Hi,
I have followed this link to train yolov3 using Pascal VOC data
https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects
finetuning using darknet53.conv.74 available weights.
after training I got yolov3.weights. I am trying to convert those weights to tensorflow using this link
https://github.com/mystic123/tensorflow-yolo-v3
and this command
python3 convert_weights_pb.py --class_names coco.names --data_format NHWC --weights_file yolov3.weights
But I am getting this error
Traceback (most recent call last):
File "convert_weights_pb.py", line 53, in <module>
tf.app.run()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "convert_weights_pb.py", line 43, in main
load_ops = load_weights(tf.global_variables(scope='detector'), FLAGS.weights_file)
File "/home/sr/yolo/tensorflow-yolo-v3/utils.py", line 114, in load_weights
(shape[3], shape[2], shape[0], shape[1]))
ValueError: cannot reshape array of size 14583 into shape (78,256,1,1)
Do I have to specify yolo cfg file somewhere in flags or I am missing something else
Any help will be appreciated
Regards
Ashish
链接已复制
For the time being I have to run it in cpu or gpu but since yolov3 needs libcpuextension.so as to be loaded , I am not able to run it in gpu also. Is there a way I can run standard yolov3 in gpu? NCS is not an option right now for me
See 4.【Optional execution】 Additional installation steps for processor graphics (GPU).
https://github.com/PINTO0309/OpenVINO-YoloV3#1-work-with-laptoppc-ubuntu-1604
GPU is compatible with Intel Graphics HDxxx series only.
I thought yolov3 needs libcpuextension.so to be link while initializing, so after following
Additional installation steps for processor graphics (GPU).
and loading this library, can we run it in gpu because that I library is for Target cpu only, correct me if I am wrong
Regards
Ashish
Apart from whether the result is correct or not, the value is now output.
b920405@ubuntu:~/git/DW2TF$ python3 openvino_tiny-yolov3_test.py
{'yolov3-tiny/convolutional13/Conv2D': array([[[[-3.4440000e+03, -4.1920000e+03, -4.2800000e+03, ...,
-5.8920000e+03, -5.6880000e+03, -2.4320000e+03],
[-6.5480000e+03, -6.8280000e+03, -6.2720000e+03, ...,
-8.4880000e+03, -7.4000000e+03, -1.6550000e+03],
[-7.5680000e+03, -8.1880000e+03, -7.0560000e+03, ...,
-6.4480000e+03, -6.0560000e+03, -1.7450000e+03],
...,
[-6.6240000e+03, -5.6600000e+03, -4.9600000e+03, ...,
-5.2480000e+03, -4.4080000e+03, -1.3320000e+03],
[-6.3520000e+03, -5.5080000e+03, -6.2560000e+03, ...,
-4.8200000e+03, -4.4480000e+03, -3.0280000e+03],
[-4.1000000e+03, -5.4800000e+03, -6.8280000e+03, ...,
-4.6520000e+03, -4.7240000e+03, -2.1600000e+03]],
[[-4.4200000e+03, -3.5360000e+03, -2.8040000e+03, ...,
-3.5780000e+03, -2.5220000e+03, -3.3950000e+02],
[-3.6440000e+03, -3.7980000e+03, -3.0300000e+03, ...,
-9.2700000e+02, -6.2600000e+02, -1.1190000e+03],
[-3.8820000e+03, -3.9780000e+03, -3.2780000e+03, ...,
-1.8560000e+03, -9.9500000e+02, -6.0100000e+02],
...,
[-8.1950000e+02, -5.5440000e+03, -5.2000000e+03, ...,
-2.6500000e+03, -2.3640000e+03, 7.3250000e+02],
[-5.4050000e+02, -6.0960000e+03, -6.3840000e+03, ...,
-2.1860000e+03, -4.0680000e+03, 1.1481250e+02],
[-2.3800000e+03, -4.2120000e+03, -4.2080000e+03, ...,
1.4000000e+03, -3.1550000e+02, 3.8375000e+01]],
[[ 1.3890000e+03, 3.9840000e+03, 4.0600000e+03, ...,
5.7680000e+03, 5.6680000e+03, 2.7060000e+03],
[ 1.6370000e+03, 4.5240000e+03, 3.6580000e+03, ...,
4.8560000e+03, 4.3920000e+03, 1.5030000e+03],
[ 2.1340000e+03, 4.6480000e+03, 2.7660000e+03, ...,
4.8840000e+03, 4.4760000e+03, 1.1830000e+03],
...,
[ 2.6740000e+03, 4.9560000e+03, 3.3560000e+03, ...,
3.1920000e+03, 3.4980000e+03, 4.3375000e+02],
