香橙派AIpro开发板评测：香橙派AIpro也能MindSpore+Jupyter Notebook开发呀和Qwen2-0.5B-Instruct-GGUF部署

python -c \
"import mindspore;mindspore.set_context(device_target='Ascend');mindspore.run_check()"

cd samples

 ./start_notebook.sh 192.168.123.119

import os
import time
import argparse
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
from acllite_model import AclLiteModel as Model
from acllite_resource import AclLiteResource as AclResource

# om模型和图片的位置
MODEL_PATH = './cnnctc.om'
IMAGE_PATH = './predict.png'
# 初始化acl资源
acl_resource = AclResource()
acl_resource.init()
#导入本地om模型
print('load model....')
model = Model(MODEL_PATH)
print('load model finished....')
# 文本与数据编码
class CTCLabelConverter():
    def __init__(self, character):
        dict_character = list(character)
        self.dict = {}
        for i, char in enumerate(dict_character):
            self.dict[char] = i + 1
        self.character = ['[blank]'] + dict_character
        self.dict['[blank]'] = 0
    #将文本转换为数字编码
    def encode(self, text):
        length = [len(s) for s in text]
        text = ''.join(text)
        text = [self.dict[char] for char in text]
        return np.array(text), np.array(length)
    # 将数字编码转换为文本
    def decode(self, text_index, length):
        texts = []
        index = 0
        for l in length:
            t = text_index[index:index + l]
            char_list = []
            for i in range(l):
                if t[i] != self.dict['[blank]'] and (
                        not (i > 0 and t[i - 1] == t[i])):
                    char_list.append(self.character[t[i]])
            text = ''.join(char_list)
            texts.append(text)
            index += l
        return texts

# 导入和处理目标图片
img_PIL = Image.open(IMAGE_PATH).convert('RGB')
img = img_PIL.resize((100, 32), resample=3)
img = np.array(img, dtype=np.float32)
img = np.expand_dims(img, axis=0)
img = np.transpose(img, [0, 3, 1, 2])
# 定义推理的时间
start = time.time()
model_predict = model.execute([img])[0]
end = time.time()
print(f'infer use time:{(end-start)*1000}ms')
# 初始化文本编码函数
character = '0123456789abcdefghijklmnopqrstuvwxyz'
converter = CTCLabelConverter(character)
# 推理过程
preds_size = np.array([model_predict.shape[1]])
preds_index = np.argmax(model_predict, 2)
preds_index = np.reshape(preds_index, [-1])
preds_str = converter.decode(preds_index, preds_size)
print('Predict: ', preds_str)

git clone https://github.com/ggerganov/llama.cpp.git

make

from modelscope import snapshot_download 
model_dir = snapshot_download('qwen/Qwen2-0.5B-Instruct-GGUF')