From e2cd01ba25a7f4509a2c42288a78779ca376b634 Mon Sep 17 00:00:00 2001
From: pwalkow <pwalkow@gpu-server.ws.clarin>
Date: Wed, 22 Mar 2023 16:23:34 +0100
Subject: [PATCH] Add dvc.lock
---
data/results/attack_basic/.gitignore | 1 +
data/results/attack_textfooler/.gitignore | 3 +
.../attack_textfooler_discard/.gitignore | 2 +
dvc.lock | 148 +++++++++++++++--
dvc.yaml | 36 ++++-
experiments/scripts/attack.py | 153 +++++++++++++-----
text_attacks/models/20_news.py | 2 +-
text_attacks/models/wiki_pl.py | 2 +-
8 files changed, 287 insertions(+), 60 deletions(-)
create mode 100644 data/results/attack_basic/.gitignore
create mode 100644 data/results/attack_textfooler/.gitignore
create mode 100644 data/results/attack_textfooler_discard/.gitignore
diff --git a/data/results/attack_basic/.gitignore b/data/results/attack_basic/.gitignore
new file mode 100644
index 0000000..bef990d
--- /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 0000000..e695872
--- /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 0000000..70a2cd6
--- /dev/null
+++ b/data/results/attack_textfooler_discard/.gitignore
@@ -0,0 +1,2 @@
+/wiki_pl
+/enron_spam
diff --git a/dvc.lock b/dvc.lock
index 18c0a65..93146df 100644
--- a/dvc.lock
+++ b/dvc.lock
@@ -17,8 +17,8 @@ stages:
--output_dir data/models/enron_spam
deps:
- path: data/preprocessed/enron_spam
- md5: b75efba1a62182dc8ac32acd1faf92ed.dir
- size: 61709260
+ md5: 30c63efbc615347ddcb5f61e011113bd.dir
+ size: 65971374
nfiles: 3
- path: experiments/scripts/get_model.py
md5: 5050f51b4019bba97af47971f6c7cab4
@@ -37,16 +37,16 @@ stages:
size: 18505614
nfiles: 6
- path: data/preprocessed/enron_spam/
- md5: b75efba1a62182dc8ac32acd1faf92ed.dir
- size: 61709260
+ md5: 30c63efbc615347ddcb5f61e011113bd.dir
+ size: 65971374
nfiles: 3
- path: experiments/scripts/classify.py
md5: 6fc1a6a0a11ba6cd99a8b6625a96d9f5
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
@@ -134,16 +134,16 @@ stages:
size: 501711136
nfiles: 7
- path: data/preprocessed/wiki_pl/
- md5: 3e9b2e1e0542777e0a751d9d7f7f4241.dir
- size: 55380570
+ md5: 0014b9bb52913cbc9a568d237ea2207b.dir
+ size: 65553079
nfiles: 3
- path: experiments/scripts/classify.py
md5: 6fc1a6a0a11ba6cd99a8b6625a96d9f5
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
@@ -169,16 +169,16 @@ stages:
size: 439008808
nfiles: 6
- path: data/preprocessed/20_news/
- md5: 1ed5ef2dabe4bc05f7377175ed11137b.dir
- size: 46845669
+ md5: 20da0980e52df537e5b7ca5db0305879.dir
+ size: 58582060
nfiles: 3
- path: experiments/scripts/classify.py
md5: 6fc1a6a0a11ba6cd99a8b6625a96d9f5
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 +239,123 @@ stages:
md5: c8ba90f9757a4e3cc4843d3791ef2446.dir
size: 232912969
nfiles: 14041
+ 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
diff --git a/dvc.yaml b/dvc.yaml
index 6862a88..03c045f 100644
--- a/dvc.yaml
+++ b/dvc.yaml
@@ -77,6 +77,38 @@ 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_basic:
foreach:
- enron_spam
@@ -86,11 +118,11 @@ 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 721d46f..78f97ca 100644
--- a/experiments/scripts/attack.py
+++ b/experiments/scripts/attack.py
@@ -1,7 +1,7 @@
"""Script for running attacks on datasets."""
