注意
点击 此处 下载完整的示例代码
TorchMultimodal 教程：FLAVA 微调¶
由于多模态 AI 的普遍性，它最近变得非常流行，从图像字幕和视觉搜索等用例到图像生成等更近期的应用。TorchMultimodal 是一个由 Pytorch 提供支持的库，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名为 FLAVA 的预训练 SoTA 模型（来自 TorchMultimodal 库）对多模态任务（即视觉问答）进行微调。该模型包含两个基于单模态 Transformer 的编码器（用于文本和图像）以及一个多模态编码器来组合这两个嵌入。它使用对比、图像文本匹配以及文本、图像和多模态掩码损失进行预训练。
安装¶
在本教程中，我们将使用 TextVQA 数据集和来自 Hugging Face 的 bert tokenizer。因此，除了 TorchMultimodal 之外，您还需要安装数据集和 transformers。
注意
在 Google Colab 中运行本教程时，请通过创建一个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中。
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 视为一个分类任务，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。
我们可以看到有 3997 个答案类别，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']
/opt/conda/envs/py_3.10/lib/python3.10/site-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用的张量 - 对于图像，我们使用 torchvision 中的转换函数将其转换为张量并调整大小到统一尺寸 - 对于文本，我们使用 Hugging Face 的BertTokenizer对其进行标记（并填充） - 对于答案（即标签），我们将最常出现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。然后将图像和问题的嵌入传递给多模态编码器。对应于 CLS token 的最终嵌入将通过一个 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))
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/opt/conda/envs/py_3.10/lib/python3.10/site-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. 我们将数据集和模型放在一个玩具训练循环中，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 = 8.290360450744629
Loss at step 1 = 8.358966827392578
Loss at step 2 = 8.274675369262695
