python optimize_anchors.py annotations.csv --no-resize --include-stride --seed=1

fails with error:

Traceback (most recent call last):
  File "optimize_anchors.py", line 10, in <module>
    from compute_overlap import compute_overlap
  File "__init__.pxd", line 872, in init compute_overlap
ValueError: numpy.ufunc has the wrong size, try recompiling. Expected 192, got 216

What's happening? My environment: a Docker container with

Ubuntu 18.04.3 LTS
Python 3.6.8

pip -list output:

Package                Version  
---------------------- ---------
absl-py                0.8.0    
anchor-optimization    0.0.1    
astor                  0.8.0    
attrs                  19.1.0   
audioread              2.1.8    
backcall               0.1.0    
bleach                 3.1.0    
cloudpickle            1.2.2    
cupy                   6.3.0    
cycler                 0.10.0   
Cython                 0.29.13  
decorator              4.4.0    
defusedxml             0.6.0    
entrypoints            0.3      
fastrlock              0.4      
future                 0.17.1   
gast                   0.2.2    
google-pasta           0.1.7    
graphsurgeon           0.4.1    
grpcio                 1.23.0   
h5py                   2.9.0    
horovod                0.18.0   
ipykernel              5.1.2    
ipython                7.8.0    
ipython-genutils       0.2.0    
jedi                   0.15.1   
Jinja2                 2.10.1   
joblib                 0.13.2   
json5                  0.8.5    
jsonschema             3.0.2    
jupyter-client         5.3.1    
jupyter-core           4.5.0    
jupyter-tensorboard    0.1.10   
jupyterlab             1.0.2    
jupyterlab-server      1.0.0    
jupytext               1.2.3    
Keras                  2.3.1    
Keras-Applications     1.0.6    
Keras-Preprocessing    1.0.5    
keras-resnet           0.1.0    
keras-retinanet        0.5.1    
kiwisolver             1.1.0    
librosa                0.6.3    
llvmlite               0.29.0   
Markdown               3.1.1    
MarkupSafe             1.1.1    
matplotlib             3.1.1    
mistune                0.8.4    
mock                   3.0.5    
mpi4py                 3.0.2    
nbconvert              5.6.0    
nbformat               4.4.0    
nltk                   3.2.5    
notebook               5.7.8    
numba                  0.44.1   
numpy                  1.14.5   
nvidia-dali            0.13.0   
nvidia-dali-tf-plugin  0.13.0   
opencv-python          4.1.1.26 
pandas                 0.23.0   
pandocfilters          1.4.2    
parso                  0.5.1    
pexpect                4.7.0    
pickleshare            0.7.5    
Pillow                 5.4.1    
pip                    19.2.3   
portpicker             1.3.1    
progressbar2           3.47.0   
prometheus-client      0.7.1    
prompt-toolkit         2.0.9    
protobuf               3.9.1    
psutil                 5.6.3    
ptyprocess             0.6.0    
pycocotools            2.0.0    
Pygments               2.4.2    
pyparsing              2.4.2    
pyrsistent             0.15.4   
python-dateutil        2.8.0    
python-speech-features 0.6      
python-utils           2.3.0    
pytz                   2019.2   
PyYAML                 5.1.2    
pyzmq                  18.1.0   
resampy                0.2.1    
sacrebleu              1.3.6    
scikit-learn           0.21.3   
scipy                  1.3.1    
Send2Trash             1.5.0    
sentencepiece          0.1.82   
setuptools             41.2.0   
six                    1.12.0   
tensorboard            1.14.0+nv
tensorflow-estimator   1.14.0   
tensorflow-gpu         1.14.0+nv
tensorrt               6.0.1.4  
termcolor              1.1.0    
terminado              0.8.2    
testpath               0.4.2    
tornado                6.0.3    
tqdm                   4.36.0   
traitlets              4.3.2    
typing                 3.7.4.1  
uff                    0.6.5    
wcwidth                0.1.7    
webencodings           0.5.1    
Werkzeug               0.15.6   
wheel                  0.33.6   
wrapt                  1.11.2 

I have python 3.7, keras-retinanet 0.5.1 from pip and tensorflow-gpu 1.13.1 as required by keras-retinanet. When I remove the tf 2.0 cpu version which is installed by default while setup a new error pops "AttributeError: module 'tensorflow' has no attribute 'name_scope'"

it doesnt exist

Hi, when I run the script, the output always shows this information as follows:

I don't know how to do next. It seems not to be the desired anchor result regarding our own data?
Please help me!
Thanks!

Hello.
I'm using SSD Inception v2 architecture and want to leverage this script to figure out the best anchor parameters for my custom dataset. In my understanding, the current codebase uses the default parameters for Keras-Retinanet and has some other minor dependencies.

e.g.

