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Signed-off-by: wxywb <xy.wang@zilliz.com>
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  1. 104
      README.md
  2. 9
      jclip.py
  3. 3
      requirements.txt

104
README.md

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# japanese-clip
#Japanese Image-Text Retrieval Embdding with CLIP
*author: David Wang*
<br />
## Description
This operator extracts features for image or text with [Japanese-CLIP](https://github.com/rinnakk/japanese-clip
) developed by [rinna Co., Ltd](https://rinna.co.jp/), which can generate embeddings for Japanese text and image by jointly training an image encoder and text encoder to maximize the cosine similarity.
<br />
## Code Example
Load an image from path './teddy.jpg' to generate an image embedding.
Read the text 'スケートボードに乗っているテディベア。' to generate an text embedding.
*Write the pipeline in simplified style*:
```python
import towhee
towhee.glob('./teddy.jpg') \
.image_decode() \
.image_text_embedding.japanese_clip(model_name='clip_vit_b32', modality='image') \
.show()
towhee.dc(["スケートボードに乗っているテディベア。"]) \
.image_text_embedding.japanese_clip(model_name='clip_vit_b32', modality='text') \
.show()
```
<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/vec1.png" alt="result1" style="height:20px;"/>
<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/vec2.png" alt="result2" style="height:20px;"/>
*Write a same pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
towhee.glob['path']('./teddy.jpg') \
.image_decode['path', 'img']() \
.image_text_embedding.japanese_clip['img', 'vec'](model_name='clip_vit_b32', modality='image') \
.select['img', 'vec']() \
.show()
towhee.dc['text'](["スケートボードに乗っているテディベア。"]) \
.image_text_embedding.japanese_clip['text','vec'](model_name='clip_vit_b32', modality='text') \
.select['text', 'vec']() \
.show()
```
<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/tabular1.png" alt="result1" style="height:60px;"/>
<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/tabular2.png" alt="result2" style="height:60px;"/>
<br />
## Factory Constructor
Create the operator via the following factory method
***japanese_clip(model_name, modality)***
**Parameters:**
***model_name:*** *str*
​ The model name of CLIP. Supported model names:
- japanese-clip-vit-b-16
- japanese-cloob-vit-b-16
***modality:*** *str*
​ Which modality(*image* or *text*) is used to generate the embedding.
<br />
## Interface
An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray.
**Parameters:**
***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)* or *str*
​ The data (image or text based on specified modality) to generate embedding.
**Returns:** *numpy.ndarray*
​ The data embedding extracted by model.

9
jclip.py

@ -33,9 +33,10 @@ class Jaclip(NNOperator):
sys.path.append(path) sys.path.append(path)
import japanese_clip as ja_clip import japanese_clip as ja_clip
sys.path.pop() sys.path.pop()
cfg = self._configs()[model_name]
self.device = "cuda" if torch.cuda.is_available() else "cpu" self.device = "cuda" if torch.cuda.is_available() else "cpu"
self._modality = modality self._modality = modality
model, preprocess = ja_clip.load("rinna/japanese-clip-vit-b-16", cache_dir="{}/weights/japanese_clip".format(path), device=self.device)
model, preprocess = ja_clip.load(cfg['weights'], cache_dir="{}/weights/japanese_clip".format(path), device=self.device)
self.model = model self.model = model
self.tfms = preprocess self.tfms = preprocess
self.tokenizer = ja_clip.load_tokenizer() self.tokenizer = ja_clip.load_tokenizer()
@ -75,6 +76,8 @@ class Jaclip(NNOperator):
def _configs(self): def _configs(self):
config = {} config = {}
config['blip_base'] = {}
config['blip_base']['weights'] = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base.pth'
config['japanese-clip-vit-b-16'] = {}
config['japanese-clip-vit-b-16']['weights'] = 'rinna/japanese-clip-vit-b-16'
config['japanese-cloob-vit-b-16'] = {}
config['japanese-cloob-vit-b-16']['weights'] = 'rinna/japanese-cloob-vit-b-16'
return config return config

3
requirements.txt

@ -0,0 +1,3 @@
torch
towhee
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