end='')
comparison_number = input()
print('Do you want to watch people younger(enter 1), older(enter 2) '
'or equal(enter 3) than {}'.format(comparison_number))
print('Enter data here', end=' ')
parameter = input()
while parameter != '1' and parameter != '2' and parameter != '3':
print('Wrong input format, you should enter 1 or 2 or 3 ')
print('Example: 1')
print('Enter your parameter here:', end=' ')
parameter = input()
func.compare_by_age(phone_book, comparison_number, parameter)
elif command == '10':
func.cls()
elif command == '11':
print('Please enter the month and day. The format of input: Day/Month(XX/XX)')
print('Example: 21/11')
date = input('Enter date here:')
while func.date_check_for_search(date) == 0:
print("Please try again,enter only date:", end=' ')
date = input()
func.search_by_month_and_day(phone_book, date)
elif command == '12':
print('Please enter the number', end=' ')
number = input()
while func.number_check(number) == 0:
print("Please try again, enter number:", end=' ')
number = input()
func.delete_person_by_number(phone_book, number)
with open('surname_numbers.txt', 'w') as out:
for key, value in phone_book.items():
out.write('{}:{}:{}\n'.format(key, *value))
print('Thank you for using our Phone book!')
print('We hope, we will see you again! Good Lick!')
print(" ")
print(" Sudoku game: Easy ")
print(" ")
## Add this to be a random drawn board
grid = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..'
display(grid_values_StartingBoard(grid))
print(" ")
for i in solutionGame:
print(i, ".", solutionGame[i])
solutionGame_choice = input(
"Please type in the number of your choice: ")
if solutionGame_choice == "1":
display(eliminate(grid_values(grid)))
printBoard = False
elif solutionGame_choice == "2":
display(search(grid_values(grid)))
printBoard = False
elif solutionGame_choice == "3":
break
elif solutionGame_choice == "4":
exit()
elif selc_levGame == "2":
print("Option not defined yet")
#fix this
elif selc_levGame == "3":
print("Option not defined yet")
elif selc_levGame == "4":
break
elif selc_levGame == "5":
exit()
else:
noresult = 'Aucun résultat pour votre recherche'
fr = '<a class = "selected" href="?lang=fr">Français</a>'
en = '<a class = "" href="?lang=en">Anglais</a>'
if ('word' in form):
word = form["word"].value
if lang == 'en':
en = '<a class = "selected" href="?word=' + word + '&lang=en">English</a>'
fr = '<a class = "" href="?word=' + word + '&lang=fr">French</a>'
else:
fr = '<a class = "selected" href="?word=' + word + '&lang=fr">Français</a>'
en = '<a class = "" href="?word=' + word + '&lang=en">Anglais</a>'
model = Word2Vec.load('./rss_models/model_' + lang)
similatities = functions.getBestSimilarities(model, word, lang, 5)
results = functions.search(word, lang)
print('''
<!DOCTYPE html>
<html lang="''' + lang + '''">
<head>
<title>''' + title + " - " + word + '''</title>
<link rel="stylesheet" href="../web/styles.css">
</head>
<body>
<div class="logo">
<img src="../web/images/logo.png" style="border: 0pt none ; width: 300px; height: 300px;">
</div>
<form class="searchfield cf">
<input type="text" name="word" value="''' + word + '''">
<input type="hidden" name="lang" value="''' + lang + '''">
<button type="submit">''' + button + '''</button>
def onsearch(self, event):
if self.search_text.GetValue() != '':
try:
fcn.search(self)
except:
self.SetStatusText('エラーが発生しました')
def DeleteAll():
memory = []
ram = []
company = []
TypeName = []
OpSys = []
companies = pd.read_csv('../Data/Companies.csv',
encoding='latin1',
index_col='Unnamed: 0')
collaborations = pd.read_csv('../Data/Collaboration_Id.csv',
encoding='latin1',
index_col='Unnamed: 0')
products = pd.read_csv('../Data/Products.csv',
encoding='latin1',
index_col='Unnamed: 0')
notebooks = ft.df_to_dict(companies, collaborations, products)
if (cvar1.get() == 1):
memory.append("Intel Core i3")
if (cvar2.get() == 1):
memory.append("Intel Core i5")
if (cvar3.get() == 1):
memory.append("Intel Core i7")
if (cvar4.get() == 1):
memory.append("Intel Core M")
if (cvar5.get() == 1):
memory.append("AMD A9-Series")
if (cvar6.get() == 1):
memory.append("AMD E-Series")
if (cvar7.get() == 1):
memory.append("AMD A6-Series")
if (cvar8.get() == 1):
memory.append("Intel Celeron")
if (cvar9.get() == 1):
memory.append("AMD Ryzen")
if (cvar10.get() == 1):
memory.append("Intel Pentium")
if (cvar11.get() == 1):
memory.append("AMD FX")
if (cvar12.get() == 1):
memory.append("Intel Xeon")
if (cvar13.get() == 1):
memory.append("AMD A10-Series")
if (cvar14.get() == 1):
memory.append("AMD A8-Series")
if (cvar15.get() == 1):
memory.append("AMD A12-Series")
if (cvar16.get() == 1):
memory.append("AMD A4-Series")
if (cvar17.get() == 1):
memory.append("Samsung Cortex")
if (cvar18.get() == 1):
memory.append("Intel Core M")
if (svar1.get() == 1):
ram.append('2GB')
if (svar2.get() == 1):
ram.append('4GB')
if (svar3.get() == 1):
ram.append('6GB')
if (svar4.get() == 1):
ram.append('8GB')
if (svar5.get() == 1):
ram.append('12GB')
if (svar6.get() == 1):
ram.append('16GB')
if (svar7.get() == 1):
ram.append('32GB')
if (svar8.get() == 1):
ram.append('24GB')
if (svar9.get() == 1):
ram.append('64GB')
if (tvar1.get() == 1):
TypeName.append('Ultrabook')
if (tvar2.get() == 1):
TypeName.append('Notebook')
if (tvar3.get() == 1):
TypeName.append('Netbook')
if (tvar4.get() == 1):
TypeName.append('Gaming')
if (tvar5.get() == 1):
TypeName.append('2 in 1 Convertible')
if (tvar6.get() == 1):
TypeName.append("Workstation")
if (covar1.get() == 1):
company.append('Apple')
if (covar2.get() == 1):
company.append('HP')
if (covar3.get() == 1):
company.append('Acer')
if (covar4.get() == 1):
company.append('Asus')
if (covar5.get() == 1):
company.append('Dell')
if (covar6.get() == 1):
company.append('Lenovo')
if (covar7.get() == 1):
company.append('Chuwi')
if (covar8.get() == 1):
company.append('MSI')
if (covar9.get() == 1):
company.append('Microsoft')
if (covar10.get() == 1):
company.append('Toshiba')
if (covar11.get() == 1):
company.append('Huawei')
if (covar12.get() == 1):
company.append('Xiaomi')
if (covar13.get() == 1):
company.append('Vero')
if (covar14.get() == 1):
company.append('Razer')
if (covar15.get() == 1):
company.append('Mediacom')
if (covar16.get() == 1):
