| # from transformers import AutoTokenizer, AutoModel | |
| # torch.set_float32_matmul_precision("high") | |
| # model_id = "FacebookAI/roberta-large" | |
| # tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| # model = AutoModel.from_pretrained(model_id).to("cuda") | |
| # text = "The capital of France is [MASK]." | |
| # inputs = tokenizer(text, return_tensors="pt").to("cuda") | |
| # outputs = model(**inputs) | |
| # # To get predictions for the mask: | |
| # masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id) | |
| # predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1) | |
| # predicted_token = tokenizer.decode(predicted_token_id) | |
| # print("Predicted token:", predicted_token) | |
| # from transformers import AutoModel, AutoTokenizer | |
| # model_name = "chandar-lab/NeoBERT" | |
| # tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) | |
| # model = AutoModel.from_pretrained(model_name, trust_remote_code=True) | |
| # text = "NeoBERT is the most efficient model of its kind!" | |
| # inputs = tokenizer(text, return_tensors="pt") | |
| # # Generate embeddings | |
| # outputs = model(**inputs) | |
| # embedding = outputs.last_hidden_state[:, 0, :] | |
| # print(embedding.shape) | |
| # import sys | |
| # # Add the folder to sys.path | |
| # sys.path.append("/gpfs/home/ym621/UniPointMap") | |
| # import torch | |
| # from PIL import Image | |
| # sys.path.append("/home/m50048399/transfered/ye_project/UniPointMap") | |
| # import open_clip | |
| # # Create model & transforms | |
| # model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16-quickgelu', pretrained='dfn2b') | |
| # model.eval() # Set model to eval mode | |
| # # Get tokenizer | |
| # tokenizer = open_clip.get_tokenizer('ViT-B-16-quickgelu') | |
| # image = preprocess(Image.open("docs/CLIP.png")).unsqueeze(0) | |
| # text = tokenizer(["a diagram", "a dog", "a cat"]) | |
| # with torch.no_grad(), torch.autocast("cuda"): | |
| # image_features = model.encode_image(image) | |
| # text_features = model.encode_text(text) | |
| # image_features /= image_features.norm(dim=-1, keepdim=True) | |
| # text_features /= text_features.norm(dim=-1, keepdim=True) | |
| # text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) | |
| # print("Label probs:", text_probs) # prints: [[1., 0., 0.]] | |
| # import torch | |
| # from PIL import Image | |
| # from transformers import ( | |
| # AutoImageProcessor, | |
| # AutoTokenizer, | |
| # AutoModelForCausalLM, | |
| # ) | |
| # model_root = "jina" | |
| # image_size=224 | |
| # model = AutoModelForCausalLM.from_pretrained(model_root,trust_remote_code=True).cuda() | |
| # device = model.device | |
| # from transformers import AutoModel, AutoTokenizer, AutoImageProcessor | |
| # model_root = 'fg-clip-base' | |
| # tokenizer = AutoTokenizer.from_pretrained(model_root) | |
| # image_processor = AutoImageProcessor.from_pretrained(model_root) | |
| # text_encoder = AutoModel.from_pretrained('jinaai/jina-clip-v2', trust_remote_code=True) | |
| # tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-clip-v2', trust_remote_code=True) | |
| # pip install -U huggingface_hub | |
| from huggingface_hub import snapshot_download | |
| # Download ONLY the light_cc3m subfolder into HF cache | |
| snapshot_download( | |
| repo_id="MatchLab/ScenePoint", | |
| repo_type="dataset", | |
| allow_patterns=["light_3rscan/**", "light_arkitscenes/**"], # only this subfolder | |
| resume_download=True, # safe to re-run | |
| max_workers=8 # parallel downloads | |
| ) | |
| print("Downloaded to Hugging Face cache only.") |