diff --git a/data/results/attack_basic/.gitignore b/data/results/attack_basic/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..bef990db996c16e172365e026cfc73df61bafa60
--- /dev/null
+++ b/data/results/attack_basic/.gitignore
@@ -0,0 +1 @@
+/wiki_pl
diff --git a/data/results/attack_textfooler/.gitignore b/data/results/attack_textfooler/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..e69587204a79358e2809c210d36b6519d6be3b81
--- /dev/null
+++ b/data/results/attack_textfooler/.gitignore
@@ -0,0 +1,3 @@
+/enron_spam
+/wiki_pl
+/20_news
diff --git a/data/results/attack_textfooler_discard/.gitignore b/data/results/attack_textfooler_discard/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..9199641fdf84d1b97fdc9832e4d9039b49bc12e2
--- /dev/null
+++ b/data/results/attack_textfooler_discard/.gitignore
@@ -0,0 +1,3 @@
+/wiki_pl
+/enron_spam
+/20_news
diff --git a/data/results/attack_xai/.gitignore b/data/results/attack_xai/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..ea22867615bba98d219c12d7f14467d051a33e80
--- /dev/null
+++ b/data/results/attack_xai/.gitignore
@@ -0,0 +1,3 @@
+/enron_spam
+/20_news
+/wiki_pl
diff --git a/dvc.lock b/dvc.lock
index d1fc740927a8e3059413fcee70c1f26a068d55e5..b1e3ff4a9045f9909434ba7f5d941251150504c6 100644
--- a/dvc.lock
+++ b/dvc.lock
@@ -45,8 +45,8 @@ stages:
size: 1181
outs:
- path: data/classification/enron_spam
- md5: 0450c0b672bc4a5db3cc7be2dac786bd.dir
- size: 10674882
+ md5: 5de1a2fcbae0de94f5fbfd2bb747d919.dir
+ size: 14585920
nfiles: 2
explain@enron_spam:
cmd: PYTHONPATH=. python experiments/scripts/explain.py --dataset_name enron_spam
@@ -142,8 +142,8 @@ stages:
size: 1181
outs:
- path: data/classification/wiki_pl
- md5: 515330772505f489b55686545bcf23a0.dir
- size: 34103198
+ md5: 88c3cea96b2cb3ddda1a82037bf6130a.dir
+ size: 44196727
nfiles: 2
preprocess_dataset@20_news:
cmd: PYTHONPATH=. python experiments/scripts/tag_dataset.py --dataset_name 20_news
@@ -177,8 +177,8 @@ stages:
size: 1181
outs:
- path: data/classification/20_news
- md5: 6831f104f7c20541548fe72250c45706.dir
- size: 31286120
+ md5: b73611443c4189af91b827c083f37e0b.dir
+ size: 42897496
nfiles: 2
attack_basic@enron_spam:
cmd: PYTHONPATH=. python experiments/scripts/attack.py --dataset_name enron_spam
@@ -239,3 +239,203 @@ stages:
md5: ad1f9f0df287078edebed1e408df2c9f.dir
size: 869336544
nfiles: 140401
+ attack_basic@wiki_pl:
+ cmd: PYTHONPATH=. python experiments/scripts/attack.py --dataset_name wiki_pl
+ --attack_type attack_basic
+ deps:
+ - path: data/models/wiki_pl
+ md5: fd453042628fb09c080ef05d34a32cce.dir
+ size: 501711136
+ nfiles: 7
+ - path: data/preprocessed/wiki_pl
+ md5: 0014b9bb52913cbc9a568d237ea2207b.dir
+ size: 65553079
+ nfiles: 3
+ - path: experiments/scripts/attack.py
+ md5: 702997933e5af85d09d8286a14e2cc05
+ size: 2486
+ outs:
+ - path: data/results/attack_basic/wiki_pl/
