import numpy as np
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import cv2
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from rknn.api import RKNN
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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=[127.5, 127.5, 127.5], std_values=[127.5, 127.5, 127.5])
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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_tensorflow(tf_pb='./ssd_mobilenet_v2.pb',
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inputs=['FeatureExtractor/MobilenetV2/MobilenetV2/input'],
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outputs=['concat_1', 'concat'],
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input_size_list=[[1,300,300,3]])
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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('--> hybrid_quantization_step1')
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ret = rknn.hybrid_quantization_step1(dataset='./dataset.txt', proposal=False)
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if ret != 0:
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print('hybrid_quantization_step1 failed!')
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exit(ret)
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print('done')
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# Tips
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print('Please modify ssd_mobilenet_v2.quantization.cfg!')
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print('==================================================================================================')
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print('Modify Method: Fill the customized_quantize_layers with the output name & dtype of the custom layer.')
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print('')
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print('For example:')
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print(' custom_quantize_layers:')
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print(' Conv__344:0: float16')
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print(' FeatureExtractor/MobilenetV2/expanded_conv/depthwise/Relu6:0: float16')
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print('Or:')
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print(' custom_quantize_layers: {')
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print(' Conv__344:0: float16,')
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print(' FeatureExtractor/MobilenetV2/expanded_conv/depthwise/Relu6:0: float16,')
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print(' }')
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print('==================================================================================================')
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rknn.release()
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