mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-01 15:40:21 +01:00
99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
|
import sys
|
||
|
|
import os
|
||
|
|
sys.path.insert(0, os.path.dirname(__file__))
|
||
|
|
from embd_input import MyModel
|
||
|
|
import numpy as np
|
||
|
|
from torch import nn
|
||
|
|
import torch
|
||
|
|
|
||
|
|
# use PandaGPT path
|
||
|
|
panda_gpt_path = os.path.join(os.path.dirname(__file__), "PandaGPT")
|
||
|
|
imagebind_ckpt_path = "./models/panda_gpt/"
|
||
|
|
|
||
|
|
sys.path.insert(0, os.path.join(panda_gpt_path,"code","model"))
|
||
|
|
from ImageBind.models import imagebind_model
|
||
|
|
from ImageBind import data
|
||
|
|
|
||
|
|
ModalityType = imagebind_model.ModalityType
|
||
|
|
max_tgt_len = 400
|
||
|
|
|
||
|
|
class PandaGPT:
|
||
|
|
def __init__(self, args):
|
||
|
|
self.visual_encoder,_ = imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path)
|
||
|
|
self.visual_encoder.eval()
|
||
|
|
self.llama_proj = nn.Linear(1024, 5120) # self.visual_hidden_size, 5120)
|
||
|
|
self.max_tgt_len = max_tgt_len
|
||
|
|
self.model = MyModel(["main", *args])
|
||
|
|
self.generated_text = ""
|
||
|
|
self.device = "cpu"
|
||
|
|
|
||
|
|
def load_projection(self, path):
|
||
|
|
state = torch.load(path, map_location="cpu")
|
||
|
|
self.llama_proj.load_state_dict({
|
||
|
|
"weight": state["llama_proj.weight"],
|
||
|
|
"bias": state["llama_proj.bias"]})
|
||
|
|
|
||
|
|
def eval_inputs(self, inputs):
|
||
|
|
self.model.eval_string("<Img>")
|
||
|
|
embds = self.extract_multimoal_feature(inputs)
|
||
|
|
for i in embds:
|
||
|
|
self.model.eval_float(i.T)
|
||
|
|
self.model.eval_string("</Img> ")
|
||
|
|
|
||
|
|
def chat(self, question):
|
||
|
|
return self.chat_with_image(None, question)
|
||
|
|
|
||
|
|
def chat_with_image(self, inputs, question):
|
||
|
|
if self.generated_text == "":
|
||
|
|
self.model.eval_string("###")
|
||
|
|
self.model.eval_string(" Human: ")
|
||
|
|
if inputs:
|
||
|
|
self.eval_inputs(inputs)
|
||
|
|
self.model.eval_string(question)
|
||
|
|
self.model.eval_string("\n### Assistant:")
|
||
|
|
ret = self.model.generate_with_print(end="###")
|
||
|
|
self.generated_text += ret
|
||
|
|
return ret
|
||
|
|
|
||
|
|
def extract_multimoal_feature(self, inputs):
|
||
|
|
features = []
|
||
|
|
for key in ["image", "audio", "video", "thermal"]:
|
||
|
|
if key + "_paths" in inputs:
|
||
|
|
embeds = self.encode_data(key, inputs[key+"_paths"])
|
||
|
|
features.append(embeds)
|
||
|
|
return features
|
||
|
|
|
||
|
|
def encode_data(self, data_type, data_paths):
|
||
|
|
|
||
|
|
type_map = {
|
||
|
|
"image": ModalityType.VISION,
|
||
|
|
"audio": ModalityType.AUDIO,
|
||
|
|
"video": ModalityType.VISION,
|
||
|
|
"thermal": ModalityType.THERMAL,
|
||
|
|
}
|
||
|
|
load_map = {
|
||
|
|
"image": data.load_and_transform_vision_data,
|
||
|
|
"audio": data.load_and_transform_audio_data,
|
||
|
|
"video": data.load_and_transform_video_data,
|
||
|
|
"thermal": data.load_and_transform_thermal_data
|
||
|
|
}
|
||
|
|
|
||
|
|
load_function = load_map[data_type]
|
||
|
|
key = type_map[data_type]
|
||
|
|
|
||
|
|
inputs = {key: load_function(data_paths, self.device)}
|
||
|
|
with torch.no_grad():
|
||
|
|
embeddings = self.visual_encoder(inputs)
|
||
|
|
embeds = embeddings[key]
|
||
|
|
embeds = self.llama_proj(embeds).cpu().numpy()
|
||
|
|
return embeds
|
||
|
|
|
||
|
|
|
||
|
|
if __name__=="__main__":
|
||
|
|
a = PandaGPT(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048", "--lora", "./models/panda_gpt/ggml-adapter-model.bin","--temp", "0"])
|
||
|
|
a.load_projection("./models/panda_gpt/adapter_model.bin")
|
||
|
|
a.chat_with_image(
|
||
|
|
{"image_paths": ["./media/llama1-logo.png"]},
|
||
|
|
"what is the text in the picture? 'llama' or 'lambda'?")
|
||
|
|
a.chat("what is the color of it?")
|