[ 1.8990000e+03, 3.0440000e+03, 2.0350000e+03, ...,
2.3440000e+03, 3.7800000e+03, 1.1190000e+03],
[-1.6540000e+03, -1.0025000e+03, -3.0825000e+02, ...,
1.4662500e+02, 1.1440000e+03, 4.3575000e+02]],
...,
[[ 2.3462500e+02, -1.5560000e+03, -1.5610000e+03, ...,
-2.3860000e+03, -2.4000000e+03, -4.7280000e+03],
[-1.2320000e+03, -3.5140000e+03, -2.9780000e+03, ...,
-2.3840000e+03, -3.1380000e+03, -5.9480000e+03],
[-1.5120000e+03, -2.9620000e+03, -2.5280000e+03, ...,
-1.9790000e+03, -3.3500000e+03, -4.7400000e+03],
...,
[-1.2510000e+03, -3.0540000e+03, -3.1260000e+03, ...,
-3.6920000e+03, -4.3680000e+03, -3.2760000e+03],
[-1.9170000e+03, -3.4920000e+03, -2.7640000e+03, ...,
-2.5380000e+03, -2.5380000e+03, -1.3880000e+03],
[-1.8200000e+03, -2.0440000e+03, -1.4660000e+03, ...,
-1.4720000e+03, -5.6000000e+02, -1.2410000e+03]],
[[-2.8340000e+03, -4.6840000e+03, -5.2480000e+03, ...,
-2.6560000e+03, -2.9860000e+03, -4.2000000e+03],
[-7.2880000e+03, -9.0720000e+03, -9.5200000e+03, ...,
-8.8400000e+03, -8.8720000e+03, -7.5520000e+03],
[-6.7840000e+03, -9.2800000e+03, -9.8960000e+03, ...,
-8.4800000e+03, -8.8160000e+03, -6.4800000e+03],
...,
[-7.7680000e+03, -8.7600000e+03, -8.4640000e+03, ...,
-8.0840000e+03, -7.5560000e+03, -5.7200000e+03],
[-7.6000000e+03, -9.6880000e+03, -8.3760000e+03, ...,
-8.5360000e+03, -7.0320000e+03, -2.8780000e+03],
[-7.2360000e+03, -1.0672000e+04, -9.4480000e+03, ...,
-7.4280000e+03, -5.1120000e+03, -1.3212500e+02]],
[[ 9.6800000e+02, 1.7340000e+03, 1.5060000e+03, ...,
1.0060000e+03, 2.5550000e+02, -4.3600000e+02],
[ 1.5350000e+03, 2.1340000e+03, 1.9410000e+03, ...,
6.1650000e+02, -1.6525000e+02, -1.3820000e+03],
[ 1.2550000e+03, 2.5840000e+03, 1.8450000e+03, ...,
1.4087500e+02, 1.1308594e+00, -1.0900000e+03],
...,
[ 1.8040000e+03, 3.0340000e+03, 1.5590000e+03, ...,
-2.5960000e+03, -1.6860000e+03, -2.1120000e+03],
[ 3.5060000e+03, 3.3960000e+03, 7.5650000e+02, ...,
-1.3170000e+03, -7.6500000e+02, -1.7440000e+03],
[ 2.3840000e+03, 2.9920000e+03, 3.8600000e+02, ...,
-4.6000000e+02, -1.1450000e+02, -8.7300000e+02]]]],
dtype=float32), 'yolov3-tiny/convolutional10/Conv2D': array([[[[ 2.001600e+04, 1.956800e+04, 1.972800e+04, ...,
1.985600e+04, 1.936000e+04, 2.046400e+04],
[ 1.956800e+04, 2.161600e+04, 2.259200e+04, ...,
2.137600e+04, 2.344000e+04, 2.099200e+04],
[ 2.044800e+04, 2.275200e+04, 2.241600e+04, ...,
2.366400e+04, 2.089600e+04, 2.100800e+04],
...,
[ 1.913600e+04, 2.153600e+04, 2.262400e+04, ...,
2.536000e+04, 2.337600e+04, 2.017600e+04],
[ 1.985600e+04, 2.024000e+04, 2.305600e+04, ...,
2.449600e+04, 2.449600e+04, 2.428800e+04],
[ 1.825600e+04, 2.179200e+04, 2.400000e+04, ...,
2.625600e+04, 2.596800e+04, 2.771200e+04]],
[[ 5.828000e+03, 4.068000e+03, 3.832000e+03, ...,
4.024000e+03, 7.475000e+02, -5.925000e+02],
[ 5.248000e+03, -3.508000e+03, -5.200000e+03, ...,
-4.360000e+03, -3.940000e+02, -7.240000e+02],
[ 5.760000e+03, -1.452000e+03, -2.112000e+03, ...,
-2.606000e+03, -8.670000e+02, -3.310000e+03],
...,
[ 6.176000e+03, 3.195000e+02, -4.965625e+01, ...,
-5.460000e+03, -1.678000e+03, -4.596000e+03],
[ 5.108000e+03, -6.305000e+02, -1.342000e+03, ...,
-6.005000e+02, -2.190000e+03, -4.520000e+03],
[ 4.980000e+03, -1.668750e+02, 4.870000e+02, ...,
-1.472000e+03, -5.056000e+03, -8.624000e+03]],
[[ 5.400000e+03, 8.992000e+03, 7.708000e+03, ...,
8.336000e+03, 8.760000e+03, 6.648000e+03],
[ 9.080000e+03, 1.444000e+04, 1.332000e+04, ...,
1.140000e+04, 1.077600e+04, 9.976000e+03],
[ 7.432000e+03, 1.050400e+04, 1.070400e+04, ...,
1.114400e+04, 1.234400e+04, 1.127200e+04],
...,
[ 8.600000e+03, 1.412800e+04, 1.365600e+04, ...,
1.200800e+04, 1.211200e+04, 1.210400e+04],
[ 7.552000e+03, 1.082400e+04, 1.087200e+04, ...,
