def bert_epoch_step(self, state, current):
model_to_save = state['model']
if self.save_best_only:
if self.monitor_op(current, self.best):
logger.info(f"\nEpoch {state['epoch']}: \
{self.monitor} improved from \
{self.best:.5f} to {current:.5f}")
self.best = current
state['best'] = self.best
model_to_save.save_pretrained(str(self.base_path))
output_config_file = self.base_path / 'config.json'
with open(str(output_config_file), 'w') as f:
f.write(model_to_save.config.to_json_string())
state.pop("model")
torch.save(state, self.base_path / 'checkpoint_info.bin')
else:
if state['epoch'] % self.epoch_freq == 0:
save_path = self.base_path / f"checkpoint-epoch-{state['epoch']}"
save_path.mkdir(exist_ok=True)
logger.info(f"\nEpoch {state['epoch']}: save model to disk.")
model_to_save.save_pretrained(save_path)
output_config_file = save_path / 'config.json'
with open(str(output_config_file), 'w') as f:
f.write(model_to_save.config.to_json_string())
state.pop("model")
torch.save(state, save_path / 'checkpoint_info.bin')
async def login(client, username, password):
def detect(Img):
logger.info(f"Detect: Detecting...")
i = transform(Img).unsqueeze(0)
predict_label1, predict_label2 = model(i)
predict_label = LabeltoStr(
[
np.argmax(predict_label1.data.numpy()[0]),
np.argmax(predict_label2.data.numpy()[0]),
]
)
logger.info(f"Detect: Result {predict_label}")
return predict_label
home = await client.get(DK_URL)
soup = BeautifulSoup(home.content, "lxml")
captcha = await client.get(CAPTCHA_URL)
valid_code = detect(Image.open(captcha))
execution = soup.find("input", attrs={"type": "hidden", "name": "execution"})
data = {
"username": username,
"password": password,
"validateCode": valid_code,
"execution": execution.get("value"),
"_eventId": "submit",
"geolocation": "",
}
post = await client.post(home.url, data=data)
logger.info(f"Login: {username} Login...")
return True if post.url == DK_URL else False
def create_examples(self, lines, example_type, cached_examples_file):
'''
Creates examples for data
'''
pbar = ProgressBar(n_total=len(lines))
if cached_examples_file.exists():
logger.info("Loading examples from cached file %s",
cached_examples_file)
examples = torch.load(cached_examples_file)
else:
examples = []
for i, line in enumerate(lines):
guid = '%s-%d' % (example_type, i)
text_a = line[0]
label = line[1]
if isinstance(label, str):
label = [np.float(x) for x in label.split(",")]
else:
label = [np.float(x) for x in list(label)]
text_b = None
example = InputExample(guid=guid,
text_a=text_a,
text_b=text_b,
label=label)
examples.append(example)
pbar.batch_step(step=i, info={}, bar_type='create examples')
logger.info("Saving examples into cached file %s",
cached_examples_file)
torch.save(examples, cached_examples_file)
return examples
def start(update, context):
message = update.message
chat = message.forward_from_chat if message.forward_from_chat else message.chat
jobs = [t.name for t in context.job_queue.jobs()]
message.reply_markdown(
f"Usage:\n/add <username> <password> \[region-num]\nregin-num: \n1 - 上海\n2 - 湖北\n3 - 其他中国地区\n5 - 国外\n/del <username>\nCHAT ID: `{chat.id}`\nCurrent Jobs: {jobs}"
)
logger.info(f"Start command: Current Jobs: {jobs}")
def detect(Img):
logger.info(f"Detect: Detecting...")
