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Implemented various fixes to get the project runing #25

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108 changes: 56 additions & 52 deletions downloader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,16 +12,18 @@

from requests.exceptions import ConnectionError, ReadTimeout, TooManyRedirects, MissingSchema, InvalidURL

from functools import partial

parser = argparse.ArgumentParser(description='ImageNet image scraper')
parser.add_argument('-scrape_only_flickr', default=True, type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('-number_of_classes', default = 10, type=int)
parser.add_argument('-images_per_class', default = 10, type=int)
parser.add_argument('-data_root', default='' , type=str)
parser.add_argument('-use_class_list', default=False,type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('-number_of_classes', default=10, type=int)
parser.add_argument('-images_per_class', default=10, type=int)
parser.add_argument('-data_root', default='', type=str)
parser.add_argument('-use_class_list', default=False, type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('-class_list', default=[], nargs='*')
parser.add_argument('-debug', default=False,type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('-debug', default=False, type=lambda x: (str(x).lower() == 'true'))

parser.add_argument('-multiprocessing_workers', default = 8, type=int)
parser.add_argument('-multiprocessing_workers', default=8, type=int)

args, args_other = parser.parse_known_args()

Expand All @@ -36,7 +38,6 @@
logging.error(f'folder {args.data_root} does not exist! please provide existing folder in -data_root arg!')
exit()


IMAGENET_API_WNID_TO_URLS = lambda wnid: f'http://www.image-net.org/api/imagenet.synset.geturls?wnid={wnid}'

current_folder = os.path.dirname(os.path.realpath(__file__))
Expand All @@ -52,11 +53,11 @@
classes_to_scrape = []

if args.use_class_list == True:
for item in args.class_list:
classes_to_scrape.append(item)
if item not in class_info_dict:
logging.error(f'Class {item} not found in ImageNete')
exit()
for item in args.class_list:
classes_to_scrape.append(item)
if item not in class_info_dict:
logging.error(f'Class {item} not found in ImageNete')
exit()

elif args.use_class_list == False:
potential_class_pool = []
Expand All @@ -70,24 +71,23 @@
potential_class_pool.append(key)

if (len(potential_class_pool) < args.number_of_classes):
logging.error(f"With {args.images_per_class} images per class there are {len(potential_class_pool)} to choose from.")
logging.error(
f"With {args.images_per_class} images per class there are {len(potential_class_pool)} to choose from.")
logging.error(f"Decrease number of classes or decrease images per class.")
exit()

picked_classes_idxes = np.random.choice(len(potential_class_pool), args.number_of_classes, replace = False)
picked_classes_idxes = np.random.choice(len(potential_class_pool), args.number_of_classes, replace=False)

for idx in picked_classes_idxes:
classes_to_scrape.append(potential_class_pool[idx])


print("Picked the following clases:")
print([ class_info_dict[class_wnid]['class_name'] for class_wnid in classes_to_scrape ])
print([class_info_dict[class_wnid]['class_name'] for class_wnid in classes_to_scrape])

imagenet_images_folder = os.path.join(args.data_root, 'imagenet_images')
if not os.path.isdir(imagenet_images_folder):
os.mkdir(imagenet_images_folder)


scraping_stats = dict(
all=dict(
tried=0,
Expand All @@ -106,33 +106,35 @@
)
)


def add_debug_csv_row(row):
with open('stats.csv', "a") as csv_f:
csv_writer = csv.writer(csv_f, delimiter=",")
csv_writer.writerow(row)


class MultiStats():
def __init__(self):

self.lock = Lock()

self.stats = dict(
all=dict(
tried=Value('d', 0),
success=Value('d',0),
time_spent=Value('d',0),
success=Value('d', 0),
time_spent=Value('d', 0),
),
is_flickr=dict(
tried=Value('d', 0),
success=Value('d',0),
time_spent=Value('d',0),
success=Value('d', 0),
time_spent=Value('d', 0),
),
not_flickr=dict(
tried=Value('d', 0),
success=Value('d', 0),
time_spent=Value('d', 0),
)
)

def inc(self, cls, stat, val):
with self.lock:
self.stats[cls][stat].value += val
Expand All @@ -142,8 +144,8 @@ def get(self, cls, stat):
ret = self.stats[cls][stat].value
return ret

multi_stats = MultiStats()

multi_stats = MultiStats()

if args.debug:
row = [
Expand All @@ -159,6 +161,7 @@ def get(self, cls, stat):
]
add_debug_csv_row(row)


def add_stats_to_debug_csv():
row = [
multi_stats.get('all', 'tried'),
Expand All @@ -173,8 +176,8 @@ def add_stats_to_debug_csv():
]
add_debug_csv_row(row)

def print_stats(cls, print_func):

def print_stats(cls, print_func):
actual_all_time_spent = time.time() - scraping_t_start.value
processes_all_time_spent = multi_stats.get('all', 'time_spent')

