def get_group_info(self):
url = 'http://s.web2.qq.com/api/get_group_name_list_mask2'
payload = {'vfwebqq': self.params['vfwebqq']}
self.s.headers[
'referer'] = 'http://s.web2.qq.com/proxy.html?v=20110412001&callback=1&id=1'
r = self.s.post(url, data=payload)
ret = json.loads(r.text)
self.groups = {g['gid']: g for g in ret['result']['gnamelist']}
url = 'http://s.web2.qq.com/api/get_group_info_ext2'
for group in self.groups.values():
payload = {
'gcode': group['code'],
'vfwebqq': self.params['vfwebqq'],
't': utils.ctime()
}
self.s.headers[
'referer'] = 'http://s.web2.qq.com/proxy.html?v=20110412001&callback=1&id=1'
r = self.s.get(url, params=payload)
ret = json.loads(r.text)
# member info
group['minfo'] = {}
for m in ret['result']['minfo']:
group['minfo'][m['uin']] = {
'nick': m['nick'],
'qq': self.get_qq_from_uin(m['uin'])
}
print self.groups
def add_answer(question_id, answer):
session = Session()
answerObj = Answer(question_id=question_id,
answer=answer,
created_at=utils.ctime())
session.add(answerObj)
session.commit()
return _get_current_ratio(question_id, answer)
def get_qq_from_uin(self, uin):
url = 'http://s.web2.qq.com/api/get_friend_uin2'
payload = {'tuin': uin, 'type': 1, 'vfwebqq': self.params['vfwebqq'], 't': utils.ctime(),
'verifysession': '', 'code': ''}
self.s.headers['referer'] = 'http://s.web2.qq.com/proxy.html?v=20110412001&callback=1&id=1'
r = self.s.get(url, params = payload)
ret = json.loads(r.text)
return ret['result']['account']
def get_qq_from_uin(self, uin):
url = 'http://s.web2.qq.com/api/get_friend_uin2'
payload = {
'tuin': uin,
'type': 1,
'vfwebqq': self.params['vfwebqq'],
't': utils.ctime(),
'verifysession': '',
'code': ''
}
self.s.headers[
'referer'] = 'http://s.web2.qq.com/proxy.html?v=20110412001&callback=1&id=1'
r = self.s.get(url, params=payload)
ret = json.loads(r.text)
return ret['result']['account']
def log_question(question_id):
session = Session()
log = QuestionLog(question_id=question_id, timestamp=utils.ctime())
session.add(log)
session.commit()
def keep_alive(self):
url = 'http://web2.qq.com/web2/get_msg_tip?uin=&tp=1&id=0&retype=1&rc=150&lv=3&t=' + utils.ctime(
)
r = self.s.get(url)
def getFactory(request):
guid = request.form['guid']
print(ctime(), guid)
return KeyDerivationFactory(guid)
def keep_alive(self):
url = 'http://web2.qq.com/web2/get_msg_tip?uin=&tp=1&id=0&retype=1&rc=150&lv=3&t=' + utils.ctime()
r = self.s.get(url)
def get_group_info(self):
url = 'http://s.web2.qq.com/api/get_group_name_list_mask2'
payload = {'vfwebqq': self.params['vfwebqq']}
self.s.headers['referer'] = 'http://s.web2.qq.com/proxy.html?v=20110412001&callback=1&id=1'
r = self.s.post(url, data = payload)
ret = json.loads(r.text)
self.groups = { g['gid']: g for g in ret['result']['gnamelist'] }
url = 'http://s.web2.qq.com/api/get_group_info_ext2'
for group in self.groups.values():
payload = {'gcode': group['code'], 'vfwebqq': self.params['vfwebqq'], 't': utils.ctime()}
self.s.headers['referer'] = 'http://s.web2.qq.com/proxy.html?v=20110412001&callback=1&id=1'
r = self.s.get(url, params = payload)
ret = json.loads(r.text)
# member info
group['minfo'] = {}
for m in ret['result']['minfo']:
group['minfo'][m['uin']] = {'nick': m['nick'], 'qq': self.get_qq_from_uin(m['uin'])}
print self.groups
def run(config, num_batches, batch_size,
model_name, class_model_name, ofile,
threshold, num_workers, epochs,
multi_gans, gan_weights, trunc_norm,
fixed_dset, transform, filter_samples):
# Instanciating generator
config['G_batch_size'] = batch_size
generator = GeneratorWrapper(config, model_name, trunc_norm, multi_gans, gan_weights)
generator_fn = generator.gen_batch
if gan_weights:
print('Using GAN weights (multi-GAN setting): ', str(gan_weights))
# Instanciating filtering classifier
if filter_samples:
print('Using ResNet20 weights: %s.pth' % class_model_name)
filter_net = Classifier('resnet20', config['n_classes'])
filter_net.load(class_model_name)
filter_fn = filter_net.filter
else:
filter_fn = None
# Creating a filtered loader using the classifier
num_classes = config['n_classes']
loader = FilteredLoader(generator_fn,
filter_fn,
num_classes,
num_batches,
batch_size,
threshold,
num_workers,
fixed_dset,
transform)
print('Training using %d generated images per epoch'
% loader.train_length())
# Creating a blank ResNet
net = resnet20(config['n_classes'], width=64).to('cuda')
# Initializing loss functions, optimizer, learning rate scheduler
cross_entropy = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=0.0001)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[100, 150])
# Evaluating the model on the test set
test_loader = utils.make_test_loader(config['dataset'],
batch_size,
transforms.Normalize(*utils.norm_vals))
# Training the model
t1 = utils.ctime()
best_acc = 0.0
for epoch in range(epochs):
print('Epoch: %3d' % (epoch+1), end=" ")
train(net, loader, batch_size, optimizer, cross_entropy)
scheduler.step()
acc = evaluate(net, test_loader)
best_acc = max(acc, best_acc)
loader.reset()
print('Val acc: %4.2f %% ' % evaluate(net, test_loader),
' | Best acc: %4.2f %%\n' % best_acc)
tt = utils.ctime() - t1
print('Finished training, total time: %4.2fs' % tt)
print('Best accuracy achieved: %4.5f %%' % best_acc)
# Saving output model
output = './output/%s.pth' % ofile
print('Saving trained classifier in %s' % output)
torch.save(net.state_dict(), output)
