TorchMultimodal 教程：微调 FLAVA
多模态AI最近因其无处不在的特性而变得非常流行，从图像字幕生成和视觉搜索等用例，到最近的从文本生成图像等应用。TorchMultimodal 是一个基于 Pytorch 的库，包含构建模块和端到端示例，旨在推动和加速多模态领域的研究。
在本教程中，我们将演示如何使用 TorchMultimodal 库中的预训练 SoTA 模型 FLAVA 来微调一个多模态任务，即视觉问答 (VQA)。该模型包含两个基于 Transformer 的单模态编码器，分别用于处理文本和图像，以及一个多模态编码器来融合这两种嵌入。它通过对比学习、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
我们将使用 TextVQA 数据集和 Hugging Face 的 bert tokenizer 来完成本教程。因此，除了 TorchMultimodal 之外，您还需要安装 datasets 和 transformers。
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目录中：
wget http://dl.fbaipublicfiles.com/pythia/data/vocab.tar.gz
tar xf vocab.tar.gz
如果您在 Google Colab 中运行本教程，请在一个新的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，我们将视觉问答（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表文件，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。
我们看到共有3997个答案类别，其中包括一个代表未知答案的类别。
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])
fromdatasetsimport 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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让我们展示数据集中的一个示例条目：
importmatplotlib.pyplotasplt
importnumpyasnp
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']
- 接下来，我们编写转换函数，将图像和文本转换为模型可处理的张量：
- 对于图像，我们使用 torchvision 中的 transforms 将其转换为张量并调整到统一大小
- 对于文本，我们使用 Hugging Face 的
BertTokenizer对其进行分词（和填充） - 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：
importtorch
fromtorchvisionimport transforms
fromcollectionsimport defaultdict
fromtransformersimport BertTokenizer
fromfunctoolsimport partial
deftransform(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)
- 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 forward 函数将图像通过视觉编码器，问题通过文本编码器。然后，图像和问题的嵌入通过多模态编码器。最终的嵌入对应于 CLS 标记，并通过一个 MLP 头，最终给出每个可能答案的概率分布。
fromtorchmultimodal.models.flava.modelimport 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: 1.43GB [00:04, 352MB/s]
我们将数据集和模型组合在一个简单的训练循环中，以演示如何对模型进行3次迭代训练：
fromtorchimport nn
BATCH_SIZE = 2
MAX_STEPS = 3
fromtorch.utils.dataimport 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