import os
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import urllib
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import traceback
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import time
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import sys
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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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import urllib.request
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ONNX_MODEL = 'resnet50v2.onnx'
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RKNN_MODEL = 'resnet50v2.rknn'
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def show_outputs(outputs):
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output = outputs
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output_sorted = sorted(output, reverse=True)
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top5_str = 'resnet50v2\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 readable_speed(speed):
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speed_bytes = float(speed)
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speed_kbytes = speed_bytes / 1024
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if speed_kbytes > 1024:
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speed_mbytes = speed_kbytes / 1024
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if speed_mbytes > 1024:
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speed_gbytes = speed_mbytes / 1024
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return "{:.2f} GB/s".format(speed_gbytes)
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else:
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return "{:.2f} MB/s".format(speed_mbytes)
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else:
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return "{:.2f} KB/s".format(speed_kbytes)
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def show_progress(blocknum, blocksize, totalsize):
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speed = (blocknum * blocksize) / (time.time() - start_time)
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speed_str = " Speed: {}".format(readable_speed(speed))
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recv_size = blocknum * blocksize
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f = sys.stdout
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progress = (recv_size / totalsize)
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progress_str = "{:.2f}%".format(progress * 100)
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n = round(progress * 50)
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s = ('#' * n).ljust(50, '-')
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f.write(progress_str.ljust(8, ' ') + '[' + s + ']' + speed_str)
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f.flush()
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f.write('\r\n')
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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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# If resnet50v2 does not exist, download it.
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# Download address:
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# https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx
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if not os.path.exists(ONNX_MODEL):
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print('--> Download {}'.format(ONNX_MODEL))
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url = 'https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx'
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download_file = ONNX_MODEL
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try:
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start_time = time.time()
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urllib.request.urlretrieve(url, download_file, show_progress)
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except:
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print('Download {} failed.'.format(download_file))
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print(traceback.format_exc())
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exit(-1)
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print('done')
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# Pre-process config
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print('--> Config model')
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rknn.config(mean_values=[123.68, 116.28, 103.53], std_values=[57.38, 57.38, 57.38])
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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_onnx(model=ONNX_MODEL)
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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')
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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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# Accuracy analysis
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print('--> Accuracy analysis')
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ret = rknn.accuracy_analysis(inputs=['./dog_224x224.jpg'], output_dir='./snapshot')
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if ret != 0:
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print('Accuracy analysis failed!')
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exit(ret)
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print('done')
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print('float32:')
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output = np.genfromtxt('./snapshot/golden/resnetv24_dense0_fwd.txt')
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show_outputs(output)
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print('quantized:')
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output = np.genfromtxt('./snapshot/simulator/resnetv24_dense0_fwd.txt')
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show_outputs(output)
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rknn.release()
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