class ClassBase(object):
"""
This class serves as inheritance base for every other class that will require universal features, like logger,
decryptor and such.
"""
def __init__(self, logger_name, logging_level):
self._logger = Logger(logger_name, logging_level)
def log(self, level, text):
"""
A simple bridge-method, used to pass values to instance's logger.
:param level: String - level of log message
:param text: String - message itself
:return: None
"""
self._logger.log(level, text)
def get_cipher(self):
"""
Creates and returns an instance of AESCipher class - wrapper for AES encryption processing.
:return: AESCipher class instance
"""
return AESCipher()
def get_current_datetime_string(self):
"""
Returns a current datetime stamp, formatted in readable string
:return: Timestamp string
"""
return datetime.now().strftime(get_time_formatter())
def MatchFiles(checkerFile, c1File, targetArch, debuggableMode):
for testCase in checkerFile.testCases:
if testCase.testArch not in [None, targetArch]:
continue
if testCase.forDebuggable != debuggableMode:
continue
# TODO: Currently does not handle multiple occurrences of the same group
# name, e.g. when a pass is run multiple times. It will always try to
# match a check group against the first output group of the same name.
c1Pass = c1File.findPass(testCase.name)
if c1Pass is None:
with file(c1File.fileName) as cfgFile:
Logger.log(''.join(cfgFile), Logger.Level.Error)
Logger.fail("Test case not found in the CFG file",
testCase.fileName, testCase.startLineNo, testCase.name)
Logger.startTest(testCase.name)
try:
MatchTestCase(testCase, c1Pass, c1File.instructionSetFeatures)
Logger.testPassed()
except MatchFailedException as e:
lineNo = c1Pass.startLineNo + e.lineNo
if e.statement.variant == TestStatement.Variant.Not:
msg = "NOT statement matched line {}"
else:
msg = "Statement could not be matched starting from line {}"
msg = msg.format(lineNo)
with file(c1File.fileName) as cfgFile:
Logger.log(''.join(cfgFile), Logger.Level.Error)
Logger.testFailed(msg, e.statement, e.variables)
class BatchCrawler():
MAX_DOCS_NUM = 100
def __init__(self, database_config_path, source_name, domain, encode, request_interval):
self.logger = Logger("crawler", domain)
self.adapter = DocRawAdapter(database_config_path, source_name, self.logger)
self.domain = domain
self.encode = encode
self.request_interval = request_interval
def run(self):
while True:
count = 0
try:
for url_hash, url in self.adapter.load_uncrawled_docs(BatchCrawler.MAX_DOCS_NUM):
count += 1
self.logger.log("crawling url %s"%url, 2)
page = common_utils.page_crawl(url)
if page == None:
self.adapter.update_doc_raw_as_crawled_failed(url_hash)
continue
if self.encode != "utf-8":
page = unicode(page, self.encode).encode("utf-8")
self.adapter.update_doc_raw_with_crawled_page(url_hash, "utf-8", page)
time.sleep(float(self.request_interval))
if count < BatchCrawler.MAX_DOCS_NUM:
break
except:
self.logger.log("mongo error")
class BatchCrawler():
MAX_DOCS_NUM = 100
def __init__(self, database_config_path, source_name, domain, encode,
request_interval):
self.logger = Logger("crawler", domain)
self.adapter = DocRawAdapter(database_config_path, source_name,
self.logger)
self.domain = domain
self.encode = encode
self.request_interval = request_interval
def run(self):
while True:
count = 0
try:
for url_hash, url in self.adapter.load_uncrawled_docs(
BatchCrawler.MAX_DOCS_NUM):
count += 1
self.logger.log("crawling url %s" % url, 2)
page = common_utils.page_crawl(url)
if page == None:
self.adapter.update_doc_raw_as_crawled_failed(url_hash)
continue
if self.encode != "utf-8":
page = unicode(page, self.encode).encode("utf-8")
self.adapter.update_doc_raw_with_crawled_page(
url_hash, "utf-8", page)
time.sleep(float(self.request_interval))
if count < BatchCrawler.MAX_DOCS_NUM:
break
except:
self.logger.log("mongo error")
def fetch(config, subreddit):
logger = Logger('main', 'fetch', plain=True)
loaders = []
try:
# pushshift
pushshift = Pushshift(root, config, subreddit)
loaders.append(pushshift)
# crawler
crawler = Crawler(root, config, subreddit)
loaders.append(crawler)
# praw
praw = Praw(root, config, subreddit)
loaders.append(praw)
# start loader threads
background = False # TODO thread implementation
for loader in loaders:
if background:
loader.start()
else:
loader.run()
# wait until abort
while background:
Sleep(1)
except KeyboardInterrupt:
for loader in loaders:
loader.stop(1)
raise KeyboardInterrupt()
except Exception as e:
logger.log(f'...fetch error {repr(e)}')
def match_files(checker_file, c1_file, target_arch, debuggable_mode,
print_cfg):
for test_case in checker_file.test_cases:
if test_case.test_arch not in [None, target_arch]:
continue
if test_case.for_debuggable != debuggable_mode:
continue
# TODO: Currently does not handle multiple occurrences of the same group
# name, e.g. when a pass is run multiple times. It will always try to
# match a check group against the first output group of the same name.
c1_pass = c1_file.find_pass(test_case.name)
if c1_pass is None:
with open(c1_file.full_file_name) as cfg_file:
Logger.log("".join(cfg_file), Logger.Level.ERROR)
Logger.fail("Test case not found in the CFG file",
c1_file.full_file_name, test_case.start_line_no,
test_case.name)
Logger.start_test(test_case.name)
try:
match_test_case(test_case, c1_pass,
c1_file.instruction_set_features)
Logger.test_passed()
except MatchFailedException as e:
line_no = c1_pass.start_line_no + e.line_no
if e.statement.variant == TestStatement.Variant.NOT:
msg = "NOT statement matched line {}"
else:
msg = "Statement could not be matched starting from line {}"
msg = msg.format(line_no)
if print_cfg:
with open(c1_file.full_file_name) as cfg_file:
Logger.log("".join(cfg_file), Logger.Level.ERROR)
Logger.test_failed(msg, e.statement, e.variables)
def test(idx, args, T, shared_net, path):
device = torch.device("cuda")
torch.manual_seed(args.seed + idx)
if args.cuda:
torch.cuda.manual_seed(args.seed + idx)
env = make_env(args.env,
stack_frames=args.stacked_frames,
max_episode_steps=args.max_episode_steps,
episodic_life=True,
reward_clipping=False)
env.seed(args.seed + idx)
state = env.reset()
state_v = torch.from_numpy(state).float().to(device)
hx = torch.zeros(1, 512).to(device)
cx = torch.zeros(1, 512).to(device)
info = True # game is real done, not end of a life (EpisodicLife)
net = A3C_LSTM(env.observation_space.shape[0],
env.action_space.n).to(device)
net.eval()
logger = Logger(name="test",
path=path,
model=shared_net,
start_time=time.time(),
print_log=True,
save_model=True)
while T.value < args.num_timesteps:
# Synchronize thread-specific parameters
if info:
net.load_state_dict(shared_net.state_dict())
# Perform action according to policy
with torch.no_grad():
value_v, logit_v, (hx, cx) = net(state_v.unsqueeze(0), (hx, cx))
prob_v = F.softmax(logit_v, dim=1)
