문제 상황
from pycocoevalcap.cider.cider import Cider import matplotlib.pyplot as plt import numpy as np import nltk import re import os import torch from nltk.tokenize import word_tokenize # NLTK 데이터 다운로드 nltk.download('punkt') # 캡션 전처리 함수 def preprocess_caption(caption): # 소문자 변환 caption = caption.lower() # 특수문자 제거 caption = re.sub(r'[^\w\s]', '', caption) # 토크나이즈 tokens = word_tokenize(caption) # 문자열로 다시 결합 return ' '.join(tokens) # CIDEr 평가 함수 def evaluate_and_visualize_with_cider(model, test_loader, word_index, device, image_dir, captions_dict, num_display=5): """ CIDEr 점수 계산 및 결과 시각화 Args: model: 캡션 생성 모델 test_loader: 테스트 데이터 로더 word_index: 단어-인덱스 매핑 device: PyTorch 디바이스 (CPU/GPU) image_dir: 이미지 디렉토리 경로 captions_dict: 이미지 이름과 참조 캡션 매핑 딕셔너리 num_display: 시각화할 이미지 수 """ model.eval() index_to_word = {idx: word for word, idx in word_index.items()} results = [] test_examples = [] with torch.no_grad(): for batch_idx, (features, captions, image_names_batch) in enumerate(test_loader): features = features.to(device) for i in range(features.size(0)): # 배치 크기만큼 반복 # Get the image name image_name = image_names_batch[i] # 캡션 생성 generated_caption = generate_caption(model, features[i], word_index) # 전처리된 캡션 generated_caption_proc = preprocess_caption(generated_caption) # 참조 캡션 가져오기 및 전처리 references = captions_dict.get(image_name, []) references_proc = [preprocess_caption(ref) for ref in references] # 평가 데이터 준비 results.append({ "image_id": image_name, "candidate": generated_caption_proc, "references": references_proc }) # 테스트 예시 저장 (num_display 개수만 저장) if len(test_examples) < num_display: image_path = os.path.join(image_dir, image_name) test_examples.append({ "image_path": image_path, "generated": generated_caption, "references": references }) # CIDEr 점수 계산 print("Calculating CIDEr scores...") cider_scorer = Cider() # gts와 res 딕셔너리 생성 gts = {} res = {} for res_item in results: image_id = res_item["image_id"] gts[image_id] = res_item["references"] # 리스트 형태의 참조 캡션들 res[image_id] = [res_item["candidate"]] # 생성된 캡션을 리스트로 감싸서 전달 # CIDEr 점수 계산 cider_score, cider_scores = cider_scorer.compute_score(gts, res) avg_cider_score = cider_score # 평균 CIDEr 점수 print(f"\nCIDEr Metric Evaluation:") print(f"Average CIDEr Score: {avg_cider_score:.4f}") # CIDEr 점수 분포 시각화 plt.figure(figsize=(10, 5)) plt.hist(cider_scores, bins=50, alpha=0.7) plt.title("Distribution of CIDEr Scores on Test Set") plt.xlabel("CIDEr Score") plt.ylabel("Count") plt.show() # 예시 출력 print("\nExample Generations:") for idx, example in enumerate(test_examples): print(f"\nExample {idx + 1}") print(f"Image Path: {example['image_path']}") print(f"Generated Caption: {example['generated']}") print(f"References: {example['references']}") return avg_cider_score, cider_scores # CIDEr 평가 실행 print("Starting CIDEr evaluation...") avg_cider_score, cider_scores = evaluate_and_visualize_with_cider( model, test_loader, word_index, device, image_dir, captions_dict ) print(f"Average CIDEr Score: {avg_cider_score:.4f}") print(f"Total samples used for CIDEr calculation: {len(cider_scores)}")
위와 같이 nltk를 이용하여 CIDEr score를 계산하려 했는데,
이미 nltk.download('punkt') 로 다운로드를 했음에도, 아래와 같은 에러가 발생함.
--------------------------------------------------------------------------- LookupError Traceback (most recent call last) Cell In[65], line 115 113 # CIDEr 평가 실행 114 print("Starting CIDEr evaluation...") --> 115 avg_cider_score, cider_scores = evaluate_and_visualize_with_cider( 116 model, test_loader, word_index, device, image_dir, captions_dict 117 ) 119 print(f"Average CIDEr Score: {avg_cider_score:.4f}") 120 print(f"Total samples used for CIDEr calculation: {len(cider_scores)}") Cell In[65], line 54 52 generated_caption = generate_caption(model, features[i], word_index) 53 # 전처리된 캡션 ---> 54 generated_caption_proc = preprocess_caption(generated_caption) 56 # 참조 캡션 가져오기 및 전처리 57 references = captions_dict.get(image_name, []) Cell In[65], line 20 18 caption = re.sub(r'[^\w\s]', '', caption) 19 # 토크나이즈 ---> 20 tokens = word_tokenize(caption) 21 # 문자열로 다시 결합 22 return ' '.join(tokens) File ~/.conda/envs/DL/lib/python3.10/site-packages/nltk/tokenize/__init__.py:142, in word_tokenize(text, language, preserve_line) 127 def word_tokenize(text, language="english", preserve_line=False): 128 """ 129 Return a tokenized copy of *text*, 130 using NLTK's recommended word tokenizer (...) 140 :type preserve_line: bool 141 """ --> 142 sentences = [text] if preserve_line else sent_tokenize(text, language) 143 return [ 144 token for sent in sentences for token in _treebank_word_tokenizer.tokenize(sent) 145 ] File ~/.conda/envs/DL/lib/python3.10/site-packages/nltk/tokenize/__init__.py:119, in sent_tokenize(text, language) 109 def sent_tokenize(text, language="english"): 110 """ 111 Return a sentence-tokenized copy of *text*, 112 using NLTK's recommended sentence tokenizer (...) 117 :param language: the model name in the Punkt corpus ... - '/usr/local/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/local/lib/nltk_data' **********************************************************************
해결
우선 아래와 같은 코드로 tokenizer가 안되는 이유를 디버깅.
import nltk from nltk.tokenize import word_tokenize try: word_tokenize("Test sentence for debugging.") print("Tokenization successful!") except LookupError as e: print("Error:", e) print("NLTK data path:", nltk.data.path)
Error: ********************************************************************** Resource punkt_tab not found. Please use the NLTK Downloader to obtain the resource: >>> import nltk >>> nltk.download('punkt_tab') For more information see: https://www.nltk.org/data.html Attempted to load tokenizers/punkt_tab/english/ Searched in: - '/home/gpu_04/nltk_data' - '/home/gpu_04/.conda/envs/DL/nltk_data' - '/home/gpu_04/.conda/envs/DL/share/nltk_data' - '/home/gpu_04/.conda/envs/DL/lib/nltk_data' - '/usr/share/nltk_data' - '/usr/local/share/nltk_data' - '/usr/lib/nltk_data' - '/usr/local/lib/nltk_data' - '/home/gpu_04/.conda/envs/DL/nltk_data' **********************************************************************
출력대로 nltk.download('punkt_tab') 으로 설치해주니 해결.
만약 path 문제라면, path를 추가해주면 될 것 같다.