| import torch |
| from PIL import Image |
| from transformers import AutoModelForCausalLM, LlamaTokenizer |
|
|
| tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5') |
| model = AutoModelForCausalLM.from_pretrained( |
| 'THUDM/cogvlm-grounding-generalist-hf', |
| torch_dtype=torch.bfloat16, |
| low_cpu_mem_usage=True, |
| trust_remote_code=True, |
| |
| ).to('cuda').eval() |
|
|
| query = 'Can you provide a description of the image and include the coordinates [[x0,y0,x1,y1]] for each mentioned object?' |
| image = Image.open(requests.get('https://github.com/THUDM/CogVLM/blob/main/examples/4.jpg?raw=true', stream=True).raw).convert('RGB') |
| inputs = model.build_conversation_input_ids(tokenizer, query=query, images=[image]) |
| inputs = { |
| 'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'), |
| 'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'), |
| 'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'), |
| 'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)]], |
| } |
| gen_kwargs = {"max_length": 2048, "do_sample": False} |
|
|
| with torch.no_grad(): |
| outputs = model.generate(**inputs, **gen_kwargs) |
| outputs = outputs[:, inputs['input_ids'].shape[1]:] |
| print(tokenizer.decode(outputs[0])) |
|
|