+ md5: f118a41e391b5f713f77611140f2f2cc.dir
+ size: 1
+ nfiles: 1
+ attack_textfooler@enron_spam:
+ cmd: 'PYTHONPATH=. python experiments/scripts/attack.py --dataset_name enron_spam
+ --attack_type attack_textfooler '
+ deps:
+ - path: data/models/enron_spam
+ md5: 3e16b22f59532c66beeadea958e0579a.dir
+ size: 18505614
+ nfiles: 6
+ - path: data/preprocessed/enron_spam
+ md5: 30c63efbc615347ddcb5f61e011113bd.dir
+ size: 65971374
+ nfiles: 3
+ - path: experiments/scripts/attack.py
+ md5: b9d9a4d9fcba1cb4dfbb554ecc3e26fb
+ size: 10083
+ outs:
+ - path: data/results/attack_textfooler/enron_spam/
+ md5: 10ecd4c940e8df1058465048ffbe78d4.dir
+ size: 3291044
+ nfiles: 2
+ attack_textfooler@20_news:
+ cmd: 'PYTHONPATH=. python experiments/scripts/attack.py --dataset_name 20_news
+ --attack_type attack_textfooler '
+ deps:
+ - path: data/models/20_news
+ md5: 43d68a67ecb8149bd6bf50db9767cb64.dir
+ size: 439008808
+ nfiles: 6
+ - path: data/preprocessed/20_news
+ md5: 20da0980e52df537e5b7ca5db0305879.dir
+ size: 58582060
+ nfiles: 3
+ - path: experiments/scripts/attack.py
+ md5: 4fe9c6210ce0f3be66b54c2565ad2daa
+ size: 10132
+ outs:
+ - path: data/results/attack_textfooler/20_news/
+ md5: 007aba16e343ca283180c7bc7b9a0190.dir
+ size: 93666157
+ nfiles: 2
+ attack_textfooler@wiki_pl:
+ cmd: 'PYTHONPATH=. python experiments/scripts/attack.py --dataset_name wiki_pl
+ --attack_type attack_textfooler '
+ deps:
+ - path: data/classification/wiki_pl
+ md5: 88c3cea96b2cb3ddda1a82037bf6130a.dir
+ size: 44196727
+ nfiles: 2
+ - path: data/models/wiki_pl
+ md5: fd453042628fb09c080ef05d34a32cce.dir
+ size: 501711136
+ nfiles: 7
+ - path: experiments/scripts/attack.py
+ md5: 2977363ba8806c393498f98d5733c013
+ size: 11497
+ outs:
+ - path: data/results/attack_textfooler/wiki_pl/
+ md5: eccc12b9a5ae383ea02067cd1955753e.dir
+ size: 20293404
+ nfiles: 2
+ attack_textfooler_discard@wiki_pl:
+ cmd: PYTHONPATH=. python experiments/scripts/attack.py --dataset_name wiki_pl
+ --attack_type attack_textfooler_discard
+ deps:
+ - path: data/classification/wiki_pl
+ md5: 88c3cea96b2cb3ddda1a82037bf6130a.dir
+ size: 44196727
+ nfiles: 2
+ - path: data/models/wiki_pl
+ md5: fd453042628fb09c080ef05d34a32cce.dir
+ size: 501711136
+ nfiles: 7
+ - path: experiments/scripts/attack.py
+ md5: 2b9ddc1ff1f56855ff667171ba04ed78
+ size: 11606
+ outs:
+ - path: data/results/attack_textfooler_discard/wiki_pl/
+ md5: e41122c3cdf76ad1b163aba49acce0f0.dir
+ size: 14396685
+ nfiles: 2
+ attack_textfooler_discard@enron_spam:
+ cmd: PYTHONPATH=. python experiments/scripts/attack.py --dataset_name enron_spam
+ --attack_type attack_textfooler_discard
+ deps:
+ - path: data/classification/enron_spam
+ md5: 5de1a2fcbae0de94f5fbfd2bb747d919.dir
+ size: 14585920
+ nfiles: 2
+ - path: data/models/enron_spam
+ md5: 3e16b22f59532c66beeadea958e0579a.dir
+ size: 18505614
+ nfiles: 6
+ - path: experiments/scripts/attack.py