company.append('Samsung')
if (covar17.get() == 1):
company.append('Google')
if (covar18.get() == 1):
company.append('Fujitsu')
if (covar19.get() == 1):
company.append('LG')
if (osvar1.get() == 1):
OpSys.append("macOS")
if (osvar2.get() == 1):
OpSys.append("No OS")
if (osvar3.get() == 1):
OpSys.append("Windows 10")
if (osvar4.get() == 1):
OpSys.append("Mac OS X")
if (osvar5.get() == 1):
OpSys.append("Linux")
if (osvar6.get() == 1):
OpSys.append("Android")
if (osvar7.get() == 1):
OpSys.append("Windows 10 S")
if (osvar8.get() == 1):
OpSys.append("Chrome OS")
if (osvar9.get() == 1):
OpSys.append("Windows 7")
if (higher_price.get() or lowest_price.get()):
pricelst = []
pricelst.append(0.0)
if (lowest_price.get()):
pricelst.append(float(lowest_price.get()))
else:
pricelst.append(0.0)
if (higher_price.get()):
pricelst.append(float(higher_price.get()))
else:
pricelst.append(9999999.9)
notebooks = ft.filter_by_price(notebooks, pricelst)
if (OpSys):
notebooks = ft.filter_by_specification(notebooks, 'OpSys', OpSys)
if (company):
notebooks = ft.filter_by_specification(notebooks, 'Company', company)
if (TypeName):
notebooks = ft.filter_by_specification(notebooks, 'TypeName', TypeName)
if (ram):
notebooks = ft.filter_by_specification(notebooks, "Ram", ram)
if (memory):
notebooks = ft.filter_by_cpu(notebooks, memory)
if (searchEntry.get() != "" and searchEntry.get() != "Search"):
# print(notebooks)
notebooks = ft.search(searchEntry.get(), notebooks)
print(searchEntry.get())
if (ram == [] and memory == [] and TypeName == [] and company == []
and OpSys == [] and higher_price.get() == ''
and lowest_price.get() == '' and searchEntry.get() == ''
and searchEntry == "Search"):
notebooks = ft.df_to_dict(companies, collaborations, products)
lbox.delete(0, END)
if (searchEntry.get() == "" or searchEntry.get() == "Search"):
for i in ft.strings(notebooks):
lbox.insert(0, i)
lbox.insert(0, '')
else:
for i in notebooks.values():
lbox.insert(0, i)
lbox.insert(0, '')
input(
"\nIntroduce an id for this password (Number between 1 and 1000): "
))
service = input("Introduce the service (Facebook, Gmail...): ")
user = input("Introduce your user: "******"Introduce your password: "******"2":
functions.showAll()
elif opcionMenu == "3":
search_service = input(
"\nOk, introduce the name of the service : ")
functions.search(search_service)
elif opcionMenu == "4":
id_edit = int(
input(
"\nIntroduce the id of the user/password you want to edit : "
))
user_edit = input("Introduce the new username : "******"Introduce the new password : "******"5":
id_remove = int(
input(
"\nOk, introduce the id of the password you want to remove : "
))
def search():
functions.search('all', '', '')
import functions as f A = f.gerarLista() f.search(A)
def main():
#GENERAL
torch.cuda.empty_cache()
root = "/home/kuru/Desktop/veri-gms-master_noise/"
train_dir = '/home/kuru/Desktop/veri-gms-master_noise/VeRispan/image_train/'
source = {'verispan'}
target = {'verispan'}
workers = 4
height = 280
width = 280
train_size = 32
train_sampler = 'RandomSampler'
#AUGMENTATION
random_erase = True
jitter = True
aug = True
#OPTIMIZATION
opt = 'adam'
lr = 0.0003
weight_decay = 5e-4
momentum = 0.9
sgd_damp = 0.0
nesterov = True
warmup_factor = 0.01
warmup_method = 'linear'
#HYPERPARAMETER
max_epoch = 80
start = 0
train_batch_size = 8
test_batch_size = 100
#SCHEDULER
lr_scheduler = 'multi_step'
stepsize = [30, 60]
gamma = 0.1
#LOSS
margin = 0.3
num_instances = 4
lambda_tri = 1
#MODEL
#arch = 'resnet101'
arch='resnet101_ibn_a'
no_pretrained = False
#TEST SETTINGS
load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet101_ibn_a.pth'
#load_weights = None
start_eval = 0
eval_freq = -1
#MISC
use_gpu = True
print_freq = 10
seed = 1
resume = ''
save_dir = '/home/kuru/Desktop/veri-gms-master_noise/spanningtree_verinoise_101_stride2/'
gpu_id = 0,1
vis_rank = True
query_remove = True
evaluate = False
dataset_kwargs = {
'source_names': source,
'target_names': target,
'root': root,
'height': height,
'width': width,
'train_batch_size': train_batch_size,
'test_batch_size': test_batch_size,
'train_sampler': train_sampler,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
transform_kwargs = {
'height': height,
'width': width,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
optimizer_kwargs = {
'optim': opt,
'lr': lr,
'weight_decay': weight_decay,
'momentum': momentum,
'sgd_dampening': sgd_damp,
'sgd_nesterov': nesterov
}
lr_scheduler_kwargs = {
'lr_scheduler': lr_scheduler,
'stepsize': stepsize,
'gamma': gamma
}
use_gpu = torch.cuda.is_available()
log_name = 'log_test.txt' if evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(save_dir, log_name))
print('Currently using GPU ', gpu_id)
cudnn.benchmark = True
print('Initializing image data manager')
dataset = init_imgreid_dataset(root='/home/kuru/Desktop/veri-gms-master_noise/', name='verispan')
train = []
num_train_pids = 0
num_train_cams = 0
print(len( dataset.train))
for img_path, pid, camid, subid, countid in dataset.train:
#print(img_path)
path = img_path[56+6:90+6]
#print(path)
folder = path[1:4]
#print(folder)
pid += num_train_pids
newidd=0
train.append((path, folder, pid, camid,subid,countid))
num_train_pids += dataset.num_train_pids
num_train_cams += dataset.num_train_cams
pid = 0
pidx = {}
for img_path, pid, camid, subid, countid in dataset.train:
path = img_path[56+6:90+6]
folder = path[1:4]
pidx[folder] = pid
pid+= 1
sub=[]
final=0
xx=dataset.train
newids=[]
print(train[0:2])
train2={}
for k in range(0,770):
for img_path, pid, camid, subid, countid in dataset.train:
if k==pid:
newid=final+subid
sub.append(newid)
#print(pid,subid,newid)
newids.append(newid)
train2[img_path]= newid
#print(img_path, pid, camid, subid, countid, newid)
final=max(sub)
#print(final)
print(len(newids),final)
#train=train2
#print(train2)
train3=[]
for img_path, pid, camid, subid, countid in dataset.train:
#print(img_path,pid,train2[img_path])
path = img_path[56:90+6]
#print(path)
folder = path[1:4]
newid=train2[img_path]
#print((path, folder, pid, camid, subid, countid,newid ))
train3.append((path, folder, pid, camid, subid, countid,newid ))
train = train3
path = '/home/kuru/Desktop/adhi/veri-final-draft-master_noise/gmsNoise776/'
