def work(self, thread_name, url_dao):
"""
This method is assigned to threads.
As long as there are items in the queue this method wil start crawling them.
:param thread_name: The name of the current thread
:param url_dao: an instance of UrlDAO
:return: Nothing
"""
try:
while True:
url = self.queue.get()
url_id = HashService.num_md5(url.url_string)
timestamp = datetime.now().strftime(
'%Y-%m-%dT%H:%M:%S.%f')[:-3] + "Z"
MyLogger.log(
LOG.CRAWLER, "url_id=" + str(url_id) + " url=" +
str(url.url_string) + " @timestamp=" + timestamp)
Spider(url, thread_name, url_dao, self.redis).run()
self.crawled.add(url)
self.parser.add_link_to_queue(url)
self.parser.start()
self.queue.task_done()
except BlacklistNotFoundError:
while self.queue.unfinished_tasks > 0:
self.queue.task_done()
raise MyThreadError
def __init__(self, cfg):
super(DefaultEvaluator, self).__init__()
self.cfg = cfg
self.debug = cfg.debug
self.callbacks = self.generate_callbacks()
self.arcs = self.load_arcs(cfg.args.arc_path)
self.writter = SummaryWriter(
os.path.join(self.cfg.logger.path, 'summary_runs'))
self.logger = MyLogger(__name__, cfg).getlogger()
self.size_acc = {
} # {'epoch1': [model_size, acc], 'epoch2': [model_size, acc], ...}
self.init_basic_settings()
def __init__(self, cfg, **kwargs):
"""
Trainer initialization.
Parameters
----------
model : nn.Module
Model with mutables.
mutator : BaseMutator
A mutator object that has been initialized with the model.
loss : callable
Called with logits and targets. Returns a loss tensor.
metrics : callable
Returns a dict that maps metrics keys to metrics data.
optimizer : Optimizer
Optimizer that optimizes the model.
num_epochs : int
Number of epochs of training.
dataset_train : torch.utils.data.Dataset
Dataset of training.
dataset_valid : torch.utils.data.Dataset
Dataset of validation/testing.
batch_size : int
Batch size.
workers : int
Number of workers used in data preprocessing.
device : torch.device
Device object. Either ``torch.device("cuda")`` or ``torch.device("cpu")``. When ``None``, trainer will
automatic detects GPU and selects GPU first.
log_frequency : int
Number of mini-batches to log metrics.
callbacks : list of Callback
Callbacks to plug into the trainer. See Callbacks.
"""
self.cfg = cfg
self.logger = MyLogger(__name__, cfg).getlogger()
self.set_up()
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
self.mutator.to(self.device)
self.loss.to(self.device)
self.callbacks = self.generate_callbacks()
for callback in self.callbacks:
callback.build(self.model, self.mutator, self)
def setup(self, args):
self.merge_from_file((args.config_file))
self.merge_from_list(args.opts)
# calibrate the path configruration
if self.model.resume_path:
self.logger.path = os.path.dirname(self.model.resume_path)
else:
if not self.logger.name:
self.logger.name = 'checkpoint'
self.logger.version = self._version_logger(self.output_root, self.logger.name)
self.logger.path = os.path.join(self.output_root, self.logger.name, f'version_{self.logger.version}')
self.logger.log_file = os.path.join(self.logger.path, 'log.txt')
cfg_name = os.path.basename(args.config_file)
self.logger.cfg_file = os.path.join(self.logger.path, cfg_name)
os.makedirs(self.logger.path, exist_ok=True)
self.freeze()
# backup cfg and args
logger = MyLogger('NAS', self).getlogger()
logger.info(self)
logger.info(args)
with open(self.logger.cfg_file, 'w') as f:
f.write(str(self))
def __crawl_page(self, url):
"""
This method is the main method of the spider class.
If the layer of the url or the size of crawled is bigger than it's corresponding property the program
will clear the queue.
If it's not it wil start crawling the page by opening a request and getting the html.
It'll then save the html to redis.
After that it'll gather all links from that page and add those links to the queue.
:param url: URL object
:return: nothing
"""
start_time = time.time()
# Check if url is already crawled or max depth of urls is not exceeded
if url.layer > Properties.SPIDER_MAX_DEPTH \
or len(self.crawled) > Properties.SPIDER_MAX_PAGES:
self.deque.clear()
else:
try:
request = self.__get_request(url)
html = self.__get_html(request)
if len(html) > 0:
self.__save_html_to_redis(html)
self.__add_links_to_queue(Spider.__gather_links(url, html))
self.crawled.add(url)
print(
self.name,
"is now crawling {}\n\t\t\t\t\t\t Queue {} | Crawled {} | Layer: {} | Duration: {}"
.format(str(url), str(len(self.deque)),
str(len(self.crawled)), str(url.layer),
time.time() - start_time))
except req.HTTPError as e:
MyLogger.log(
LOG.SPIDER, "HTTP Error occurred [{0}]: {1} {2}".format(
str(e.code), e.filename, e.reason))
except req.URLError as e:
MyLogger.log(LOG.SPIDER,
"URL Error occurred: {0}".format(e.reason))
except ssl.SSLError as e:
MyLogger.log(LOG.SPIDER, "SSL Error occurred: {0}".format(e))
except socket.timeout as e:
MyLogger.log(LOG.SPIDER, "Timeout occurred: {0}".format(e))
class BaseTransforms(object):
def __init__(self, cfg):
self.cfg = cfg
self.logger_print = MyLogger(__name__, cfg).getlogger()
self.is_train = cfg.dataset.is_train
def get_transform(self):
if not self.is_train:
self.logger_print.info('Generating validation transform ...')