+ md5: 2b9ddc1ff1f56855ff667171ba04ed78
+ size: 11606
+ outs:
+ - path: data/results/attack_textfooler_discard/enron_spam/
+ md5: 8a78484bd77916f82021a72338342a44.dir
+ size: 2816160
+ nfiles: 2
+ attack_textfooler_discard@20_news:
+ cmd: PYTHONPATH=. python experiments/scripts/attack.py --dataset_name 20_news
+ --attack_type attack_textfooler_discard
+ deps:
+ - path: data/classification/20_news
+ md5: b73611443c4189af91b827c083f37e0b.dir
+ size: 42897496
+ nfiles: 2
+ - path: data/models/20_news
+ md5: 43d68a67ecb8149bd6bf50db9767cb64.dir
+ size: 439008808
+ nfiles: 6
+ - path: experiments/scripts/attack.py
+ md5: 9e913b341cb0993625a41c401d64a30b
+ size: 12017
+ outs:
+ - path: data/results/attack_textfooler_discard/20_news/
+ md5: 82d89b00a710e9de0a2157357fed5894.dir
+ size: 24977923
+ nfiles: 2
+ attack_xai@enron_spam:
+ cmd: PYTHONPATH=. python experiments/scripts/attack.py --dataset_name enron_spam
+ --attack_type attack_xai
+ deps:
+ - path: data/classification/enron_spam
+ md5: 5de1a2fcbae0de94f5fbfd2bb747d919.dir
+ size: 14585920
+ nfiles: 2
+ - path: data/models/enron_spam
+ md5: 3e16b22f59532c66beeadea958e0579a.dir
+ size: 18505614
+ nfiles: 6
+ - path: experiments/scripts/attack.py
+ md5: 87f54ee4e2a08f1259d9d8b2d01fe1b9
+ size: 12061
+ outs:
+ - path: data/results/attack_xai/enron_spam/
+ md5: ad19831866da140de113e64862da0bce.dir
+ size: 2860109
+ nfiles: 2
+ attack_xai@20_news:
+ cmd: PYTHONPATH=. python experiments/scripts/attack.py --dataset_name 20_news
+ --attack_type attack_xai
+ deps:
+ - path: data/classification/20_news
+ md5: b73611443c4189af91b827c083f37e0b.dir
+ size: 42897496
+ nfiles: 2
+ - path: data/models/20_news
+ md5: 43d68a67ecb8149bd6bf50db9767cb64.dir
+ size: 439008808
+ nfiles: 6
+ - path: experiments/scripts/attack.py
+ md5: 87f54ee4e2a08f1259d9d8b2d01fe1b9
+ size: 12061
+ outs:
+ - path: data/results/attack_xai/20_news/
+ md5: af00c730d4d73a0a8e2a047882c0d9aa.dir
+ size: 8739816
+ nfiles: 2
+ attack_xai@wiki_pl:
+ cmd: PYTHONPATH=. python experiments/scripts/attack.py --dataset_name wiki_pl
+ --attack_type attack_xai
+ deps:
+ - path: data/classification/wiki_pl
+ md5: 88c3cea96b2cb3ddda1a82037bf6130a.dir
+ size: 44196727
+ nfiles: 2
+ - path: data/models/wiki_pl
+ md5: fd453042628fb09c080ef05d34a32cce.dir
+ size: 501711136
+ nfiles: 7
+ - path: experiments/scripts/attack.py
+ md5: 87f54ee4e2a08f1259d9d8b2d01fe1b9
+ size: 12061
+ outs:
+ - path: data/results/attack_xai/wiki_pl/
+ md5: e24c456f63d8e13b92fcab51e0726141.dir
+ size: 8287334
+ nfiles: 2
diff --git a/dvc.yaml b/dvc.yaml
index 6862a88561c9e3c9841ae258749e018d430215ad..0455a1bc2a3849add2fac2a63f8c1303129e1e09 100644
--- a/dvc.yaml
+++ b/dvc.yaml
@@ -77,6 +77,102 @@ stages:
- data/preprocessed/${item}
outs:
- data/explanations/${item}/
+ attack_textfooler:
+ foreach:
+ - enron_spam
+ - 20_news
+ - wiki_pl
+ do:
+ wdir: .
