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import numpy as np
from rknn.api import RKNN
def show_outputs(outputs):
np.save('./caffe_mobilenet_v2_0.npy', outputs[0])
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=False)
# 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='rk3562')
print('done')
# Load model
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='../../caffe/mobilenet_v2/dataset.txt')
if ret != 0:
print('Build model 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')
print('--> Generate cpp demo')
ret = rknn.codegen(output_path='./rknn_app_demo', inputs=['../../caffe/mobilenet_v2/dog_224x224.jpg'], overwrite=True)
if ret != 0:
print('Generate cpp demo failed!')
exit(ret)
print('done')
rknn.release()
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