pkl = {}
#pkl[0] = pickle.load('/home/kuru/Desktop/veri-gms-master/gms/620.pkl')
entries = os.listdir(path)
for name in entries:
f = open((path+name), 'rb')
ccc=(path+name)
#print(ccc)
if name=='featureMatrix.pkl':
s = name[0:13]
else:
s = name[0:3]
#print(s)
#with open (ccc,"rb") as ff:
# pkl[s] = pickle.load(ff)
#print(pkl[s])
pkl[s] = pickle.load(f)
f.close
#print(len(pkl))
with open('cids.pkl', 'rb') as handle:
b = pickle.load(handle)
#print(b)
with open('index.pkl', 'rb') as handle:
c = pickle.load(handle)
transform_t = train_transforms(**transform_kwargs)
data_tfr = vdspan(pkl_file='index_veryspan_noise.pkl', dataset = train, root_dir='/home/kuru/Desktop/veri-gms-master_noise/VeRispan/image_train/', transform=transform_t)
print("lllllllllllllllllllllllllllllllllllllllllllline 433")
df2=[]
data_tfr_old=data_tfr
for (img,label,index,pid, cid,subid,countid,newid) in data_tfr :
#print((img,label,index,pid, cid,subid,countid,newid) )
#print("datframe",(label))
#print(countid)
if countid > 4 :
#print(countid)
df2.append((img,label,index,pid, cid,subid,countid,newid))
print("filtered final trainset length",len(df2))
data_tfr=df2
trainloader = DataLoader(data_tfr, sampler=None,batch_size=train_batch_size, shuffle=True, num_workers=workers,pin_memory=True, drop_last=True)
#data_tfr = vd(pkl_file='index.pkl', dataset = train, root_dir=train_dir,transform=transforms.Compose([Rescale(64),RandomCrop(32),ToTensor()]))
#dataloader = DataLoader(data_tfr, batch_size=batch_size, shuffle=False, num_workers=0)
for batch_idx, (img,label,index,pid, cid,subid,countid,newid) in enumerate(trainloader):
#print("trainloader",batch_idx, (label,index,pid, cid,subid,countid,newid))
print("trainloader",batch_idx, (label))
break
print('Initializing test data manager')
dm = ImageDataManager(use_gpu, **dataset_kwargs)
testloader_dict = dm.return_dataloaders()
print('Initializing model: {}'.format(arch))
model = models.init_model(name=arch, num_classes=num_train_pids, loss={'xent', 'htri'},
pretrained=not no_pretrained, last_stride =2 )
print('Model size: {:.3f} M'.format(count_num_param(model)))
if load_weights is not None:
print("weights loaded")
load_pretrained_weights(model, load_weights)
print(torch.cuda.device_count())
model = nn.DataParallel(model).cuda() if use_gpu else model
optimizer = init_optimizer(model, **optimizer_kwargs)
#optimizer = init_optimizer(model)
scheduler = init_lr_scheduler(optimizer, **lr_scheduler_kwargs)
criterion_xent = CrossEntropyLoss(num_classes=num_train_pids, use_gpu=use_gpu, label_smooth=True)
criterion_htri = TripletLoss(margin=margin)
ranking_loss = nn.MarginRankingLoss(margin = margin)
if evaluate:
print('Evaluate only')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
_, distmat = test(model, queryloader, galleryloader, train_batch_size, use_gpu, return_distmat=True)
if vis_rank:
visualize_ranked_results(
distmat, dm.return_testdataset_by_name(name),
save_dir=osp.join(save_dir, 'ranked_results', name),
topk=20
)
return
time_start = time.time()
ranklogger = RankLogger(source, target)
print('=> Start training')
data_index = search(pkl)
print(len(data_index))
for epoch in range(start, max_epoch):
losses = AverageMeter()
#xent_losses = AverageMeter()
htri_losses = AverageMeter()
accs = AverageMeter()
batch_time = AverageMeter()
xent_losses=AverageMeter()
model.train()
for p in model.parameters():
p.requires_grad = True # open all layers
end = time.time()
for batch_idx, (img,label,index,pid, cid,subid,countid,newid) in enumerate(trainloader):
trainX, trainY = torch.zeros((train_batch_size*3,3,height, width), dtype=torch.float32), torch.zeros((train_batch_size*3), dtype = torch.int64)
#pids = torch.zeros((batch_size*3), dtype = torch.int16)
for i in range(train_batch_size):
#print("dfdsfs")
labelx = label[i]
indexx = index[i]
cidx = pid[i]
if indexx >len(pkl[labelx])-1:
indexx = len(pkl[labelx])-1
#maxx = np.argmax(pkl[labelx][indexx])
a = pkl[labelx][indexx]
minpos = np.argmin(ma.masked_where(a==0, a))
#print(minpos)
#print(np.array(data_index).shape)
#print(data_index[cidx][1])
pos_dic = data_tfr_old[data_index[cidx][1]+minpos]
neg_label = int(labelx)
while True:
neg_label = random.choice(range(1, 770))
#print(neg_label)
if neg_label is not int(labelx) and os.path.isdir(os.path.join('/home/kuru/Desktop/adiusb/veri-split/train', strint(neg_label))) is True:
break
negative_label = strint(neg_label)
neg_cid = pidx[negative_label]
neg_index = random.choice(range(0, len(pkl[negative_label])))
neg_dic = data_tfr_old[data_index[neg_cid][1]+neg_index]
trainX[i] = img[i]
trainX[i+train_batch_size] = pos_dic[0]
trainX[i+(train_batch_size*2)] = neg_dic[0]
trainY[i] = cidx
trainY[i+train_batch_size] = pos_dic[3]
trainY[i+(train_batch_size*2)] = neg_dic[3]
trainX = trainX.cuda()
trainY = trainY.cuda()
outputs, features = model(trainX)
xent_loss = criterion_xent(outputs[0:train_batch_size], trainY[0:train_batch_size])
htri_loss = criterion_htri(features, trainY)
#tri_loss = ranking_loss(features)
#ent_loss = xent_loss(outputs[0:batch_size], trainY[0:batch_size], num_train_pids)
loss = htri_loss+xent_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
losses.update(loss.item(), trainY.size(0))
htri_losses.update(htri_loss.item(), trainY.size(0))
xent_losses.update(xent_loss.item(), trainY.size(0))
accs.update(accuracy(outputs[0:train_batch_size], trainY[0:train_batch_size])[0])
if (batch_idx) % 50 == 0:
print('Train ', end=" ")
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'TriLoss {loss.val:.4f} ({loss.avg:.4f})\t'
'XLoss {xloss.val:.4f} ({xloss.avg:.4f})\t'
'OveralLoss {oloss.val:.4f} ({oloss.avg:.4f})\t'
'Acc {acc.val:.2f} ({acc.avg:.2f})\t'
'lr {lrrr} \t'.format(
epoch + 1, batch_idx + 1, len(trainloader),
batch_time=batch_time,
loss = htri_losses,
xloss = xent_losses,
oloss = losses,
acc=accs ,
lrrr=lrrr,
))
end = time.time()
scheduler.step()
print('=> Test')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader, test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
rank2, distmat2 = test_rerank(model, queryloader, galleryloader, test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank2)
#if (epoch + 1) == max_epoch:
if (epoch + 1) % 2 == 0:
