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91 lines
2.5 KiB
91 lines
2.5 KiB
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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