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---
license: mit
language:
- zh
tags:
- gpt2
- vit
---
# 模型介绍
1. vit对图像做encoder，然后再用gpt2做decoder
2. vit模型使用的是`google/vit-base-patch16-224`, gpt2使用的是`yuanzhoulvpi/gpt2_chinese`
3. 本模型支持中文
# 训练代码
[https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/vit-gpt2-image-chinese-captioning](https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/vit-gpt2-image-chinese-captioning)
# 推理代码
# infer
```python
from transformers import (VisionEncoderDecoderModel,
AutoTokenizer,ViTImageProcessor)
import torch
from PIL import Image
```
```python
vision_encoder_decoder_model_name_or_path = "yuanzhoulvpi/vit-gpt2-image-chinese-captioning"#"vit-gpt2-image-chinese-captioning/checkpoint-3200"
processor = ViTImageProcessor.from_pretrained(vision_encoder_decoder_model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(vision_encoder_decoder_model_name_or_path)
model = VisionEncoderDecoderModel.from_pretrained(vision_encoder_decoder_model_name_or_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
```
```python
max_length = 16
num_beams = 4
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def predict_step(image_paths):
images = []
for image_path in image_paths:
i_image = Image.open(image_path)
if i_image.mode != "RGB":
i_image = i_image.convert(mode="RGB")
images.append(i_image)
pixel_values = processor(images=images, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
output_ids = model.generate(pixel_values, **gen_kwargs)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
predict_step(['bigdata/image_data/train-1000200.jpg'])
```
# 效果
## example 1
## example 2
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