action_v = torch.multinomial(prob_v, num_samples=1)
action = int(action_v.item())
# Receive reward and new state
state, reward, done, info = env.step(action)
state_v = torch.from_numpy(state).float().to(device)
logger.log(T.value, reward, info)
if done:
state = env.reset()
state_v = torch.from_numpy(state).float().to(device)
hx = torch.zeros(1, 512).to(device)
cx = torch.zeros(1, 512).to(device)
def DumpPass(outputFilename, passName):
c1File = ParseC1visualizerStream(os.path.basename(outputFilename), open(outputFilename, "r"))
compiler_pass = c1File.findPass(passName)
if compiler_pass:
maxLineNo = compiler_pass.startLineNo + len(compiler_pass.body)
lenLineNo = len(str(maxLineNo)) + 2
curLineNo = compiler_pass.startLineNo
for line in compiler_pass.body:
Logger.log((str(curLineNo) + ":").ljust(lenLineNo) + line)
curLineNo += 1
else:
Logger.fail("Pass \"" + passName + "\" not found in the output")
def DumpPass(outputFilename, passName):
c1File = ParseC1visualizerStream(os.path.basename(outputFilename), open(outputFilename, "r"))
compiler_pass = c1File.findPass(passName)
if compiler_pass:
maxLineNo = compiler_pass.startLineNo + len(compiler_pass.body)
lenLineNo = len(str(maxLineNo)) + 2
curLineNo = compiler_pass.startLineNo
for line in compiler_pass.body:
Logger.log((str(curLineNo) + ":").ljust(lenLineNo) + line)
curLineNo += 1
else:
Logger.fail("Pass \"" + passName + "\" not found in the output")
def dump_pass(output_filename, pass_name):
c1_file = parse_c1_visualizer_stream(output_filename,
open(output_filename, "r"))
compiler_pass = c1_file.find_pass(pass_name)
if compiler_pass:
max_line_no = compiler_pass.start_line_no + len(compiler_pass.body)
len_line_no = len(str(max_line_no)) + 2
cur_line_no = compiler_pass.start_line_no
for line in compiler_pass.body:
Logger.log((str(cur_line_no) + ":").ljust(len_line_no) + line)
cur_line_no += 1
else:
Logger.fail('Pass "{}" not found in the output'.format(pass_name))
def check_stop(self):
# check stop scan
task = self.web_api.get_scan(self.new_task)
current_id = task["start_id"]
"""Check stopped"""
if current_id != self.start_id:
self.new_task["status"] = 3
self.web_api.put(self.new_task)
self.web_api.post_notify(
message=unicode(u"Stopped scanning task : " +
str(self.new_task["id"])),
status=1)
Logger.log("Stopped task " + str(self.new_task["id"]))
return True
return False
class Testset:
"""Top class for testset, a collection of testcases."""
def __init__(self, working_dir : str = None, testcases : list = [], runner : str = None, logger : Logger = None, testset_name=""):
"""Testset constructor."""
self.working_dir = working_dir if working_dir is not None else ""
self.testcases = testcases
self.logger = Logger() if logger is None else logger
self.testset_name = testset_name
def append(self, testcase : Testcase):
"""Appends a testcase to the testset."""
self.testcases.append(testcase)
def run(self):
"""Run all testcases in testset."""
logname = os.path.join("", *[self.working_dir, self.testset_name + ".log"])
self.logger.open_file_log(filename=logname)
self.results = []
n_testcases = len(self.testcases)
i = 1
for testcase in self.testcases:
testcase.set_logger(self.logger)
print ("Running test %d / %d ..." % (i, n_testcases))
i += 1
if testcase.expected_to_fail:
self.results.append(not testcase.run())
else:
self.results.append(testcase.run())
self.log_results()
if False in self.results:
print("%d / %d testcase(s) FAILED" % (self.results.count(False), n_testcases))
else:
print("All %d testcase(s) PASSED" % (n_testcases))
self.logger.close()
return self.results
def log_results(self, results=None):
"""Logs all testcases in testset and their pass / fail status."""
results = results if results is not None else self.results
testcase_max_length = len(max(self.testcases, key=lambda t : len(t.testcase_name)).testcase_name) # go over all testcase names, find (one of) the biggest and get the size of it
result_fmt_str = " %%-%ds : %%s" % (testcase_max_length)
self.logger.log("Results:")
for test_result in zip(self.testcases, self.results):
self.logger.log(result_fmt_str % (test_result[0].testcase_name, "PASSED" if test_result[1] is True else "FAILED"))
def publish(interval, kaggle):
logger = Logger('main', 'publish', plain=True)
path = os.path.join('data', 'export')
try:
# upload disabled
if not interval:
return
# update datapackage
kaggle.update(path)
# start upload
elapsed = kaggle.timer.stop(run=False) / 1000
if elapsed > interval:
logger.log(f'\n{"-"*45}{"UPLOADING":^15}{"-"*45}\n')
kaggle.upload(path)
logger.log(f'\n{"-"*45}{"PUBLISHED":^15}{"-"*45}\n')
kaggle.timer.reset()
except Exception as e:
logger.log(f'...publish error {repr(e)}')
print('loss Player2 %s' % all_losses2[-1])
print("---------------------------")
timer.update(time.time())
timediff = timer.getTimeDiff()
total_time = timer.getTotalTime()
loopstogo = (num_frames - i_update) / 100
estimatedtimetogo = timer.getTimeToGo(loopstogo)
logger.printDayFormat("runntime last epochs: ", timediff)
logger.printDayFormat("total runtime: ", total_time)
logger.printDayFormat("estimated time to run: ", estimatedtimetogo)
print("######## {0} ########".format(sys.argv[1]))
rollout1.after_update() # player1
rollout2.after_update() # player2
if i_update % 1000 == 0 and i_update > 0:
logger.log(all_rewards1, "Data/", "all_rewards_p1_{0}_{1}.txt".format(sys.argv[1], swich_variable))
logger.log(all_losses1, "Data/", "all_losses_p1_{0}_{1}.txt".format(sys.argv[1], swich_variable))
logger.log_state_dict(agent1.state_dict(), "Data/agents/agent1_{0}_{1}".format(sys.argv[1], swich_variable))
logger.log(all_rewards2, "Data/", "all_rewards_p2_{0}_{1}.txt".format(sys.argv[1], swich_variable))
logger.log(all_losses2, "Data/", "all_losses_p2_{0}_{1}.txt".format(sys.argv[1], swich_variable))
logger.log_state_dict(agent2.state_dict(), "Data/agents/agent2_{0}_{1}".format(sys.argv[1], swich_variable))
swich_variable += 1
swich_variable %= 2
logger.log(all_rewards1, "Data/", "all_rewards_p1_{0}_{1}.txt".format(sys.argv[1], swich_variable))
logger.log(all_losses1, "Data/", "all_losses_p1_{0}_{1}.txt".format(sys.argv[1], swich_variable))
logger.log_state_dict(agent1.state_dict(), "Data/agents/agent1_{0}_{1}".format(sys.argv[1], swich_variable))
logger.log(all_rewards2, "Data/", "all_rewards_p2_{0}_{1}.txt".format(sys.argv[1], swich_variable))
logger.log(all_losses2, "Data/", "all_losses_p2_{0}_{1}.txt".format(sys.argv[1], swich_variable))
logger.log_state_dict(agent2.state_dict(), "Data/agents/agent2_{0}_{1}".format(sys.argv[1], swich_variable))
class Service(ServiceBase):
""" Siterummage Page Store microservice class """
## Title text logged during initialisation.
title_text = 'Site Rummagge Page Store Microservice'
## Copyright text logged on initialisation etc.
copyright_text = 'Copyright 2021 Site Rummage'
