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import platform
import cv2
import numpy as np
import platform
from rknnlite.api import RKNNLite
# decice tree for rk356x/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 'rk3588' in device_compatible_str:
host = 'RK3588'
else:
host = 'RK356x'
except IOError:
print('Read device node {} failed.'.format(DEVICE_COMPATIBLE_NODE))
exit(-1)
else:
host = os_machine
return host
INPUT_SIZE = 224
RK356X_RKNN_MODEL = 'resnet18_for_rk356x.rknn'
RK3588_RKNN_MODEL = 'resnet18_for_rk3588.rknn'
def show_top5(result):
output = result[0].reshape(-1)
# softmax
output = np.exp(output)/sum(np.exp(output))
output_sorted = sorted(output, reverse=True)
top5_str = 'resnet18\n-----TOP 5-----\n'
for i in range(5):
value = output_sorted[i]
index = np.where(output == value)
for j in range(len(index)):
if (i + j) >= 5:
break
if value > 0:
topi = '{}: {}\n'.format(index[j], value)
else:
topi = '-1: 0.0\n'
top5_str += topi
print(top5_str)
if __name__ == '__main__':
host_name = get_host()
if host_name == 'RK356x':
rknn_model = RK356X_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)
# init runtime environment
print('--> Init runtime environment')
# run on RK356x/RK3588 with Debian OS, do not need specify target.
if host_name == 'RK3588':
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_top5(outputs)
print('done')
rknn_lite.release()