CLIP使用教程

原理篇

前言

本文主要介绍如何调用Hugging Face中openai提供的CLIP API.

注意

1. 如果碰到模型无法自动下载，可手动下载到本地，注意本地调用路径后缀加/。

下载config.json、preprocessor_config.json、pytorch_model.bin、tokenizer.json

2. 其中processor中
text表示待检索文本，支持多语句搜索
images表示输入图片，支持多张图片搜索
return_tensors表示返回结果格式，
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return NumPy np.ndarray objects.
- 'jax': Return JAX jnp.ndarray objects.

使用

准备一张图片，本示例中图片000000039769.jpg如下，

from PIL import Image
import requests

from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
# model = CLIPModel.from_pretrained("./clip-vit-base-patch32/")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
#  processor = CLIPProcessor.from_pretrained("./clip-vit-base-patch32/")

img_path = "./data/clip/000000039769.jpg"
image = Image.open(img_path)

inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)

outputs = model(**inputs)
logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
print(logits_per_image )
probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
print(probs)

打印结果如下，

tensor([[24.5701, 19.3049]], grad_fn=<PermuteBackward0>)
tensor([[0.9949, 0.0051]], grad_fn=<SoftmaxBackward0>)

返回logits_per_image 并非[0,1]，对于多条语句比对时，可通过softmax归一化；
但当输入一条语句（“a photo of a cat”）及一张图片时，无法获得[0,1]之间相似度，难以设定阈值过滤

tensor([[24.5701]], grad_fn=<PermuteBackward0>)
tensor([[1.]], grad_fn=<SoftmaxBackward0>)

outputs结构如下，

return CLIPOutput(
            loss=loss,
            logits_per_image=logits_per_image,
            logits_per_text=logits_per_text,
            text_embeds=text_embeds,
            image_embeds=image_embeds,
            text_model_output=text_outputs,
            vision_model_output=vision_outputs,
        )

此时可通过本地计算text_embeds与image_embeds之间余弦相似度，完整代码如下，

from PIL import Image
import torch
from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("./clip-vit-base-patch32/")
processor = CLIPProcessor.from_pretrained("./clip-vit-base-patch32/")

img_path = "./data/clip/000000039769.jpg"
image = Image.open(img_path)

# inputs = processor(text=["a photo of a cat"], images=image, return_tensors="pt", padding=True)
inputs = processor(text=["a photo of a cat","a photo of a dog"], images=image, return_tensors="pt", padding=True)

outputs = model(**inputs)
# logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
# print(logits_per_image)
# probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
# print(probs)

similarity = torch.cosine_similarity(outputs.text_embeds, outputs.image_embeds, dim=1)
print(similarity)

输出结果如下，

tensor([0.2457, 0.1930], grad_fn=<SumBackward1>)

猫相似度为0.2457，狗相似度为0.1930

其他示例

“a photo of iron man”
以下靓图similarity分别为：[0.3081, 0.2685]


可能电影相关任务，所有第二张图相似度比较高。