Expand All @@ -183,17 +186,18 @@ def print_stats(cls, print_func):
else:
actual_processes_ratio = actual_all_time_spent / processes_all_time_spent

#print(f"actual all time: {actual_all_time_spent} proc all time {processes_all_time_spent}")
# print(f"actual all time: {actual_all_time_spent} proc all time {processes_all_time_spent}")

print_func(f'STATS For class {cls}:')
print_func(f' tried {multi_stats.get(cls, "tried")} urls with'
f' {multi_stats.get(cls, "success")} successes')

if multi_stats.get(cls, "tried") > 0:
print_func(f'{100.0 * multi_stats.get(cls, "success")/multi_stats.get(cls, "tried")}% success rate for {cls} urls ')
print_func(
f'{100.0 * multi_stats.get(cls, "success") / multi_stats.get(cls, "tried")}% success rate for {cls} urls ')
if multi_stats.get(cls, "success") > 0:
print_func(f'{multi_stats.get(cls,"time_spent") * actual_processes_ratio / multi_stats.get(cls,"success")} seconds spent per {cls} succesful image download')

print_func(
f'{multi_stats.get(cls, "time_spent") * actual_processes_ratio / multi_stats.get(cls, "success")} seconds spent per {cls} succesful image download')


lock = Lock()
Expand All @@ -202,16 +206,15 @@ def print_stats(cls, print_func):
class_folder = ''
class_images = Value('d', 0)

def get_image(img_url):

#print(f'Processing {img_url}')
def get_image(class_folder, img_url):
# print(f'Processing {img_url}')

#time.sleep(3)
# time.sleep(3)

if len(img_url) <= 1:
return


cls_imgs = 0
with lock:
cls_imgs = class_images.value
Expand All @@ -237,11 +240,11 @@ def finish(status):
multi_stats.inc(cls, 'time_spent', t_spent)
multi_stats.inc('all', 'time_spent', t_spent)

multi_stats.inc(cls,'tried', 1)
multi_stats.inc(cls, 'tried', 1)
multi_stats.inc('all', 'tried', 1)

if status == 'success':
multi_stats.inc(cls,'success', 1)
multi_stats.inc(cls, 'success', 1)
multi_stats.inc('all', 'success', 1)

elif status == 'failure':
Expand All @@ -251,7 +254,6 @@ def finish(status):
exit()
return


with lock:
url_tries.value += 1
if url_tries.value % 250 == 0:
Expand All @@ -263,7 +265,7 @@ def finish(status):
add_stats_to_debug_csv()

try:
img_resp = requests.get(img_url, timeout = 1)
img_resp = requests.get(img_url, timeout=1)
except ConnectionError:
logging.debug(f"Connection Error for url {img_url}")
return finish('failure')
Expand Down Expand Up @@ -314,26 +316,28 @@ def finish(status):
return finish('success')


for class_wnid in classes_to_scrape:
if __name__ == '__main__':

class_name = class_info_dict[class_wnid]["class_name"]
print(f'Scraping images for class \"{class_name}\"')
url_urls = IMAGENET_API_WNID_TO_URLS(class_wnid)
for class_wnid in classes_to_scrape:

time.sleep(0.05)
resp = requests.get(url_urls)
class_name = class_info_dict[class_wnid]["class_name"]
print(f'Scraping images for class \"{class_name}\"')
url_urls = IMAGENET_API_WNID_TO_URLS(class_wnid)

class_folder = os.path.join(imagenet_images_folder, class_name)
if not os.path.exists(class_folder):
os.mkdir(class_folder)
time.sleep(0.05)
resp = requests.get(url_urls)

class_images.value = 0
class_folder = os.path.join(imagenet_images_folder, class_name)
if not os.path.exists(class_folder):
os.mkdir(class_folder)

urls = [url.decode('utf-8') for url in resp.content.splitlines()]
class_images.value = 0

#for url in urls:
# get_image(url)
urls = [url.decode('utf-8') for url in resp.content.splitlines()]

print(f"Multiprocessing workers: {args.multiprocessing_workers}")
with Pool(processes=args.multiprocessing_workers) as p:
p.map(get_image,urls)
# for url in urls:
# get_image(url)
part = partial(get_image, class_folder)
print(f"Multiprocessing workers: {args.multiprocessing_workers}")
with Pool(processes=args.multiprocessing_workers) as p:
p.map(part, urls)