89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
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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, 1000))
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fp = open('./labels.txt', 'r')
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labels = fp.readlines()
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for batch, output in enumerate(output_):
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index = sorted(range(len(output)), key=lambda k : output[k], reverse=True)
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top5_str = '----- Batch {}: TOP 5 -----\n'.format(batch)
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for i in range(5):
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value = output[index[i]]
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if value > 0:
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topi = '[{:>3d}] score:{:.6f} class:"{}"\n'.format(index[i], value, labels[index[i]].strip().split(':')[-1])
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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.strip())
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def show_perfs(perfs):
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perfs = 'perfs: {}\n'.format(outputs)
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print(perfs)
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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=[103.94, 116.78, 123.68], std_values=[58.82, 58.82, 58.82], quant_img_RGB2BGR=True, target_platform='rk3566')
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print('done')
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# Load model (from https://github.com/shicai/MobileNet-Caffe)
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print('--> Loading model')
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ret = rknn.load_caffe(model='../../caffe/mobilenet_v2/mobilenet_v2_deploy.prototxt',
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blobs='../../caffe/mobilenet_v2/mobilenet_v2.caffemodel')
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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', rknn_batch_size=4)
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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_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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# Set inputs
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img_0 = cv2.imread('./dog_224x224.jpg')
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img_0 = np.expand_dims(img_0, 0)
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img_1 = cv2.imread('./goldfish_224x224.jpg')
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img_1 = np.expand_dims(img_1, 0)
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img_2 = cv2.imread('./space_shuttle_224.jpg')
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img_2 = np.expand_dims(img_2, 0)
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img = np.concatenate((img_0, img_1, img_2, img_0), axis=0) # the inputs data need to be merged together using np.concatenate.
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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('./functions_multi_batch_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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