7.900000e+03, 1.100000e+04, 7.892000e+03],
[ 5.108000e+03, 8.084000e+03, 8.664000e+03, ...,
7.032000e+03, 8.208000e+03, 4.812000e+03]],
...,
[[ 2.265600e+04, 2.817600e+04, 2.507200e+04, ...,
1.705600e+04, 2.280000e+03, -5.414400e+04],
[ 3.134400e+04, 3.344000e+04, 2.300800e+04, ...,
2.806400e+04, 9.792000e+03, -5.747200e+04],
[ 1.872000e+04, 1.575200e+04, -8.110000e+02, ...,
1.071200e+04, -1.637000e+03, -5.408000e+04],
...,
[ 1.995200e+04, 1.948800e+04, 1.809600e+04, ...,
1.894400e+04, 1.533600e+04, -3.753600e+04],
[ 2.201600e+04, 1.360800e+04, 1.889600e+04, ...,
2.196800e+04, -1.566400e+04, -2.739200e+04],
[-3.030000e+03, 5.876000e+03, 7.700000e+03, ...,
-5.124000e+03, -2.694400e+04, -4.772000e+03]],
[[ 8.320000e+03, 1.628800e+04, 1.777600e+04, ...,
1.076800e+04, -7.492000e+03, -5.305600e+04],
[ 3.217600e+04, 5.276800e+04, 4.796800e+04, ...,
3.404800e+04, 2.358400e+04, -3.875200e+04],
[ 1.784000e+04, 3.275200e+04, 2.931200e+04, ...,
4.016000e+04, 2.123200e+04, -4.432000e+04],
...,
[ 2.470400e+04, 4.473600e+04, 4.688000e+04, ...,
4.886400e+04, 3.036800e+04, -3.468800e+04],
[ 2.620800e+04, 4.460800e+04, 5.462400e+04, ...,
4.748800e+04, 8.856000e+03, -3.881600e+04],
[ 1.984000e+04, 3.449600e+04, 3.795200e+04, ...,
3.033600e+04, -1.886400e+04, -3.240000e+04]],
[[-2.160000e+03, 4.512000e+03, 5.612000e+03, ...,
-3.686000e+03, -1.883200e+04, -5.164800e+04],
[ 1.363200e+04, 3.179200e+04, 2.388800e+04, ...,
8.520000e+03, 4.168000e+03, -3.721600e+04],
[ 7.715000e+02, 6.044000e+03, 7.520000e+03, ...,
1.365600e+04, 3.748000e+03, -3.824000e+04],
...,
[ 5.676000e+03, 1.705600e+04, 1.776000e+04, ...,
2.241600e+04, 6.552000e+03, -3.051200e+04],
[ 9.408000e+03, 2.260800e+04, 3.664000e+04, ...,
2.979200e+04, 3.600000e+02, -3.472000e+04],
[ 9.504000e+03, 2.672000e+04, 2.547200e+04, ...,
1.897600e+04, -1.737600e+04, -3.014400e+04]]]], dtype=float32)}
I am still worried about running the converted model with openvino as my first aim is to reduce the detection time of yolov3, if you are able to run openvino converted version of yolov3 for custom model with correct outputs, kindly update.
Any help will be appreciated
Regards
Ashish
Hyodo, Katsuya wrote:It may be that the operation overflows while converting the model.
If the following command is executed, will an overflow warning be displayed?$ sudo python3 /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/mo_tf.py \
--input_model data/frozen_yolov3.pb \
--output_dir . \
--data_type FP32 \
--batch 1 \
--input yolov3/net1 \
--output yolov3/convolutional59/BiasAdd,yolov3/convolutional67/BiasAdd,yolov3/convolutional75/BiasAdd \
--log_level WARNINGor
$ sudo python3 /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/mo_tf.py \
--input_model data/frozen_yolov3.pb \
--output_dir . \
--data_type FP32 \
--batch 1 \
--input yolov3/net1 \
--output yolov3/convolutional59/BiasAdd,yolov3/convolutional67/BiasAdd,yolov3/convolutional75/BiasAdd \
--log_level DEBUG
Does the same procedure works on yolov3-tiny?
Please share the Json file of yolov3-tiny
@Batra, Dhruv
I did all the work with tiny-YoloV3.
JSON file is unnecessary.
However, I still need to adjust "negative_slope".
https://github.com/PINTO0309/OpenVINO-YoloV3/wiki/Reference-repository#conversion-success-2
If you follow Intel's standard tutorial, please refer to the following URL.
https://github.com/PINTO0309/OpenVINO-YoloV3/blob/master/script.txt
I tried to adjust negative_slope on full yolo but no success. I think Json file is necessary for Full Yolo because without it, I am not able to convert it in xml and bin format.
Did you got any success running converted yolov3 model with correct outputs?
