cfa0750bc9
* add interface for float input * fixed inpL shape and type * add examples of input floats * add test example for embd input * fixed sampling * add free for context * fixed add end condition for generating * add examples for llava.py * add READMD for llava.py * add READMD for llava.py * add example of PandaGPT * refactor the interface and fixed the styles * add cmake build for embd-input * add cmake build for embd-input * Add MiniGPT-4 example * change the order of the args of llama_eval_internal * fix ci error
71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
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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from transformers import CLIPVisionModel, CLIPImageProcessor
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from PIL import Image
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# model parameters from 'liuhaotian/LLaVA-13b-delta-v1-1'
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vision_tower = "openai/clip-vit-large-patch14"
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select_hidden_state_layer = -2
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# (vision_config.image_size // vision_config.patch_size) ** 2
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image_token_len = (224//14)**2
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class Llava:
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def __init__(self, args):
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self.image_processor = CLIPImageProcessor.from_pretrained(vision_tower)
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self.vision_tower = CLIPVisionModel.from_pretrained(vision_tower)
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self.mm_projector = nn.Linear(1024, 5120)
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self.model = MyModel(["main", *args])
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def load_projection(self, path):
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state = torch.load(path)
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self.mm_projector.load_state_dict({
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"weight": state["model.mm_projector.weight"],
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"bias": state["model.mm_projector.bias"]})
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def chat(self, question):
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self.model.eval_string("user: ")
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self.model.eval_string(question)
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self.model.eval_string("\nassistant: ")
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return self.model.generate_with_print()
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def chat_with_image(self, image, question):
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with torch.no_grad():
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embd_image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
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image_forward_out = self.vision_tower(embd_image.unsqueeze(0), output_hidden_states=True)
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select_hidden_state = image_forward_out.hidden_states[select_hidden_state_layer]
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image_feature = select_hidden_state[:, 1:]
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embd_image = self.mm_projector(image_feature)
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embd_image = embd_image.cpu().numpy()[0]
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self.model.eval_string("user: ")
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self.model.eval_token(32003-2) # im_start
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self.model.eval_float(embd_image.T)
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for i in range(image_token_len-embd_image.shape[0]):
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self.model.eval_token(32003-3) # im_patch
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self.model.eval_token(32003-1) # im_end
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self.model.eval_string(question)
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self.model.eval_string("\nassistant: ")
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return self.model.generate_with_print()
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if __name__=="__main__":
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# model form liuhaotian/LLaVA-13b-delta-v1-1
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a = Llava(["--model", "./models/ggml-llava-13b-v1.1.bin", "-c", "2048"])
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# Extract from https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin.
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# Also here can use pytorch_model-00003-of-00003.bin directly.
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a.load_projection(os.path.join(
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os.path.dirname(__file__) ,
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"llava_projetion.pth"))
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respose = a.chat_with_image(
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Image.open("./media/llama1-logo.png").convert('RGB'),
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"what is the text in the picture?")
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respose
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a.chat("what is the color of it?")
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