참고
Click here to download the full example code
TorchMultimodal 튜토리얼: FLAVA 미세조정¶
번역: 김찬
멀티 모달 AI는 최근에 이미지 자막추가, 시각적 검색부터 텍스트로부터 이미지를 생성같은 최근의 응용까지 그 사용이 빠르게 확산되고 있습니다. TorchMultimodal은 PyTorch를 기반으로 하는 라이브러리로, 멀티 모달 연구를 가능하게 하고 가속화하기 위한 빌딩 블록과 end-to-end 예제들을 제공합니다.
본 튜토리얼에서는 사전 훈련된 SoTA 모델인 FLAVA 를 TorchMultimodal 라이브러리에서 사용하여 멀티 모달 작업인 시각적 질의 응답(VQA)에 미세조정하는 방법을 보여 드리겠습니다. 이 모델은 텍스트와 이미지를 위한 두 개의 단일 모달 트랜스포머 기반 인코더와 두 임베딩을 결합하는 다중 모달 인코더로 구성되어 있습니다. 이 모델은 대조적, 이미지-텍스트 매칭, 그리고 텍스트, 이미지 및 다중 모달 마스킹 손실을 사용하여 사전 훈련되었습니다.
설치¶
이 튜토리얼을 위해서는 TextVQA 데이터셋과 Hugging Face의 bert 토크나이저 를 사용할 것입니다.
따라서 TorchMultimodal 외에도 datasets과 transformers를 설치해야 합니다.
참고
이 튜토리얼을 Google Colab에서 실행할 경우, 새로운 셀을 만들고 다음의 명령어를 실행하여 필요한 패키지를 설치하세요:
!pip install torchmultimodal-nightly
!pip install datasets
!pip install transformers
단계¶
다음 명령어를 실행하여 Hugging Face 데이터셋을 컴퓨터의 디렉토리에 다운로드하세요:
wget http://dl.fbaipublicfiles.com/pythia/data/vocab.tar.gz tar xf vocab.tar.gz
참고
이 튜토리얼을 Google Colab에서 실행하는 경우, 새 셀에서 이 명령어를 실행하고 명령어 앞에 느낌표 (!)를 붙이세요.
본 튜토리얼에서는 VQA를 이미지와 질문(텍스트)이 입력되고 출력이 답변 클래스인 분류 작업으로 취급합니다. 따라서 답변 클래스와 레이블 매핑을 생성할 단어장 파일을 다운로드해야 합니다.
또한 Hugging Face에서 textvqa 데이터셋 을 불러오는데, 이 데이터셋은 34602개의 훈련 샘플(이미지, 질문, 답변)을 포함하고 있습니다.
3997개의 답변 클래스가 있음을 확인할 수 있으며, 이에는 알 수 없는 답변을 나타내는 클래스도 포함되어 있습니다.
with open("data/vocabs/answers_textvqa_more_than_1.txt") as f:
vocab = f.readlines()
answer_to_idx = {}
for idx, entry in enumerate(vocab):
answer_to_idx[entry.strip("\n")] = idx
print(len(vocab))
print(vocab[:5])
from datasets import load_dataset
dataset = load_dataset("facebook/textvqa")
3997
['<unk>\n', 'nokia\n', 'ec\n', 'virgin\n', '2011\n']
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데이터셋에서 샘플 엔트리를 표시해 봅시다:
import matplotlib.pyplot as plt
import numpy as np
idx = 5
print("Question: ", dataset["train"][idx]["question"])
print("Answers: " ,dataset["train"][idx]["answers"])
im = np.asarray(dataset["train"][idx]["image"].resize((500,500)))
plt.imshow(im)
plt.show()
Question: what year is shown in the photo?
Answers: ['2011', '2011', '2011', '2011', '2011', '2011', '2011', '2011', '2011', '2011']
3. 다음으로, 이미지와 텍스트를 모델에서 사용할 수 있는 텐서로 변환하기 위한 변환 함수를 작성합니다.
- 이미지의 경우, torchvision의 변환을 사용하여 텐서로 변환하고 일정한 크기로 조정합니다.
- 텍스트의 경우, Hugging Face의 BertTokenizer 를 사용하여 토큰화(및 패딩)합니다.