import importlib
import json
-
+from collections import defaultdict
import click
import pandas as pd
import os
@@ -22,6 +22,7 @@ LEMMAS = "lemmas"
TAGS = "tags"
ORTHS = "orths"
ID = "id"
+PRED = "pred_label"
ATTACK_SUMMARY = "attacks_summary"
ATTACK_SUCCEEDED = "attacks_succeeded"
@@ -36,6 +37,10 @@ ACTUAL = "actual"
COSINE_SCORE = "cosine_score"
CLASS = "class"
QUEUE_SIZE = 1000
+FEATURES = "features"
+IMPORTANCE = "importance"
+SYNONYM = "synonym"
+DISCARD = "discard"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -49,19 +54,20 @@ DEFAULT_RES = {
def data_producer(queue_out, dataset_df):
for i, cols in tqdm(
- dataset_df[[TEXT, ID, LEMMAS, TAGS, ORTHS]].iterrows(), total=len(dataset_df)
+ dataset_df[[TEXT, ID, LEMMAS, TAGS, ORTHS, PRED]].iterrows(), total=len(dataset_df)
):
- 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])
+ 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, que_kill, to_kill_nbr):
+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:
@@ -74,13 +80,17 @@ def data_saver(queue_in, queue_log, output_file, output_dir, cases_nbr, que_kill
ch_all += spoiled[ATTACK_SUMMARY][ALL]
spoiled_sents.append(spoiled)
if processed_nbr == cases_nbr:
- [que_kill.put(None) for _ in range(to_kill_nbr)]
- with open(output_file, 'a') as fd:
- fd.write(
- pd.DataFrame(spoiled_sents).to_json(
- orient="records", lines=True
- )
- )
+ 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')
@@ -89,7 +99,8 @@ def data_saver(queue_in, queue_log, output_file, output_dir, cases_nbr, que_kill
fd.write(f"succeeded {ch_suc} all {ch_all}")
-def classify_queue(queue_in, queue_out, queue_log, dataset_name):
+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",
@@ -101,21 +112,19 @@ def classify_queue(queue_in, queue_out, queue_log, dataset_name):
item = queue_in.get()
queue_log.put("Classify got from queue")
if item is not None:
- sent_id, org_sentence, changed_sents = item
- sentences = [org_sentence]
- sentences.extend([sent[TEXT] for sent in changed_sents])
+ 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)
- queue_out.put((sent_id, org_sentence, changed_sents, classified))
+ 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}")
- queue_out.put(None)
def log_queues(queues):
while True:
sizes = [q.qsize() for q in queues]
print(sizes, flush=True)
- sleep(2)
+ sleep(10)
def log_info_queue(queue):
@@ -125,6 +134,31 @@ def log_info_queue(queue):
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")
+ 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",
@@ -145,42 +179,77 @@ def main(dataset_name: str, attack_type: str):
"wiki_pl": "pl",
}[dataset_name]
params = {
- "attack_textfooler": [lang],
- "attack_basic": [lang, 0.5, 0.4, 0.3]
+ "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_type]
output_dir = f"data/results/{attack_type}/{dataset_name}/"
- input_file = f"data/preprocessed/{dataset_name}/test.jsonl"
+ 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")
dataset_df = pd.read_json(input_file, lines=True)
+
+ # xai_global, xai_local = load_xai_importance(
+ # f"data/explanations/{dataset_name}"
+ # ) if attack_type == "attack_xai" else {}, {}
+
max_sub = 1
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)), # spoiling 0 -> 1
- 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, )), # 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[0], 11))] # saving 4 -> file_out
+ 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 = 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 = Thread(target=log_info_queue, args=(queues[5],))
info_que.daemon = True
info_que.start()
# wait for all processes to finish
diff --git a/text_attacks/models/20_news.py b/text_attacks/models/20_news.py
index 53712fa..8bebdac 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 1ad1539..18e9f9f 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,
--
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