Chinese-CLIP/├── run_scripts/│ ├── muge_finetune_vit-b-16_rbt-base.sh # 训练脚本，官方样例│ ├── flickr30k_finetune_vit-b-16_rbt-base.sh # 训练脚本，官方样例│ └── cn_clip/ ├── clip/ ├── eval/ ├── preprocess/ └── training/${DATAPAT...
text_tokenizer = BertTokenizer.from_pretrained("./clip") text_encoder = BertForSequenceClassification.from_pretrained("./clip").eval() text = text_tokenizer(labels,return_tensors='pt', padding=True)['input_ids'] # 加载CLIP的image encoder clip_model = CLIPModel.from_pretrained("openai32/")...
model = CLIPModel().to(CFG.device) model.load_state_dict(torch.load(model_path, map_location=CFG.device)) model.eval() valid_image_embeddings = [] with torch.no_grad(): for batch in tqdm(valid_loader): image_features = model.image_encoder(batch["image"].to(CFG.device)) image_embed...
device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = load_from_name("ViT-B-16", device=device, download_root='./') model.eval() image = preprocess(Image.open("examples/pokemon.jpeg")).unsqueeze(0).to(device) text = clip.tokenize(["杰尼龟", "妙蛙种子", "...
# 加载Taiyi 中文 text encodertext_tokenizer=BertTokenizer.from_pretrained("./clip")text_encoder=BertForSequenceClassification.from_pretrained("./clip").eval()text=text_tokenizer(labels,return_tensors='pt',padding=True)['input_ids']# 加载CLIP的image encoderclip_model=CLIPModel.from_pretrained("...
model.eval() with torch.no_grad(): similarity = model(image, input_ids, attention_mask) print("Similarity:", similarity) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. ...
├── eval/ ├── preprocess/ └── training/ ${DATAPATH} # 作者为 KG_finetune ├── pretrained_weights/ # 预训练骨架 ├── experiments/ # 训练模型导出地址 ├── deploy/ # 用于存放 pt 转换 ONNX └── datasets/ ├── KG_GE/ ...
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image - CLIP/clip/model.py at a9b1bf5920416aaeaec965c25dd9e8f98c864f16 · openai/CLIP
importtorchfromPILimportImageimportopen_clipmodel,_,preprocess=open_clip.create_model_and_transforms('ViT-B-32',pretrained='laion2b_s34b_b79k')model.eval()# model in train mode by default, impacts some models with BatchNorm or stochastic depth activetokenizer=open_clip.get_tokenizer('ViT-B-...
load_caption_model 这个方法用于加载图像描述模型。首先判断配置中是否直接传入了图像描述模型对象,并且是否指定了图像描述模型名称。如果没有直接传入模型对象并且指定了模型名称,则根据模型名称加载对应的模型。加载过程中根据模型名称的不同选择不同的加载方式。加载完成后,将模型设置为eval模式,并根据配置决定是否将模型...