i = transform(Img).unsqueeze(0)
predict_label1, predict_label2 = model(i)
predict_label = LabeltoStr([
np.argmax(predict_label1.data.numpy()[0]),
np.argmax(predict_label2.data.numpy()[0]),
])
logger.info(f"Detect: Result {predict_label}")
return predict_label
def add(update, context):
message = update.message
chat = message.chat
data = message.text.split(" ")
if len(data) < 3:
message.reply_text("用法:\n"
"添加数字平台账户:\n"
"/add <学号> <密码> \\[地区]\n"
"地区:\n"
"1 - 上海\n"
"2 - 湖北\n"
"3 - 其他中国地区\n"
"5 - 国外")
return
username, password = data[1], data[2]
region = 1 if len(data) <= 3 else data[3]
chat_id = chat.id if len(data) <= 4 else data[4]
for job in context.job_queue.get_jobs_by_name(username):
job.schedule_removal()
jobs = [t.name for t in context.job_queue.jobs()]
context.job_queue.run_daily(
checkin_queue,
datetime.time(
0,
min(3 + len(jobs), 59),
SystemRandom().randrange(60),
SystemRandom().randrange(1000000),
datetime.timezone(datetime.timedelta(hours=8)),
),
context={
"username": username,
"password": password,
"region": region,
"chat": chat_id,
},
name=username,
)
jobs.append(username)
context.job_queue.run_once(
checkin_queue,
1,
context={
"username": username,
"password": password,
"region": region,
"chat": chat_id,
},
)
message.reply_text(
f"添加成功!\n学号: {username}\n密码: {password}\n地区: {region}\n现在的任务列表: {jobs}"
)
logger.info(f"Added Jobs: {username}, Current Jobs: {jobs}")
def main():
parser = get_argparse()
parser.add_argument("--fine_tunning_model",
type=str,
required=True,
help="fine_tuning model path")
args = parser.parse_args()
print(
json.dumps(vars(args),
sort_keys=True,
indent=4,
separators=(', ', ': '),
ensure_ascii=False))
init_logger(log_file="./log/{}.log".format(
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())))
seed_everything(args.seed)
# save path
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
# device
args.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
# tokenizer
tokenizer = BertTokenizerFast.from_pretrained(args.model_name_or_path)
# Dataset & Dataloader
test_dataset = MrcDataset(args,
json_path="./data/test1.json",
tokenizer=tokenizer)
test_iter = DataLoader(test_dataset,
shuffle=False,
batch_size=args.per_gpu_eval_batch_size,
collate_fn=collate_fn,
num_workers=24)
logger.info("The nums of the test_dataset examples is {}".format(
len(test_dataset.examples)))
logger.info("The nums of the test_dataset features is {}".format(
len(test_dataset)))
# model
model = MRC_model(args.model_name_or_path)
model.to(args.device)
model.load_state_dict(torch.load(args.fine_tunning_model))
# predict test
model.eval()
evaluate(args, test_iter, model, prefix="test")
def create_dataset(self, features, is_sorted=False):
if is_sorted:
logger.info("sorted data by th length of input")
features = sorted(features,
key=lambda x: x.input_len,
reverse=True)
all_input_ids = torch.tensor([f.input_ids for f in features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_ids for f in features],
dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_ids, all_input_ids,
all_label_ids)
return dataset
def load_json(filename="config.json"):
try:
with open(filename, "r") as file:
config = json.load(file)
except FileNotFoundError:
try:
filename = f"{os.path.split(os.path.realpath(__file__))[0]}/{filename}"
with open(filename, "r") as file:
config = json.load(file)
except FileNotFoundError:
logger.exception(f"Cannot find {filename}.")
sys.exit(1)
logger.info(f"Json: Loaded {filename}")
return config
def train_val_split(self, X: list, y: list, valid_size: float,
data_name=None, data_dir=None, save=True):
logger.info('split train data into train and valid')
Xy = []
for i in range(len(X)):
Xy.append((X[i], y[i]))
train, valid = train_test_split(
Xy, test_size=valid_size, random_state=42)
if save:
train_path = data_dir / "{}.train.pkl".format(data_name)
valid_path = data_dir / "{}.valid.pkl".format(data_name)
save_pickle(data=train, file_path=train_path)
save_pickle(data=valid, file_path=valid_path)
return train, valid
def checkin(s, username, region):
data = {
"xgh": username,
"lon": "",
"lat": "",
"region": region,
"rylx": 4,
"status": 0,
}
s.post(CHECKIN_URL, data=data)
logger.info(f"Checkin: {username} Checkin...")