## License text logged on initialisation etc.
license_text = 'All Rights Reserved. Proprietary and confidential'
def __init__(self, new_instance):
super().__init__()
self._quart = new_instance
## Instance of the logging wrapper class
self._logger = Logger()
## _is_initialised is inherited from parent class ServiceThread
self._is_initialised = False
self._configuration = None
self._db_interface = None
self._api_health = ApiHealth(self._quart)
self._api_webpage = None
def _initialise(self) -> bool:
self._logger.write_to_console = True
self._logger.initialise()
self._logger.log(
LogType.Info,
f'{self.title_text} {VERSION} (Core Version {CORE_VERSION})')
self._logger.log(LogType.Info, self.copyright_text)
self._logger.log(LogType.Info, self.license_text)
config_mgr = ConfigurationManager()
config_file = os.getenv('SITERUMMAGE_PAGESTORE_CONFIG')
self._configuration = config_mgr.parse_config_file(config_file)
if not self._configuration:
self._logger.log(LogType.Error, config_mgr.last_error_msg)
return False
self._logger.log(LogType.Info, '+=== Configuration Settings ===+')
self._logger.log(LogType.Info, '+==============================+')
db_config = self._configuration.db_settings
self._logger.log(LogType.Info, '+== Database Settings :->')
self._logger.log(LogType.Info, f'+= database : {db_config.database}')
self._logger.log(LogType.Info, f'+= host : {db_config.host}')
self._logger.log(LogType.Info, f'+= username : {db_config.username}')
self._logger.log(LogType.Info, f'+= port : {db_config.port}')
self._logger.log(LogType.Info, f'+= pool_name : {db_config.pool_name}')
self._logger.log(LogType.Info, f'+= pool_size : {db_config.pool_size}')
self._logger.log(LogType.Info, '+==============================+')
self._db_interface = DatabaseInterface(self._logger,
self._configuration)
if not self._db_interface.database_connection_valid():
return False
self._api_webpage = ApiWebpage(self._quart, self._db_interface,
self._configuration)
self._is_initialised = True
return True
async def _main_loop(self):
# if not self._master_thread_class.initialise():
# return False
pass
def _shutdown(self):
self._logger.log(LogType.Info, 'Shutting down...')
class SpiderBase():
def __init__(self,
data_adapter_config_path,
source_name,
encode="utf-8",
parse_try_limit=3):
self.logger = Logger("spider", source_name)
self.doc_raw_adapter = DocRawAdapter(data_adapter_config_path,
source_name, self.logger)
self.data_raw_adapter = DataRawAdapter(data_adapter_config_path,
source_name, self.logger)
self.image_store_adapter = ImageStoreAdapter(data_adapter_config_path,
self.logger)
self.source_name = source_name
self.encode = encode
self.parse_try_limit = parse_try_limit
self.exploring_times = 0
def url_exists_in_doc_raw(self, url):
url_hash = common_utils.gen_url_hash(url)
return self.doc_raw_adapter.has_doc_raw_by_url_hash(url_hash)
def url_hash_exists_in_data_raw(self, url_hash):
return self.data_raw_adapter.has_data_raw_by_url_hash(url_hash)
def parse(self, url_hash, page, encode, stage, context, created_at,
page_crawled_at):
'''
you must override this function
'''
self.logger.log(
"what the hell!!!you have to override to implement parse logic!!!")
features = {}
images = []
images.append({
"name": "test_image_name",
"url": "test_image_url",
"image_format": "jpg"
})
next_update_time = None
children = []
children.append({
"url": "test_url",
"stage": "test_stage",
"context": "test_context",
"operation_flag": SpiderChildNodeOperationFlag.NEW_ADD
})
return features, images, next_update_time, children
def explore_child(self, father_url_hash, url, url_hash, stage, context,
operation_flag):
if operation_flag == SpiderChildNodeOperationFlag.NEW_ADD:
if not self.doc_raw_adapter.has_doc_raw_by_url_hash(url_hash):
self.doc_raw_adapter.create_doc_raw(url_hash, url, stage,
context, father_url_hash)
self.logger.log("child [%s] %s new added." % (url_hash, url))
else:
if self.doc_raw_adapter.has_doc_raw_by_url_hash(url_hash):
if operation_flag == SpiderChildNodeOperationFlag.UPDATE_INFO_ONLY:
self.doc_raw_adapter.update_doc_raw_with_node_info(
url_hash,
stage=stage,
context=context,
father=father_url_hash)
self.logger.log("child [%s]'s info is updated." %
(url_hash))
elif operation_flag == SpiderChildNodeOperationFlag.FORCE_TO_REPARSE:
self.doc_raw_adapter.update_doc_raw_with_node_info(
url_hash,
stage=stage,
context=context,
father=father_url_hash,
status_flag=DocRawStatus.PAGE_CRAWLED)
self.logger.log("child [%s] is set to reparse data." %
(url_hash))
elif operation_flag == SpiderChildNodeOperationFlag.FORCE_TO_RECRAWL:
self.doc_raw_adapter.update_doc_raw_with_node_info(
url_hash,
stage=stage,
context=context,
father=father_url_hash,
status_flag=DocRawStatus.NEW_ADDED)
self.logger.log("child [%s]'s is set to recrawled page." %
(url_hash))
def spider_run(self):
for url_hash, url, stage, page, encode, context, created_at, page_crawled_at in self.doc_raw_adapter.load_unparsed_doc_raw(
):
try:
self.logger.log("parsing [%s]." % (url_hash))
features, images, next_update_time, children = self.parse(
url_hash, page, encode, stage, context, created_at,
page_crawled_at)
if images != None:
for i in range(0, len(images)):
try:
image_id = common_utils.gen_url_hash(
images[i]["url"])
if not self.image_store_adapter.has_image_index_by_image_id(
image_id):
images[i]["image_id"] = image_id
self.image_store_adapter.create_image_index(
image_id, images[i]["image_format"],
images[i]["url"])
self.logger.log(
"image [%s] created for [%s]." %
(image_id, url_hash))
except BaseException, e:
self.logger.log(
"Error occured when creating image index: %s" %
(e))
if features != None:
if not self.url_hash_exists_in_data_raw(url_hash):
self.data_raw_adapter.create_data_raw(
url_hash, url, features, images)
self.logger.log("features for [%s] is added." %
(url_hash))
else:
self.data_raw_adapter.update_data_raw(
url_hash, features, images)
self.logger.log("features for [%s] is updated." %
(url_hash))
children_url_hashes = None
if children != None:
children_url_hashes = []
for child in children:
try:
url_new = child["url"]
url_hash_new = common_utils.gen_url_hash(
child["url"])
stage_new = child["stage"]
context_new = child["context"]
operation_flag = child["operation_flag"]
self.explore_child(url_hash, url_new, url_hash_new,
stage_new, context_new,
operation_flag)
children_url_hashes.append(url_hash_new)
except BaseException, e:
self.logger.log(
"Error occured when exploring child: %s" % (e))