+ cmd: >-
+ PYTHONPATH=. python experiments/scripts/attack.py
+ --dataset_name ${item} --attack_type attack_textfooler
+ deps:
+ - experiments/scripts/attack.py
+ - data/models/${item}
+ - data/classification/${item}
+ outs:
+ - data/results/attack_textfooler/${item}/
+ attack_textfooler_discard:
+ foreach:
+ - enron_spam
+ - 20_news
+ - wiki_pl
+ do:
+ wdir: .
+ cmd: >-
+ PYTHONPATH=. python experiments/scripts/attack.py
+ --dataset_name ${item} --attack_type attack_textfooler_discard
+ deps:
+ - experiments/scripts/attack.py
+ - data/models/${item}
+ - data/classification/${item}
+ outs:
+ - data/results/attack_textfooler_discard/${item}/
+ attack_xai:
+ foreach:
+ - enron_spam
+ - 20_news
+ - wiki_pl
+ do:
+ wdir: .
+ cmd: >-
+ PYTHONPATH=. python experiments/scripts/attack.py
+ --dataset_name ${item} --attack_type attack_xai
+ deps:
+ - experiments/scripts/attack.py
+ - data/models/${item}
+ - data/classification/${item}
+ outs:
+ - data/results/attack_xai/${item}/
+ attack_xai_discard:
+ foreach:
+ - enron_spam
+ - 20_news
+ - wiki_pl
+ do:
+ wdir: .
+ cmd: >-
+ PYTHONPATH=. python experiments/scripts/attack.py
+ --dataset_name ${item} --attack_type attack_xai_discard
+ deps:
+ - experiments/scripts/attack.py
+ - data/models/${item}
+ - data/classification/${item}
+ outs:
+ - data/results/attack_xai_discard/${item}/
+ attack_xai_local:
+ foreach:
+ - enron_spam
+ - 20_news
+ - wiki_pl
+ do:
+ wdir: .
+ cmd: >-
+ PYTHONPATH=. python experiments/scripts/attack.py
+ --dataset_name ${item} --attack_type attack_xai_local
+ deps:
+ - experiments/scripts/attack.py
+ - data/models/${item}
+ - data/classification/${item}
+ outs:
+ - data/results/attack_xai_local/${item}/
+ attack_xai_discard_local:
+ foreach:
+ - enron_spam
+ - 20_news
+ - wiki_pl
+ do:
+ wdir: .
+ cmd: >-
+ PYTHONPATH=. python experiments/scripts/attack.py
+ --dataset_name ${item} --attack_type attack_xai_discard_local
+ deps:
+ - experiments/scripts/attack.py
+ - data/models/${item}
+ - data/classification/${item}
+ outs:
+ - data/results/attack_xai_discard_local/${item}/
attack_basic:
foreach:
- enron_spam
@@ -86,11 +182,11 @@ stages:
wdir: .
cmd: >-
PYTHONPATH=. python experiments/scripts/attack.py
- --dataset_name ${item}
+ --dataset_name ${item} --attack_type attack_basic
deps:
- experiments/scripts/attack.py
- data/models/${item}
- - data/preprocessed/${item}
+ - data/classification/${item}
outs:
- data/results/attack_basic/${item}/
diff --git a/experiments/scripts/attack.py b/experiments/scripts/attack.py
index 79c954f8670871ba9fdab8e98a69b4860f5527bf..dde6b08909a48bf187ad5fec3963dcbe852ff18d 100644
--- a/experiments/scripts/attack.py
+++ b/experiments/scripts/attack.py
@@ -1,16 +1,28 @@
"""Script for running attacks on datasets."""