transform = self.valid_transform
self.logger_print.info(f'Valid transform={transform}')
else:
self.logger_print.info('Generating training transform ...')
transform = self.train_transform
self.logger_print.info(f'Train transform={transform}')
return transform
@property
def valid_transform(self):
raise NotImplementedError
@property
def train_transform(self):
raise NotImplementedError
class Trainer(BaseTrainer):
def __init__(self, cfg, **kwargs):
"""
Trainer initialization.
Parameters
----------
model : nn.Module
Model with mutables.
mutator : BaseMutator
A mutator object that has been initialized with the model.
loss : callable
Called with logits and targets. Returns a loss tensor.
metrics : callable
Returns a dict that maps metrics keys to metrics data.
optimizer : Optimizer
Optimizer that optimizes the model.
num_epochs : int
Number of epochs of training.
dataset_train : torch.utils.data.Dataset
Dataset of training.
dataset_valid : torch.utils.data.Dataset
Dataset of validation/testing.
batch_size : int
Batch size.
workers : int
Number of workers used in data preprocessing.
device : torch.device
Device object. Either ``torch.device("cuda")`` or ``torch.device("cpu")``. When ``None``, trainer will
automatic detects GPU and selects GPU first.
log_frequency : int
Number of mini-batches to log metrics.
callbacks : list of Callback
Callbacks to plug into the trainer. See Callbacks.
"""
self.cfg = cfg
self.logger = MyLogger(__name__, cfg).getlogger()
self.set_up()
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
self.mutator.to(self.device)
self.loss.to(self.device)
self.callbacks = self.generate_callbacks()
for callback in self.callbacks:
callback.build(self.model, self.mutator, self)
def set_up(self):
# model
self.model = build_model(self.cfg)
self.logger.info(f"Building model {self.cfg.model.name} ...")
# mutator
# self.logger.info('Cell choices: {}'.format(model.layers[0].nodes[0].cell_x.op_choice.choices))
self.mutator = build_mutator(self.model, self.cfg)
for x in self.mutator.mutables:
if isinstance(x, nni.nas.pytorch.mutables.LayerChoice):
self.logger.info('Cell choices: {}'.format(x.choices))
break
self.logger.info(f"Building mutator {self.cfg.mutator.name} ...")
# dataset
self.batch_size = self.cfg.dataset.batch_size
self.workers = self.cfg.dataset.workers
self.dataset_train, self.dataset_valid = build_dataset(self.cfg)
self.logger.info(f"Building dataset {self.cfg.dataset.name} ...")
# loss
self.loss = build_loss_fn(self.cfg)
self.logger.info(f"Building loss function {self.cfg.loss.name} ...")
# optimizer
self.optimizer = generate_optimizer(
model=self.model,
optim_name=self.cfg.optim.name,
lr=self.cfg.optim.base_lr,
momentum=self.cfg.optim.momentum,
weight_decay=self.cfg.optim.weight_decay)
self.logger.info(f"Building optimizer {self.cfg.optim.name} ...")
# scheduler
self.scheduler_params = parse_cfg_for_scheduler(
self.cfg, self.cfg.optim.scheduler.name)
self.lr_scheduler = generate_scheduler(self.optimizer,
self.cfg.optim.scheduler.name,
**self.scheduler_params)
self.logger.info(
f"Building optimizer scheduler {self.cfg.optim.scheduler.name} ..."
)
# miscellaneous
self.num_epochs = self.cfg.trainer.num_epochs
self.log_frequency = self.cfg.logger.log_frequency
self.start_epoch = 0
@abstractmethod
def train_one_epoch(self, epoch):
pass
@abstractmethod
def validate_one_epoch(self, epoch):
pass
@abstractmethod
def test_one_epoch(self, epoch):
pass
def train(self, validate=True):
self.resume()
self.train_meters = None
self.valid_meters = None
for epoch in range(self.start_epoch, self.num_epochs):
for callback in self.callbacks:
callback.on_epoch_begin(epoch)
# training
self.logger.info("Epoch {} Training".format(epoch))
self.train_meters = self.train_one_epoch(epoch)
self.logger.info("Final training metric: {}".format(
self.train_meters))
if validate:
# validation
self.logger.info("Epoch {} Validatin".format(epoch))
self.valid_meters = self.validate_one_epoch(epoch)
self.logger.info("Final test metric: {}".format(
self.valid_meters))
for callback in self.callbacks:
if isinstance(callback, CheckpointCallback):
if self.valid_meters:
meters = self.valid_meters
else:
meters = self.train_meters
callback.update_best_metric(
meters.meters['save_metric'].avg)
callback.on_epoch_end(epoch)
def validate(self):
self.validate_one_epoch(-1)
def test(self):
return self.test_one_epoch(-1)
def export(self, file):
"""
Call ``mutator.export()`` and dump the architecture to ``file``.