self.doc_raw_adapter.update_doc_raw_with_node_info(
url_hash,
next_update_time=next_update_time,
children=children_url_hashes,
status_flag=DocRawStatus.DATA_PARSED)
except BaseException, e:
self.logger.log("Error occured in main spider_run: %s" % (e))
if url_hash != None:
parse_try_times = self.doc_raw_adapter.get_doc_raw_parse_try_times(
url_hash)
if parse_try_times + 1 >= self.parse_try_limit:
self.doc_raw_adapter.update_doc_raw_with_node_info(
url_hash,
status_flag=DocRawStatus.ERROR_FAILED_TO_PARSED)
else:
self.doc_raw_adapter.update_doc_raw_with_node_info(
url_hash,
next_update_time=datetime.datetime.now() +
datetime.timedelta(86400),
parse_try_times=parse_try_times + 1,
status_flag=DocRawStatus.NEW_ADDED)
class Defender(Trainer):
""" Perform various adversarial attacks and defense on a pretrained model
Scheme generates Tensor, not Variable
"""
def __init__(self, val_loader, args, **kwargs):
self.val_loader = val_loader
self.args = args
self.model = get_model(args)
self.step = 0
self.cuda = self.args.cuda
self.log_path = (
PROJECT_ROOT / Path("experiments") /
Path(datetime.now().strftime("%Y%m%d%H%M%S") + "-")).as_posix()
self.log_path = Path(self.get_dirname(self.log_path, args))
if not Path.exists(self.log_path):
Path(self.log_path).mkdir(parents=True, exist_ok=True)
self.logger = Logger("defense", self.log_path, args.verbose)
self.logger.log("Checkpoint files will be saved in {}".format(
self.log_path))
self.logger.add_level("ATTACK", 21, 'yellow')
self.logger.add_level("DEFENSE", 22, 'cyan')
self.logger.add_level("TEST", 23, 'white')
self.logger.add_level("DIST", 11, 'white')
self.kwargs = kwargs
if args.domain_restrict:
self.artifact = get_artifact(self.model, val_loader, args)
self.kwargs['artifact'] = self.artifact
def defend(self):
self.model.eval()
defense_scheme = getattr(defenses,
self.args.defense)(self.model, self.args,
**self.kwargs)
source = self.model
if self.args.source is not None and (self.args.ckpt_name !=
self.args.ckpt_src):
target = self.args.ckpt_name
self.args.model = self.args.source
self.args.ckpt_name = self.args.ckpt_src
source = get_model(self.args)
self.logger.log("Transfer attack from {} -> {}".format(
self.args.ckpt_src, target))
attack_scheme = getattr(attacks, self.args.attack)(source, self.args,
**self.kwargs)
eval_metrics = EvaluationMetrics(
['Test/Acc', 'Test/Top5', 'Test/Time'])
eval_def_metrics = EvaluationMetrics(
['Def-Test/Acc', 'Def-Test/Top5', 'Def-Test/Time'])
attack_metrics = EvaluationMetrics(
['Attack/Acc', 'Attack/Top5', 'Attack/Time'])
defense_metrics = EvaluationMetrics(
['Defense/Acc', 'Defense/Top5', 'Defense/Time'])
dist_metrics = EvaluationMetrics(['L0', 'L1', 'L2', 'Li'])
for i, (images, labels) in enumerate(self.val_loader):
self.step += 1
if self.cuda:
images = images.cuda()
labels = labels.cuda()
if self.args.half: images = images.half()
# Inference
st = time.time()
outputs = self.model(self.to_var(images, self.cuda, True))
outputs = outputs.float()
_, preds = torch.topk(outputs, 5)
acc = (labels == preds.data[:, 0]).float().mean()
top5 = torch.sum(
(labels.unsqueeze(1).repeat(1, 5) == preds.data).float(),
dim=1).mean()
eval_metrics.update('Test/Acc', float(acc), labels.size(0))
eval_metrics.update('Test/Top5', float(top5), labels.size(0))
eval_metrics.update('Test/Time', time.time() - st, labels.size(0))
# Attacker
st = time.time()
adv_images, adv_labels = attack_scheme.generate(images, labels)
if isinstance(adv_images, Variable):
adv_images = adv_images.data
attack_metrics.update('Attack/Time',
time.time() - st, labels.size(0))
# Lp distance
diff = torch.abs(
denormalize(adv_images, self.args.dataset) -
denormalize(images, self.args.dataset))
L0 = torch.sum((torch.sum(diff, dim=1) > 1e-3).float().view(
labels.size(0), -1),
dim=1).mean()
diff = diff.view(labels.size(0), -1)
L1 = torch.norm(diff, p=1, dim=1).mean()
L2 = torch.norm(diff, p=2, dim=1).mean()
Li = torch.max(diff, dim=1)[0].mean()
dist_metrics.update('L0', float(L0), labels.size(0))
dist_metrics.update('L1', float(L1), labels.size(0))
dist_metrics.update('L2', float(L2), labels.size(0))
dist_metrics.update('Li', float(Li), labels.size(0))
# Defender
st = time.time()
def_images, def_labels = defense_scheme.generate(
adv_images, adv_labels)
if isinstance(
def_images, Variable
): # FIXME - Variable in Variable out for all methods
def_images = def_images.data
defense_metrics.update('Defense/Time',
time.time() - st, labels.size(0))
self.calc_stats('Attack', adv_images, images, adv_labels, labels,
attack_metrics)
self.calc_stats('Defense', def_images, images, def_labels, labels,
defense_metrics)
# Defense-Inference for shift of original image
st = time.time()
def_images_org, _ = defense_scheme.generate(images, labels)
if isinstance(
def_images_org, Variable
): # FIXME - Variable in Variable out for all methods
def_images_org = def_images_org.data
outputs = self.model(self.to_var(def_images_org, self.cuda, True))
outputs = outputs.float()
_, preds = torch.topk(outputs, 5)
acc = (labels == preds.data[:, 0]).float().mean()
top5 = torch.sum(
(labels.unsqueeze(1).repeat(1, 5) == preds.data).float(),
dim=1).mean()
eval_def_metrics.update('Def-Test/Acc', float(acc), labels.size(0))
eval_def_metrics.update('Def-Test/Top5', float(top5),
labels.size(0))
eval_def_metrics.update('Def-Test/Time',
time.time() - st, labels.size(0))
if self.step % self.args.log_step == 0 or self.step == len(
self.val_loader):
self.logger.scalar_summary(eval_metrics.avg, self.step, 'TEST')
self.logger.scalar_summary(eval_def_metrics.avg, self.step,
'TEST')
self.logger.scalar_summary(attack_metrics.avg, self.step,
'ATTACK')
self.logger.scalar_summary(defense_metrics.avg, self.step,
'DEFENSE')
self.logger.scalar_summary(dist_metrics.avg, self.step, 'DIST')
defense_rate = eval_metrics.avg[
'Test/Acc'] - defense_metrics.avg['Defense/Acc']
if eval_metrics.avg['Test/Acc'] - attack_metrics.avg[
'Attack/Acc']:
defense_rate /= eval_metrics.avg[
'Test/Acc'] - attack_metrics.avg['Attack/Acc']
else:
defense_rate = 0
defense_rate = 1 - defense_rate
defense_top5 = eval_metrics.avg[
'Test/Top5'] - defense_metrics.avg['Defense/Top5']
if eval_metrics.avg['Test/Top5'] - attack_metrics.avg[
'Attack/Top5']:
defense_top5 /= eval_metrics.avg[
'Test/Top5'] - attack_metrics.avg['Attack/Top5']
else:
defense_top5 = 0
defense_top5 = 1 - defense_top5
self.logger.log(
"Defense Rate Top1: {:5.3f} | Defense Rate Top5: {:5.3f}".
