Darknet model conversion

MernokAdriaan

Hi all,

I am trying to export the weights of a yolov4-tiny model that I have trained using darknet (https://github.com/hank-ai/darknet) to use it on a OAK-D type camera (actually a custom baseboard using the SoM).

Attached is my cfg file.

After training, I used the yolo2openvino repo (https://github.com/luxonis/yolo2openvino) to convert my model to TF model weights with the following command:

python ~/repos/yolo2openvino/convert_weights_pb.py \
--yolo 4 \
--weights_file ~/nn/test_project/test_project_best.weights \
--class_names ~/nn/test_project/test_project.names \
--output ~/nn/test_project/test_project.pb \
--tiny \
-h 160 \
-w 160 \
-a 4,18,9,35,15,61,27,100,48,138,95,151

Next, I converted to the OpenVINO IR format using the model optimizer (v2022.1), json config attached:

mo \
--input_model ~/nn/test_project/test_project.pb \
--tensorflow_use_custom_operations_config ~/nn/test_project/test_project.json \
--batch 1 \
--data_type FP16 \
--reverse_input_channel \
--model_name test_project \
--output_dir ~/nn/test_project/

Finally, I converted to a blobfile using blobconverter:

python -m blobconverter \
-ox nn/test_project/test_project.xml \
-ob nn/test_project/test_project.bin \
-sh 6 \
-v 2022.1 \
-o nn/test_project

Uploading this blob to the device, results in the following errors:

[14442C1071F33FD700] [1.1] [1.803] [SpatialDetectionNetwork(8)] [warning] Input image (160x160) does not match NN (3x160)
[14442C1071F33FD700] [1.1] [1.813] [SpatialDetectionNetwork(8)] [error] Mask is not defined for output layer with width '5'. Define at pipeline build time using: 'setAnchorMasks' for 'side5'.
[14442C1071F33FD700] [1.1] [1.813] [SpatialDetectionNetwork(8)] [error] Mask is not defined for output layer with width '10'. Define at pipeline build time using: 'setAnchorMasks' for 'side10'.

Here is a snippet from my YoloSpatialDetectionNetwork configuration:

spatial_nn.setNumClasses(1)
spatial_nn.setCoordinateSize(4)
spatial_nn.setAnchors([4, 18, 9, 35, 15, 61, 27, 100, 48, 138, 95, 151])
spatial_nn.setAnchorMasks({"side26": [0, 1, 2], "side13": [3, 4, 5]})
spatial_nn.setIouThreshold(0.5)

It is clear that somewhere along the line I've messed up the layers during the conversion.

Any tips on where I can start looking for errors?

I am using Python 3.7.17

MernokAdriaan

I wasn't able to upload the config or json files, so here goes:

test_project.cfg:

DarkMark v1.9.5-1 output for Darknet

# Project .... /home/adriaan/nn/test_project

# Config ..... /home/adriaan/nn/test_project/test_project.cfg

# Template ... /opt/darknet/cfg/yolov4-tiny-custom.cfg

# Username ... adriaan@ZOBLZO21X5GY3

# Timestamp .. Mon 2024-11-18 16:53:42 SAST

#

# WARNING: If you re-generate the darknet files for this project you'll

# lose any customizations you are about to make in this file!

#

[net]

# Testing

#batch=1

#subdivisions=1

# Training

batch=64

subdivisions=1

width=160

height=160

channels=3

momentum=0.9

decay=0.0005

angle=0

saturation=0.000000

exposure=0.000000

hue=0.000000

learning_rate=0.002610

burn_in=1000

max_batches=1000

policy=steps

steps=800,900

scales=.1,.1

cutmix=0

flip=0

max_chart_loss=6.000000

mixup=0

mosaic=0

use_cuda_graph=0

[convolutional]

batch_normalize=1

filters=32

size=3

stride=2

pad=1

activation=leaky

[convolutional]

batch_normalize=1

filters=64

size=3

stride=2

pad=1

activation=leaky

[convolutional]

batch_normalize=1

filters=64

size=3

stride=1

pad=1

activation=leaky

[route]

layers=-1

groups=2

group_id=1

[convolutional]

batch_normalize=1

filters=32

size=3

stride=1

pad=1

activation=leaky

[convolutional]

batch_normalize=1

filters=32

size=3

stride=1

pad=1

activation=leaky

[route]

layers = -1,-2

[convolutional]

batch_normalize=1

filters=64

size=1

stride=1

pad=1

activation=leaky

[route]

layers = -6,-1

[maxpool]

size=2

stride=2

[convolutional]

batch_normalize=1

filters=128

size=3

stride=1

pad=1

activation=leaky

[route]

layers=-1

groups=2

group_id=1

[convolutional]

batch_normalize=1

filters=64

size=3

stride=1

pad=1

activation=leaky

[convolutional]

batch_normalize=1

filters=64

size=3

stride=1

pad=1

activation=leaky

[route]

layers = -1,-2

[convolutional]

batch_normalize=1

filters=128

size=1

stride=1

pad=1

activation=leaky

[route]

layers = -6,-1

[maxpool]

size=2

stride=2

[convolutional]

batch_normalize=1

filters=256

size=3

stride=1

pad=1

activation=leaky

[route]

layers=-1

groups=2

group_id=1

[convolutional]

batch_normalize=1

filters=128

size=3

stride=1

pad=1

activation=leaky

[convolutional]

