import numpy as np
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import cv2
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from rknn.api import RKNN
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def show_outputs(outputs):
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output = outputs[0].reshape(-1)
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output_sorted = sorted(output, reverse=True)
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top5_str = 'mobilenet_v2\n-----TOP 5-----\n'
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for i in range(5):
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value = output_sorted[i]
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index = np.where(output == value)
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for j in range(len(index)):
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if (i + j) >= 5:
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break
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if value > 0:
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topi = '{}: {}\n'.format(index[j], value)
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else:
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topi = '-1: 0.0\n'
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top5_str += topi
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print(top5_str)
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if __name__ == '__main__':
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# Create RKNN object
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rknn = RKNN(verbose=True)
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# Pre-process config
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print('--> Config model')
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rknn.config(mean_values=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82],
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quant_img_RGB2BGR=True, target_platform='rk3588')
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print('done')
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# Load model
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print('--> Loading model')
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ret = rknn.load_caffe(model='./../../caffe/mobilenet_v2/mobilenet_v2.prototxt',
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blobs='./../../caffe/mobilenet_v2/mobilenet_v2.caffemodel')
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if ret != 0:
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print('Load model failed!')
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exit(ret)
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print('done')
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# Build model
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print('--> Building model')
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ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
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if ret != 0:
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print('Build model failed!')
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exit(ret)
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print('done')
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# Export rknn model
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print('--> Export rknn model')
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ret = rknn.export_rknn('./mobilenet_v2.rknn')
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if ret != 0:
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print('Export rknn model failed!')
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exit(ret)
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print('done')
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# Export encrypted RKNN model
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print('--> Export encrypted rknn model')
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ret = rknn.export_encrypted_rknn_model('./mobilenet_v2.rknn', None, 3)
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# load rknn model
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print('--> Load rknn model')
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ret = rknn.load_rknn('./mobilenet_v2.rknn')
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if ret != 0:
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print('Load rknn model failed!')
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exit(ret)
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print('done')
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# Set inputs
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img = cv2.imread('./dog_224x224.jpg')
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print('--> List devices')
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rknn.list_devices()
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# Init runtime environment
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print('--> Init runtime environment')
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ret = rknn.init_runtime(target='rk3588', perf_debug=True, eval_mem=True)
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if ret != 0:
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print('Init runtime environment failed!')
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exit(ret)
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print('done')
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print('--> Get sdk version')
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sdk_version = rknn.get_sdk_version()
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print(sdk_version)
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# eval perf
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print('--> Eval perf')
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rknn.eval_perf(inputs=[img])
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# eval perf
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print('--> Eval memory')
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rknn.eval_memory()
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# Inference
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print('--> Running model')
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outputs = rknn.inference(inputs=[img])
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np.save('./functions_board_test_0.npy', outputs[0])
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show_outputs(outputs)
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print('done')
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# Accuracy analysis
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print('--> Accuracy analysis')
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ret = rknn.accuracy_analysis(inputs=['./dog_224x224.jpg'], output_dir='./snapshot', target='rk3588')
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if ret != 0:
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print('Accuracy analysis failed!')
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exit(ret)
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print('done')
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rknn.release()
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