Delete demo_test.py
Browse files- demo_test.py +0 -44
demo_test.py
DELETED
@@ -1,44 +0,0 @@
|
|
1 |
-
# from modeling_llavanext_for_embedding import LLaVANextForEmbedding
|
2 |
-
# from transformers import LlavaNextProcessor
|
3 |
-
|
4 |
-
# model = LLaVANextForEmbedding.from_pretrained("/share/junjie/code/VISTA2/240920mllmemb/llm_dense_retriever/MMRet-release/test").cuda()
|
5 |
-
# processor = LlavaNextProcessor.from_pretrained("/share/junjie/code/VISTA2/240920mllmemb/llm_dense_retriever/MMRet-release/test")
|
6 |
-
|
7 |
-
# texts = "find a image of a dog"
|
8 |
-
|
9 |
-
# inputs = processor(texts, return_tensors="pt").to("cuda")
|
10 |
-
# outputs = model(**inputs)
|
11 |
-
# print(outputs)
|
12 |
-
|
13 |
-
|
14 |
-
import torch
|
15 |
-
from transformers import LlavaNextProcessor, AutoModel
|
16 |
-
|
17 |
-
model = AutoModel.from_pretrained("/share/junjie/code/VISTA2/240920mllmemb/llm_dense_retriever/MMRet-release/MMRet-MLLM", trust_remote_code=True).cuda()
|
18 |
-
model = model.eval()
|
19 |
-
processor = LlavaNextProcessor.from_pretrained("/share/junjie/code/VISTA2/240920mllmemb/llm_dense_retriever/MMRet-release/MMRet-MLLM")
|
20 |
-
|
21 |
-
texts = "[INST] \n <instruct> <query> find a image of a dog \n [/INST]"
|
22 |
-
|
23 |
-
inputs = processor(texts, return_tensors="pt").to("cuda")
|
24 |
-
outputs = model(**inputs)[:, -1, :]
|
25 |
-
embeddings = torch.nn.functional.normalize(outputs, dim=-1)
|
26 |
-
|
27 |
-
print(embeddings)
|
28 |
-
|
29 |
-
|
30 |
-
|
31 |
-
from transformers import LlavaNextProcessor, AutoModel
|
32 |
-
import torch
|
33 |
-
|
34 |
-
model_name = "/share/junjie/code/VISTA2/240920mllmemb/llm_dense_retriever/MMRet-release/MMRet-MLLM"
|
35 |
-
model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()
|
36 |
-
model = model.eval()
|
37 |
-
model.set_processor(model_name)
|
38 |
-
inputs = model.data_process(text="find a image of a dog", q_or_c="query")
|
39 |
-
|
40 |
-
model_output = model(**inputs, output_hidden_states=True)
|
41 |
-
embeddings = model_output[:, -1, :]
|
42 |
-
embeddings = torch.nn.functional.normalize(embeddings, dim=-1)
|
43 |
-
|
44 |
-
print(embeddings)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|