Parameters
----------
file : str
A file path. Expected to be a JSON.
"""
mutator_export = self.mutator.export()
with open(file, "w") as f:
json.dump(mutator_export,
f,
indent=2,
sort_keys=True,
cls=TorchTensorEncoder)
def checkpoint(self):
"""
Return trainer checkpoint.
"""
raise NotImplementedError("Not implemented yet")
def enable_visualization(self):
"""
Enable visualization. Write graph and training log to folder ``logs/<timestamp>``.
"""
sample = None
for x, _ in self.train_loader:
sample = x.to(self.device)[:2]
break
if sample is None:
self.logger.warning("Sample is %s.", sample)
self.logger.info(
"Creating graph json, writing to %s. Visualization enabled.",
self.cfg.logger.path)
with open(os.path.join(self.cfg.logger.path, "graph.json"), "w") as f:
json.dump(self.mutator.graph(sample), f)
self.visualization_enabled = True
def _write_graph_status(self):
if hasattr(self,
"visualization_enabled") and self.visualization_enabled:
print(json.dumps(self.mutator.status()),
file=self.status_writer,
flush=True)
def model_size(self, name='size'):
assert name in ['size', 'flops']
size = self.cfg.input.size
if self.cfg.dataset.is_3d:
input_size = (1, 1, self.cfg.dataset.slice_num, *size)
else:
input_size = (1, 3, *size)
return flops_size_counter(self.model, input_size)[name]
def generate_callbacks(self):
'''
Args：
func: a function to generate other callbacks, must return a list
Return:
a list of callbacks.
'''
self.ckpt_callback = CheckpointCallback(
checkpoint_dir=self.cfg.logger.path,
name='best_search.pth',
mode=self.cfg.callback.checkpoint.mode)
self.arch_callback = ArchitectureCheckpoint(self.cfg.logger.path)
self.relevance_callback = RelevanceCallback(
save_path=self.cfg.logger.path,
filename=self.cfg.callback.relevance.filename)
callbacks = [
self.ckpt_callback, self.arch_callback, self.relevance_callback
]
return callbacks
def metrics(self, *args, **kwargs):
return metrics(*args, **kwargs)
def resume(self):
self.best_metric = -999
path = self.cfg.model.resume_path
if path:
assert os.path.exists(path), "{} does not exist".format(path)
ckpt = torch.load(path)
self.start_epoch = ckpt['epoch'] + 1
self.model.load_state_dict(ckpt['model_state_dict'])
self.mutator.load_state_dict(ckpt['mutator_state_dict'])
self.optimizer.load_state_dict(ckpt['optimizer_state_dict'])
self.lr_scheduler.load_state_dict(ckpt['lr_scheduler_state_dict'])
self.best_metric = ckpt['best_metric']
self.logger.info('Resuming training from epoch {}'.format(
self.start_epoch))
for callback in self.callbacks:
if isinstance(callback, CheckpointCallback):
if self.best_metric == -999:
self.best_metric = callback.best_metric
else:
callback.best_metric = self.best_metric
if len(self.cfg.trainer.device_ids) > 1:
device_ids = self.cfg.trainer.device_ids
num_gpus_available = torch.cuda.device_count()
assert num_gpus_available >= len(
device_ids), "you can only use {} device(s)".format(
num_gpus_available)
self.model = torch.nn.DataParallel(self.model,
device_ids=device_ids)
# self.mutator = torch.nn.DataParallel(self.mutator, device_ids=device_ids) # mutator doesn't support dataparallel yet.
def save_cfg(self):
cfg_file = self.cfg.logger.cfg_file
with open(cfg_file, 'w') as f:
f.write(str(self.cfg))
print(f'Saving config file to {cfg_file}')
def main(args, stream):
if not args.class_wise_sampling and args.data_seed is None:
args.data_seed = random.randint(1, 1e8)
# * Prepare data_module *
dm = DInterface(**vars(args))
args.class_dict = dm.init_data['class_dict']
args.classes = list(args.class_dict.keys())
args.num_classes = len(args.class_dict)
global_bs = args.gpus * args.batch_size if args.gpus > 1 else args.batch_size
# * Build model *
net = build_model(**vars(args))
if args.load_pretrained:
pretrained_path = load_pretrain_path_by_args(args, '.pth.tar')
bl_layers = None
if args.mode_name in ['train', 'finetune']:
bl_layers = ['classifier', 'fc']
net = load_ckpt(net, pretrained_path,
train=(args.mode_name == 'train'),
block_layers=bl_layers,
map_keys=args.map_keys,
verbose=True)
model = BasicModel(net, **vars(args))
# Resume
load_path = load_model_path_by_args(args)
if load_path is not None:
model.load_from_checkpoint(checkpoint_path=load_path, strict=False)
# * validate mode *
if args.mode_name in ['val', 'test']:
model.final_val = True
trainer = Trainer.from_argparse_args(args)
trainer.validate(model, datamodule=dm)
return
