注意
点击 这里 下载完整示例代码
PyTorch 多模态教程：微调 FLAVA¶
创建日期：2022年10月27日 | 最后更新日期：2023年4月11日 | 最后验证日期：2024年11月5日
多模态AI由于其无处不在的特性最近变得非常流行，从图像描述、视觉搜索等用例到更近期的应用如从文本生成图像。 TorchMultimodal是一个由Pytorch驱动的库，包含构建模块和端到端示例，旨在启用和加速多模态研究。
在本教程中，我们将演示如何使用来自 TorchMultimodal 库的名为FLAVA的预训练最先进模型对多模态任务（即视觉问题回答）进行微调（VQA）。该模型包含两个用于文本和图像的单模态基于变换器的编码器，以及一个将两者嵌入组合起来的多模态编码器。它使用对比、图文匹配以及文本、图像和多模态掩码损失进行预训练。
安装¶
我们将使用TextVQA数据集和 bert tokenizer 从Hugging Face进行此
教程。因此，除了TorchMultimodal之外，您还需要安装datasets和transformers。
注意
在 Google Colab 中运行此教程时，通过创建一个新单元格并运行以下命令来安装所需的包：
!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中：
wget http://dl.fbaipublicfiles.com/pythia/data/vocab.tar.gz tar xf vocab.tar.gz
注意
如果你是在 Google Colab 中运行此教程，请在新单元格中运行这些命令，并在命令前添加一个感叹号 (!)
对于本教程，我们将VQA视为一个分类任务，其中输入是图像和问题（文本），输出是一个答案类别。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。
我们还加载了来自Hugging Face的textvqa 数据集，其中包含34602个训练样本 （图像、问题和答案）
我们看到共有 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://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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让我们显示数据集中的一个示例条目：
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消费的张量 - 对于图像，我们使用torchvision中的转换方法将其转换为张量并调整为统一尺寸 - 对于文本，我们使用Hugging Face提供的BertTokenizer进行分词（并填充） - 对于答案（即标签），我们采用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。
模型的前向函数将图像通过视觉编码器，将问题通过文本编码器。图像和问题的嵌入随后被传递给多模态编码器。最终对应于 CLS 标记的嵌入通过一个 MLP 头部，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))
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flava_for_pretraining_unified_text_encoder.pt: 1.43GB [00:14, 98.5MB/s]
/usr/local/lib/python3.10/dist-packages/torchmultimodal/utils/common.py:106: FutureWarning:
You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
5. 我们在一个玩具训练循环中将数据集和模型组合在一起，以演示如何对模型进行3次迭代的训练：
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