rknn.inference执行问题

[复制链接] · 发表于 2019-6-3 14:53:58

TF转完rknn模型之后，执行rknn模型，但在卡在执行rknn.inference处，过了30min仍没有执行完，具体是什么问题造成的呢？

只看该作者 · 发表于 2019-6-4 10:05:33

补充

模型转换

# To use Inference Engine backend, specify location of plugins:

# export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH



import cv2 as cv

import numpy as np

import argparse

import compileall

import time

import datetime

from PIL import        Image

from rknn.api import RKNN

compileall.compile_dir(r'./')





#inWidth = args.width

#inHeight = args.height



# Create RKNN object

rknn = RKNN()



# Config for Model Input PreProcess

rknn.config(channel_mean_value='128 128 128 128', reorder_channel='0 1 2')

#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')





#Robert Lee

print('--> Loading model')



ret = rknn.load_tflite(model='./graph_opt.tflite')

if ret != 0:

    print('Load failed!')

    exit(ret)





print('done')





# Build Model

print('--> Building model')

rknn.build(do_quantization=True,dataset='./dataset.txt')

print('done')



# Export RKNN Model

rknn.export_rknn('./graph_opt_test.rknn')



rknn.release()



exit(0)





复制代码

模型推理

# To use Inference Engine backend, specify location of plugins:

# export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH



import cv2 as cv

import numpy as np

import argparse

import compileall

import time

from rknn.api import RKNN

import pickle

compileall.compile_dir(r'./')



parser = argparse.ArgumentParser()

parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')

parser.add_argument('--thr', default=0.2, type=float, help='Threshold value for pose parts heat map')

parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.')

parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.')



args = parser.parse_args()



BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,

               "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,

               "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14,

               "LEye": 15, "REar": 16, "LEar": 17, "Background": 18 }



POSE_PAIRS = [ ["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"],

               ["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"],

               ["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],

               ["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],

               ["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ]



inWidth = args.width

inHeight = args.height

starttime = time.time()

#=====================================================================================

# Create RKNN object

rknn = RKNN()

# Config for Model Input PreProcess

rknn.config(channel_mean_value='128 128 128 128', reorder_channel='0 1 2')

#rknn.config(channel_mean_value='0 0 0 255', reorder_channel='0 1 2')

#=====================================================================================

ret = rknn.load_rknn(path='./graph_opt_test.rknn')

#net = cv.dnn.readNetFromTensorflow("graph_opt.pb")



print('--> Init runtime environment')

ret = rknn.init_runtime()

if ret != 0:

     print('Init runtime environment failed')

     exit(ret)

print('done')

#=====================================================================================

frame = cv.imread('./apink1_crop.jpg')



vid_writer = None

print("[INFO] iamge processed, elapsed time: %f" % (time.time() - starttime))        



frameWidth = frame.shape[1]

frameHeight = frame.shape[0]    





outlist = rknn.inference(inputs=[cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (127.5, 127.5, 127.5), swapRB=True, crop=False)],data_type='float')



print("[INFO] one frame processed, elapsed time: %f" % (time.time() - starttime))

#out = out[:, :19, :, :]  # MobileNet output [1, 57, -1, -1], we only need the first 19 elements

out1 = outlist[0]

out = out1.reshape(1,57,46,46)

out = out[:, :19, :, :]

data = out

print(data.shape)

print(type(data))

#=====================================================================================

print("[INFO] one frame processed, elapsed time: %f" % (time.time() - starttime))        



fw = open('dataFiletest1.txt','wb')

pickle.dump(outlist[0],fw)

fw.close



复制代码

只看该作者 · 发表于 2019-6-11 19:33:00

BlackBoy_Xuan 发表于 2019-6-4 10:05
补充

模型转换

rknn版本已经是1.0.0了吗

只看该作者 · 发表于 2019-6-12 09:54:59

是的，rknn版本已经更新至最新的了。

rknn.inference执行问题

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