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