134 lines
3.7 KiB
Python
134 lines
3.7 KiB
Python
import os
|
|
import urllib
|
|
import traceback
|
|
import time
|
|
import sys
|
|
import numpy as np
|
|
import cv2
|
|
from rknn.api import RKNN
|
|
|
|
ONNX_MODEL = 'resnet50v2.onnx'
|
|
RKNN_MODEL = 'resnet50v2.rknn'
|
|
|
|
|
|
def show_outputs(outputs):
|
|
output = outputs[0][0]
|
|
output_sorted = sorted(output, reverse=True)
|
|
top5_str = 'resnet50v2\n-----TOP 5-----\n'
|
|
for i in range(5):
|
|
value = output_sorted[i]
|
|
index = np.where(output == value)
|
|
for j in range(len(index)):
|
|
if (i + j) >= 5:
|
|
break
|
|
if value > 0:
|
|
topi = '{}: {}\n'.format(index[j], value)
|
|
else:
|
|
topi = '-1: 0.0\n'
|
|
top5_str += topi
|
|
print(top5_str)
|
|
|
|
|
|
def readable_speed(speed):
|
|
speed_bytes = float(speed)
|
|
speed_kbytes = speed_bytes / 1024
|
|
if speed_kbytes > 1024:
|
|
speed_mbytes = speed_kbytes / 1024
|
|
if speed_mbytes > 1024:
|
|
speed_gbytes = speed_mbytes / 1024
|
|
return "{:.2f} GB/s".format(speed_gbytes)
|
|
else:
|
|
return "{:.2f} MB/s".format(speed_mbytes)
|
|
else:
|
|
return "{:.2f} KB/s".format(speed_kbytes)
|
|
|
|
|
|
def show_progress(blocknum, blocksize, totalsize):
|
|
speed = (blocknum * blocksize) / (time.time() - start_time)
|
|
speed_str = " Speed: {}".format(readable_speed(speed))
|
|
recv_size = blocknum * blocksize
|
|
|
|
f = sys.stdout
|
|
progress = (recv_size / totalsize)
|
|
progress_str = "{:.2f}%".format(progress * 100)
|
|
n = round(progress * 50)
|
|
s = ('#' * n).ljust(50, '-')
|
|
f.write(progress_str.ljust(8, ' ') + '[' + s + ']' + speed_str)
|
|
f.flush()
|
|
f.write('\r\n')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
# Create RKNN object
|
|
rknn = RKNN(verbose=True)
|
|
|
|
# If resnet50v2 does not exist, download it.
|
|
# Download address:
|
|
# https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx
|
|
if not os.path.exists(ONNX_MODEL):
|
|
print('--> Download {}'.format(ONNX_MODEL))
|
|
url = 'https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.onnx'
|
|
download_file = ONNX_MODEL
|
|
try:
|
|
start_time = time.time()
|
|
urllib.request.urlretrieve(url, download_file, show_progress)
|
|
except:
|
|
print('Download {} failed.'.format(download_file))
|
|
print(traceback.format_exc())
|
|
exit(-1)
|
|
print('done')
|
|
|
|
# pre-process config
|
|
print('--> config model')
|
|
rknn.config(mean_values=[123.675, 116.28, 103.53], std_values=[58.82, 58.82, 58.82], target_platform='rk3566')
|
|
print('done')
|
|
|
|
# Load model
|
|
print('--> Loading model')
|
|
ret = rknn.load_onnx(model=ONNX_MODEL)
|
|
if ret != 0:
|
|
print('Load model failed!')
|
|
exit(ret)
|
|
print('done')
|
|
|
|
# Build model
|
|
print('--> Building model')
|
|
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
|
|
if ret != 0:
|
|
print('Build model failed!')
|
|
exit(ret)
|
|
print('done')
|
|
|
|
# Export rknn model
|
|
print('--> Export rknn model')
|
|
ret = rknn.export_rknn(RKNN_MODEL)
|
|
if ret != 0:
|
|
print('Export rknn model failed!')
|
|
exit(ret)
|
|
print('done')
|
|
|
|
# Set inputs
|
|
img = cv2.imread('./dog_224x224.jpg')
|
|
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
|
|
|
# Init runtime environment
|
|
print('--> Init runtime environment')
|
|
ret = rknn.init_runtime()
|
|
if ret != 0:
|
|
print('Init runtime environment failed!')
|
|
exit(ret)
|
|
print('done')
|
|
|
|
# Inference
|
|
print('--> Running model')
|
|
outputs = rknn.inference(inputs=[img])
|
|
np.save('./onnx_resnet50v2_0.npy', outputs[0])
|
|
x = outputs[0]
|
|
output = np.exp(x)/np.sum(np.exp(x))
|
|
outputs = [output]
|
|
show_outputs(outputs)
|
|
print('done')
|
|
|
|
rknn.release()
|