import numpy as np
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import cv2
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from rknn.api import RKNN
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import torchvision.models as models
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import torch
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import os
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def show_outputs(output):
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output_sorted = sorted(output, reverse=True)
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top5_str = '\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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def show_perfs(perfs):
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perfs = 'perfs: {}\n'.format(perfs)
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print(perfs)
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def softmax(x):
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return np.exp(x)/sum(np.exp(x))
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def torch_version():
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import torch
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torch_ver = torch.__version__.split('.')
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torch_ver[2] = torch_ver[2].split('+')[0]
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return [int(v) for v in torch_ver]
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if __name__ == '__main__':
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if torch_version() < [1, 9, 0]:
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import torch
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print("Your torch version is '{}', in order to better support the Quantization Aware Training (QAT) model,\n"
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"Please update the torch version to '1.9.0' or higher!".format(torch.__version__))
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exit(0)
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model = './resnet18_i8.pt'
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input_size_list = [[1, 3, 224, 224]]
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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=[123.675, 116.28, 103.53], std_values=[58.395, 58.395, 58.395])
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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_pytorch(model=model, input_size_list=input_size_list)
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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=False)
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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('./resnet_18.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('./space_shuttle_224.jpg')
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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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('./pytorch_resnet18_qat_0.npy', outputs[0])
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show_outputs(softmax(np.array(outputs[0][0])))
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print('done')
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rknn.release()
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