format(defense_rate, defense_top5), 'DEFENSE')
if self.step % self.args.img_log_step == 0:
image_dict = {
'Original':
to_np(denormalize(images, self.args.dataset))[0],
'Attacked':
to_np(denormalize(adv_images, self.args.dataset))[0],
'Defensed':
to_np(denormalize(def_images, self.args.dataset))[0],
'Perturbation':
to_np(denormalize(images - adv_images,
self.args.dataset))[0]
}
self.logger.image_summary(image_dict, self.step)
def calc_stats(self, method, gen_images, images, gen_labels, labels,
metrics):
"""gen_images: Generated from attacker or defender
Currently just calculating acc and artifact
"""
success_rate = 0
if not isinstance(gen_images, Variable):
gen_images = self.to_var(gen_images.clone(), self.cuda, True)
gen_outputs = self.model(gen_images)
gen_outputs = gen_outputs.float()
_, gen_preds = torch.topk(F.softmax(gen_outputs, dim=1), 5)
if isinstance(gen_preds, Variable):
gen_preds = gen_preds.data
gen_acc = (labels == gen_preds[:, 0]).float().mean()
gen_top5 = torch.sum(
(labels.unsqueeze(1).repeat(1, 5) == gen_preds).float(),
dim=1).mean()
metrics.update('{}/Acc'.format(method), float(gen_acc), labels.size(0))
metrics.update('{}/Top5'.format(method), float(gen_top5),
labels.size(0))
def to_var(self, x, cuda, volatile=False):
"""For CPU inference manual cuda setting is needed
"""
if cuda:
x = x.cuda()
return torch.autograd.Variable(x, volatile=volatile)
sTestCaseDir = cfg.sResultDir + sTestName + '/'
sSampleDir = sTestCaseDir + 'samples/'
sCheckpointDir = sTestCaseDir + 'checkpoint/'
makedirs(sCheckpointDir)
makedirs(sSampleDir)
makedirs(sTestCaseDir + 'code/')
from common.logger import Logger
logger = Logger()
logger.set_dir(sTestCaseDir)
logger.set_casename(sTestName)
logger.log(sTestCaseDir)
commandline = ''
for arg in ['CUDA_VISIBLE_DEVICES="0" python3'] + sys.argv:
commandline += arg + ' '
logger.log(commandline)
logger.log(str_flags(cfg.__flags))
copydir(cfg.sSourceDir, sTestCaseDir + 'code')
############################################################################################################################################
tf.logging.set_verbosity(tf.logging.ERROR)
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, intra_op_parallelism_threads=0, inter_op_parallelism_threads=0)
from client.config import GENERAL
from common.logger import Logger, LogLevel
logger = Logger("main", logging_level=GENERAL["logging_level"])
logger.log(LogLevel.INFO, "Starting up")
"""
We mimic the Node-like architecture by separating parts of app into self-contained modules.
Initialization is performed during module import (in __init__.py files).
"""
# Service imports. Used to initialize modules and their respective processes/variables. DO NOT REMOVE THEM!
logger.log(LogLevel.INFO, "Starting GPIO adapter")
import client.gpio
logger.log(LogLevel.INFO, "Starting Device client")
import client.socket_connector
logger.log(LogLevel.INFO, "Starting Device controller module")
import client.board_controller
# End of service imports
logger.log(LogLevel.INFO, "Start-up complete")
class Service(ServiceBase):
""" Siterummage Big Broker microservice class """
## Title text logged during initialisation.
title_text = 'Site Rummagge Big Broker Microservice'
## Copyright text logged on initialisation etc.
copyright_text = 'Copyright 2021 Site Rummage'
## License text logged on initialisation etc.
license_text = 'All Rights Reserved. Proprietary and confidential'
def __init__(self, new_instance):
super().__init__()
self._quart = new_instance
## Instance of the logging wrapper class
self._logger = Logger()
## _is_initialised is inherited from parent class ServiceThread
self._is_initialised = False
self._configuration = None
self._api_schedule = None
self._api_node_management = None
self._scrape_node_list = ScrapeNodeList()
def _initialise(self) -> bool:
self._logger.write_to_console = True
self._logger.initialise()
self._logger.log(LogType.Info,
f'{self.title_text} {VERSION} (Core Version {CORE_VERSION})')
self._logger.log(LogType.Info, self.copyright_text)
self._logger.log(LogType.Info, self.license_text)
config_mgr = ConfigurationManager()
config_file = os.getenv('SITERUMMAGE_BIGBROKER_CONFIG')
self._logger.log(LogType.Info, f'Configuration file: {config_file}')
self._configuration = config_mgr.parse_config_file(config_file)
if not self._configuration:
self._logger.log(LogType.Error, config_mgr.last_error_msg)
return False
self._logger.log(LogType.Info, '+=== Configuration Settings ===+')
self._logger.log(LogType.Info, '+==============================+')
page_store_cfg = self._configuration.page_store_api
self._logger.log(LogType.Info, '+== Page Store Api :->')
self._logger.log(LogType.Info, f'+= host : {page_store_cfg.host}')
self._logger.log(LogType.Info, f'+= port : {page_store_cfg.port}')
processing_queue_cfg = self._configuration.processing_queue_api
self._logger.log(LogType.Info, '+== Page Store Api :->')
self._logger.log(LogType.Info, f'+= host : {processing_queue_cfg.host}')
self._logger.log(LogType.Info, f'+= port : {processing_queue_cfg.port}')
self._logger.log(LogType.Info, '+==============================+')
self._api_schedule = ApiSchedule(self._quart, self._configuration)
self._api_node_management = ApiNodeManagement(self._quart,
self._configuration,
self._scrape_node_list)
self._is_initialised = True
return True
async def _main_loop(self):
# if not self._master_thread_class.initialise():
# return False
pass
def _shutdown(self):
self._logger.log(LogType.Info, 'Shutting down...')
sTestName = cfg.sDataSet + ('_' + sResultTag if len(sResultTag) else '')
sTestCaseDir = cfg.sResultDir + sTestName + '/'
sSampleDir = sTestCaseDir + '/sample/'
sCheckpointDir = sTestCaseDir + 'checkpoint/'
makedirs(sSampleDir)
makedirs(sCheckpointDir)
makedirs(sTestCaseDir + 'source/code/')
makedirs(sTestCaseDir + 'source/common/')
logger = Logger()
logger.set_dir(sTestCaseDir)
logger.set_casename(sTestName)
logger.linebreak()
logger.log(sTestCaseDir)
commandline = ''
for arg in ['python3'] + sys.argv:
commandline += arg + ' '
logger.log('\n' + commandline + '\n')
logger.log(str_flags(cfg.__flags))
logger.log('Using GPU%d\n' % GPU_ID)
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
intra_op_parallelism_threads=0,
inter_op_parallelism_threads=0)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
value_loss = advantages.pow(2).mean()
action_loss = -(advantages.data * action_log_probs).mean()
optimizer.zero_grad()
loss = value_loss * value_loss_coef + action_loss - entropy * entropy_coef
loss.backward()
nn.utils.clip_grad_norm_(actor_critic.parameters(), max_grad_norm)
optimizer.step()
if i_update % 100 == 0:
all_rewards.append(final_rewards.mean())
all_losses.append(loss.item())
timer.update(time.time())
loopstogo = (num_frames - i_update) / 100
estimatedtimetogo = timer.getTimeToGo(loopstogo)
print('epoch %s. reward: %s' % (i_update, np.mean(all_rewards[-10:])))
print('loss %s' % all_losses[-1])
logger.printDayFormat("estimated time to run: ", estimatedtimetogo)
print("######## AC_Pacman_{0} ########".format(mode))
rollout.after_update()
logger.log(all_rewards, "Data/", "all_rewards_{0}.txt".format(mode))
logger.log(all_losses, "Data/", "all_losses_{0}.txt".format(mode))
logger.log_state_dict(actor_critic.state_dict(), "Data/actor_critic_{0}".format(mode))
from common.logger import Logger, LogLevel
from server.config import GENERAL
logger = Logger("main", logging_level=GENERAL["logging_level"])
logger.log(LogLevel.INFO, "Starting up")
"""
We mimic the Node-like architecture by separating parts of app into self-contained modules.
Initialization is performed during module import (in __init__.py files).