batch_normalize=1

filters=128

size=3

stride=1

pad=1

activation=leaky

[route]

layers = -1,-2

[convolutional]

batch_normalize=1

filters=256

size=1

stride=1

pad=1

activation=leaky

[route]

layers = -6,-1

[maxpool]

size=2

stride=2

[convolutional]

batch_normalize=1

filters=512

size=3

stride=1

pad=1

activation=leaky

##################################

[convolutional]

batch_normalize=1

filters=256

size=1

stride=1

pad=1

activation=leaky

[convolutional]

batch_normalize=1

filters=512

size=3

stride=1

pad=1

activation=leaky

[convolutional]

size=1

stride=1

pad=1

filters=18

activation=linear

[yolo]

mask = 3,4,5

anchors=4, 18, 9, 35, 15, 61, 27, 100, 48, 138, 95, 151

classes=1

num=6

jitter=.3

scale_x_y = 1.05

cls_normalizer=1.0

iou_normalizer=0.07

iou_loss=ciou

ignore_thresh = .7

truth_thresh = 1

random=0

resize=1.5

nms_kind=greedynms

beta_nms=0.6

[route]

layers = -4

[convolutional]

batch_normalize=1

filters=128

size=1

stride=1

pad=1

activation=leaky

[upsample]

stride=2

[route]

layers = -1, 23

[convolutional]

batch_normalize=1

filters=256

size=3

stride=1

pad=1

activation=leaky

[convolutional]

size=1

stride=1

pad=1

filters=18

activation=linear

[yolo]

mask = 0,1,2

anchors=4, 18, 9, 35, 15, 61, 27, 100, 48, 138, 95, 151

classes=1

num=6

jitter=.3

scale_x_y = 1.05

cls_normalizer=1.0

iou_normalizer=0.07

iou_loss=ciou

ignore_thresh = .7

truth_thresh = 1

random=0

resize=1.5

nms_kind=greedynms

beta_nms=0.6

test_project.json:

[

{

"id": "TFYOLOV3",

"match_kind": "general",

"custom_attributes": {

  "classes": 1,

  "anchors": [4, 18, 9, 35, 15, 61, 27, 100, 48, 138, 95, 151],

  "coords": 4,

  "num": 6,

  "masks": [[3, 4, 5], [0, 1, 2]],

  "entry_points": ["detector/yolo-v4-tiny/Reshape", "detector/yolo-v4-tiny/Reshape_4"]

}

}

]

jakaskerl

MernokAdriaan
Make sure you convert yolo-type models using https://tools.luxonis.com/ .

Thanks,
Jaka

MernokAdriaan

jakaskerl, this is a yolov4-tiny model though? That tool only seems to support YOLOv5 and onwards. I am trying to avoid the Ultralytics licensing costs while we commercialize our product while simultaneously learning more about the models and their limitations - if we get to a point where we cannot push the yolov4-tiny models further and there are actual measurable improvements with the Ultralytics models, then we will look at getting the required licensing.

jakaskerl

MernokAdriaan
Uh, right; We have v4 models available so it should be possible (maybe even through tools, but idk) cc @JanCuhel on what the differences are

Thanks,
Jaka

JanCuhel

Hi @MernokAdriaan,

yes, you're correct. Tools support conversion of YOLOv5 and later (that includes YOLOv6 and v7, both with GPL-3.0 license which aren't originally part of Ultralytics ecosystem).

Regarding these errors:
[14442C1071F33FD700] [1.1] [1.813] [SpatialDetectionNetwork(8)] [error] Mask is not defined for output layer with width '5'. Define at pipeline build time using: 'setAnchorMasks' for 'side5'.
[14442C1071F33FD700] [1.1] [1.813] [SpatialDetectionNetwork(8)] [error] Mask is not defined for output layer with width '10'. Define at pipeline build time using: 'setAnchorMasks' for 'side10'.

You need to update the keys of the dictionary in here:
spatial_nn.setAnchorMasks({"side26": [0, 1, 2], "side13": [3, 4, 5]})
to this (following this formula: sideX and sideY, where X = width/16 and Y = width/32)
spatial_nn.setAnchorMasks({"side10": [0, 1, 2], "side5": [3, 4, 5]})

Regarding this warning:
[14442C1071F33FD700] [1.1] [1.803] [SpatialDetectionNetwork(8)] [warning] Input image (160x160) does not match NN (3x160)
Please make sure that your model expects images 3x160x160. You use use netron.app and upload the generated .xml file there and you should see that the input should have the following shape: 1x3x160x160.

Best,
Jan

MernokAdriaan

Hi JanCuhel,

Thanks so much for the prompt response and the explanation on where the sideX and sideY comes from!
That seems to have solved the error.

W.r.t. the shape, here is a screenshot from the intermediary .pb file:

And then the .XML file:

So definitely something going wrong here. I'm curious why the shape of the .pb file is [?,160,160,3].
Sorry if these are stupid questions, I'm not familiar with all the weight file formats etc…

Another nudge in the right direction would be greatly appreciated!

MernokAdriaan

As a side note: I'm aware that v6 and v7 do not fall under the Ultralytics ecosystem, but the GPL-3.0 license is still a problem if I plan to use the models in closed source code no?

The legal jargon is very confusing to me.

PS: the licensing of the output feels really strange as many text editors are also licensed as such, but the works created from them do not fall under the same umbrella - sure you've heard this rant a million times before.