print('=> Test')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader, test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
# if vis_rank:
# visualize_ranked_results(
# distmat, dm.return_testdataset_by_name(name),
# save_dir=osp.join(save_dir, 'ranked_results', name),
# topk=20)
save_checkpoint({
'state_dict': model.state_dict(),
'rank1': rank1,
'epoch': epoch + 1,
'arch': arch,
'optimizer': optimizer.state_dict(),
}, save_dir)
def main():
torch.backends.cudnn.deterministic = True
cudnn.benchmark = True
#parser = argparse.ArgumentParser(description="ReID Baseline Training")
#parser.add_argument(
#"--config_file", default="", help="path to config file", type=str)
#parser.add_argument("opts", help="Modify config options using the command-line", default=None, nargs=argparse.REMAINDER)
#args = parser.parse_args()
config_file = 'configs/baseline_veri_r101_a.yml'
if config_file != "":
cfg.merge_from_file(config_file)
#cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = setup_logger("reid_baseline", output_dir, if_train=True)
logger.info("Saving model in the path :{}".format(cfg.OUTPUT_DIR))
logger.info(config_file)
if config_file != "":
logger.info("Loaded configuration file {}".format(config_file))
with open(config_file, 'r') as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
path = 'D:/Python_SMU/Veri/verigms/gms/'
pkl = {}
entries = os.listdir(path)
for name in entries:
f = open((path + name), 'rb')
if name == 'featureMatrix.pkl':
s = name[0:13]
else:
s = name[0:3]
pkl[s] = pickle.load(f)
f.close
with open('cids.pkl', 'rb') as handle:
b = pickle.load(handle)
with open('index.pkl', 'rb') as handle:
c = pickle.load(handle)
train_transforms, val_transforms, dataset, train_set, val_set = make_dataset(
cfg, pkl_file='index.pkl')
num_workers = cfg.DATALOADER.NUM_WORKERS
num_classes = dataset.num_train_pids
#pkl_f = 'index.pkl'
pid = 0
pidx = {}
for img_path, pid, _, _ in dataset.train:
path = img_path.split('\\')[-1]
folder = path[1:4]
pidx[folder] = pid
pid += 1
if 'triplet' in cfg.DATALOADER.SAMPLER:
train_loader = DataLoader(train_set,
batch_size=cfg.SOLVER.IMS_PER_BATCH,
sampler=RandomIdentitySampler(
dataset.train, cfg.SOLVER.IMS_PER_BATCH,
cfg.DATALOADER.NUM_INSTANCE),
num_workers=num_workers,
pin_memory=True,
collate_fn=train_collate_fn)
elif cfg.DATALOADER.SAMPLER == 'softmax':
print('using softmax sampler')
train_loader = DataLoader(train_set,
batch_size=cfg.SOLVER.IMS_PER_BATCH,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
collate_fn=train_collate_fn)
else:
print('unsupported sampler! expected softmax or triplet but got {}'.
format(cfg.SAMPLER))
print("train loader loaded successfully")
val_loader = DataLoader(val_set,
batch_size=cfg.TEST.IMS_PER_BATCH,
shuffle=False,
num_workers=num_workers,
pin_memory=True,
collate_fn=train_collate_fn)
print("val loader loaded successfully")
if cfg.MODEL.PRETRAIN_CHOICE == 'finetune':
model = make_model(cfg, num_class=576)
model.load_param_finetune(cfg.MODEL.PRETRAIN_PATH)
print('Loading pretrained model for finetuning......')
else:
model = make_model(cfg, num_class=num_classes)
loss_func, center_criterion = make_loss(cfg, num_classes=num_classes)
optimizer, optimizer_center = make_optimizer(cfg, model, center_criterion)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_EPOCHS,
cfg.SOLVER.WARMUP_METHOD)
print("model,optimizer, loss, scheduler loaded successfully")
height, width = cfg.INPUT.SIZE_TRAIN
log_period = cfg.SOLVER.LOG_PERIOD
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
eval_period = cfg.SOLVER.EVAL_PERIOD
device = "cuda"
epochs = cfg.SOLVER.MAX_EPOCHS
logger = logging.getLogger("reid_baseline.train")
logger.info('start training')
if device:
if torch.cuda.device_count() > 1:
print('Using {} GPUs for training'.format(
torch.cuda.device_count()))
model = nn.DataParallel(model)
model.to(device)
loss_meter = AverageMeter()
acc_meter = AverageMeter()
evaluator = R1_mAP_eval(len(dataset.query),
max_rank=50,
feat_norm=cfg.TEST.FEAT_NORM)
model.base._freeze_stages()
logger.info('Freezing the stages number:{}'.format(cfg.MODEL.FROZEN))
data_index = search(pkl)
print("Ready for training")
for epoch in range(1, epochs + 1):
start_time = time.time()
loss_meter.reset()
acc_meter.reset()
evaluator.reset()
scheduler.step()
model.train()
for n_iter, (img, label, index, pid, cid) in enumerate(train_loader):
optimizer.zero_grad()
optimizer_center.zero_grad()
#img = img.to(device)
#target = vid.to(device)
trainX, trainY = torch.zeros(
(train_loader.batch_size * 3, 3, height, width),
dtype=torch.float32), torch.zeros(
(train_loader.batch_size * 3), dtype=torch.int64)
for i in range(train_loader.batch_size):
labelx = label[i]
indexx = index[i]
cidx = pid[i]
if indexx > len(pkl[labelx]) - 1:
indexx = len(pkl[labelx]) - 1
a = pkl[labelx][indexx]
minpos = np.argmin(ma.masked_where(a == 0, a))
pos_dic = train_set[data_index[cidx][1] + minpos]
#print(pos_dic[1])
neg_label = int(labelx)
while True:
neg_label = random.choice(range(1, 770))
if neg_label is not int(labelx) and os.path.isdir(
os.path.join('D:/datasets/veri-split/train',
strint(neg_label))) is True:
break
negative_label = strint(neg_label)
neg_cid = pidx[negative_label]
neg_index = random.choice(range(0, len(pkl[negative_label])))
neg_dic = train_set[data_index[neg_cid][1] + neg_index]
trainX[i] = img[i]
trainX[i + train_loader.batch_size] = pos_dic[0]
trainX[i + (train_loader.batch_size * 2)] = neg_dic[0]
trainY[i] = cidx
trainY[i + train_loader.batch_size] = pos_dic[3]
trainY[i + (train_loader.batch_size * 2)] = neg_dic[3]
#print(trainY)
trainX = trainX.cuda()
trainY = trainY.cuda()
score, feat = model(trainX, trainY)
loss = loss_func(score, feat, trainY)
loss.backward()
optimizer.step()
if 'center' in cfg.MODEL.METRIC_LOSS_TYPE:
for param in center_criterion.parameters():
param.grad.data *= (1. / cfg.SOLVER.CENTER_LOSS_WEIGHT)
optimizer_center.step()
acc = (score.max(1)[1] == trainY).float().mean()
loss_meter.update(loss.item(), img.shape[0])
acc_meter.update(acc, 1)
if (n_iter + 1) % log_period == 0:
logger.info(
"Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}"
.format(epoch, (n_iter + 1), len(train_loader),
loss_meter.avg, acc_meter.avg,