"""
# Service imports. Used to initialize modules and their respective processes/variables. DO NOT REMOVE THEM!
logger.log(LogLevel.INFO, "Starting Web and Database service")
import server.web
logger.log(LogLevel.INFO, "Starting Device server")
import server.socket_server
# End of service imports
logger.log(LogLevel.INFO, "Start-up complete")
print("---------------------------")
timer.update(time.time())
timediff = timer.getTimeDiff()
total_time = timer.getTotalTime()
loopstogo = (num_frames - i_update) / 100
estimatedtimetogo = timer.getTimeToGo(loopstogo)
logger.printDayFormat("runntime last epochs: ", timediff)
logger.printDayFormat("total runtime: ", total_time)
logger.printDayFormat("estimated time to run: ", estimatedtimetogo)
print("######## AC_KeyCollect ########")
rollout.after_update()
# snapshot of weights, data and optimzer every 1000 epochs
if i_update % 1000 == 0 and i_update > 0:
logger.log(all_rewards, "Data/", "all_rewards_KeyCollect.txt")
logger.log(all_losses, "Data/", "all_losses_KeyCollect.txt")
logger.log_state_dict(actor_critic.state_dict(), "Data/actor_critic_KeyCollect")
logger.log_state_dict(optimizer.state_dict(), "Data/actor_critic_optimizer_KeyCollect")
# final save
logger.log(all_rewards, "Data/", "all_rewards_KeyCollect.txt")
logger.log(all_losses, "Data/", "all_losses_KeyCollect.txt")
logger.log_state_dict(actor_critic.state_dict(), "Data/actor_critic_KeyCollect")
logger.log_state_dict(optimizer.state_dict(), "Data/actor_critic_optimizer_KeyCollect")
class SpiderBase():
def __init__(self, data_adapter_config_path, source_name, encode = "utf-8", parse_try_limit = 3):
self.logger = Logger("spider", source_name)
self.doc_raw_adapter = DocRawAdapter(data_adapter_config_path, source_name, self.logger)
self.data_raw_adapter = DataRawAdapter(data_adapter_config_path, source_name, self.logger)
self.image_store_adapter = ImageStoreAdapter(data_adapter_config_path, self.logger)
self.source_name = source_name
self.encode = encode
self.parse_try_limit = parse_try_limit
self.exploring_times = 0
def url_exists_in_doc_raw(self, url):
url_hash = common_utils.gen_url_hash(url)
return self.doc_raw_adapter.has_doc_raw_by_url_hash(url_hash)
def url_hash_exists_in_data_raw(self, url_hash):
return self.data_raw_adapter.has_data_raw_by_url_hash(url_hash)
def parse(self, url_hash, page, encode, stage, context, created_at, page_crawled_at):
'''
you must override this function
'''
self.logger.log("what the hell!!!you have to override to implement parse logic!!!")
features = {}
images = []
images.append({"name" : "test_image_name", "url" : "test_image_url", "image_format" : "jpg"})
next_update_time = None
children = []
children.append({"url" : "test_url", "stage" : "test_stage", "context" : "test_context", "operation_flag" : SpiderChildNodeOperationFlag.NEW_ADD})
return features, images, next_update_time, children
def explore_child(self, father_url_hash, url, url_hash, stage, context, operation_flag):
if operation_flag == SpiderChildNodeOperationFlag.NEW_ADD:
if not self.doc_raw_adapter.has_doc_raw_by_url_hash(url_hash):
self.doc_raw_adapter.create_doc_raw(url_hash, url, stage, context, father_url_hash)
self.logger.log("child [%s] %s new added."%(url_hash, url))
else:
if self.doc_raw_adapter.has_doc_raw_by_url_hash(url_hash):
if operation_flag == SpiderChildNodeOperationFlag.UPDATE_INFO_ONLY:
self.doc_raw_adapter.update_doc_raw_with_node_info(url_hash,
stage = stage,
context = context,
father = father_url_hash)
self.logger.log("child [%s]'s info is updated."%(url_hash))
elif operation_flag == SpiderChildNodeOperationFlag.FORCE_TO_REPARSE:
self.doc_raw_adapter.update_doc_raw_with_node_info(url_hash,
stage = stage,
context = context,
father = father_url_hash,
status_flag = DocRawStatus.PAGE_CRAWLED)
self.logger.log("child [%s] is set to reparse data."%(url_hash))
elif operation_flag == SpiderChildNodeOperationFlag.FORCE_TO_RECRAWL:
self.doc_raw_adapter.update_doc_raw_with_node_info(url_hash,
stage = stage,
context = context,
father = father_url_hash,
status_flag = DocRawStatus.NEW_ADDED)
self.logger.log("child [%s]'s is set to recrawled page."%(url_hash))
def spider_run(self):
for url_hash, url, stage, page, encode, context, created_at, page_crawled_at in self.doc_raw_adapter.load_unparsed_doc_raw():
try:
self.logger.log("parsing [%s]."%(url_hash))
features, images, next_update_time, children = self.parse(url_hash, page, encode, stage, context, created_at, page_crawled_at)
if images != None:
for i in range(0, len(images)):
try:
image_id = common_utils.gen_url_hash(images[i]["url"])
if not self.image_store_adapter.has_image_index_by_image_id(image_id):
images[i]["image_id"] = image_id
self.image_store_adapter.create_image_index(image_id, images[i]["image_format"], images[i]["url"])
self.logger.log("image [%s] created for [%s]."%(image_id, url_hash))
except BaseException, e:
self.logger.log("Error occured when creating image index: %s"%(e))
if features != None:
if not self.url_hash_exists_in_data_raw(url_hash):
self.data_raw_adapter.create_data_raw(url_hash, url, features, images)
self.logger.log("features for [%s] is added."%(url_hash))
else:
self.data_raw_adapter.update_data_raw(url_hash, features, images)
self.logger.log("features for [%s] is updated."%(url_hash))
children_url_hashes = None
if children != None:
children_url_hashes = []
for child in children:
try:
url_new = child["url"]
url_hash_new = common_utils.gen_url_hash(child["url"])
stage_new = child["stage"]
context_new = child["context"]
operation_flag = child["operation_flag"]
self.explore_child(url_hash, url_new, url_hash_new, stage_new, context_new, operation_flag)
children_url_hashes.append(url_hash_new)
except BaseException, e:
self.logger.log("Error occured when exploring child: %s"%(e))
self.doc_raw_adapter.update_doc_raw_with_node_info(url_hash,
next_update_time = next_update_time,
children = children_url_hashes,
status_flag = DocRawStatus.DATA_PARSED)
except BaseException, e:
self.logger.log("Error occured in main spider_run: %s"%(e))
if url_hash != None:
parse_try_times = self.doc_raw_adapter.get_doc_raw_parse_try_times(url_hash)
if parse_try_times + 1 >= self.parse_try_limit:
self.doc_raw_adapter.update_doc_raw_with_node_info(url_hash,
status_flag = DocRawStatus.ERROR_FAILED_TO_PARSED)
else:
self.doc_raw_adapter.update_doc_raw_with_node_info(url_hash,
next_update_time = datetime.datetime.now() + datetime.timedelta(86400),
parse_try_times = parse_try_times + 1,
status_flag = DocRawStatus.NEW_ADDED)
sTestCaseDir = cfg.sResultDir + sTestName + '/'
sSampleDir = sTestCaseDir + '/samples/'
sCheckpointDir = sTestCaseDir + '/checkpoint/'
makedirs(cfg.sResultDir)
makedirs(sTestCaseDir)
makedirs(sSampleDir)
makedirs(sCheckpointDir)
makedirs(sTestCaseDir + '/code/')
logger = Logger()
logger.set_dir(sTestCaseDir)
logger.set_casename(sTestName)
logger.log(sTestCaseDir)
commandline = ''
for arg in ['CUDA_VISIBLE_DEVICES="0" python3'] + sys.argv:
commandline += arg + ' '
logger.log(commandline)
logger.log(str_flags(cfg.__flags))
copydir(SOURCE_DIR + "code/", sTestCaseDir + '/source/code/')
copydir(SOURCE_DIR + "common/", sTestCaseDir + '/source/common/')
tf.logging.set_verbosity(tf.logging.ERROR)
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
config.gpu_options.allow_growth = True
def ListPasses(outputFilename):
c1File = ParseC1visualizerStream(os.path.basename(outputFilename), open(outputFilename, "r"))
for compiler_pass in c1File.passes:
Logger.log(compiler_pass.name)
class Trainer:
""" Train and Validation with single GPU """
def __init__(self, train_loader, val_loader, args):
self.train_loader = train_loader
self.val_loader = val_loader
self.args = args
self.model = get_model(args)
self.epochs = args.epochs
self.total_step = len(train_loader) * args.epochs
self.step = 0
self.epoch = 0
self.start_epoch = 1
self.lr = args.learning_rate
self.best_acc = 0
# Log
self.log_path = (
PROJECT_ROOT / Path(SAVE_DIR) /
Path(datetime.now().strftime("%Y%m%d%H%M%S") + "-")
).as_posix()
self.log_path = Path(self.get_dirname(self.log_path, args))
if not Path.exists(self.log_path):
Path(self.log_path).mkdir(parents=True, exist_ok=True)
self.logger = Logger("train", self.log_path, args.verbose)