- 답변(즉, 레이블)의 경우, 가장 빈번하게 나타나는 답변을 훈련 레이블로 사용합니다:
import torch
from torchvision import transforms
from collections import defaultdict
from transformers import BertTokenizer
from functools import partial
def transform(tokenizer, input):
batch = {}
image_transform = transforms.Compose([transforms.ToTensor(), transforms.Resize([224,224])])
image = image_transform(input["image"][0].convert("RGB"))
batch["image"] = [image]
tokenized=tokenizer(input["question"],return_tensors='pt',padding="max_length",max_length=512)
batch.update(tokenized)
ans_to_count = defaultdict(int)
for ans in input["answers"][0]:
ans_to_count[ans] += 1
max_value = max(ans_to_count, key=ans_to_count.get)
ans_idx = answer_to_idx.get(max_value,0)
batch["answers"] = torch.as_tensor([ans_idx])
return batch
tokenizer=BertTokenizer.from_pretrained("bert-base-uncased",padding="max_length",max_length=512)
transform=partial(transform,tokenizer)
dataset.set_transform(transform)
4. 마지막으로, torchmultimodal 에서 flava_model_for_classification 을 가져옵니다.
이것은 기본적으로 사전 훈련된 FLAVA 체크포인트를 로드하고 분류 헤드를 포함합니다.
모델의 순방향 함수는 이미지를 시각 인코더에 통과시키고 질문을 텍스트 인코더에 통과시킵니다. 이미지와 질문의 임베딩은 그 후 멀티 모달 인코더를 통과합니다. 최종 임베딩은 CLS 토큰에 해당하며, 이는 MLP 헤드를 통과하여 각 가능한 답변에 대한 확률 분포를 제공합니다.
from torchmultimodal.models.flava.model import flava_model_for_classification
model = flava_model_for_classification(num_classes=len(vocab))
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flava_for_pretraining_unified_text_encoder.pt: 80%|######## | 1.15G/1.43G [00:54<00:13, 21.6MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 82%|########2 | 1.18G/1.43G [00:55<00:11, 22.1MB/s]
flava_for_pretraining_unified_text_encoder.pt: 82%|########2 | 1.18G/1.43G [00:55<00:11, 21.9MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 83%|########3 | 1.19G/1.43G [00:56<00:11, 22.0MB/s]
flava_for_pretraining_unified_text_encoder.pt: 83%|########3 | 1.19G/1.43G [00:56<00:10, 22.2MB/s]
flava_for_pretraining_unified_text_encoder.pt: 84%|########3 | 1.20G/1.43G [00:56<00:10, 22.0MB/s]
flava_for_pretraining_unified_text_encoder.pt: 84%|########3 | 1.20G/1.43G [00:56<00:10, 22.0MB/s]
flava_for_pretraining_unified_text_encoder.pt: 84%|########4 | 1.20G/1.43G [00:57<00:10, 21.9MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 85%|########4 | 1.21G/1.43G [00:57<00:09, 22.1MB/s]
flava_for_pretraining_unified_text_encoder.pt: 85%|########5 | 1.22G/1.43G [00:57<00:09, 21.9MB/s]
flava_for_pretraining_unified_text_encoder.pt: 85%|########5 | 1.22G/1.43G [00:57<00:09, 22.2MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 86%|########6 | 1.23G/1.43G [00:58<00:08, 22.1MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 88%|########8 | 1.26G/1.43G [00:59<00:07, 21.4MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 89%|########9 | 1.28G/1.43G [01:00<00:06, 21.9MB/s]
flava_for_pretraining_unified_text_encoder.pt: 90%|########9 | 1.28G/1.43G [01:00<00:06, 22.3MB/s]
flava_for_pretraining_unified_text_encoder.pt: 90%|######### | 1.29G/1.43G [01:00<00:06, 22.6MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 91%|######### | 1.30G/1.43G [01:01<00:05, 22.4MB/s]
flava_for_pretraining_unified_text_encoder.pt: 91%|#########1| 1.30G/1.43G [01:01<00:05, 22.1MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 92%|#########2| 1.32G/1.43G [01:02<00:04, 22.2MB/s]