scheduler.get_lr()[0]))
end_time = time.time()
time_per_batch = (end_time - start_time) / (n_iter + 1)
logger.info(
"Epoch {} done. Time per batch: {:.3f}[s] Speed: {:.1f}[samples/s]"
.format(epoch, time_per_batch,
train_loader.batch_size / time_per_batch))
if epoch % checkpoint_period == 0:
torch.save(
model.state_dict(),
os.path.join(cfg.OUTPUT_DIR,
cfg.MODEL.NAME + '_{}.pth'.format(epoch)))
if epoch % eval_period == 0:
model.eval()
for n_iter, (img, vid, camid, _, _) in enumerate(val_loader):
with torch.no_grad():
img = img.to(device)
feat = model(img)
evaluator.update((feat, vid, camid))
cmc, mAP, _, _, _, _, _ = evaluator.compute()
logger.info("Validation Results - Epoch: {}".format(epoch))
logger.info("mAP: {:.1%}".format(mAP))
for r in [1, 5, 10]:
logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(
r, cmc[r - 1]))
def main():
#GENERAL
torch.cuda.empty_cache()
root = "/home/kuru/Desktop/veri-gms-master/"
train_dir = '/home/kuru/Desktop/veri-gms-master/VeRispan/image_train/'
source = {'verispan'}
target = {'verispan'}
workers = 4
height = 320
width = 320
train_sampler = 'RandomSampler'
#AUGMENTATION
random_erase = True
jitter = True
aug = True
#OPTIMIZATION
opt = 'adam'
lr = 0.001
weight_decay = 5e-4
momentum = 0.9
sgd_damp = 0.0
nesterov = True
warmup_factor = 0.01
warmup_method = 'linear'
STEPS = (30, 60)
GAMMA = 0.1
WARMUP_FACTOR = 0.01
WARMUP_EPOCHS = 10
WARMUP_METHOD = 'linear'
#HYPERPARAMETER
max_epoch = 80
start = 0
train_batch_size = 16
test_batch_size = 50
#SCHEDULER
lr_scheduler = 'multi_step'
stepsize = [30, 60]
gamma = 0.1
#LOSS
margin = 0.3
num_instances = 4
lambda_tri = 1
#MODEL
#arch = 'resnet101'
arch = 'resnet50_ibn_a'
no_pretrained = False
#TEST SETTINGS
#load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet101_ibn_a.pth'
#load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet101_ibn_a.pth'
load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet50_ibn_a.pth'
#load_weights = None
start_eval = 0
eval_freq = -1
num_classes = 776
feat_dim = 2048
CENTER_LR = 0.5
CENTER_LOSS_WEIGHT = 0.0005
center_criterion = CenterLoss(num_classes=num_classes,
feat_dim=feat_dim,
use_gpu=True)
optimizer_center = torch.optim.SGD(center_criterion.parameters(),
lr=CENTER_LR)
#MISC
use_gpu = True
#use_gpu = False
print_freq = 10
seed = 1
resume = ''
save_dir = '/home/kuru/Desktop/veri-gms-master_noise/spanningtree_veri_pure/'
gpu_id = 0, 1
vis_rank = True
query_remove = True
evaluate = False
dataset_kwargs = {
'source_names': source,
'target_names': target,
'root': root,
'height': height,
'width': width,
'train_batch_size': train_batch_size,
'test_batch_size': test_batch_size,
'train_sampler': train_sampler,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
transform_kwargs = {
'height': height,
'width': width,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
optimizer_kwargs = {
'optim': opt,
'lr': lr,
'weight_decay': weight_decay,
'momentum': momentum,
'sgd_dampening': sgd_damp,
'sgd_nesterov': nesterov
}
lr_scheduler_kwargs = {
'lr_scheduler': lr_scheduler,
'stepsize': stepsize,
'gamma': gamma
}
use_gpu = torch.cuda.is_available()
log_name = 'log_test.txt' if evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(save_dir, log_name))
print('Currently using GPU ', gpu_id)
cudnn.benchmark = True
print('Initializing image data manager')
#dataset = init_imgreid_dataset(root='/home/kuru/Desktop/veri-gms-master/', name='veri')
dataset = init_imgreid_dataset(root='/home/kuru/Desktop/veri-gms-master/',
name='verispan')
train = []
num_train_pids = 0
num_train_cams = 0
print(len(dataset.train))
for img_path, pid, camid, subid, countid in dataset.train:
#print(img_path)
path = img_path[56:90 + 6]
#print(path)
folder = path[1:4]
#print(folder)
#print(img_path, pid, camid,subid,countid)
pid += num_train_pids
camid += num_train_cams
newidd = 0
train.append((path, folder, pid, camid, subid, countid))
#print(train)
#break
num_train_pids += dataset.num_train_pids
num_train_cams += dataset.num_train_cams
pid = 0
pidx = {}
for img_path, pid, camid, subid, countid in dataset.train:
path = img_path[56:90 + 6]
folder = path[1:4]
pidx[folder] = pid
pid += 1
#print(pidx)
sub = []
final = 0
xx = dataset.train
newids = []
print(train[0:2])
train2 = {}
for k in range(0, 770):
for img_path, pid, camid, subid, countid in dataset.train:
if k == pid:
newid = final + subid
sub.append(newid)
#print(pid,subid,newid)
newids.append(newid)
train2[img_path] = newid
#print(img_path, pid, camid, subid, countid, newid)
final = max(sub)
#print(final)
print(len(newids), final)
#train=train2
#print(train2)
train3 = []
for img_path, pid, camid, subid, countid in dataset.train:
#print(img_path,pid,train2[img_path])
path = img_path[56:90 + 6]
#print(path)
folder = path[1:4]
newid = train2[img_path]
#print((path, folder, pid, camid, subid, countid,newid ))
train3.append((path, folder, pid, camid, subid, countid, newid))
train = train3
# for (path, folder, pid, camid, subid, countid,newid) in train:
# print(path, folder)
#path = '/home/kuru/Desktop/adhi/veri-final-draft-master_noise/gmsNoise776/'
path = '/home/kuru/Desktop/veri-gms-master/gms/'
pkl = {}
#pkl[0] = pickle.load('/home/kuru/Desktop/veri-gms-master/gms/620.pkl')
entries = os.listdir(path)
for name in entries:
f = open((path + name), 'rb')
ccc = (path + name)
#print(ccc)
if name == 'featureMatrix.pkl':
s = name[0:13]
else:
s = name[0:3]
#print(s)
#with open (ccc,"rb") as ff:
# pkl[s] = pickle.load(ff)
#print(pkl[s])
pkl[s] = pickle.load(f)
f.close
#print(len(pkl))
print('=> pickle indexing')
data_index = search(pkl)
print(len(data_index))
transform_t = train_transforms(**transform_kwargs)
#print(train[0],train[10])
#data_tfr = vd(pkl_file='index.pkl', dataset = train, root_dir='/home/kuru/Desktop/veri-gms-master/VeRi/image_train/', transform=transform_t)
data_tfr = vdspan(
pkl_file='index_veryspan.pkl',
dataset=train,
root_dir='/home/kuru/Desktop/veri-gms-master/VeRispan/image_train/',
transform=transform_t)
#print(data_tfr)
#print(trainloader)
#data_tfr2=list(data_tfr)
print("lllllllllllllllllllllllllllllllllllllllllllline 433")
df2 = []
data_tfr_old = data_tfr
for (img, label, index, pid, cid, subid, countid, newid) in data_tfr:
