Merge branch 'xai_fooler' into 'master'

Xai fooler

See merge request adversarial-attacks/text-attacks!5

data/results/attack_basic/.gitignore

+/wiki_pl

data/results/attack_textfooler/.gitignore

+/enron_spam
+/wiki_pl
+/20_news

data/results/attack_textfooler_discard/.gitignore

+/wiki_pl
+/enron_spam
+/20_news

data/results/attack_xai/.gitignore

+/enron_spam
+/20_news
+/wiki_pl

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

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}/
 

experiments/scripts/attack.py

 """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__":

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,

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,