+import importlib
+import json
+from collections import defaultdict
import click
import pandas as pd
import os
-from tqdm import tqdm
-from text_attacks.utils import get_classify_function
-from textfooler import Attack, TextFooler, BaseLine, process
+import torch
+from tqdm import tqdm
+from textfooler import Attack, TextFooler, Similarity, BaseLine, \
+ process, run_queue, filter_similarity_queue, spoil_queue
+from time import sleep, time
+from multiprocessing import Process
+from multiprocessing import Queue, Manager
+from threading import Thread
+from sklearn.metrics import classification_report, confusion_matrix
+import numpy as np
TEXT = "text"
LEMMAS = "lemmas"
TAGS = "tags"
ORTHS = "orths"
+ID = "id"
+PRED = "pred_label"
ATTACK_SUMMARY = "attacks_summary"
ATTACK_SUCCEEDED = "attacks_succeeded"
@@ -24,6 +36,14 @@ EXPECTED = "expected"
ACTUAL = "actual"
COSINE_SCORE = "cosine_score"
CLASS = "class"
+QUEUE_SIZE = 1000
+FEATURES = "features"
+IMPORTANCE = "importance"
+SYNONYM = "synonym"
+DISCARD = "discard"
+GLOBAL = "global"
+LOCAL = "local"
+
os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -35,13 +55,126 @@ DEFAULT_RES = {
}
+def data_producer(queue_out, dataset_df):
+ for i, cols in tqdm(
+ dataset_df[[TEXT, ID, LEMMAS, TAGS, ORTHS, PRED]].iterrows(), total=len(dataset_df)
+ ):
+ sentence, sent_id, lemmas, tags, orths, y_pred = cols[0], cols[1], \
+ cols[2], cols[3], cols[4], cols[5]
+ queue_out.put([sentence, orths, [], lemmas, tags, sent_id, y_pred])
+
+
+def data_saver(queue_in, queue_log, output_file, output_dir, cases_nbr, queues_kill, to_kill_nbr):
+ processed_nbr, start = 0, time()
+ item = 1
+ test_y, pred_y = [], []
+ spoiled_sents = []
+ ch_suc, ch_all = 0, 0
+ end_time = time()
+ while item is not None:
+ item = queue_in.get()
+ if item is not None:
+ processed_nbr += 1
+ spoiled, class_test, class_pred = item
+ test_y.append(class_test)
+ pred_y.append(class_pred)
+ queue_log.put(f"Processed and saved {processed_nbr} in {time() - start} s")
+ ch_suc += spoiled[ATTACK_SUMMARY][SUCCEEDED]
+ ch_all += spoiled[ATTACK_SUMMARY][ALL]
+ spoiled_sents.append(spoiled)
+ if processed_nbr == cases_nbr:
+ for que_kill in queues_kill:
+ [que_kill.put(None) for _ in range(to_kill_nbr)]
+ if processed_nbr == cases_nbr - 10:
+ end_time = time()
+ if processed_nbr >= cases_nbr - 10:
+ if sum([q.qsize() for q in queues_kill]) == 0 and (time() - end_time) > 3600:
+ for que_kill in queues_kill:
+ [que_kill.put(None) for _ in range(to_kill_nbr)]
+ with open(output_file, 'wt') as fd:
+ fd.write(pd.DataFrame(spoiled_sents).to_json(
+ orient="records", lines=True))