home = s.get(DK_URL)
soup = BeautifulSoup(home.content, "lxml")
return (True if "success" in str(
soup.find("div", attrs={"class": "form-group"})) else False)
def build_filter_sets(imgs, num_classes, mode, id2trainid=None):
if not (mode == "train"):
return imgs
json_fn = os.path.join(cfg.DATASET.PCL_DIR, "filter_set.json")
if os.path.isfile(json_fn):
logger.info("[*] Loading Filter sets file: {}".format(json_fn))
with open(json_fn, "r") as f:
records = json.load(f)
else:
logger.info("[*] Didn\'t find {}, so building it.".format(json_fn))
records = generate_filter_all(imgs, num_classes, id2trainid)
with open(json_fn, "w") as f:
json.dump(records, f, indent=4)
return records
def login(s, username, password):
home = s.get(DK_URL)
soup = BeautifulSoup(home.content, "lxml")
captcha = s.get(CAPTCHA_URL, stream=True)
valid_code = detect(Image.open(captcha.raw))
execution = soup.find("input", attrs={"type": "hidden", "name": "execution"})
data = {
"username": username,
"password": password,
"validateCode": valid_code,
"execution": execution.get("value"),
"_eventId": "submit",
"geolocation": "",
}
post = s.post(home.url, data=data)
logger.info(f"Login: {username} Login...")
return True if post.url == DK_URL else False
def checkin_queue(context):
job = context.job
username, password, region, chat = (
job.context.get("username"),
job.context.get("password"),
job.context.get("region"),
job.context.get("chat"),
)
s = requests.Session()
s.headers.update({
"User-Agent":
"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.87 Safari/537.36"
})
retry_count = 5
for i in range(retry_count):
try:
if login(s, username, password):
logger.info(f"Login: {username} Success!")
break
except:
continue
logger.warning(f"Login: {username} Fail {i}")
for i in range(retry_count):
try:
if checkin(s, username, region):
logger.info(f"Checkin: {username} Success!")
context.bot.send_message(chat,
f"任务: {username} 执行成功！",
disable_notification=True)
return
except:
continue
logger.warning(f"Checkin: {username} Fail {i}")
context.bot.send_message(chat, f"任务: {username} 执行失败！预计下个小时将继续执行。")
context.job_queue.run_once(
checkin_queue,
SystemRandom().randint(1800, 3600),
context={
"username": username,
"password": password,
"region": region,
"chat": chat,
},
)
logger.warning(f"Job: {username} fail -> run in next hour")
def save_pretrained(self, save_directory):
""" Save a model and its configuration file to a directory, so that it
can be re-loaded using the `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.
"""
assert os.path.isdir(save_directory), "\
Saving path should be a directory where the model and configuration can be saved"
# Only save the model itself if we are using distributed training
model_to_save = self.module if hasattr(self, 'module') else self
# Save configuration file
# model_to_save.config.save_pretrained(save_directory)
# If we save using the predefined names,
# we can load using `from_pretrained`
output_model_file = os.path.join(save_directory, "pytorch_model.bin")
torch.save(model_to_save.state_dict(), output_model_file)
logger.info("Model weights saved in {}".format(output_model_file))
def main():
model = ResNet(ResidualBlock)
model.eval()
model.load_state_dict(torch.load("model/best.pkl", map_location=device))
logger.info("Valid: loaded model")
predict_dataloader = get_predict_data_loader()
for i, (images, labels) in enumerate(predict_dataloader):
predict_label1, predict_label2 = model(images)
predict_label = LabeltoStr([
np.argmax(predict_label1.data.numpy()[0]),
np.argmax(predict_label2.data.numpy()[0]),
])
true_label = LabeltoStr(labels.data.numpy()[0])
logger.info(
f"Test: {i}, Expect: {true_label}, Predict: {predict_label}, Result: {True if true_label == predict_label else False}"
)
def start(update, context):
message = update.message
jobs = [t.name for t in context.job_queue.jobs()]
message.reply_text("用法:\n"
"添加数字平台账户:\n"
"/add <学号> <密码> [地区]\n"
"地区(默认上海):\n"
"1 - 上海\n"
"2 - 湖北\n"
"3 - 其他中国地区\n"
"5 - 国外\n"
"移除数字平台账户:\n"
"/del <学号>\n"
"立即运行:\n"
"/run [学号]\n"
f"现在的任务列表: {jobs}")
logger.info(
f"Start command: Current Jobs: {[t.context for t in context.job_queue.jobs()]}"
)
def delete(update, context):
message = update.message
chat = message.chat
data = message.text.split(" ")
if len(data) < 2:
message.reply_text("用法:\n移除数字平台账户:\n/del <学号>")
return
username = data[1]
deleted_flag = False
jobs = [t.name for t in context.job_queue.jobs()]
for job in context.job_queue.get_jobs_by_name(username):
if job.context.get("chat") in [chat.id, ADMIN]:
deleted_flag = True
job.schedule_removal()
logger.info(f"Deleted Jobs: {username}, Current Jobs: {jobs}")
if deleted_flag:
message.reply_text(f"删除成功!\n学号: {username}\n现在的任务列表: {jobs}")
else:
message.reply_text("您没有删除此账户的权限.")
