import platform
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import cv2
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import numpy as np
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import platform
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from rknnlite.api import RKNNLite
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# decice tree for rk356x/rk3588
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DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible'
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def get_host():
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# get platform and device type
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system = platform.system()
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machine = platform.machine()
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os_machine = system + '-' + machine
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if os_machine == 'Linux-aarch64':
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try:
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with open(DEVICE_COMPATIBLE_NODE) as f:
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device_compatible_str = f.read()
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if 'rk3588' in device_compatible_str:
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host = 'RK3588'
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else:
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host = 'RK356x'
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except IOError:
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print('Read device node {} failed.'.format(DEVICE_COMPATIBLE_NODE))
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exit(-1)
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else:
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host = os_machine
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return host
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INPUT_SIZE = 224
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RK356X_RKNN_MODEL = 'resnet18_for_rk356x.rknn'
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RK3588_RKNN_MODEL = 'resnet18_for_rk3588.rknn'
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def show_top5(result):
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output = result[0].reshape(-1)
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# softmax
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output = np.exp(output)/sum(np.exp(output))
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output_sorted = sorted(output, reverse=True)
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top5_str = 'resnet18\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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host_name = get_host()
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if host_name == 'RK356x':
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rknn_model = RK356X_RKNN_MODEL
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elif host_name == 'RK3588':
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rknn_model = RK3588_RKNN_MODEL
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else:
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print("This demo cannot run on the current platform: {}".format(host_name))
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exit(-1)
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rknn_lite = RKNNLite()
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# load RKNN model
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print('--> Load RKNN model')
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ret = rknn_lite.load_rknn(rknn_model)
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if ret != 0:
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print('Load RKNN model failed')
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exit(ret)
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print('done')
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ori_img = cv2.imread('./space_shuttle_224.jpg')
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img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
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# init runtime environment
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print('--> Init runtime environment')
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# run on RK356x/RK3588 with Debian OS, do not need specify target.
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if host_name == 'RK3588':
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ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
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else:
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ret = rknn_lite.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_lite.inference(inputs=[img])
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show_top5(outputs)
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print('done')
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rknn_lite.release()
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