110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
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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for output in output_:
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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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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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# The multiple sets of input shapes specified by the user, to simulate the function of dynamic input.
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# Please make sure the model can be dynamic when enable 'config.dynamic_input', and shape in dynamic_input are correctly!
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dynamic_input = [
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[[1,3,192,192]], # set 0: [input0_192]
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[[1,3,256,256]], # set 1: [input0_256]
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[[1,3,160,160]], # set 2: [input0_160]
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[[1,3,224,224]], # set 3: [input0_224]
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]
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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', dynamic_input=dynamic_input)
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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='../../caffe/mobilenet_v2/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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# 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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# 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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# Set inputs
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img = cv2.imread('./dog_224x224.jpg')
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# Inference
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print('--> Running model')
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img2 = cv2.resize(img, (224,224))
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img2 = np.expand_dims(img2, 0)
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img2 = np.transpose(img2, (0,3,1,2)) # [1,3,224,224]
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outputs = rknn.inference(inputs=[img2], data_format=['nchw'])
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np.save('./functions_dynamic_input_0.npy', outputs[0])
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show_outputs(outputs)
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img3 = cv2.resize(img, (160,160))
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img3 = np.expand_dims(img3, 0)
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img3 = np.transpose(img3, (0,3,1,2)) # [1,3,160,160]
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outputs = rknn.inference(inputs=[img3], data_format=['nchw'])
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np.save('./functions_dynamic_input_1.npy', outputs[0])
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show_outputs(outputs)
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img4 = cv2.resize(img, (256,256))
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img4 = np.expand_dims(img4, 0)
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img4 = np.transpose(img4, (0,3,1,2)) # [1,3,256,256]
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outputs = rknn.inference(inputs=[img4], data_format=['nchw'])
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np.save('./functions_dynamic_input_2.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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