# * Callbacks *
# Checkpoint callbacks
if args.ckpt == 'debug' or not args.save_ckpt:
# ckpt_callback = get_checkpoint_callback(args.ckpt, save_last=False, save_top_k=0)
ckpt_callback = get_checkpoint_callback(f'Task_models/{args.net_suffix}', save_last=False, save_top_k=1)
else:
cpDir = '{}/{}_{}'.format(args.ckpt, args.model_name, args.net_suffix)
every_n_train_steps = dm.num_samples//global_bs
if args.ckpt_ever_n_epoch:
every_n_train_steps *= args.ckpt_ever_n_epoch
ckpt_callback = get_checkpoint_callback(
cpDir, 'val/acc', 'max',
filename='{epoch}_{val_acc:.2f}',
every_n_train_steps=every_n_train_steps)
# Logging callbacks
if args.train_scale >= 1:
version_str = f'{args.dataset}_ts={int(args.train_scale):d}'
else:
version_str = f'{args.dataset}_ts={args.train_scale:.2%}'
logger_tb = pl_log.TensorBoardLogger(args.log_dir, args.exp_name, version_str)
log_dir = logger_tb.log_dir
args.logger = [logger_tb]
if pl.utilities.distributed._get_rank() == 0:
os.makedirs(log_dir)
stream.all_to_file(log_dir+'/{}.log'.format(
args.exp_name), flush=True)
# logger_eren = MyLogger(log_dir, 'exp_log')
logger_eren = MyLogger(None)
args.progress_bar_refresh_rate = 0 # Use MyLogger() install of progress_bar
lr_monitor = pl.callbacks.LearningRateMonitor(logging_interval='epoch')
args.callbacks = [
ckpt_callback, logger_eren, lr_monitor
]
# * Accelerating *
if args.gpus > 1 and (args.accelerator is None and args.plugins is None):
args.accelerator = 'ddp'
if args.accelerator == 'ddp':
args.plugins = pl.plugins.DDPPlugin(find_unused_parameters=False)
if args.mode_name in ['train', 'finetune']:
args.benchmark = True
# * Begin training and testing *
trainer = Trainer.from_argparse_args(args)
# Begin training
trainer.fit(model, datamodule=dm)
# Final test
model.final_val = True
trainer.validate(model, ckpt_path='best', datamodule=dm)
# Other operations
print('Best ckpt: {}'.format(trainer.checkpoint_callback.best_model_path))
if args.ckpt != 'debug' and args.save_ckpt:
checkpoint_standardize(cpDir)
import os
import re
import numpy as np
import pandas as pd
import requests
import sqlite3
import json
import base64
import hashlib
import socket
import struct
from utils import Configuration
from utils import MyLogger
config = Configuration()
LOGGER_CONTROL = MyLogger("IPhandler", config=config)
LOGGER_CONTROL.disable_file()
LOGGER = LOGGER_CONTROL.getLogger()
requests.packages.urllib3.disable_warnings()
IPBEHAVIOUR_DB = 'ipbehaviour.db'
regular = r'^(((25[0-5]|2[0-4]\d|1\d{2})|([1-9]?\d))\.){3}((25[0-5]|2[0-4]\d|1\d{2})|([1-9]?\d))$'
def check_ip(fuc):
def wrapper(*args, **kwargs):
if kwargs.get('ip'):
ip = kwargs['ip']
else:
ip = args[1]
nargs=argparse.REMAINDER)
args = parser.parse_args()
config_file = args.config_file
if os.path.isdir(args.arc_path) and args.arc_path[-1] != '/':
args.arc_path += '/'
arc_path = args.arc_path
assert config_file and arc_path, f"please check whether {config_file} and {arc_path} exists"
# configuration
cfg = setup_cfg(args)
with open(os.path.join(cfg.logger.path, 'retrain.yaml'), 'w') as f:
f.write(str(cfg))
cfg.update({'args': args})
logger = MyLogger(__name__, cfg).getlogger()
logger.info('args:{}'.format(args))
if args.cam_only:
model = build_model(cfg)
apply_fixed_architecture(model, args.arc_path)
cam = CAM3D(cfg, model)
cam.run()
else:
evaluator = build_evaluator(cfg)
if os.path.isdir(arc_path):
best_arch_info = evaluator.compare()
evaluator.run(best_arch_info['arc'])
elif os.path.isfile(arc_path):
evaluator.run(arc_path, validate=True, test=args.test_only)
else:
import struct
import time
import math
import pickle
import random
#import Levenshtein
import numpy as np
import pandas as pd
###user layer
from utils import check_ip
from utils import check_content
from utils import Configuration
from utils import MyLogger
from gst.detection_api import url_detection_api
config = Configuration()
LOGGER_CONTROL = MyLogger("Feature_engine", config=config)
LOGGER_CONTROL.disable_file()
LOGGER = LOGGER_CONTROL.getLogger()
class Feature_engine(object):
# @check_content
def __init__(self, http_log_content=None):
self.http_log_content = http_log_content
ac_tree = './ac/dir_traver.pkl'
with open(ac_tree, 'br') as f:
self.dir_traver_ac = pickle.load(f)
#self.feature_columns=['Sum_sec_flow','Feq_sec_flow','Sum_xss','Feq_xss','Sum_dir','Feq_dir',\
# 'Sum_404_error','Feq_404_error','H_status','src_ip','dest_ip','Malics_urls']
self.feature_columns=['Sum_flow','Sum_sec_flow','Feq_sec_flow','Sum_xss','Feq_xss','Sum_dir','Feq_dir',\
