82 lines
2.1 KiB
Python
Executable File
82 lines
2.1 KiB
Python
Executable File
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()
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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], reorder_channel=True)
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print('done')
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# Load tensorflow model
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print('--> Loading model')
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ret = rknn.load_caffe(model='./mobilenet_v2.prototxt',
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proto='caffe',
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blobs='./mobilenet_v2.caffemodel')
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if ret != 0:
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print('Load mobilenet_v2 failed! Ret = {}'.format(ret))
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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 mobilenet_v2 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 mobilenet_v2.rknn 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('./goldfish_224x224.jpg')
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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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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show_outputs(outputs)
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print('done')
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# perf
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print('--> Begin evaluate model performance')
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perf_results = rknn.eval_perf(inputs=[img])
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print('done')
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rknn.release()
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