import numpy as np
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import cv2
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from rknn.api import RKNN
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from ssd_post_process import ssd_post_process
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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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# Build model
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print('--> hybrid_quantization_step2')
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ret = rknn.hybrid_quantization_step2(model_input='./ssd_mobilenet_v2.model',
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data_input='./ssd_mobilenet_v2.data',
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model_quantization_cfg='./ssd_mobilenet_v2.quantization.cfg')
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if ret != 0:
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print('hybrid_quantization_step2 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('./ssd_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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# Accuracy analysis
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print('--> Accuracy analysis')
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ret = rknn.accuracy_analysis(inputs=['./dog_bike_car_300x300.jpg'], output_dir=None)
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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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# Set inputs
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img = cv2.imread('./dog_bike_car_300x300.jpg')
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# Init runtime environment
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print('--> Init runtime environment')
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ret = rknn.init_runtime()
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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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# Inference
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print('--> Running model')
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outputs = rknn.inference(inputs=[img])
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np.save('./functions_hybrid_quant_0.npy', outputs[0])
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np.save('./functions_hybrid_quant_1.npy', outputs[1])
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ssd_post_process(np.reshape(outputs[0], (-1)), np.reshape(outputs[1], (-1)), './dog_bike_car_300x300.jpg', './')
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print('done')
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rknn.release()
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