+ np.savetxt(f"{output_dir}/metrics.txt", confusion_matrix(test_y, pred_y))
+ with open(f"{output_dir}/metrics.txt", mode="at") as fd:
+ fd.write('\n')
+ fd.write(classification_report(test_y, pred_y))
+ fd.write('\n')
+ fd.write(f"succeeded {ch_suc} all {ch_all}")
+
+
+def classify_queue(queue_in, queue_out, queue_log, dataset_name, cuda_device):
+ os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
+ fun = getattr(
+ importlib.import_module(f"text_attacks.models.{dataset_name}"),
+ "get_classify_function",
+ )
+ classify_fun = fun(device="cuda" if torch.cuda.is_available() else "cpu")
+ queue_log.put(f"Classify device {'cuda' if torch.cuda.is_available() else 'cpu'}")
+ item = True
+ while item is not None:
+ item = queue_in.get()
+ queue_log.put("Classify got from queue")
+ if item is not None:
+ sent_id, org_sentence, y_pred, changed_sents = item
+ sentences = [sent[TEXT] for sent in changed_sents]
+ queue_log.put(f"Classifying sentences {len(sentences)}, id {sent_id}")
+ classified = classify_fun(sentences) if sentences else []
+ queue_out.put((sent_id, org_sentence, changed_sents, y_pred, classified))
+ queue_log.put(f"Classified sentences {sent_id}")
+
+
+def log_queues(queues):
+ while True:
+ sizes = [q.qsize() for q in queues]
+ print(sizes, flush=True)
+ sleep(10)
+
+
+def log_info_queue(queue):
+ print("Logging queue")
+ while True:
+ item = queue.get()
+ print(item)
+
+
+def load_dir_files(dir_path):
+ result = {}
+ for filename in os.listdir(dir_path):
+ with open(os.path.join(dir_path, filename), 'r') as fin:
+ importance = json.load(fin)
+ result[filename.split("__")[0]] = {
+ word: importance[IMPORTANCE][idx]
+ for idx, word in importance[FEATURES].items()
+ if word
+ }
+ return result
+
+
+def load_xai_importance(input_dir):
+ global_xai_dir = os.path.join(input_dir, "global")
+ local_xai_dir = os.path.join(input_dir, "local", "test")
+ local_dirs = os.listdir(local_xai_dir)
+ local_class_to_file = {dir_name: load_dir_files(os.path.join(local_xai_dir, dir_name))
+ for dir_name in local_dirs}
+ local_file_to_class = defaultdict(dict)
+ for c_name in local_dirs:
+ for f_name, value_df in local_class_to_file[c_name].items():
+ local_file_to_class[f_name][c_name] = value_df
+ return load_dir_files(global_xai_dir), local_file_to_class
+
+
@click.command()
@click.option(
"--dataset_name",
help="Dataset name",
type=str,
)
-def main(dataset_name: str):
+@click.option(
+ "--attack_type",
+ help="Attack type",
+ type=str,
+)
+def main(dataset_name: str, attack_type: str):
"""Downloads the dataset to the output directory."""
