mirror of
https://github.com/ggml-org/llama.cpp.git
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100 lines
3.4 KiB
Python
Executable File
100 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python3
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import sys
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import os
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sys.path.insert(0, os.path.dirname(__file__))
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from embd_input import MyModel
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import numpy as np
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from torch import nn
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import torch
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# use PandaGPT path
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panda_gpt_path = os.path.join(os.path.dirname(__file__), "PandaGPT")
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imagebind_ckpt_path = "./models/panda_gpt/"
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sys.path.insert(0, os.path.join(panda_gpt_path,"code","model"))
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from ImageBind.models import imagebind_model
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from ImageBind import data
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ModalityType = imagebind_model.ModalityType
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max_tgt_len = 400
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class PandaGPT:
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def __init__(self, args):
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self.visual_encoder,_ = imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path)
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self.visual_encoder.eval()
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self.llama_proj = nn.Linear(1024, 5120) # self.visual_hidden_size, 5120)
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self.max_tgt_len = max_tgt_len
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self.model = MyModel(["main", *args])
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self.generated_text = ""
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self.device = "cpu"
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def load_projection(self, path):
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state = torch.load(path, map_location="cpu")
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self.llama_proj.load_state_dict({
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"weight": state["llama_proj.weight"],
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"bias": state["llama_proj.bias"]})
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def eval_inputs(self, inputs):
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self.model.eval_string("<Img>")
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embds = self.extract_multimoal_feature(inputs)
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for i in embds:
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self.model.eval_float(i.T)
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self.model.eval_string("</Img> ")
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def chat(self, question):
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return self.chat_with_image(None, question)
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def chat_with_image(self, inputs, question):
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if self.generated_text == "":
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self.model.eval_string("###")
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self.model.eval_string(" Human: ")
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if inputs:
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self.eval_inputs(inputs)
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self.model.eval_string(question)
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self.model.eval_string("\n### Assistant:")
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ret = self.model.generate_with_print(end="###")
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self.generated_text += ret
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return ret
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def extract_multimoal_feature(self, inputs):
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features = []
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for key in ["image", "audio", "video", "thermal"]:
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if key + "_paths" in inputs:
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embeds = self.encode_data(key, inputs[key+"_paths"])
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features.append(embeds)
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return features
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def encode_data(self, data_type, data_paths):
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type_map = {
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"image": ModalityType.VISION,
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"audio": ModalityType.AUDIO,
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"video": ModalityType.VISION,
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"thermal": ModalityType.THERMAL,
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}
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load_map = {
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"image": data.load_and_transform_vision_data,
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"audio": data.load_and_transform_audio_data,
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"video": data.load_and_transform_video_data,
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"thermal": data.load_and_transform_thermal_data
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}
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load_function = load_map[data_type]
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key = type_map[data_type]
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inputs = {key: load_function(data_paths, self.device)}
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with torch.no_grad():
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embeddings = self.visual_encoder(inputs)
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embeds = embeddings[key]
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embeds = self.llama_proj(embeds).cpu().numpy()
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return embeds
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if __name__=="__main__":
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a = PandaGPT(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048", "--lora", "./models/panda_gpt/ggml-adapter-model.bin","--temp", "0"])
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a.load_projection("./models/panda_gpt/adapter_model.bin")
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a.chat_with_image(
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{"image_paths": ["./media/llama1-logo.png"]},
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"what is the text in the picture? 'llama' or 'lambda'?")
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a.chat("what is the color of it?")
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