def reload_model():
global model, tokenizer, checkpoint_fn
try:
start = time.time()
logging.info('prediction requested')
params = get_params()
logger.debug(json.dumps(params, indent=2))
args_model_checkpoint = args.model_checkpoint
full_model_checkpoint = os.path.join(
os.path.dirname(args_model_checkpoint), params['model_checkpoint'])
model, tokenizer, checkpoint_fn = load_model(
model_checkpoint=full_model_checkpoint,
model_type=params['model_type'])
response = Response(
f'loaded model {params["model_checkpoint"]} successfully')
logger.info("Time spent handling the request: %f" %
(time.time() - start))
except Exception as e:
tb = traceback.format_exc()
logger.error(tb)
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
raise InvalidUsage('%s: %s @line %s in %s' %
(type(e).__name__, str(e), exc_tb.tb_lineno, fname))
return response
def load():
"""
load
"""
return interact.load_model()
])
return res
return _context_fetcher
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__file__)
args = get_args()
random.seed(args.seed)
torch.random.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
model, tokenizer, checkpoint_fn = load_model(
model_checkpoint=args.model_checkpoint, model_type=args.model)
try:
logger.info('create wikipedia context fetcher ...')
context_fetcher = create_wikipedia_context_fetcher(
wikipedia_file=args.wikipedia_dump,
base_url=args.entity_linking_service_url)
except IOError as e:
logger.warning(
'could not create a context fetcher. Please provide a context with every request.'
% args.spacy_model)
context_fetcher = None
logger.info('Starting the API')
if args.deploy:
logger.info('use deployment server')
def load():
"""
load
"""
return interact.load_model(), load_candidate_set(
"../data/candidate_set.txt")
def pred_attacks(df,
model_path,
args,
output_clm,
context='title',
attacks_clm='premise_counter_premise_pairs',
post_clm='post',
rand_premise_idx=-1,
rand_premises_clm='',
baseline=False,
random=False):
model, tokenizer = interact.load_model(model_path)
predictions = []
for row_idx, row in df.iterrows():
post_attacks = []
for premise_counter in row[attacks_clm]:
if context == 'title+full_post':
argument = [tokenizer.encode(row['title'])] + [
tokenizer.encode(sent) for sent in row[post_clm]
]
else:
argument = [tokenizer.encode(
row['title'])] if context == 'title' else [
tokenizer.encode(sent) for sent in row[post_clm]
]
#print([row['title']]+row[post_clm])
#print(premise_counter[0])
if baseline:
argument = trim_argument(argument, 510, args)
pred_counter = interact.sample_sequence(argument, [],
tokenizer,
model,
args,
baseline=True)
elif random:
if rand_premise_idx != None:
weak_premise_encoded = [
tokenizer.encode(
row[rand_premises_clm][rand_premise_idx])
]
else:
weak_premise_encoded = [
tokenizer.encode(row[rand_premises_clm])
]
argument = trim_argument(argument, 510, args)
pred_counter = interact.sample_sequence(
argument, weak_premise_encoded, tokenizer, model, args)
else:
weak_premise_encoded = [
tokenizer.encode(premise) for premise in premise_counter[0]
]
argument = trim_argument(argument, 510, args)
pred_counter = interact.sample_sequence(
argument, weak_premise_encoded, tokenizer, model, args)
#remove quoted text
#pred_counter = tokenizer.decode(pred_counter)
#for p in premise_counter[0]:
# pred_counter = pred_counter.replace(p.lower(), '')
post_attacks.append([premise_counter[0], pred_counter])
predictions.append(post_attacks)
df[output_clm] = predictions
return df
def perform_attacks_hua_df(df,
model_path,
args,
output_clm,
context,
weak_premise_clm,
weak_premise_idx=None,
baseline=False):
model, tokenizer = interact.load_model(model_path)
pred_attacks = []
for idx, row in df.iterrows():
if baseline:
argument = [row['claim']] + row['post']
argument = [tokenizer.encode(sentence) for sentence in argument]
argument = trim_argument(argument, 510, args)
pred_counter = interact.sample_sequence(argument, [],
tokenizer,
model,
args,
baseline=True)
pred_counter = pred_counter.replace(row['claim'].lower(), '')
for sent in row['post']:
pred_counter = pred_counter.replace(sent.lower(), '')
else:
if context == 'title+full_post':
argument = [tokenizer.encode(row['claim'])] + [
tokenizer.encode(sent) for sent in row['post']
]
else:
argument = [tokenizer.encode(row['claim'])
] if context == 'title' else [
tokenizer.encode(sent) for sent in row['post']
]
if weak_premise_idx != None:
weak_premise_encoded = [
tokenizer.encode(premise)
for premise in row[weak_premise_clm][weak_premise_idx]
]
else:
weak_premise_encoded = [
tokenizer.encode(premise)
for premise in row[weak_premise_clm]
]
if args.premise_extra:
argument = trim_argument(
argument, 510 -
len([token for p in weak_premise_encoded for token in p]),
args)
else:
argument = trim_argument(argument, 510, args)
pred_counter = interact.sample_sequence(argument,
weak_premise_encoded,
tokenizer, model, args)
#remove quoted text
#pred_counter = tokenizer.decode(pred_counter)
for p in row[weak_premise_clm]:
pred_counter = pred_counter.replace(p.lower(), '')
pred_attacks.append(pred_counter)
df[output_clm] = pred_attacks
return df