TorchMultimodal 教程：微调 FLAVA¶

创建于：2022 年 10 月 27 日 | 最后更新：2023 年 4 月 11 日 | 最后验证：2024 年 11 月 5 日

多模态 AI 最近因其无处不在的特性而变得非常流行，从图像字幕和视觉搜索等用例到更近期的文本生成图像等应用。TorchMultimodal 是一个由 Pytorch 支持的库，包含构建块和端到端示例，旨在支持和加速多模态研究。

在本教程中，我们将演示如何使用 TorchMultimodal 库中的一个预训练的 SOTA 模型 FLAVA 在多模态任务（即视觉问答 (VQA)）上进行微调。该模型由用于文本和图像的两个单模态 transformer 编码器以及一个用于结合这两个嵌入的多模态编码器组成。它使用对比损失、图像文本匹配损失以及文本、图像和多模态掩码损失进行预训练。

安装¶

本教程将使用 Hugging Face 的 TextVQA 数据集和 bert tokenizer。因此，除了 TorchMultimodal 之外，您还需要安装 datasets 和 transformers。

注意

在 Google Colab 中运行本教程时，通过创建新单元格并运行以下命令来安装所需的软件包

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

步骤¶

通过运行以下命令将 Hugging Face 数据集下载到您计算机上的一个目录
```
wget http://dl.fbaipublicfiles.com/pythia/data/vocab.tar.gz
tar xf vocab.tar.gz
```
注意

如果您在 Google Colab 中运行本教程，请在新单元格中运行这些命令，并在这些命令前加上感叹号 (!)
在本教程中，我们将 VQA 视为分类任务，其中输入是图像和问题（文本），输出是答案类别。因此，我们需要下载包含答案类别的词汇文件，并创建答案到标签的映射。

我们还从 Hugging Face 加载了包含 34602 个训练样本（图像、问题和答案）的textvqa 数据集

我们看到有 3997 个答案类别，其中包括一个表示未知答案的类别。

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://hugging-face.cn/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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让我们显示数据集中的一个样本条目

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. 接下来，我们编写转换函数，将图像和文本转换为模型可使用的张量 - 对于图像，我们使用 torchvision 中的变换将其转换为张量并调整大小为统一尺寸 - 对于文本，我们使用 Hugging Face 的 BertTokenizer 对其进行分词（并填充） - 对于答案（即标签），我们选择出现频率最高的答案作为训练标签

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. 最后，我们从 torchmultimodal 中导入 flava_model_for_classification。它默认加载预训练的 FLAVA 检查点，并包含一个分类头。

模型的正向传播函数将图像通过视觉编码器，问题通过文本编码器。图像和问题的嵌入随后通过多模态编码器。对应于 CLS token 的最终嵌入通过一个 MLP 头，该 MLP 头最终给出每个可能答案的概率分布。

from torchmultimodal.models.flava.model import flava_model_for_classification
model = flava_model_for_classification(num_classes=len(vocab))

flava_for_pretraining_unified_text_encoder.pt: 0.00B [00:00, ?B/s]
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flava_for_pretraining_unified_text_encoder.pt:  71%|#######1  | 1.02G/1.43G [00:02<00:01, 365MB/s]
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flava_for_pretraining_unified_text_encoder.pt:  82%|########1 | 1.17G/1.43G [00:03<00:00, 369MB/s]
flava_for_pretraining_unified_text_encoder.pt:  84%|########4 | 1.21G/1.43G [00:03<00:00, 380MB/s]
flava_for_pretraining_unified_text_encoder.pt:  87%|########7 | 1.25G/1.43G [00:03<00:00, 388MB/s]
flava_for_pretraining_unified_text_encoder.pt:  90%|########9 | 1.29G/1.43G [00:03<00:00, 385MB/s]
flava_for_pretraining_unified_text_encoder.pt:  93%|#########2| 1.33G/1.43G [00:03<00:00, 389MB/s]
flava_for_pretraining_unified_text_encoder.pt:  95%|#########5| 1.37G/1.43G [00:03<00:00, 391MB/s]
flava_for_pretraining_unified_text_encoder.pt:  98%|#########8| 1.41G/1.43G [00:03<00:00, 393MB/s]
flava_for_pretraining_unified_text_encoder.pt: 1.43GB [00:03, 371MB/s]

5. 我们将数据集和模型放在一个简单的训练循环中，以演示如何训练模型 3 个迭代

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 = 8.31214427947998
Loss at step 1 = 8.427092552185059
Loss at step 2 = 8.25987720489502

结论¶

本教程介绍了如何使用 TorchMultimodal 中的 FLAVA 在多模态任务上进行微调的基础知识。请同时查看库中的其他示例，例如用于目标检测的多模态模型 MDETR 以及涵盖图像、视频和 3D 分类的多任务模型 Omnivore。

脚本总运行时间： ( 1 分 57.472 秒)

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TorchMultimodal 教程：微调 FLAVA¶

安装¶

步骤¶

结论¶

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