diff --git a/experiments/scripts/attack.py b/experiments/scripts/attack.py
index 0683156ac327a6cd8607b1d3dcd204095f483537..d9a0db83f2372636a31bbba4924b46fc71224086 100644
--- a/experiments/scripts/attack.py
+++ b/experiments/scripts/attack.py
@@ -4,13 +4,16 @@ import json
import click
import pandas as pd
import os
+
+import torch
from tqdm import tqdm
from text_attacks.utils import get_classify_function
-from textfooler import Attack, TextFooler, BaseLine, process, run_queue, filter_similarity_queue
-from queue import Full, Empty
+from textfooler import Attack, TextFooler, Similarity, BaseLine, \
+ process, run_queue, filter_similarity_queue, spoil_queue
from time import sleep
from multiprocessing import Process
from multiprocessing import Queue, Manager
+import multiprocess
from threading import Thread
TEXT = "text"
@@ -31,7 +34,6 @@ EXPECTED = "expected"
ACTUAL = "actual"
COSINE_SCORE = "cosine_score"
CLASS = "class"
-SLEEP_TIME = 0.01
QUEUE_SIZE = 1000
os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -49,48 +51,33 @@ def data_producer(queue_out, input_file):
for i, cols in tqdm(
dataset_df[[TEXT, ID, LEMMAS, TAGS, ORTHS]].iterrows(), total=len(dataset_df)
):
- try:
- sentence, sent_id, lemmas, tags, orths = cols[0], cols[1], \
- cols[2], cols[3], cols[4]
- queue_out.put([sentence, orths, [], lemmas, tags, sent_id])
- except Full:
- sleep(SLEEP_TIME)
- try:
- queue_out.put(None)
- except Full:
- sleep(SLEEP_TIME)
+ sentence, sent_id, lemmas, tags, orths = cols[0], cols[1], \
+ cols[2], cols[3], cols[4]
+ queue_out.put([sentence, orths, [], lemmas, tags, sent_id])
+ queue_out.put(None)
def data_saver(queue_in, output_file):
item = 1
while item is not None:
- try:
- item = queue_in.get(block=False)
- except Empty:
- sleep(SLEEP_TIME)
- continue
+ item = queue_in.get()
if item is not None:
with open(output_file, 'a') as file_out:
json.dump(item, file_out, indent=2)
-def classify_queue(queue_in, queue_out, classify_fun):
+def classify_queue(queue_in, queue_out, queue_log, classify_fun):
item = True
while item is not None:
- try:
- item = queue_in.get(block=False)
- except Empty:
- sleep(SLEEP_TIME)
- continue
+ item = queue_in.get()
+ queue_log.put("Classify got from queue")
if item is not None:
- try:
- sent_id, org_sentence, changed_sents = item
- sentences = [org_sentence].extend([sent[TEXT] for sent in changed_sents])
- classified = classify_fun(sentences)
- queue_out.put((sent_id, org_sentence, changed_sents, classified))
- except Full:
- sleep(SLEEP_TIME)
- continue
+ sent_id, org_sentence, changed_sents = item
+ sentences = [org_sentence]
+ sentences.extend([sent[TEXT] for sent in changed_sents])
+ queue_log.put(f"Classifying sentences {sentences[:100]}")
+ classified = classify_fun(sentences)
+ queue_out.put((sent_id, org_sentence, changed_sents, classified))
queue_out.put(None)
@@ -99,8 +86,15 @@ def log_queues(queues):
sizes = [q.qsize() for q in queues]
print(sizes, flush=True)
sleep(2)
-
-
+
+
+def log_info_queue(queue):
+ print("Logging queue")
+ while True:
+ item = queue.get()
+ print(item)
+
+
@click.command()
@click.option(
"--dataset_name",
@@ -120,27 +114,25 @@ def main(dataset_name: str, attack_type: str):
"20_news": "en",
"wiki_pl": "pl",
}[dataset_name]
-
- attack = {
- "attack_textfooler": TextFooler(lang),
- "attack_basic": BaseLine(lang, 0.5, 0.4, 0.3)
+ params = {
+ "attack_textfooler": [lang],
+ "attack_basic": [lang, 0.5, 0.4, 0.3]
}[attack_type]
- # sim = Similarity(0.95, "distiluse-base-multilingual-cased-v1")
output_dir = f"data/results/{attack_type}/{dataset_name}/"
input_file = f"data/preprocessed/{dataset_name}/test.jsonl"
os.makedirs(output_dir, exist_ok=True)
output_path = os.path.join(output_dir, "test.jsonl")
- classify = get_classify_function(dataset_name=dataset_name)
+ classify = get_classify_function(dataset_name=dataset_name, device="cpu")
max_sub = 1
m = Manager()
- queues = [m.Queue(maxsize=QUEUE_SIZE) for _ in range(5)]
+ queues = [m.Queue(maxsize=QUEUE_SIZE) for _ in range(6)]
+ sim = Similarity(queues[5], 0.95, "distiluse-base-multilingual-cased-v1")
processes = [Process(target=data_producer, args=(queues[0], input_file,)), # loading data file_in -> 0
- Process(target=attack.spoil_queue, args=(queues[0], queues[1], max_sub,)), # spoiling 0 -> 1
- Process(target=filter_similarity_queue, args=(queues[1], queues[2], 0.95,
- "distiluse-base-multilingual-cased-v1",)), # cosim 1 -> 2
- Process(target=classify_queue, args=(queues[2], queues[3], classify, )), # classify changed 2 -> 3
+ Process(target=spoil_queue, args=(queues[0], queues[1], max_sub, attack_type, params)), # spoiling 0 -> 1
+ Process(target=filter_similarity_queue, args=(queues[1], queues[2], queues[5], sim)), # cosim 1 -> 2
+ multiprocess.Process(target=classify_queue, args=(queues[2], queues[3], queues[5], classify, )), # classify changed 2 -> 3
Process(target=run_queue, args=(queues[3], queues[4], process,)), # process 3 -> 4
Process(target=data_saver, args=(queues[4], output_path,))] # saving 4 -> file_out
[p.start() for p in processes]
@@ -148,9 +140,13 @@ def main(dataset_name: str, attack_type: str):
log_que = Thread(target=log_queues, args=(queues, ))
log_que.daemon = True
log_que.start()
+ info_que = Thread(target=log_info_queue, args=(queues[5], ))
+ info_que.daemon = True
+ info_que.start()
# wait for all processes to finish
[p.join() for p in processes]
log_que.join(timeout=0.5)
+ info_que.join(timeout=0.5)
if __name__ == "__main__":