'Sum_404_error','Feq_404_error','H_status','Cls_url','Sum_packets','Mean_packets',\
##sys layer
import os
import json
import pdb
import time
import numpy as np
import pandas as pd
##user layer
from datalib import IPHandle
from feature_engine2 import Feature_engine
from behaviour import Behaviour
from utils import Configuration
from utils import MyLogger
config = Configuration()
LOGGER_CONTROL = MyLogger("main",config=config)
LOGGER_CONTROL.disable_file()
LOGGER = LOGGER_CONTROL.getLogger()
starttime = time.time()
#filename="train/awvs_xss.log"
filename="train/http.log.3"
#filename="train/scan.log"
#filename="train/nikto.log"
#filename="train/netspark.log"
handle = IPHandle(http_log=filename)
handle.read_http_log()
#wangsu_ip = handle.db_wsip()
engine = Feature_engine(handle.http_log_content)
import json
import pdb
import time
import sqlite3
import numpy as np
import pandas as pd
##user layer
import datalib
import feature_engine
from utils import Configuration
#from utils import check_ip
#from utils import check_content
from utils import Configuration
from utils import MyLogger
config = Configuration()
LOGGER_CONTROL = MyLogger("behaviour", config=config)
LOGGER_CONTROL.disable_file()
LOGGER = LOGGER_CONTROL.getLogger()
starttime = time.time()
filename = "http.log.3"
handle = datalib.IPHandle(http_log=filename)
handle.read_http_log()
wangsu_ip = handle.get_wsip()
engine = feature_engine.Feature_engine(handle.http_log_content)
df = engine.data_clean()
all_tb, ws_tb, os_tb = engine.webscan_feature(input_data=df[:100],
ws_ipall=wangsu_ip)
print(all_tb)
def __get_html(self, request):
"""
This method will return the HTML of the request.
:param request: Request object
:return: The HTML of the request object
"""
html_string = ''
try:
response = req.urlopen(request, timeout=self.TIMEOUT_TIME)
if 'text/html' in response.getheader('Content-Type'):
html_bytes = response.read()
html_string = html_bytes.decode("utf-8").strip()
except UnicodeDecodeError as e:
MyLogger.log(LOG.SPIDER,
"UnicodeDecodeError occurred: {0}".format(e))
except socket.timeout as e:
MyLogger.log(LOG.SPIDER, "Timeout occurred: {0}".format(e))
except ConnectionResetError as e:
MyLogger.log(
LOG.SPIDER, "ConnectionResetError occurred [{0}]: {1}".format(
str(e.errno), e.strerror))
except ssl.CertificateError as e:
MyLogger.log(LOG.SPIDER,
"SSL CertificateError: {0}".format(e.args))
except BadStatusLine as e:
MyLogger.log(LOG.SPIDER, "BadStatusLine: {0}".format(e.args))
except IncompleteRead as e:
MyLogger.log(LOG.SPIDER, "IncompleteRead: {0}".format(e.args))
return html_string
def __init__(self, cfg):
self.cfg = cfg
self.logger_print = MyLogger(__name__, cfg).getlogger()
self.is_train = cfg.dataset.is_train
import os
import pdb
import json
import time
from time import ctime
import numpy as np
import pandas as pd
import argparse
import threading
from concurrent.futures import ThreadPoolExecutor
##user layer
from datalib import IPHandle
from utils import Configuration
from utils import MyLogger
config = Configuration()
LOGGER_CONTROL = MyLogger("update", config=config, update=True)
LOGGER_CONTROL.disable_file()
LOGGER = LOGGER_CONTROL.getLogger()
PARSER = argparse.ArgumentParser()
## Input Arguments
PARSER.add_argument('--filename',
'-f',
help='filename',
type=str,
default='http.log')
param, _ = PARSER.parse_known_args()
filename = param.filename
#最大线程个数是10，通过线程池设置
if config.noconf == False:
class DefaultEvaluator(BaseEvaluator):
def __init__(self, cfg):
super(DefaultEvaluator, self).__init__()
self.cfg = cfg
self.debug = cfg.debug
self.callbacks = self.generate_callbacks()
self.arcs = self.load_arcs(cfg.args.arc_path)
self.writter = SummaryWriter(
os.path.join(self.cfg.logger.path, 'summary_runs'))
self.logger = MyLogger(__name__, cfg).getlogger()
self.size_acc = {
} # {'epoch1': [model_size, acc], 'epoch2': [model_size, acc], ...}
self.init_basic_settings()
def init_basic_settings(self):
'''init train_epochs, device, loss_fn, dataset, and dataloaders
'''
# train epochs
try:
self.train_epochs = self.cfg.args.train_epochs
except:
self.train_epochs = 1
# device
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.logger.info(f"Using device: {self.device}")
# loss_fn
self.loss_fn = build_loss_fn(self.cfg)
self.loss_fn.to(self.device)
self.logger.info(f"Building loss function ...")
