# require modelscope>=0.3.7，目前默认已经超过，您检查一下即可
# 按照更新镜像的方法处理或者下面的方法
# pip install --upgrade modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
# 需要单独安装decord，安装方法：pip install decord
import torch
from modelscope.utils.constant import Tasks
from modelscope.pipelines import pipeline
from modelscope.preprocessors.image import load_image

pipeline = pipeline(task=Tasks.multi_modal_embedding,
                    model='damo/multi-modal_clip-vit-large-patch14_336_zh', model_revision='v1.0.1')
input_img = load_image('https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg') # 支持皮卡丘示例图片路径/本地图片 返回PIL.Image
input_texts = ["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"]

# 支持一张图片(PIL.Image)或多张图片(List[PIL.Image])输入，输出归一化特征向量
img_embedding = pipeline.forward({'img': input_img})['img_embedding'] # 2D Tensor, [图片数, 特征维度]

# 支持一条文本(str)或多条文本(List[str])输入，输出归一化特征向量
text_embedding = pipeline.forward({'text': input_texts})['text_embedding'] # 2D Tensor, [文本数, 特征维度]

# 计算图文相似度
with torch.no_grad():
    # 计算内积得到logit，考虑模型temperature
    logits_per_image = (img_embedding / pipeline.model.temperature) @ text_embedding.t()
    # 根据logit计算概率分布
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()

print("图文匹配概率:", probs)