def get_inconsistencies():
files = utils.get_all_images()
parsed = {}
for f in files:
parts = os.path.splitext(f)[0].split('_')
webcompatID = int(parts[0])
if webcompatID not in parsed:
parsed[webcompatID] = {}
if len(parts) > 2:
sequence = int(parts[-2])
else:
sequence = -1
if sequence not in parsed[webcompatID]:
parsed[webcompatID][sequence] = []
parsed[webcompatID][sequence].append(parts[-1])
incons = []
for key, value in parsed.items():
for sequence, browsers in value.items():
if len(browsers) < 2:
incons.append([
key, sequence, 'firefox' in browsers, 'chrome' in browsers
])
incons.sort(key=lambda x: (x[2], x[0]))
return incons
def print_statistics(file_name, incons):
n_incons = len(incons)
f = utils.get_all_images()
total_img = len(f)
firefox = []
chrome = []
for line in incons:
firefox.append(line[2])
chrome.append(line[3])
incons_f = [int(x) for x in firefox]
incons_c = [int(x) for x in chrome]
print("Number of photos: {} ".format(total_img))
print("Number of pairs of images: {} ".format(
int((total_img - n_incons) / 2)))
print("Number of pairs of images possible: {} ".format(
int((total_img - n_incons) / 2 + n_incons)))
print("Percentage of Firefox inconsistencies: {} ".format(
int(((n_incons - sum(incons_f)) / n_incons) * 100)))
print("Percentage of Chrome inconsistencies: {} ".format(
int(((n_incons - sum(incons_c)) / n_incons) * 100)))
from functools import lru_cache
import itertools
import random
from urllib.parse import urlparse
from autowebcompat import network, utils
bugs = utils.get_bugs()
utils.prepare_images()
all_images = utils.get_all_images()[:3000] # 3000
image = utils.load_image(all_images[0])
input_shape = image.shape
BATCH_SIZE = 32
EPOCHS = 50
bugs_to_website = {}
for bug in bugs:
bugs_to_website[bug['id']] = urlparse(bug['url']).netloc
@lru_cache(maxsize=len(all_images))
def site_for_image(image):
bug = image[:image.index('_')]
return bugs_to_website[int(bug)]
def are_same_site(image1, image2):
return site_for_image(image1) == site_for_image(image2)
parser.add_argument('optimizer',
type=str,
choices=network.SUPPORTED_OPTIMIZERS,
help='Select the optimizer to use for training')
parser.add_argument(
'--early_stopping',
dest='early_stopping',
action='store_true',
help=
'Stop training training when validation accuracy has stopped improving.')
args = parser.parse_args()
bugs = utils.get_bugs()
utils.prepare_images()
all_images = utils.get_all_images()[:SAMPLE_SIZE]
image = utils.load_image(all_images[0])
input_shape = image.shape
TRAIN_SAMPLE = 80 * (SAMPLE_SIZE // 100)
VALIDATION_SAMPLE = 10 * (SAMPLE_SIZE // 100)
TEST_SAMPLE = SAMPLE_SIZE - (TRAIN_SAMPLE + VALIDATION_SAMPLE)
bugs_to_website = {}
for bug in bugs:
bugs_to_website[bug['id']] = urlparse(bug['url']).netloc
@lru_cache(maxsize=len(all_images))
def site_for_image(image):
bug = image[:image.index('_')]