self.logger.log("Checkpoint files will be saved in {}".format(self.log_path))
self.logger.add_level('STEP', 21, 'green')
self.logger.add_level('EPOCH', 22, 'cyan')
self.logger.add_level('EVAL', 23, 'yellow')
self.criterion = nn.CrossEntropyLoss()
if self.args.cuda:
self.criterion = self.criterion.cuda()
if args.half:
self.model.half()
self.criterion.half()
params = self.model.parameters()
self.optimizer = get_optimizer(args.optimizer, params, args)
def train(self):
self.eval()
for self.epoch in range(self.start_epoch, self.args.epochs+1):
self.adjust_learning_rate([int(self.args.epochs/2), int(self.args.epochs*3/4)], factor=0.1)
self.train_epoch()
self.eval()
self.logger.writer.export_scalars_to_json(
self.log_path.as_posix() + "/scalars-{}-{}-{}.json".format(
self.args.model,
self.args.seed,
self.args.activation
)
)
self.logger.writer.close()
def train_epoch(self):
self.model.train()
eval_metrics = EvaluationMetrics(['Loss', 'Acc', 'Time'])
for i, (images, labels) in enumerate(self.train_loader):
st = time.time()
self.step += 1
images = torch.autograd.Variable(images)
labels = torch.autograd.Variable(labels)
if self.args.cuda:
images = images.cuda()
labels = labels.cuda()
if self.args.half: images = images.half()
outputs, loss = self.compute_loss(images, labels)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
outputs = outputs.float()
loss = loss.float()
elapsed_time = time.time() - st
_, preds = torch.max(outputs, 1)
accuracy = (labels == preds.squeeze()).float().mean()
batch_size = labels.size(0)
eval_metrics.update('Loss', float(loss), batch_size)
eval_metrics.update('Acc', float(accuracy), batch_size)
eval_metrics.update('Time', elapsed_time, batch_size)
if self.step % self.args.log_step == 0:
self.logger.scalar_summary(eval_metrics.val, self.step, 'STEP')
# Histogram of parameters
for tag, p in self.model.named_parameters():
tag = tag.split(".")
tag = "Train_{}".format(tag[0]) + "/" + "/".join(tag[1:])
try:
self.logger.writer.add_histogram(tag, p.clone().cpu().data.numpy(), self.step)
self.logger.writer.add_histogram(tag+'/grad', p.grad.clone().cpu().data.numpy(), self.step)
except Exception as e:
print("Check if variable {} is not used: {}".format(tag, e))
self.logger.scalar_summary(eval_metrics.avg, self.step, 'EPOCH')
def eval(self):
self.model.eval()
eval_metrics = EvaluationMetrics(['Loss', 'Acc', 'Time'])
for i, (images, labels) in enumerate(self.val_loader):
st = time.time()
images = torch.autograd.Variable(images)
labels = torch.autograd.Variable(labels)
if self.args.cuda:
images = images.cuda()
labels = labels.cuda()
if self.args.half: images = images.half()
outputs, loss = self.compute_loss(images, labels)
outputs = outputs.float()
loss = loss.float()
elapsed_time = time.time() - st
_, preds = torch.max(outputs, 1)
accuracy = (labels == preds.squeeze()).float().mean()
batch_size = labels.size(0)
eval_metrics.update('Loss', float(loss), batch_size)
eval_metrics.update('Acc', float(accuracy), batch_size)
eval_metrics.update('Time', elapsed_time, batch_size)
# Save best model
if eval_metrics.avg['Acc'] > self.best_acc:
self.save()
self.logger.log("Saving best model: epoch={}".format(self.epoch))
self.best_acc = eval_metrics.avg['Acc']
self.maybe_delete_old_pth(log_path=self.log_path.as_posix(), max_to_keep=1)
self.logger.scalar_summary(eval_metrics.avg, self.step, 'EVAL')
def get_dirname(self, path, args):
path += "{}-".format(getattr(args, 'dataset'))
path += "{}-".format(getattr(args, 'seed'))
path += "{}".format(getattr(args, 'model'))
return path
def save(self, filename=None):
if filename is None:
filename = os.path.join(self.log_path, 'model-{}.pth'.format(self.epoch))
torch.save({
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'epoch': self.start_epoch,
'best_acc': self.best_acc,
'args': self.args
}, filename)
def load(self, filename=None):
if filename is None: filename = self.log_path
S = torch.load(filename) if self.args.cuda else torch.load(filename, map_location=lambda storage, location: storage)
self.model.load_state_dict(S['model'])
self.optimizer.load_state_dict(S['optimizer'])
self.epoch = S['epoch']
self.best_acc = S['best_acc']
self.args = S['args']
def maybe_delete_old_pth(self, log_path, max_to_keep):
"""Model filename must end with xxx-xxx-[epoch].pth
"""
# filename and time
pths = [(f, int(f[:-4].split("-")[-1])) for f in os.listdir(log_path) if f.endswith('.pth')]
if len(pths) > max_to_keep:
sorted_pths = sorted(pths, key=lambda tup: tup[1])
for i in range(len(pths) - max_to_keep):
os.remove(os.path.join(log_path, sorted_pths[i][0]))
def show_current_model(self):
print("\n".join("{}: {}".format(k, v) for k, v in sorted(vars(self.args).items())))
model_parameters = filter(lambda p: p.requires_grad, self.model.parameters())
total_params = np.sum([np.prod(p.size()) for p in model_parameters])
print('%s\n\n'%(type(self.model)))
print('%s\n\n'%(inspect.getsource(self.model.__init__)))
print('%s\n\n'%(inspect.getsource(self.model.forward)))
# Total 95
print("*"*40 + "%10s" % self.args.model + "*"*45)
print("*"*40 + "PARAM INFO" + "*"*45)
print("-"*95)
print("| %40s | %25s | %20s |" % ("Param Name", "Shape", "Number of Params"))
print("-"*95)
for name, param in self.model.named_parameters():
if param.requires_grad:
print("| %40s | %25s | %20d |" % (name, list(param.size()), np.prod(param.size())))
print("-"*95)
print("Total Params: %d" % (total_params))
print("*"*95)
def adjust_learning_rate(self, milestone, factor=0.1):
if self.epoch in milestone:
self.lr *= factor
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.lr
def compute_loss(self, images, labels):
outputs = self.model(images)
loss = self.criterion(outputs, labels)
return outputs, loss
help='run loaders periodically in background')
argp.add_argument('-publish',
type=int,
default=None,
help='publish datasets to kaggle every x seconds')
argp.add_argument('-pause',
type=int,
default=None,
help='pause x seconds after fetching a subreddit')
args = argp.parse_args()
# handle process termination
signal.signal(signal.SIGTERM, terminate)
try:
logger.log(f'\n{"-"*45}{"ENVIRONMENT":^15}{"-"*45}\n')
logger.log(Env.init())
logger.log(f'\n{"-"*45}{"STARTED":^15}{"-"*45}\n')
# load config
root = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(root, args.config)) as f:
config = json.load(f)
# kaggle client
kaggle = Kaggle(config=os.path.join('config', 'kaggle.json'))
# start background tasks
while not terminated:
for subreddit in args.subreddits:
class ImageCrawler:
NUM_PER_FETCH = 100
NUM_PROCESSES = 10
def __init__(self, database_config_path):
self.queue = JoinableQueue()
self.logger = Logger("image_crawler")
self.adapter = ImageStoreAdapter(database_config_path, self.logger)
def produce(self):
while True:
if self.queue.empty():
for image_id, link in self.adapter.load_undownloaded_images(
self.NUM_PER_FETCH):
self.logger.log("Producer: add new image to crawl:" +
image_id + " " + link)
self.queue.put((image_id, link))
time.sleep(10)
def consume(self, process_id):
while True:
self.logger.log("Consumer process:" + str(process_id) +
" fetch new image from queue")
if not self.queue.empty():
image_id, link = self.queue.get()
self.logger.log("Consumer process:" + str(process_id) +
" start crawling " + str(link))
image = common_utils.page_crawl(link)
if image != None:
self.logger.log(link + "crawled successfully")
self.adapter.store_image(image_id, image)
else:
self.logger.log(link + " failed at crawling")
self.adapter.update_image_status(
image_id, ImageIndexStatus.DOWNLOAD_FAILED)
self.queue.task_done()
time.sleep(1)
else:
self.logger.log("Queue empty")
time.sleep(10)
def run(self):
producer = Process(target=self.produce)
producer.start()
consumers = []
for i in range(self.NUM_PROCESSES):
consumer = Process(target=self.consume, args=(i, ))
consumers.append(consumer)
consumer.start()
for consumer in consumers:
consumer.join()
producer.join()
self.queue.join()
def list_passes(output_filename):
c1_file = parse_c1_visualizer_stream(output_filename,
open(output_filename, "r"))
for compiler_pass in c1_file.passes:
Logger.log(compiler_pass.name)
class Service(ServiceBase):
""" Siterummage Processing Queue microservice class """