#print((img,label,index,pid, cid,subid,countid,newid) )
#print("datframe",(label))
#print(countid)
if countid > 4:
#print(countid)
df2.append((img, label, index, pid, cid, subid, countid, newid))
print("filtered final trainset length", len(df2))
data_tfr = df2
# with open('df2noise_ex.pkl', 'wb') as handle:
# b = pickle.dump(df2, handle, protocol=pickle.HIGHEST_PROTOCOL)
# with open('df2noise.pkl', 'rb') as handle:
# df2 = pickle.load(handle)
# data_tfr=df2
# for (img,label,index,pid, cid,subid,countid,newid) in data_tfr :
# print("datframe",(label))
#data_tfr = vdspansort( dataset = train, root_dir='/home/kuru/Desktop/veri-gms-master_noise/VeRispan/image_train/', transform=transform_t)
#trainloader = DataLoader(df2, sampler=None,batch_size=train_batch_size, shuffle=True, num_workers=workers,pin_memory=True, drop_last=True)
trainloader = DataLoader(data_tfr,
sampler=None,
batch_size=train_batch_size,
shuffle=True,
num_workers=workers,
pin_memory=True,
drop_last=True)
for batch_idx, (img, label, index, pid, cid, subid, countid,
newid) in enumerate(trainloader):
#print("trainloader",batch_idx, (label,index,pid, cid,subid,countid,newid))
print("trainloader", batch_idx, (label))
break
print('Initializing test data manager')
dm = ImageDataManager(use_gpu, **dataset_kwargs)
testloader_dict = dm.return_dataloaders()
print('Initializing model: {}'.format(arch))
model = models.init_model(name=arch,
num_classes=num_train_pids,
loss={'xent', 'htri'},
pretrained=not no_pretrained,
last_stride=2)
print('Model size: {:.3f} M'.format(count_num_param(model)))
if load_weights is not None:
print("weights loaded")
load_pretrained_weights(model, load_weights)
#checkpoint = torch.load('/home/kuru/Desktop/veri-gms-master/logg/model.pth.tar-19')
#model._load_from_state_dict(checkpoint['state_dict'])
#model.load_state_dict(checkpoint['state_dict'])
#optimizer.load_state_dict(checkpoint['optimizer'])
#print(checkpoint['epoch'])
#print(checkpoint['rank1'])
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
print(torch.cuda.device_count())
model = nn.DataParallel(model).cuda() if use_gpu else model
optimizer = init_optimizer(model, **optimizer_kwargs)
#optimizer = init_optimizer(model)
#optimizer.load_state_dict(checkpoint['optimizer'])
scheduler = init_lr_scheduler(optimizer, **lr_scheduler_kwargs)
# scheduler = WarmupMultiStepLR(optimizer, STEPS, GAMMA,
# WARMUP_FACTOR,
# WARMUP_EPOCHS, WARMUP_METHOD)
criterion_xent = CrossEntropyLoss(num_classes=num_train_pids,
use_gpu=use_gpu,
label_smooth=True)
criterion_htri = TripletLoss(margin=margin)
ranking_loss = nn.MarginRankingLoss(margin=margin)
if evaluate:
print('Evaluate only')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
_, distmat = test(model,
queryloader,
galleryloader,
train_batch_size,
use_gpu,
return_distmat=True)
if vis_rank:
visualize_ranked_results(distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(
save_dir, 'ranked_results', name),
topk=20)
return
time_start = time.time()
ranklogger = RankLogger(source, target)
# # checkpoint = torch.load('/home/kuru/Desktop/market_all/ibna_model/model.pth.tar-79')
# # model.load_state_dict(checkpoint['state_dict'])
# # optimizer.load_state_dict(checkpoint['optimizer'])
# # print(checkpoint['epoch'])
# # start_epoch=checkpoint['epoch']
# # start=start_epoch
# checkpoint = torch.load('/home/kuru/Desktop/veri-gms-master/spanningtreeveri/model.pth.tar-2')
# model.load_state_dict(checkpoint['state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer'])
# print(checkpoint['epoch'])
# start_epoch=checkpoint['epoch']
# start=start_epoch
##start_epoch=resume_from_checkpoint('/home/kuru/Desktop/veri-gms-master/logg/model.pth.tar-20', model, optimizer=None)
print('=> Start training')
for epoch in range(start, max_epoch):
print(epoch, scheduler.get_lr()[0])
#print( torch.cuda.memory_allocated(0))
losses = AverageMeter()
#xent_losses = AverageMeter()
htri_losses = AverageMeter()
accs = AverageMeter()
batch_time = AverageMeter()
xent_losses = AverageMeter()
model.train()
for p in model.parameters():
p.requires_grad = True # open all layers
end = time.time()
for batch_idx, (img, label, index, pid, cid, subid, countid,
newid) in enumerate(trainloader):
trainX, trainY = torch.zeros(
(train_batch_size * 3, 3, height, width),
dtype=torch.float32), torch.zeros((train_batch_size * 3),
dtype=torch.int64)
#pids = torch.zeros((batch_size*3), dtype = torch.int16)
#batchcount=0
for i in range(train_batch_size):
if (countid[i] > 4):
#batchcount=batchcount+1
#print("dfdsfs")
labelx = label[i]
indexx = index[i]
cidx = pid[i]
if indexx > len(pkl[labelx]) - 1:
indexx = len(pkl[labelx]) - 1
#maxx = np.argmax(pkl[labelx][indexx])
a = pkl[labelx][indexx]
minpos = np.argmin(ma.masked_where(a == 0, a))
# print(len(a))
# print(a)
# print(ma.masked_where(a==0, a))
# print(labelx,index,pid,cidx,minpos)
# print(np.array(data_index).shape)
# print(data_index[cidx][1])
pos_dic = data_tfr_old[data_index[cidx][1] + minpos]
#print('posdic', pos_dic)
neg_label = int(labelx)
while True:
neg_label = random.choice(range(1, 770))
#print(neg_label)
if neg_label is not int(labelx) and os.path.isdir(
os.path.join(
'/home/kuru/Desktop/veri-gms-master_noise/veriNoise_train_spanning_folder',
strint(neg_label))) is True:
break
negative_label = strint(neg_label)
neg_cid = pidx[negative_label]
neg_index = random.choice(
range(0, len(pkl[negative_label])))
#print(negative_label,neg_cid,neg_index,data_index[neg_cid] )
neg_dic = data_tfr_old[data_index[neg_cid][1] + neg_index]
#print('negdic', neg_dic)
trainX[i] = img[i]
trainX[i + train_batch_size] = pos_dic[0]
trainX[i + (train_batch_size * 2)] = neg_dic[0]
trainY[i] = cidx
trainY[i + train_batch_size] = pos_dic[3]
trainY[i + (train_batch_size * 2)] = neg_dic[3]
# trainY[i+train_batch_size] = pos_dic[7]
# trainY[i+(train_batch_size*2)] = neg_dic[7]
#break
# else:
# print("skiped",countid[i],subid[i],label[i])
#break
#print(batchcount)
trainX = trainX.cuda()
trainY = trainY.cuda()
outputs, features = model(trainX)
xent_loss = criterion_xent(outputs[0:train_batch_size],
trainY[0:train_batch_size])
htri_loss = criterion_htri(features, trainY)
centerloss = CENTER_LOSS_WEIGHT * center_criterion(
features, trainY)
