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][0]
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output_sorted = sorted(output, reverse=True)
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top5_str = 'mobilenet_v1\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=[128, 128, 128], std_values=[128, 128, 128],
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quantized_method='layer', quantized_algorithm='mmse')
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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='mobilenet_v1.pb',
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inputs=['input'],
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input_size_list=[[1, 224, 224, 3]],
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outputs=['MobilenetV1/Logits/SpatialSqueeze'])
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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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# Accuracy analysis
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print('--> Accuracy analysis')
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ret = rknn.accuracy_analysis(inputs=['dog_224x224.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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f = open('./snapshot/error_analysis.txt')
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lines = f.readlines()
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cos = lines[-1].split()[1]
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if float(cos) >= 0.965:
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print('cos = {}, mmse work!'.format(cos))
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else:
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print('cos = {} < 0.965, mmse abnormal!'.format(cos))
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f.close()
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# Set inputs
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img = cv2.imread('./dog_224x224.jpg')
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = np.expand_dims(img, 0)
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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_mmse_0.npy', outputs[0])
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show_outputs(outputs)
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print('done')
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rknn.release()
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