# dataset
self.train_dataset, self.test_dataset = build_dataset(self.cfg)
# dataloader
self.train_loader = torch.utils.data.DataLoader(
self.train_dataset,
batch_size=self.cfg.dataset.batch_size,
shuffle=True,
num_workers=self.cfg.dataset.workers,
pin_memory=True)
self.test_loader = torch.utils.data.DataLoader(
self.test_dataset,
batch_size=self.cfg.dataset.batch_size,
shuffle=False,
num_workers=self.cfg.dataset.workers,
pin_memory=True)
self.logger.info(f"Building dataset and dataloader ...")
def load_arcs(self, arc_path):
'''load arch json files
Args:
arc_path:
(file): [arc_path]
(dir): [arc_path/epoch_0.json, arc_path/epoch_1.json, ...]
'''
if os.path.isfile(arc_path):
return [arc_path]
else:
arcs = os.listdir(arc_path)
arcs = [
os.path.join(arc_path, arc) for arc in arcs
if arc.split('.')[-1] == 'json'
]
arcs = sorted(
arcs,
key=lambda x: int(
os.path.splitext(os.path.basename(x))[0].split('_')[1]))
return arcs
def reset(self):
'''mutable can be only initialized for once, hence it needs to
reset model, optimizer, scheduler when run a new trial.
'''
# model
self.model = build_model(self.cfg)
self.model.to(self.device)
self.logger.info(f"Building model {self.cfg.model.name} ...")
# load teacher model if using knowledge distillation
if hasattr(self.cfg, 'kd') and self.cfg.kd.enable:
self.kd_model = load_kd_model(self.cfg).to(self.device)
self.kd_model.eval()
self.logger.info(
f"Building teacher model {self.cfg.kd.model.name} ...")
else:
self.kd_model = None
# optimizer
self.optimizer = generate_optimizer(
model=self.model,
optim_name=self.cfg.optim.name,
lr=self.cfg.optim.base_lr,
momentum=self.cfg.optim.momentum,
weight_decay=self.cfg.optim.weight_decay)
self.logger.info(f"Building optimizer {self.cfg.optim.name} ...")
# scheduler
self.scheduler_params = parse_cfg_for_scheduler(
self.cfg, self.cfg.optim.scheduler.name)
self.lr_scheduler = generate_scheduler(self.optimizer,
self.cfg.optim.scheduler.name,
**self.scheduler_params)
self.logger.info(
f"Building optim.scheduler {self.cfg.optim.scheduler.name} ...")
def compare(self):
self.logger.info("=" * 20)
self.logger.info("Selecting the best architecture ...")
self.enable_writter = False
# split train dataset into train and valid dataset
train_size = int(0.8 * len(self.train_dataset))
valid_size = len(self.train_dataset) - train_size
self.train_dataset_part, self.valid_dataset_part = torch.utils.data.random_split(
self.train_dataset, [train_size, valid_size])
# dataloader
self.train_loader_part = torch.utils.data.DataLoader(
self.train_dataset_part,
batch_size=self.cfg.dataset.batch_size,
shuffle=True,
num_workers=self.cfg.dataset.workers,
pin_memory=True)
self.valid_loader_part = torch.utils.data.DataLoader(
self.valid_dataset_part,
batch_size=self.cfg.dataset.batch_size,
shuffle=True,
num_workers=self.cfg.dataset.workers,
pin_memory=True)
# choose the best architecture
for arc in self.arcs:
self.reset()
self.mutator = apply_fixed_architecture(self.model, arc)
size = self.model_size()
arc_name = os.path.basename(arc)
self.logger.info(f"{arc} Model size={size*4/1024**2} MB")
# train
for epoch in range(self.train_epochs):
self.train_one_epoch(epoch, self.train_loader_part)
val_acc = self.valid_one_epoch(-1, self.valid_loader_part)
self.size_acc[arc_name] = {
'size': size,
'val_acc': val_acc,
'arc': arc
}
sorted_size_acc = sorted(
self.size_acc.items(),
key=lambda x: x[1]['val_acc']['save_metric'].avg,
reverse=True)
return sorted_size_acc[0][1]
def run(self, arc, validate=True, test=False):
'''retrain the best-performing arch from scratch
arc: the json file path of the best-performing arch
'''
self.logger.info("=" * 20)
self.logger.info("Retraining the best architecture ...")
self.enable_writter = True
self.reset()
# init model and mutator
self.mutator = apply_fixed_architecture(self.model, arc)
size = self.model_size()
arc_name = os.path.basename(arc)
self.logger.info(f"{arc_name} Model size={size*4/1024**2} MB")
# callbacks
for callback in self.callbacks:
callback.build(self.model, self.mutator, self)
# resume
self.start_epoch = 0
self.resume()