lang = {
"enron_spam": "en",
@@ -49,32 +182,89 @@ def main(dataset_name: str):
"20_news": "en",
"wiki_pl": "pl",
}[dataset_name]
- output_dir = f"data/results/{dataset_name}"
- input_file = f"data/preprocessed/{dataset_name}/test.jsonl"
+ xai_global, xai_local = {}, {}
+ if "attack_xai" in attack_type:
+ importance = load_xai_importance(f"data/explanations/{dataset_name}")
+ xai_global, xai_local = importance[0], importance[1]
+ xai_sub = 5
+ params = {
+ "attack_textfooler": [lang, SYNONYM],
+ "attack_textfooler_discard": [lang, DISCARD],
+ "attack_basic": [lang, 0.5, 0.4, 0.3], # prawopodobieństwa spacji > usunięcia znaku > usunięcia słowa
+ "attack_xai": [lang, xai_global, xai_local, GLOBAL, SYNONYM, xai_sub],
+ "attack_xai_discard": [lang, xai_global, xai_local, GLOBAL, DISCARD, xai_sub],
+ "attack_xai_local": [lang, xai_global, xai_local, LOCAL, SYNONYM, xai_sub],
+ "attack_xai_discard_local": [lang, xai_global, xai_local, LOCAL, DISCARD, xai_sub]
+ }[attack_type]
+
+ output_dir = f"data/results/{attack_type}/{dataset_name}/"
+ input_file = f"data/classification/{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)
dataset_df = pd.read_json(input_file, lines=True)
- spoiled, results = [], []
- similarity, max_sub = 0.95, 1
- classes = classify(dataset_df[TEXT].tolist())
- attack = TextFooler(lang)
+ max_sub = 1
- for i, cols in tqdm(
- dataset_df[[TEXT, LEMMAS, TAGS, ORTHS]].iterrows(), total=len(dataset_df)
- ):
- sentence, lemmas, tags, orths = cols[0], cols[1], cols[2], cols[3]
- changed_sent = attack.spoil(sentence, [], lemmas, tags, orths, similarity, max_sub)
- if changed_sent:
- spoiled.append(process(changed_sent, classes[i], classify))
-
- with open(output_path, mode="wt") as fd:
- fd.write(
- pd.DataFrame({"spoiled": spoiled}).to_json(
- orient="records", lines=True
- )
- )
+ m = Manager()
+ 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], dataset_df,)), # loading data file_in -> 0
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ # Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ # Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ # Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ # Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ # Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ Process(target=spoil_queue, args=(queues[0], queues[1], queues[5], max_sub, attack_type, params)),
+ # spoiling 0 -> 1
+ Process(target=filter_similarity_queue, args=(queues[1], queues[2], queues[5], sim)),
+ Process(target=filter_similarity_queue, args=(queues[1], queues[2], queues[5], sim)), # cosim 1 -> 2
+ Process(target=classify_queue, args=(queues[2], queues[3], queues[5], dataset_name, "6")),
+ Process(target=classify_queue, args=(queues[2], queues[3], queues[5], dataset_name, "4")),
+ # classify changed 2 -> 3
+ Process(target=run_queue, args=(queues[3], queues[4], queues[5], process,)), # process 3 -> 4
+ Process(target=data_saver, args=(queues[4], queues[5], output_path, output_dir, len(dataset_df), queues, 30))
+ # saving 4 -> file_out
+ ]
+ [p.start() for p in processes]
+
+ log_que = Thread(target=log_queues, args=(queues[:5],))
+ 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__":
diff --git a/text_attacks/models/20_news.py b/text_attacks/models/20_news.py
index 53712fa403a55b03a12aaf6962cdfcd6f4c503ce..8bebdac020574212b917f3a39e55c5b1f1703cb1 100644
--- a/text_attacks/models/20_news.py
+++ b/text_attacks/models/20_news.py
@@ -23,7 +23,7 @@ def get_classify_function(device="cpu"):
logits = list()
i = 0
for chunk in tqdm(
- [texts[pos:pos + 256] for pos in range(0, len(texts), 256)]
+ [texts[pos:pos + 128] for pos in range(0, len(texts), 128)]
):
encoded_inputs = tokenizer(
chunk,
diff --git a/text_attacks/models/wiki_pl.py b/text_attacks/models/wiki_pl.py
index 1ad153955d6e5acfc89f4f922465fb624c1ecf5d..18e9f9f5ee263dfaa6a12183d8e179cf5f50adf5 100644
--- a/text_attacks/models/wiki_pl.py
+++ b/text_attacks/models/wiki_pl.py
@@ -23,7 +23,7 @@ def get_classify_function(device="cpu"):
logits = list()
i = 0
for chunk in tqdm(
- [texts[pos:pos + 256] for pos in range(0, len(texts), 256)]
+ [texts[pos:pos + 128] for pos in range(0, len(texts), 128)]
):
encoded_inputs = tokenizer(
chunk,