#tri_loss = ranking_loss(features)
#ent_loss = xent_loss(outputs[0:batch_size], trainY[0:batch_size], num_train_pids)
loss = htri_loss + xent_loss + centerloss
loss = htri_loss + xent_loss
optimizer.zero_grad()
optimizer_center.zero_grad()
loss.backward()
optimizer.step()
# for param in center_criterion.parameters():
# param.grad.data *= (1. /CENTER_LOSS_WEIGHT)
# optimizer_center.step()
for param_group in optimizer.param_groups:
#print(param_group['lr'] )
lrrr = str(param_group['lr'])
batch_time.update(time.time() - end)
losses.update(loss.item(), trainY.size(0))
htri_losses.update(htri_loss.item(), trainY.size(0))
xent_losses.update(xent_loss.item(), trainY.size(0))
accs.update(
accuracy(outputs[0:train_batch_size],
trainY[0:train_batch_size])[0])
if (batch_idx) % 50 == 0:
print('Train ', end=" ")
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'TriLoss {loss.val:.4f} ({loss.avg:.4f})\t'
'XLoss {xloss.val:.4f} ({xloss.avg:.4f})\t'
'OveralLoss {oloss.val:.4f} ({oloss.avg:.4f})\t'
'Acc {acc.val:.2f} ({acc.avg:.2f})\t'
'lr {lrrr} \t'.format(
epoch + 1,
batch_idx + 1,
len(trainloader),
batch_time=batch_time,
loss=htri_losses,
xloss=xent_losses,
oloss=losses,
acc=accs,
lrrr=lrrr,
))
end = time.time()
# del loss
# del htri_loss
# del xent_loss
# del htri_losses
# del losses
# del outputs
# del features
# del accs
# del trainX
# del trainY
scheduler.step()
print('=> Test')
save_checkpoint(
{
'state_dict': model.state_dict(),
#'rank1': rank1,
'epoch': epoch + 1,
'arch': arch,
'optimizer': optimizer.state_dict(),
},
save_dir)
GPUtil.showUtilization()
print(torch.cuda.memory_allocated(), torch.cuda.memory_cached())
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
rank2, distmat2 = test_rerank(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank2)
del queryloader
del galleryloader
del distmat
print(torch.cuda.memory_allocated(), torch.cuda.memory_cached())
torch.cuda.empty_cache()
if (epoch + 1) == max_epoch:
#if (epoch + 1) % 10 == 0:
print('=> Test')
save_checkpoint(
{
'state_dict': model.state_dict(),
'rank1': rank1,
'epoch': epoch + 1,
'arch': arch,
'optimizer': optimizer.state_dict(),
}, save_dir)
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
# del queryloader
# del galleryloader
# del distmat
if vis_rank:
visualize_ranked_results(
distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(save_dir, 'ranked_results', name),
topk=20)
else:
raise err.FieldNotProvidedError
except err.FieldNotProvidedError:
txt.print_red(
"FieldNotProvidedError: Cannot execute: not enough arguments!"
)
# if the command is 'search', try to get a substring and
# a string. If the substring is inside the string, print a
# formatted version of the search_success_string, notifying
# the user that it was successful. If it's not, notify the
# user that nothing was found. If some of the fields aren't
# provided, print an error message.
elif commands[0].lower() == "search" or commands[0].lower() == "gss":
try:
if len(commands) >= 3:
fn.search(commands, search_success_string)
else:
raise err.FieldNotProvidedError
except err.FieldNotProvidedError:
txt.print_red(
"FieldNotProvidedError: Cannot execute: not enough arguments!"
)
except err.InstanceNotFoundError:
txt.print_red(
"InstanceNotFoundError: No instances were found!")
# if the command is 'say-options', print each option in the
# say_options list.
elif commands[0].lower() == "say-options" or commands[0].lower(
) == "sopt":
fn.say_options()
# if the command is 'time' display current time using
def main():
#GENERAL
root = "/home/kuru/Desktop/veri-gms-master/"
train_dir = '/home/kuru/Desktop/veri-gms-master/VeRispan/image_train/'
source = {'veri'}
target = {'veri'}
workers = 2
height = 320
width = 320
train_sampler = 'RandomSampler'
#AUGMENTATION
random_erase = True
jitter = True
aug = True
#OPTIMIZATION
opt = 'adam'
lr = 0.0003
weight_decay = 5e-4
momentum = 0.9
sgd_damp = 0.0
nesterov = True
warmup_factor = 0.01
warmup_method = 'linear'
#HYPERPARAMETER
max_epoch = 80
start = 0
train_batch_size = 16
test_batch_size = 50
#SCHEDULER
lr_scheduler = 'multi_step'
stepsize = [30, 60]
gamma = 0.1
#LOSS
margin = 0.3
num_instances = 6
lambda_tri = 1
#MODEL
arch = 'resnet101_ibn_a'
no_pretrained = False
#TEST SETTINGS
load_weights = '/home/kuru/Desktop/veri-gms-master/IBN-Net_pytorch0.4.1/resnet101_ibn_a.pth'
#load_weights = None
start_eval = 0
eval_freq = -1
#MISC
use_gpu = True
use_amp = True
print_freq = 50
seed = 1
resume = ''
save_dir = '/home/kuru/Desktop/veri-gms-master/logapex/'
gpu_id = 0
vis_rank = True
query_remove = True
evaluate = False
dataset_kwargs = {
'source_names': source,
'target_names': target,
'root': root,
'height': height,
'width': width,
'train_batch_size': train_batch_size,
'test_batch_size': test_batch_size,
'train_sampler': train_sampler,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
transform_kwargs = {
'height': height,
'width': width,
'random_erase': random_erase,
'color_jitter': jitter,
'color_aug': aug
}
optimizer_kwargs = {
'optim': opt,
'lr': lr,
'weight_decay': weight_decay,
'momentum': momentum,
'sgd_dampening': sgd_damp,
'sgd_nesterov': nesterov
}
lr_scheduler_kwargs = {
'lr_scheduler': lr_scheduler,
'stepsize': stepsize,
'gamma': gamma
}
use_gpu = torch.cuda.is_available()
log_name = 'log_test.txt' if evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(save_dir, log_name))
print('Currently using GPU ', gpu_id)
cudnn.benchmark = True
print('Initializing image data manager')
dataset = init_imgreid_dataset(root='/home/kuru/Desktop/veri-gms-master/',
name='veri')
train = []
num_train_pids = 0
num_train_cams = 0
for img_path, pid, camid in dataset.train:
path = img_path[52:77]
#print(path)
folder = path[1:4]
pid += num_train_pids
camid += num_train_cams
train.append((path, folder, pid, camid))
num_train_pids += dataset.num_train_pids
num_train_cams += dataset.num_train_cams
pid = 0
pidx = {}
for img_path, pid, camid in dataset.train:
path = img_path[52:77]
folder = path[1:4]
pidx[folder] = pid
pid += 1
path = '/home/kuru/Desktop/veri-gms-master/gms/'
pkl = {}
entries = os.listdir(path)
for name in entries:
f = open((path + name), 'rb')
if name == 'featureMatrix.pkl':
s = name[0:13]
else:
s = name[0:3]