# fintune
# todo: improve robustness, bug of optimizer resume
# if self.cfg.model.finetune:
# self.logger.info("Freezing params of conv part ...")
# for name, param in self.model.named_parameters():
# if 'dense' not in name:
# param.requires_grad = False
# dataparallel
if len(self.cfg.trainer.device_ids) > 1:
device_ids = self.cfg.trainer.device_ids
num_gpus_available = torch.cuda.device_count()
assert num_gpus_available >= len(
device_ids), "you can only use {} device(s)".format(
num_gpus_available)
self.model = torch.nn.DataParallel(self.model,
device_ids=device_ids)
if self.kd_model:
self.kd_model = torch.nn.DataParallel(self.kd_model,
device_ids=device_ids)
if test:
meters = self.test_one_epoch(-1, self.test_loader)
self.logger.info(f"Final test metrics= {meters}")
return meters
# start training
for epoch in range(self.start_epoch, self.cfg.evaluator.num_epochs):
for callback in self.callbacks:
callback.on_epoch_begin(epoch)
self.logger.info("Epoch %d Training", epoch)
self.train_one_epoch(epoch, self.train_loader)
if validate:
self.logger.info("Epoch %d Validating", epoch)
self.valid_one_epoch(epoch, self.test_loader)
self.lr_scheduler.step()
self.cur_meters = getattr(self, 'valid_meters', self.train_meters)
for callback in self.callbacks:
if isinstance(callback, CheckpointCallback):
callback.update_best_metric(
self.cur_meters.meters['save_metric'].avg)
callback.on_epoch_end(epoch)
self.logger.info("Final best Prec@1 = {:.4%}".format(self.best_metric))
def train_one_epoch(self, epoch, dataloader):
config = self.cfg
self.train_meters = AverageMeterGroup()
cur_lr = self.optimizer.param_groups[0]["lr"]
self.logger.info("Epoch %d LR %.6f", epoch, cur_lr)
if self.enable_writter:
self.writter.add_scalar("lr", cur_lr, global_step=epoch)
self.model.train()
for step, (x, y) in enumerate(dataloader):
if self.debug and step > 1:
break
for callback in self.callbacks:
callback.on_batch_begin(epoch)
x, y = x.to(self.device,
non_blocking=True), y.to(self.device,
non_blocking=True)
bs = x.size(0)
# mixup data
if config.mixup.enable:
x, y_a, y_b, lam = mixup_data(x, y, config.mixup.alpha)
mixup_y = [y_a, y_b, lam]
# forward
logits = self.model(x)
# loss
if isinstance(logits, tuple):
logits, aux_logits = logits
if config.mixup.enable:
aux_loss = mixup_loss_fn(self.loss_fn, aux_logits,
*mixup_y)
else:
aux_loss = self.loss_fn(aux_logits, y)
else:
aux_loss = 0.
if config.mixup.enable:
loss = mixup_loss_fn(self.loss_fn, logits, *mixup_y)
else:
loss = self.loss_fn(logits, y)
if config.model.aux_weight > 0:
loss += config.model.aux_weight * aux_loss
if self.kd_model:
teacher_output = self.kd_model(x)
loss += (1 - config.kd.loss.alpha) * loss + loss_fn_kd(
logits, teacher_output, self.cfg.kd.loss)
# backward
loss.backward()
# gradient clipping
# nn.utils.clip_grad_norm_(model.parameters(), 20)
if (step + 1) % config.trainer.accumulate_steps == 0:
self.optimizer.step()
self.optimizer.zero_grad()
# post-processing
accuracy = metrics(logits, y,
topk=(1, 3)) # e.g. {'acc1':0.65, 'acc3':0.86}
self.train_meters.update(accuracy)
self.train_meters.update({'train_loss': loss.item()})
if step % config.logger.log_frequency == 0 or step == len(
dataloader) - 1:
self.logger.info(
"Train: [{:3d}/{}] Step {:03d}/{:03d} {}".format(
epoch + 1, config.trainer.num_epochs, step,
len(dataloader) - 1, self.train_meters))
for callback in self.callbacks:
callback.on_batch_end(epoch)
if self.enable_writter:
self.writter.add_scalar("loss/train",
self.train_meters['train_loss'].avg,
global_step=epoch)
self.writter.add_scalar("acc1/train",
self.train_meters['acc1'].avg,
global_step=epoch)
self.writter.add_scalar("acc3/train",
self.train_meters['acc3'].avg,
global_step=epoch)
self.logger.info("Train: [{:3d}/{}] Final result {}".format(
epoch + 1, config.trainer.num_epochs, self.train_meters))
return self.train_meters
def valid_one_epoch(self, epoch, dataloader):
config = self.cfg
self.valid_meters = AverageMeterGroup()
self.model.eval()
y_true = []
y_pred = []
with torch.no_grad():
for step, (X, y) in enumerate(dataloader):
if self.debug and step > 1:
break
X, y = X.to(self.device,
non_blocking=True), y.to(self.device,
non_blocking=True)
bs = X.size(0)
# forward
logits = self.model(X)
# loss
if isinstance(logits, tuple):
logits, aux_logits = logits
aux_loss = self.loss_fn(aux_logits, y)
else:
aux_loss = 0.
