58 lines
1.3 KiB
Python
58 lines
1.3 KiB
Python
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from rknn.api import RKNN
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import cv2
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import numpy as np
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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], target_platform='rk3566')
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print('done')
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# Load model (from https://www.tensorflow.org/lite/guide/hosted_models?hl=zh-cn)
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print('--> Loading model')
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ret = rknn.load_tflite(model='mobilenet_v1_1.0_224.tflite')
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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_v1.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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# 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], data_format=['nhwc'])
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np.save('./tflite_mobilenet_v1_0.npy', outputs[0])
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show_outputs(outputs)
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print('done')
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rknn.release() |