flava_for_pretraining_unified_text_encoder.pt: 93%|#########2| 1.32G/1.43G [01:02<00:04, 22.1MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 93%|#########3| 1.33G/1.43G [01:02<00:04, 22.1MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 94%|#########3| 1.34G/1.43G [01:03<00:03, 22.5MB/s]
flava_for_pretraining_unified_text_encoder.pt: 94%|#########4| 1.35G/1.43G [01:03<00:03, 22.2MB/s]
flava_for_pretraining_unified_text_encoder.pt: 94%|#########4| 1.35G/1.43G [01:03<00:03, 22.0MB/s]
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flava_for_pretraining_unified_text_encoder.pt: 95%|#########4| 1.36G/1.43G [01:04<00:03, 21.9MB/s]
flava_for_pretraining_unified_text_encoder.pt: 95%|#########5| 1.36G/1.43G [01:04<00:03, 21.9MB/s]
flava_for_pretraining_unified_text_encoder.pt: 95%|#########5| 1.37G/1.43G [01:04<00:03, 21.7MB/s]
flava_for_pretraining_unified_text_encoder.pt: 96%|#########5| 1.37G/1.43G [01:04<00:02, 21.8MB/s]
flava_for_pretraining_unified_text_encoder.pt: 96%|#########5| 1.37G/1.43G [01:04<00:02, 21.6MB/s]
flava_for_pretraining_unified_text_encoder.pt: 96%|#########6| 1.38G/1.43G [01:04<00:02, 21.7MB/s]
flava_for_pretraining_unified_text_encoder.pt: 97%|#########6| 1.38G/1.43G [01:05<00:02, 22.0MB/s]
flava_for_pretraining_unified_text_encoder.pt: 97%|#########6| 1.39G/1.43G [01:05<00:02, 21.8MB/s]
flava_for_pretraining_unified_text_encoder.pt: 97%|#########7| 1.39G/1.43G [01:05<00:01, 22.2MB/s]
flava_for_pretraining_unified_text_encoder.pt: 97%|#########7| 1.39G/1.43G [01:05<00:01, 22.2MB/s]
flava_for_pretraining_unified_text_encoder.pt: 98%|#########7| 1.40G/1.43G [01:05<00:01, 22.0MB/s]
flava_for_pretraining_unified_text_encoder.pt: 98%|#########7| 1.40G/1.43G [01:06<00:01, 22.0MB/s]
flava_for_pretraining_unified_text_encoder.pt: 98%|#########8| 1.41G/1.43G [01:06<00:01, 21.7MB/s]
flava_for_pretraining_unified_text_encoder.pt: 98%|#########8| 1.41G/1.43G [01:06<00:00, 21.9MB/s]
flava_for_pretraining_unified_text_encoder.pt: 99%|#########8| 1.41G/1.43G [01:06<00:00, 22.2MB/s]
flava_for_pretraining_unified_text_encoder.pt: 99%|#########9| 1.42G/1.43G [01:06<00:00, 22.1MB/s]
flava_for_pretraining_unified_text_encoder.pt: 99%|#########9| 1.42G/1.43G [01:06<00:00, 22.8MB/s]
flava_for_pretraining_unified_text_encoder.pt: 100%|#########9| 1.43G/1.43G [01:07<00:00, 22.6MB/s]
flava_for_pretraining_unified_text_encoder.pt: 100%|#########9| 1.43G/1.43G [01:07<00:00, 22.3MB/s]
flava_for_pretraining_unified_text_encoder.pt: 1.43GB [01:07, 21.3MB/s]
5. 데이터셋과 모델을 함께 모아 3회 반복을 위한 간단한 훈련 루프를 작성하여 모델 훈련 방법을 보여줍니다:
from torch import nn
BATCH_SIZE = 2
MAX_STEPS = 3
from torch.utils.data import DataLoader
train_dataloader = DataLoader(dataset["train"], batch_size= BATCH_SIZE)
optimizer = torch.optim.AdamW(model.parameters())
epochs = 1
for _ in range(epochs):
for idx, batch in enumerate(train_dataloader):
optimizer.zero_grad()
out = model(text = batch["input_ids"], image = batch["image"], labels = batch["answers"])
loss = out.loss
loss.backward()
optimizer.step()
print(f"Loss at step {idx} = {loss}")
if idx >= MAX_STEPS-1:
break
Loss at step 0 = 8.290360450744629
Loss at step 1 = 8.358965873718262
Loss at step 2 = 8.274676322937012