loss = self.loss_fn(logits, y)
if config.model.aux_weight > 0:
loss = loss + config.model.aux_weight * aux_loss
# post-processing
y_true.append(y.cpu().detach())
y_pred.append(logits.cpu().detach())
accuracy = metrics(logits, y, topk=(1, 3))
self.valid_meters.update(accuracy)
self.valid_meters.update({'valid_loss': loss.item()})
if step % config.logger.log_frequency == 0 or step == len(
dataloader) - 1:
self.logger.info(
"Valid: [{:3d}/{}] Step {:03d}/{:03d} {}".format(
epoch + 1, config.trainer.num_epochs, step,
len(dataloader) - 1, self.valid_meters))
y_true = torch.cat(y_true)
y_pred = torch.cat(y_pred)
self.valid_report = parse_preds(
np.array(y_true.detach().cpu().numpy()),
np.array(y_pred.detach().cpu().numpy()))
self.valid_report['acc1'] = self.valid_meters['acc1'].avg
self.valid_report['epoch'] = epoch
self.logger.info(self.valid_report['cls_report'])
self.logger.info(self.valid_report['covid_auc'])
if self.enable_writter and epoch > 0:
self.writter.add_scalar("loss/valid",
self.valid_meters['valid_loss'].avg,
global_step=epoch)
self.writter.add_scalar("acc1/valid",
self.valid_meters['acc1'].avg,
global_step=epoch)
self.writter.add_scalar("acc3/valid",
self.valid_meters['acc3'].avg,
global_step=epoch)
self.logger.info("Valid: [{:3d}/{}] Final result {}".format(
epoch + 1, config.trainer.num_epochs, self.valid_meters))
return self.valid_meters
# if self.cfg.callback.checkpoint.mode: # the more the better, e.g. acc
# return self.valid_meters['acc1'].avg
# else: # the less, the better, e.g. epe
# return self.valid_meters['valid_loss'].avg
def test_one_epoch(self, epoch, dataloader):
config = self.cfg
self.valid_meters = AverageMeterGroup()
self.model.eval()
y_true = []
y_pred = []
with torch.no_grad():
for step, (X, y) in enumerate(dataloader):
if self.debug and step > 1:
break
X, y = X.to(self.device,
non_blocking=True), y.to(self.device,
non_blocking=True)
bs = X.size(0)
# forward
logits = self.model(X)
# loss
if isinstance(logits, tuple):
logits, aux_logits = logits
aux_loss = self.loss_fn(aux_logits, y)
else:
aux_loss = 0.
loss = self.loss_fn(logits, y)
if config.model.aux_weight > 0:
loss = loss + config.model.aux_weight * aux_loss
# post-processing
y_true.append(y.cpu().detach())
y_pred.append(logits.cpu().detach())
accuracy = metrics(logits, y, topk=(1, 3))
self.valid_meters.update(accuracy)
self.valid_meters.update({'valid_loss': loss.item()})
if step % config.logger.log_frequency == 0 or step == len(
dataloader) - 1:
self.logger.info(
"Test: [{:3d}/{}] Step {:03d}/{:03d} {}".format(
epoch + 1, config.trainer.num_epochs, step,
len(dataloader) - 1, self.valid_meters))
y_true = torch.cat(y_true)
y_pred = torch.cat(y_pred)
self.valid_report = parse_preds(
np.array(y_true.detach().cpu().numpy()),
np.array(y_pred.detach().cpu().numpy()))
self.valid_report['acc1'] = self.valid_meters['acc1'].avg
self.valid_report['epoch'] = epoch
self.logger.info(self.valid_report['cls_report'])
self.logger.info(self.valid_report['covid_auc'])
torch.save(
self.valid_report,
os.path.join(config.logger.path,
f'best_epoch{epoch}_valid_report.pth'))
# if self.enable_writter and epoch > 0:
# self.writter.add_scalar("loss/valid", self.valid_meters['valid_loss'].avg, global_step=epoch)
# self.writter.add_scalar("acc1/valid", self.valid_meters['acc1'].avg, global_step=epoch)
# self.writter.add_scalar("acc3/valid", self.valid_meters['acc3'].avg, global_step=epoch)
self.logger.info("Test: [{:3d}/{}] Final result {}".format(
epoch + 1, config.trainer.num_epochs, self.valid_meters))
return self.valid_meters
def resume(self, mode=True):
self.best_metric = -999
path = self.cfg.model.resume_path
if path:
assert os.path.exists(path), "{} does not exist".format(path)
ckpt = torch.load(path)
try:
self.model.load_state_dict(ckpt['model_state_dict'])
except:
self.logger.info('Loading from DataParallel model...')
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in ckpt['model_state_dict'].items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
# load params
self.model.load_state_dict(new_state_dict)
self.optimizer.load_state_dict(ckpt['optimizer_state_dict'])
self.lr_scheduler.load_state_dict(ckpt['lr_scheduler_state_dict'])
self.logger.info('Resuming training from epoch {}'.format(
self.start_epoch))
self.best_metric = ckpt['best_metric']
self.start_epoch = ckpt['epoch'] + 1
for callback in self.callbacks:
if isinstance(callback, CheckpointCallback):
if self.best_metric == -999:
self.best_metric = callback.best_metric
else:
callback.best_metric = self.best_metric
def generate_callbacks(self):
'''
Args:
func: a function to generate other callbacks, must return a list
Return:
a list of callbacks.
'''
self.ckpt_callback = CheckpointCallback(
checkpoint_dir=self.cfg.logger.path,
name='best_retrain.pth',
mode=self.cfg.callback.checkpoint.mode)
self.cam_callback = CAMCallback(self.cfg)
callbacks = [self.ckpt_callback, self.cam_callback]
return callbacks
def model_size(self, name='size'):
assert name in ['size', 'flops']
size = self.cfg.input.size
if self.cfg.dataset.is_3d:
input_size = (1, 1, self.cfg.dataset.slice_num, *size)
else:
input_size = (1, 3, *size)
return flops_size_counter(self.model, input_size)[name]