一、adb
adb shell，进入板子的根目录
exit，退出到linux
adb pull 板子 linux（从板子到linux）
adb push linux 板子（从linux到板子）
二、测试AI功能
2.1 resnet18
import cv2
import numpy as np
import platform
from synset_label import labels
from rknnlite.api import RKNNLite
# decice tree for RK356x/RK3576/RK3588
DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible'
def get_host():
# get platform and device type
system = platform.system()
machine = platform.machine()
os_machine = system + '-' + machine
if os_machine == 'Linux-aarch64':
try:
with open(DEVICE_COMPATIBLE_NODE) as f:
device_compatible_str = f.read()
if 'rk3562' in device_compatible_str:
host = 'RK3562'
elif 'rk3576' in device_compatible_str:
host = 'RK3576'
elif 'rk3588' in device_compatible_str:
host = 'RK3588'
else:
host = 'RK3566_RK3568'
except IOError:
print('Read device node {} failed.'.format(DEVICE_COMPATIBLE_NODE))
exit(-1)
else:
host = os_machine
return host
INPUT_SIZE = 224
RK3566_RK3568_RKNN_MODEL = 'resnet18_for_rk3566_rk3568.rknn'
RK3588_RKNN_MODEL = 'resnet18_for_rk3588.rknn'
RK3562_RKNN_MODEL = 'resnet18_for_rk3562.rknn'
RK3576_RKNN_MODEL = 'resnet18_for_rk3576.rknn'
def show_top5(result):
output = result[0].reshape(-1)
# Softmax
output = np.exp(output) / np.sum(np.exp(output))
# Get the indices of the top 5 largest values
output_sorted_indices = np.argsort(output)[::-1][:5]
top5_str = 'resnet18\n-----TOP 5-----\n'
for i, index in enumerate(output_sorted_indices):
value = output[index]
if value > 0:
topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index, value, labels[index])
else:
topi = '-1: 0.0\n'
top5_str += topi
print(top5_str)
if __name__ == '__main__':
# Get device information
host_name = get_host()
if host_name == 'RK3566_RK3568':
rknn_model = RK3566_RK3568_RKNN_MODEL
elif host_name == 'RK3562':
rknn_model = RK3562_RKNN_MODEL
elif host_name == 'RK3576':
rknn_model = RK3576_RKNN_MODEL
elif host_name == 'RK3588':
rknn_model = RK3588_RKNN_MODEL
else:
print("This demo cannot run on the current platform: {}".format(host_name))
exit(-1)
rknn_lite = RKNNLite()
# Load RKNN model
print('--> Load RKNN model')
ret = rknn_lite.load_rknn(rknn_model)
if ret != 0:
print('Load RKNN model failed')
exit(ret)
print('done')
ori_img = cv2.imread('./space_shuttle_224.jpg')
img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
img = np.expand_dims(img, 0)
# Init runtime environment
print('--> Init runtime environment')
# Run on RK356x / RK3576 / RK3588 with Debian OS, do not need specify target.
if host_name in ['RK3576', 'RK3588']:
# For RK3576 / RK3588, specify which NPU core the model runs on through the core_mask parameter.
ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
else:
ret = rknn_lite.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Inference
print('--> Running model')
outputs = rknn_lite.inference(inputs=[img])
# Show the classification results
show_top5(outputs)
print('done')
rknn_lite.release()
2.2 对于安装
1.安装anacoda
2. 安装adb
3. 安装交叉编译工具链
4. 安装RKNN Toolkit Lite2
5. 更新NPU驱动（待）
6. 查看和调整CPU与NPU频率（待）
7. 安装rknn-toolkit2（待）
8. 安装conda（待）
RKNN Toolkit Lite2和rknn-toolkit2
rknn-toolkit2 的出现解决了这一难题。它提供了一个完整的解决方案，允许开发者将已经训练好的深度学习模型（如传统的TensorFlow、Pytorch、onnx 等格式的模型）转换为优化的rknn 格式，这些格式的模型能够充分利用RK3568、RK3588 等设备的硬件特性，从而在保证模型性能的同时，降低了对计算资源的需求。（优化，变成RKNN）
RKNN Toolkit Lite2 是一个轻量级的工具，专门为带有Rockchip NPU平台提供Python编程接口，帮助用户部署使用RKNN-Toolkit2导出的RKNN模型。它更侧重于在开发板上的部署和推理，通常用于实际的硬件环境中。RKNN Toolkit Lite2的开发文档位于 external/rknn/rknn-toolkit2/rknn-toolkit-lite2/doc 目录下，并且在Toybrick Debian11系统中已经预装了RKNN Toolkit Lite2的whl包 。
rknn-toolkit2 是一个更全面的开发套件，它不仅包括了Lite2的功能，还提供了更多的工具和接口，用于在PC平台上进行模型转换、推理和性能评估。它支持的功能更多，包括模型转换、量化、性能和内存评估等。rknn-toolkit2的开发文档位于 external/rknn/rknn-toolkit2/doc 目录下 。
