mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 02:44:36 +00:00
130 lines
4.7 KiB
Python
Executable File
130 lines
4.7 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
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
|
|
from PIL import Image
|
|
|
|
minigpt4_path = os.path.join(os.path.dirname(__file__), "MiniGPT-4")
|
|
sys.path.insert(0, minigpt4_path)
|
|
from minigpt4.models.blip2 import Blip2Base
|
|
from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor
|
|
|
|
|
|
class MiniGPT4(Blip2Base):
|
|
"""
|
|
MiniGPT4 model from https://github.com/Vision-CAIR/MiniGPT-4
|
|
"""
|
|
def __init__(self,
|
|
args,
|
|
vit_model="eva_clip_g",
|
|
q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
|
|
img_size=224,
|
|
drop_path_rate=0,
|
|
use_grad_checkpoint=False,
|
|
vit_precision="fp32",
|
|
freeze_vit=True,
|
|
freeze_qformer=True,
|
|
num_query_token=32,
|
|
llama_model="",
|
|
prompt_path="",
|
|
prompt_template="",
|
|
max_txt_len=32,
|
|
end_sym='\n',
|
|
low_resource=False, # use 8 bit and put vit in cpu
|
|
device_8bit=0
|
|
):
|
|
super().__init__()
|
|
self.img_size = img_size
|
|
self.low_resource = low_resource
|
|
self.preprocessor = Blip2ImageEvalProcessor(img_size)
|
|
|
|
print('Loading VIT')
|
|
self.visual_encoder, self.ln_vision = self.init_vision_encoder(
|
|
vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
|
|
)
|
|
print('Loading VIT Done')
|
|
print('Loading Q-Former')
|
|
self.Qformer, self.query_tokens = self.init_Qformer(
|
|
num_query_token, self.visual_encoder.num_features
|
|
)
|
|
self.Qformer.cls = None
|
|
self.Qformer.bert.embeddings.word_embeddings = None
|
|
self.Qformer.bert.embeddings.position_embeddings = None
|
|
for layer in self.Qformer.bert.encoder.layer:
|
|
layer.output = None
|
|
layer.intermediate = None
|
|
self.load_from_pretrained(url_or_filename=q_former_model)
|
|
print('Loading Q-Former Done')
|
|
self.llama_proj = nn.Linear(
|
|
self.Qformer.config.hidden_size, 5120 # self.llama_model.config.hidden_size
|
|
)
|
|
self.max_txt_len = max_txt_len
|
|
self.end_sym = end_sym
|
|
self.model = MyModel(["main", *args])
|
|
# system prompt
|
|
self.model.eval_string("Give the following image: <Img>ImageContent</Img>. "
|
|
"You will be able to see the image once I provide it to you. Please answer my questions."
|
|
"###")
|
|
|
|
def encode_img(self, image):
|
|
image = self.preprocessor(image)
|
|
image = image.unsqueeze(0)
|
|
device = image.device
|
|
if self.low_resource:
|
|
self.vit_to_cpu()
|
|
image = image.to("cpu")
|
|
|
|
with self.maybe_autocast():
|
|
image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
|
|
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
|
|
|
|
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
|
|
query_output = self.Qformer.bert(
|
|
query_embeds=query_tokens,
|
|
encoder_hidden_states=image_embeds,
|
|
encoder_attention_mask=image_atts,
|
|
return_dict=True,
|
|
)
|
|
|
|
inputs_llama = self.llama_proj(query_output.last_hidden_state)
|
|
# atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
|
|
return inputs_llama
|
|
|
|
def load_projection(self, path):
|
|
state = torch.load(path)["model"]
|
|
self.llama_proj.load_state_dict({
|
|
"weight": state["llama_proj.weight"],
|
|
"bias": state["llama_proj.bias"]})
|
|
|
|
def chat(self, question):
|
|
self.model.eval_string("Human: ")
|
|
self.model.eval_string(question)
|
|
self.model.eval_string("\n### Assistant:")
|
|
return self.model.generate_with_print(end="###")
|
|
|
|
def chat_with_image(self, image, question):
|
|
with torch.no_grad():
|
|
embd_image = self.encode_img(image)
|
|
embd_image = embd_image.cpu().numpy()[0]
|
|
self.model.eval_string("Human: <Img>")
|
|
self.model.eval_float(embd_image.T)
|
|
self.model.eval_string("</Img> ")
|
|
self.model.eval_string(question)
|
|
self.model.eval_string("\n### Assistant:")
|
|
return self.model.generate_with_print(end="###")
|
|
|
|
|
|
if __name__=="__main__":
|
|
a = MiniGPT4(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048"])
|
|
a.load_projection(os.path.join(
|
|
os.path.dirname(__file__) ,
|
|
"pretrained_minigpt4.pth"))
|
|
respose = a.chat_with_image(
|
|
Image.open("./media/llama1-logo.png").convert('RGB'),
|
|
"what is the text in the picture?")
|
|
a.chat("what is the color of it?")
|