def go(self):
if self.IS_HEROKU_MODE:
logger.info('Heroku Mode start')
# Get Heroku app name from config vars.
name = os.environ.get('APP_NAME')
# Get Heroku app port from config vars.
port = os.environ.get('PORT')
webhook_url = "https://{}.herokuapp.com/{}".format(name, self.TOKEN)
self.updater.start_webhook(listen="0.0.0.0",
# port=int(port),
port=80,
url_path=self.TOKEN)
self.updater.bot.setWebhook(webhook_url)
else:
logger.info('Local Mode start')
self.updater.start_polling()
self.updater.idle()
def build_filter_sets(imgs, num_classes, mode, id2trainid=None):
if not (mode == "train"):
return imgs
json_fn = os.path.join(cfg.DATASET.PCL_DIR, "classwised_set.json")
if os.path.isfile(json_fn):
logger.info("[*] Loading Class-wised file: {}".format(json_fn))
with open(json_fn, "r") as f:
records = json.load(f)
records = {int(k): v for k, v in records.items()}
logger.info("[*] Found {} classes.".format(len(records)))
else:
logger.info("[*] Didn\'t find {}, so building it.".format(json_fn))
records = uniform.generate_classwised_all(imgs, num_classes, id2trainid)
with open(json_fn, "w") as f:
json.dump(records, f, indent=4)
loss_fn = os.path.join(cfg.DATASET.PCL_DIR, "loss_info.json")
if os.path.isfile(loss_fn):
logger.info("[*] Loading Loss info file: {}".format(loss_fn))
with open(loss_fn, "r") as f:
loss_info = json.load(f)
new_records = defaultdict(list)
for k, v in records.items():
for item in v:
img_n = os.path.basename(item[0]).split(".")[0]
loss = loss_info[img_n]["ce_loss"]
new_records[k].append((*item, loss))
else:
logger.info("[*] Didn\'t find {}, so didn\'t use it.")
new_records = defaultdict(list)
for k, v in records.items():
for item in v:
new_records[k].append((*item, 0))
records = new_records
return records
def build_epoch(imgs, records, num_classes, mode):
if not (mode == "train"):
return imgs
one_class_filter_pct = cfg.DATASET.ONE_CLASS_FILTER_PCT
logger.info("[*] One Class Filter Percentage: {}".format(
str(one_class_filter_pct)))
num_imgs = int(len(imgs))
logger.info("[*] Number of images: {}".format(str(num_imgs)))
imgs_one_classes = records["True"]
num_one_class = int(len(imgs_one_classes) * one_class_filter_pct)
imgs_multi_classes = records["False"]
one_class_uniform = ramdom_sampling(records["True"], num_one_class)
imgs_multi_classes.extend(one_class_uniform)
return imgs_multi_classes
def epoch_step(self, state, current):
'''
:param state: 需要保存的信息
:param current: 当前判断指标
:return:
'''
if self.save_best_only:
if self.monitor_op(current, self.best):
logger.info(f"\nEpoch {state['epoch']}: \
{self.monitor} improved from \
{self.best:.5f} to {current:.5f}")
self.best = current
state['best'] = self.best
best_path = self.base_path / self.model_name
torch.save(state, str(best_path))
else:
filename = self.base_path / f"epoch_{state['epoch']}_{state[self.monitor]}_{self.arch}_model.bin"
if state['epoch'] % self.epoch_freq == 0:
logger.info(f"\nEpoch {state['epoch']}: save model to disk.")