pkl[s] = pickle.load(f)
f.close
transform_t = train_transforms(**transform_kwargs)
data_tfr = vd(
pkl_file='index.pkl',
dataset=train,
root_dir='/home/kuru/Desktop/veri-gms-master/VeRi/image_train/',
transform=transform_t)
trainloader = DataLoader(data_tfr,
sampler=None,
batch_size=train_batch_size,
shuffle=True,
num_workers=workers,
pin_memory=False,
drop_last=True)
#data_tfr = vd(pkl_file='index.pkl', dataset = train, root_dir=train_dir,transform=transforms.Compose([Rescale(64),RandomCrop(32),ToTensor()]))
#dataloader = DataLoader(data_tfr, batch_size=batch_size, shuffle=False, num_workers=0)
print('Initializing test data manager')
dm = ImageDataManager(use_gpu, **dataset_kwargs)
testloader_dict = dm.return_dataloaders()
print('Initializing model: {}'.format(arch))
model = models.init_model(name=arch,
num_classes=num_train_pids,
loss={'xent', 'htri'},
last_stride=1,
pretrained=not no_pretrained,
use_gpu=use_gpu)
print('Model size: {:.3f} M'.format(count_num_param(model)))
if load_weights is not None:
print("weights loaded")
load_pretrained_weights(model, load_weights)
model = (model).cuda() if use_gpu else model
#model = nn.DataParallel(model).cuda() if use_gpu else model
optimizer = init_optimizer(model, **optimizer_kwargs)
#optimizer = init_optimizer(model)
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O2",
keep_batchnorm_fp32=True,
loss_scale="dynamic")
model = nn.DataParallel(model).cuda() if use_gpu else model
scheduler = init_lr_scheduler(optimizer, **lr_scheduler_kwargs)
criterion_xent = CrossEntropyLoss(num_classes=num_train_pids,
use_gpu=use_gpu,
label_smooth=True)
criterion_htri = TripletLoss(margin=margin)
ranking_loss = nn.MarginRankingLoss(margin=margin)
if evaluate:
print('Evaluate only')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
_, distmat = test(model,
queryloader,
galleryloader,
train_batch_size,
use_gpu,
return_distmat=True)
if vis_rank:
visualize_ranked_results(distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(
save_dir, 'ranked_results', name),
topk=20)
return
time_start = time.time()
ranklogger = RankLogger(source, target)
print('=> Start training')
data_index = search(pkl)
for epoch in range(start, max_epoch):
losses = AverageMeter()
#xent_losses = AverageMeter()
htri_losses = AverageMeter()
accs = AverageMeter()
batch_time = AverageMeter()
model.train()
for p in model.parameters():
p.requires_grad = True # open all layers
end = time.time()
for batch_idx, (img, label, index, pid, cid) in enumerate(trainloader):
trainX, trainY = torch.zeros(
(train_batch_size * 3, 3, height, width),
dtype=torch.float32), torch.zeros((train_batch_size * 3),
dtype=torch.int64)
#pids = torch.zeros((batch_size*3), dtype = torch.int16)
for i in range(train_batch_size):
labelx = str(label[i])
indexx = int(index[i])
cidx = int(pid[i])
if indexx > len(pkl[labelx]) - 1:
indexx = len(pkl[labelx]) - 1
#maxx = np.argmax(pkl[labelx][indexx])
a = pkl[labelx][indexx]
minpos = np.argmax(ma.masked_where(a == 0, a))
pos_dic = data_tfr[data_index[cidx][1] + minpos]
neg_label = int(labelx)
while True:
neg_label = random.choice(range(1, 770))
if neg_label is not int(labelx) and os.path.isdir(
os.path.join(
'/home/kuru/Desktop/adiusb/veri-split/train',
strint(neg_label))) is True:
break
negative_label = strint(neg_label)
neg_cid = pidx[negative_label]
neg_index = random.choice(range(0, len(pkl[negative_label])))
neg_dic = data_tfr[data_index[neg_cid][1] + neg_index]
trainX[i] = img[i]
trainX[i + train_batch_size] = pos_dic[0]
trainX[i + (train_batch_size * 2)] = neg_dic[0]
trainY[i] = cidx
trainY[i + train_batch_size] = pos_dic[3]
trainY[i + (train_batch_size * 2)] = neg_dic[3]
#print("anc",labelx,'posdic', pos_dic[1],pos_dic[2],'negdic', neg_dic[1],neg_dic[2])
trainX = trainX.cuda()
trainY = trainY.cuda()
outputs, features = model(trainX)
xent_loss = criterion_xent(outputs[0:train_batch_size],
trainY[0:train_batch_size])
htri_loss = criterion_htri(features, trainY)
#tri_loss = ranking_loss(features)
#ent_loss = xent_loss(outputs[0:batch_size], trainY[0:batch_size], num_train_pids)
loss = htri_loss + xent_loss
optimizer.zero_grad()
if use_amp:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
#loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
losses.update(loss.item(), trainY.size(0))
htri_losses.update(htri_loss.item(), trainY.size(0))
accs.update(
accuracy(outputs[0:train_batch_size],
trainY[0:train_batch_size])[0])
if (batch_idx) % print_freq == 0:
print('Train ', end=" ")
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc {acc.val:.2f} ({acc.avg:.2f})\t'.format(
epoch + 1,
batch_idx + 1,
len(trainloader),
batch_time=batch_time,
loss=htri_losses,
acc=accs))
end = time.time()
scheduler.step()
print('=> Test')
for name in target:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1, distmat = test(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
rank2, distmat2 = test_rerank(model, queryloader, galleryloader,
test_batch_size, use_gpu)
ranklogger.write(name, epoch + 1, rank2)
del queryloader
del galleryloader
del distmat
#print(torch.cuda.memory_allocated(),torch.cuda.memory_cached())
torch.cuda.empty_cache()
if (epoch + 1) == max_epoch:
#if (epoch + 1) % 10 == 0:
print('=> Test')
save_checkpoint(
{
'state_dict': model.state_dict(),
'rank1': rank1,
'epoch': epoch + 1,
'arch': arch,
'optimizer': optimizer.state_dict(),
}, save_dir)
if vis_rank:
visualize_ranked_results(distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(
save_dir, 'ranked_results', name),
topk=20)
from functions import findXmls, getMeetingInfosFromXml, convertBBBMeeting2mp4, addLogos, search
from os.path import dirname, abspath, join
import os
import sys
os.chdir("/home/karabinaa/Desktop/pythondenemeleri/bbbrecorder/bbb-recorder")
pathList = findXmls("bbb18")
query = input("Sorguyu Giriniz : ")
searcher = search(query.replace("I", "ı").replace("İ", "i").lower())
matchDict = {}
key = 1
for path in pathList:
try:
ilist = getMeetingInfosFromXml(path)
except:
ilist = [""]
if searcher(ilist[0].replace("I", "ı").replace("İ", "i").lower()) == True:
matchDict.update({key: ilist})
key += 1
if len(query) > 0:
for key, value in matchDict.items():
print(str(key) + " : " + value[0])
selection = input(
