TorchMultimodal 教學：微調 FLAVA¶

建立於：2022 年 10 月 27 日 | 最後更新：2023 年 4 月 11 日 | 最後驗證：2024 年 11 月 05 日

由於其普遍性，從影像字幕和視覺搜尋等用例到最近的應用（例如從文字產生影像），多模態 AI 近來變得非常流行。TorchMultimodal 是一個由 Pytorch 驅動的函式庫，包含構建模組和端到端範例，旨在啟用並加速多模態研究。

在本教學中，我們將示範如何使用 來自 TorchMultimodal 函式庫的預訓練 SoTA 模型 FLAVA 在多模態任務（即視覺問答）上進行微調 (VQA)。該模型包含兩個基於 unimodal transformer 的編碼器，分別用於文字和影像，以及一個多模態編碼器，用於組合這兩個嵌入。它使用對比、影像文字匹配以及文字、影像和多模態遮罩損失進行預訓練。

安裝¶

我們將使用 TextVQA 資料集和來自 Hugging Face 的 bert tokenizer 進行本教學。因此，除了 TorchMultimodal 之外，您還需要安裝 datasets 和 transformers。

注意

在 Google Colab 中執行本教學時，建立一個新儲存格並執行以下指令來安裝所需的套件

!pip install torchmultimodal-nightly
!pip install datasets
!pip install transformers

步驟¶

透過執行以下指令將 Hugging Face 資料集下載到您電腦上的目錄
```
wget http://dl.fbaipublicfiles.com/pythia/data/vocab.tar.gz
tar xf vocab.tar.gz
```
注意

如果您在 Google Colab 中執行本教學，請在一個新儲存格中執行這些指令，並在這些指令前加上驚嘆號 (!)
在本教學中，我們將 VQA 視為分類任務，其中輸入是影像和問題（文字），輸出是答案類別。因此，我們需要下載包含答案類別的 vocab 檔案，並建立答案到標籤的對應。

我們也從 Hugging Face 載入包含 34602 個訓練樣本（影像、問題和答案）的 textvqa 資料集

我們看到有 3997 個答案類別，包含代表未知答案的類別。

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("textvqa")

3997
['<unk>\n', 'nokia\n', 'ec\n', 'virgin\n', '2011\n']
/usr/local/lib/python3.10/dist-packages/datasets/load.py:1491: FutureWarning:

The repository for textvqa contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/textvqa
You can avoid this message in future by passing the argument `trust_remote_code=True`.
Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.


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讓我們顯示資料集中的一個樣本條目

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. 接下來，我們編寫轉換函數，將影像和文字轉換為我們的模型可用的張量 - 對於影像，我們使用 torchvision 中的轉換來轉換為張量並調整為統一大小 - 對於文字，我們使用來自 Hugging Face 的 BertTokenizer 對其進行分詞（和填充）- 對於答案（即標籤），我們採用最常出現的答案作為訓練標籤

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. 最後，我們從 torchmultimodal 匯入 flava_model_for_classification。它預設載入預訓練的 FLAVA 檢查點，並包含一個分類頭。

模型正向函數將影像傳遞到視覺編碼器，並將問題傳遞到文字編碼器。然後，影像和問題嵌入會傳遞到多模態編碼器。對應於 CLS 符記的最終嵌入會傳遞到 MLP 頭，最終給出每個可能答案的機率分佈。

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:  79%|#######8  | 1.13G/1.43G [00:03<00:00, 327MB/s]
flava_for_pretraining_unified_text_encoder.pt:  81%|########  | 1.16G/1.43G [00:03<00:00, 326MB/s]
flava_for_pretraining_unified_text_encoder.pt:  83%|########3 | 1.19G/1.43G [00:03<00:00, 322MB/s]
flava_for_pretraining_unified_text_encoder.pt:  85%|########5 | 1.22G/1.43G [00:03<00:00, 322MB/s]
flava_for_pretraining_unified_text_encoder.pt:  88%|########7 | 1.25G/1.43G [00:04<00:00, 322MB/s]
flava_for_pretraining_unified_text_encoder.pt:  90%|########9 | 1.29G/1.43G [00:04<00:00, 322MB/s]
flava_for_pretraining_unified_text_encoder.pt:  92%|#########2| 1.32G/1.43G [00:04<00:00, 322MB/s]
flava_for_pretraining_unified_text_encoder.pt:  94%|#########4| 1.35G/1.43G [00:04<00:00, 323MB/s]
flava_for_pretraining_unified_text_encoder.pt:  97%|#########6| 1.38G/1.43G [00:04<00:00, 323MB/s]
flava_for_pretraining_unified_text_encoder.pt:  99%|#########8| 1.42G/1.43G [00:04<00:00, 323MB/s]
flava_for_pretraining_unified_text_encoder.pt: 1.43GB [00:04, 315MB/s]

5. 我們將資料集和模型放在一個玩具訓練迴圈中，以示範如何訓練模型 3 次迭代

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 = 8.358966827392578
Loss at step 2 = 8.274675369262695

結論¶

本教學介紹了如何使用來自 TorchMultimodal 的 FLAVA 在多模態任務上進行微調的基礎知識。也請查看函式庫中的其他範例，例如 MDETR，這是一個用於物件偵測的多模態模型，以及 Omnivore，這是一個涵蓋影像、影片和 3d 分類的多任務模型。

腳本的總執行時間： (2 分鐘 36.427 秒)

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TorchMultimodal 教學：微調 FLAVA¶

安裝¶

步驟¶

結論¶

文件

教學

資源