torch.save(state, str(filename))
def delete(update, context):
message = update.message
chat = message.chat
data = message.text.split(" ")
if len(data) < 2:
message.reply_text("Usage:\n/del <username>")
return
username = data[1]
deleted_flag = False
jobs = [t.name for t in context.job_queue.jobs()]
for job in context.job_queue.get_jobs_by_name(username):
if job.context.get("chat") == chat.id:
deleted_flag = True
job.schedule_removal()
logger.info(f"Deleted Jobs: {username}, Current Jobs: {jobs}")
if deleted_flag:
message.reply_text(
f"Deleted successfully!\nusername: {username}\nCurrent Jobs: {jobs}"
)
else:
message.reply_text("You cannot delete it.")
def build_epoch(imgs, records, num_classes, mode):
if not (mode == "train"):
return imgs
logger.info("[*] Filter the data with high loss.")
records = loss_filter(records, cfg.DATASET.LOSS_UPPER_BOUND)
logger.info("[*] Sampling the image with max num of image.")
imgs_sampling = []
class_counter = 0
for class_id in range(num_classes):
num_sampling = cfg.DATASET.NUM_IMG_PER_CLASS
records_len = len(records[class_id])
if records_len == 0:
pass
else:
class_records = random_sampling(records[class_id], num_sampling)
imgs_sampling.extend(class_records)
class_counter += 1
logger.info("[*] Sampling including {} classes.".format(class_counter))
imgs_sampling = filter_duplicate(imgs_sampling)
return imgs_sampling
def add(update, context):
message = update.message
chat = message.chat
data = message.text.split(" ")
if len(data) < 3:
message.reply_text(
"Usage:\n/add <username> <password> [region-num]\nregin-num: \n1 - 上海\n2 - 湖北\n3 - 其他中国地区\n5 - 国外\n"
)
return
username, password = data[1], data[2]
region = 1 if len(data) == 3 else data[3]
for job in context.job_queue.get_jobs_by_name(username):
job.schedule_removal()
jobs = [t.name for t in context.job_queue.jobs()]
context.job_queue.run_once(
checkin_queue,
1,
context={
"username": username,
"password": password,
"region": region,
"chat": chat.id,
},
)
context.job_queue.run_daily(
checkin_queue,
datetime.time(
0,
min(2 + len(jobs), 59),
0,
0,
datetime.timezone(datetime.timedelta(hours=8)),
),
context={"username": username, "password": password, "chat": chat.id},
name=username,
)
message.reply_text(
f"Added successfully!\nusername: {username}\npassword: {password}\nCurrent Jobs: {jobs}"
)
logger.info(f"Added Jobs: {username}, Current Jobs: {jobs}")
def build_epoch(imgs, records, num_classes, mode):
"""
Generate an epoch of image using uniform sampling
Will not apply uniform sampling if not train or class uniform is off.
