Created
December 28, 2023 12:26
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minimalistic inference
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| import cv2 | |
| import numpy as np | |
| import time | |
| from rknnpool import rknnPoolExecutor | |
| def myFunc(rknn_lite, IMG): | |
| outputs = rknn_lite.inference(inputs=[IMG]) | |
| return outputs | |
| if __name__ == '__main__': | |
| ori_img = cv2.imread('./space_shuttle_224126.jpg') | |
| img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB) | |
| img = np.expand_dims(img, 0) | |
| modelPath = "DINOv2g.rknn" | |
| TPEs = 12 | |
| pool = rknnPoolExecutor( | |
| rknnModel=modelPath, | |
| TPEs=TPEs, | |
| func=myFunc) | |
| frames, loopTime, initTime = 0, time.time(), time.time() | |
| for j in range(1000): | |
| pool.put(img) | |
| for j in range(1001): | |
| frame, flag = pool.get() | |
| if flag == False: | |
| break | |
| if j % 100 == 0: | |
| print("100 images time:\t", (time.time() - loopTime), "sec") | |
| loopTime = time.time() | |
| pool.release() |
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