#pylint: disable=too-many-instance-attributes
## Title text logged during initialisation.
title_text = 'Site Rummagge Processing Queue Microservice'
## Copyright text logged on initialisation etc.
copyright_text = 'Copyright 2021 Site Rummage'
## License text logged on initialisation etc.
license_text = 'All Rights Reserved. Proprietary and confidential'
def __init__(self, new_instance):
super().__init__()
self._quart = new_instance
## Instance of the logging wrapper class
self._logger = Logger()
## _is_initialised is inherited from parent class ServiceThread
self._is_initialised = False
self._configuration = None
self._db_interface = None
self._api_queue = None
self._queue_cache = None
self._processing_queue = None
def _initialise(self) -> bool:
self._logger.write_to_console = True
self._logger.initialise()
self._logger.log(
LogType.Info,
f'{self.title_text} {VERSION} (Core Version {CORE_VERSION})')
self._logger.log(LogType.Info, self.copyright_text)
self._logger.log(LogType.Info, self.license_text)
config_mgr = ConfigurationManager()
config_file = os.getenv('SITERUMMAGE_PROCESSINGQUEUE_CONFIG')
self._configuration = config_mgr.parse_config_file(config_file)
if not self._configuration:
self._logger.log(LogType.Error, config_mgr.last_error_msg)
return False
self._logger.log(LogType.Info, '+=== Configuration Settings ===+')
self._logger.log(LogType.Info, '+==============================+')
db_config = self._configuration.db_settings
self._logger.log(LogType.Info, '+== Database Settings :->')
self._logger.log(LogType.Info,
f'+= Cache Size : {db_config.cache_size}')
self._logger.log(
LogType.Info,
f'+= DB Filename : {db_config.database_file}')
self._logger.log(
LogType.Info,
f'+= Fail On No Database : {db_config.fail_on_no_database}')
self._logger.log(LogType.Info, '+== Api Settings :->')
self._logger.log(LogType.Info, '+= Auth Key : ******')
self._logger.log(LogType.Info, '+==============================+')
self._db_interface = DbInterface(db_config.database_file)
if not self._db_interface.database_exists():
if self._configuration.db_settings.fail_on_no_database:
self._logger.log(LogType.Error,
"DB doesn't exist and fail on create is set")
return False
if not self._db_interface.build_database():
self._logger.log(LogType.Error,
self._db_interface.last_error_message)
return False
self._logger.log(LogType.Info, 'Database created successfully')
if not self._db_interface.open():
self._logger.log(LogType.Error,
self._db_interface.last_error_message)
return False
self._processing_queue = UrlsBeingProcessed()
self._queue_cache = QueueCache(self._db_interface, self._configuration,
self._logger, self._processing_queue)
self._api_queue = ApiQueue(self._quart, self._db_interface,
self._configuration, self._processing_queue,
self._queue_cache)
self._is_initialised = True
return True
async def _main_loop(self):
...
def _shutdown(self):
self._logger.log(LogType.Info, 'Shutting down...')
if self._db_interface.is_connected:
self._db_interface.close()
self._logger.log(LogType.Info, '|-> Database connection closed')
def ListPasses(outputFilename):
c1File = ParseC1visualizerStream(os.path.basename(outputFilename),
open(outputFilename, "r"))
for compiler_pass in c1File.passes:
Logger.log(compiler_pass.name)
sTestCaseDir = cfg.sResultDir + sTestName + '/'
sSampleDir = sTestCaseDir + '/samples/'
sCheckpointDir = sTestCaseDir + '/checkpoint/'
makedirs(cfg.sResultDir)
makedirs(sTestCaseDir)
makedirs(sSampleDir)
makedirs(sCheckpointDir)
makedirs(sTestCaseDir + '/code/')
logger = Logger()
logger.set_dir(sTestCaseDir)
logger.set_casename(sTestName)
logger.log(sTestCaseDir)
commandline = ''
for arg in ['CUDA_VISIBLE_DEVICES="0" python3'] + sys.argv:
commandline += arg + ' '
logger.log(commandline)
logger.log(str_flags(cfg.__flags))
copydir(SOURCE_DIR + "code/", sTestCaseDir + '/source/code/')
copydir(SOURCE_DIR + "common/", sTestCaseDir + '/source/common/')
tf.logging.set_verbosity(tf.logging.ERROR)
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
config.gpu_options.allow_growth = True
class ImageCrawler:
NUM_PER_FETCH = 100
NUM_PROCESSES = 10
def __init__(self, database_config_path):
self.queue = JoinableQueue()
self.logger = Logger("image_crawler")
self.adapter = ImageStoreAdapter(database_config_path, self.logger)
def produce(self):
while True:
if self.queue.empty():
for image_id, link in self.adapter.load_undownloaded_images(self.NUM_PER_FETCH):
self.logger.log("Producer: add new image to crawl:" + image_id + " " + link)
self.queue.put((image_id, link))
time.sleep(10)
def consume(self, process_id):
while True:
self.logger.log("Consumer process:" + str(process_id) + " fetch new image from queue")
if not self.queue.empty():
image_id, link = self.queue.get()
self.logger.log("Consumer process:"+ str(process_id) + " start crawling " + str(link))
image = common_utils.page_crawl(link)
if image != None:
self.logger.log(link + "crawled successfully")
self.adapter.store_image(image_id, image)
else:
self.logger.log(link + " failed at crawling")
self.adapter.update_image_status(image_id, ImageIndexStatus.DOWNLOAD_FAILED)
self.queue.task_done()
time.sleep(1)
else:
self.logger.log("Queue empty")
time.sleep(10)
def run(self):
producer = Process(target=self.produce)
producer.start()
consumers = []
for i in range(self.NUM_PROCESSES):
consumer = Process(target=self.consume, args=(i,))
consumers.append(consumer)
consumer.start()
for consumer in consumers:
consumer.join()
producer.join()
self.queue.join()