Args:
imgs: list of images: (img_fn, mask_fn)
records: dict of classes which is list including img_fn, mask_fn, class_id
num_classes: int
mode: str
Returns:
imgs: list of images
"""
class_uniform_pct = cfg.DATASET.CLASS_UNIFORM_PCT
if not (mode == "train" and class_uniform_pct):
return imgs
logger.info("[*] Class Uniform Percentage: {}".format(
str(class_uniform_pct)))
num_epoch = int(len(imgs))
logger.info("[*] Class Uniform items per Epoch: {}".format(str(num_epoch)))
num_per_class = int((num_epoch * class_uniform_pct) / num_classes)
class_uniform_count = num_per_class * num_classes
num_rand = num_epoch - class_uniform_count
imgs_uniform = ramdom_sampling(imgs, num_rand)
for class_id in range(num_classes):
num_per_class_biased = num_per_class
records_len = len(records[class_id])
if records_len == 0:
pass
else:
class_records = ramdom_sampling(records[class_id],
num_per_class_biased)
imgs_uniform.extend(class_records)
return imgs_uniform
def checkin_queue(context):
job = context.job
username, password, region, chat = (
job.context.get("username"),
job.context.get("password"),
job.context.get("region"),
job.context.get("chat"),
)
s = requests.Session()
s.headers.update(
{
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.87 Safari/537.36"
}
)
retry_count = 5
message = context.bot.send_message(chat, f"Job: Running for {username}")
for i in range(retry_count):
result = login(s, username, password)
if result:
append_text = f"Login: {username} Successful!"
logger.info(append_text)
message = message.edit_text(f"{message.text}\n{append_text}")
break
else:
append_text = f"Login: {username} Fail {i}"
logger.warning(append_text)
message = message.edit_text(f"{message.text}\n{append_text}")
for i in range(retry_count):
result = checkin(s, username, region)
if result:
append_text = f"Checkin: {username} Successful!"
logger.info(append_text)
message = message.edit_text(f"{message.text}\n{append_text}")
break
else:
append_text = f"Checkin: {username} Fail {i}"
logger.warning(append_text)
message = message.edit_text(f"{message.text}\n{append_text}")
def checkin():
data = {
"xgh": USERNAME,
"lon": "",
"lat": "",
"region": 1,
"rylx": 4,
"status": 0,
}
post = s.post(CHECKIN_URL, data=data)
data = {
"xgh": USERNAME,
"alwaysinsh": 1,
"fromaddr": "",
"fromtime": "",
"totime": "",
"jtgj": "",
"status": 0
}
post = s.post(ARRSH_URL, data=data)
logger.info("Checkin: Checkin...")
soup = BeautifulSoup(post.content, "lxml")
return (True if "success" in str(
soup.find("div", attrs={"class": "form-group"})) else False)
def main():
model = ResNet(ResidualBlock).to(device)
model.reload()
model.train()
logger.info("Train: Init model")
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler_after = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=30,
gamma=0.5)
scheduler = GradualWarmupScheduler(optimizer,
8,
10,
after_scheduler=scheduler_after)
train_dataloader = get_train_data_loader()
loss_best = 1
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(tqdm(train_dataloader)):
images = images.to(device)
labels = labels.to(device)
labels = labels.long()
label1, label2 = labels[:, 0], labels[:, 1]
optimizer.zero_grad()
y1, y2 = model(images)
loss1, loss2 = criterion(y1, label1), criterion(y2, label2)
loss = loss1 + loss2
# outputs = model(images)
# loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
scheduler.step()
logger.info(f"epoch: {epoch}, step: {i}, loss: {loss.item()}")
model.save()
if loss_best > loss.item():
loss_best = loss.item()
torch.save(model.state_dict(), "model/best.pkl")
logger.info("Train: Saved best model")
torch.save(model.state_dict(), "model/final.pkl")
logger.info("Train: Saved last model")
def build_classwised_sets(imgs, num_classes, mode, id2trainid=None):
"""
Build the Class-wised image sets
"""
if not (mode == "train" and cfg.DATASET.CLASS_UNIFORM_PCT):
return []
json_fn = os.path.join(cfg.DATASET.PCL_DIR, "classwised_set.json")
if os.path.isfile(json_fn):
logger.info("[*] Loading Class-wised file: {}".format(json_fn))
with open(json_fn, "r") as f:
records = json.load(f)
records = {int(k): v for k, v in records.items()}
logger.info("[*] Found {} classes".format(len(records)))
else:
logger.info("[*] Didn\'t find {}, so building it.".format(json_fn))
records = generate_classwised_all(imgs, num_classes, id2trainid)
with open(json_fn, "w") as f:
json.dump(records, f, indent=4)
return records