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import numpy as np
import cv2
from rknn.api import RKNN
def show_outputs(outputs):
output = outputs[0].reshape(-1)
index = sorted(range(len(output)), key=lambda k : output[k], reverse=True)
fp = open('./labels.txt', 'r')
labels = fp.readlines()
top5_str = 'mobilenet_v2\n-----TOP 5-----\n'
for i in range(5):
value = output[index[i]]
if value > 0:
topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1])
else:
topi = '[ -1]: 0.0\n'
top5_str += topi
print(top5_str.strip())
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN(verbose=True)
# Pre-process config
print('--> Config model')
rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82],
quant_img_RGB2BGR=True, target_platform='rk3588')
print('done')
# Load model (from https://github.com/shicai/MobileNet-Caffe)
print('--> Loading model')
ret = rknn.load_caffe(model='./../../caffe/mobilenet_v2/mobilenet_v2_deploy.prototxt',
blobs='./../../caffe/mobilenet_v2/mobilenet_v2.caffemodel')
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Accuracy analysis
print('--> Accuracy analysis')
ret = rknn.accuracy_analysis(inputs=['./dog_224x224.jpg'], output_dir='./snapshot', target='rk3588')
if ret != 0:
print('Accuracy analysis failed!')
exit(ret)
print('done')
# Export rknn model
print('--> Export rknn model')
ret = rknn.export_rknn('./mobilenet_v2.rknn')
if ret != 0:
print('Export rknn model failed!')
exit(ret)
print('done')
# Export encrypted RKNN model
print('--> Export encrypted rknn model')
ret = rknn.export_encrypted_rknn_model('./mobilenet_v2.rknn', None, 3)
rknn.release()
# Create RKNN object
rknn = RKNN(verbose=True)
# load rknn model
print('--> Load rknn model')
ret = rknn.load_rknn('./mobilenet_v2.rknn')
if ret != 0:
print('Load rknn model failed!')
exit(ret)
print('done')
# Set inputs
img = cv2.imread('./dog_224x224.jpg')
img = np.expand_dims(img, 0)
print('--> List devices')
rknn.list_devices()
# Init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime(target='rk3588', perf_debug=True, eval_mem=True)
if ret != 0:
print('Init runtime environment failed!')
exit(ret)
print('done')
print('--> Get sdk version')
sdk_version = rknn.get_sdk_version()
print(sdk_version)
# eval perf
print('--> Eval perf')
rknn.eval_perf()
# eval perf
print('--> Eval memory')
rknn.eval_memory()
# Inference
print('--> Running model')
outputs = rknn.inference(inputs=[img], data_format=['nhwc'])
np.save('./functions_npu_device_test_0.npy', outputs[0])
show_outputs(outputs)
print('done')
rknn.release()
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