I just wanted to initiate the discussion and see if any work has already been done along the line. Also, if required, lay down the list of tasks that are needed to achieve the above objective.

Thank you,
Aneesh

Give me this error
Using TensorFlow backend. Loading object dimensions. Traceback (most recent call last): File "anchor-optimization/optimize_anchors.py", line 131, in <module> x1, y1, x2, y2 = list(map(lambda x: int(x), row[1:5])) ValueError: not enough values to unpack (expected 4, got 1)

Hi, i would be really grateful if you could please help out in following queries:

i. Is this a good approach/recommended to change anchor area from 32^ - 512^2 to 16^2 to 256^ to meet the small size objects? Since increasing the no. of anchors also increases the computational time.
ii. If i change anchor size from [32, 64, 128, 256, 512] to [16, 32, 64, 128, 256] then how would i change the aspect ratio and scale values, means how to set the ratio and scale parameters values when i change the anchor size.
iii. In a particular dataset, how may i know that what is the smallest and largest size (in pixels) of an object so that i could decide about anchor sizes and areas.

Thanks,

Please tell me how do I run the code using my Nvidia GPU. I have already installed tensorflow-gpu.

Hello @martinzlocha

Thank you for the nice anchor-optimization tool!

I was wondering how I can interpret the anchor scale output? Is it a fraction of the default anchor size:

My goal is to optimize the anchor aspect ratios and the scales for YOLACT++. The aspect ratios can be easily copy-pasted, but the scales are in a different format. YOLACT uses absolute values instead of fractions. YOLACT uses a maximum image-size of 550 pixels: https://github.com/dbolya/yolact/blob/57b8f2d95e62e2e649b382f516ab41f949b57239/data/config.py#L664

When I executed the command below:
anchor-optimization yolact/data/custom/train/boxes.csv --ratios=3 --scales=5 --image-max-side 550

This output was generated:
State: 0.12784
Ratios: [0.461, 1.0, 2.168]
Scales: [0.4, 0.458, 0.527, 0.689, 1.2]

I'm a bit confused as the 'pred_scales' of YOLACT are:
[24, 48, 96, 192, 384]
https://github.com/dbolya/yolact/blob/57b8f2d95e62e2e649b382f516ab41f949b57239/data/config.py#L779

Can someone help me with the right interpretation?
Thanks in advance,
Pieter

File "c:\programdata\miniconda3\lib\site-packages\anchor_optimization\optimize_anchors.py", line 193, in anchors_optimize
x1, y1, x2, y2 = list(map(lambda x: int(x), row[1:5]))
ValueError: not enough values to unpack (expected 4, got 0)

and this problem：

Invalid argument: 'C:\Users\Administrator\Desktop\\ufeff"C:\Users\Administrator\Desktop\all1020\2021102010.jpg'

which one is the final result? I'm so obfuscated..

My dataset contains many images with large size (above 2000*2000) and have different shapes and size of classes. From what I saw when use debug.py file from keras-retinanet, all small objects are fine with generated anchors, but with large objects ( > 20% of image width), no anchor matches. One of them is below:

I think many red boxes has made my traning process went into a trouble, when mAP value that calculated at the end of every epoch was 0.0000. How can I deal with this problem?

Hi guys, for the requirements I see you are using tensorflow rather than tensorflow-gpu.

I have tensorflow-gpu installed. Can I remove tensorflow from the requirements to be downloaded and run the code normally?

Line 193 in optimize_anchors.py:

Negative examples contain an empty entry for the bbox coordinates and not 0. We should check to skip these before trying to convert them to int to avoid ValueError.

PFA fix.

I run code in Python 3.6, I haven't 3.7 available,
When I run python optimize_anchors.py annotations.csv I get the error:

Using TensorFlow backend.
Loading object dimensions.
Traceback (most recent call last):
  File "optimize_anchors.py", line 137, in <module>
    scale = compute_resize_scale(img.shape, min_side=args.image_min_side, max_side=args.image_max_side)
AttributeError: 'JpegImageFile' object has no attribute 'shape'

Hi!
First of all thanks for the contribution. In the default configuration the script worked out fine for me. I have a dataset with very small rectangles (256x256 images with small objects on it). For that reason I changed to bounds to allow a smaller value (I changed it here: https://github.com/martinzlocha/anchor-optimization/blob/master/optimize_anchors.py#L174) which worked fine and it printed that there are only 9 labels without a matching anchor, which is very good for my 2000+ images dataset. The full print here:

Current best anchor configuration
Ratios: [0.343, 0.77, 1.0, 1.298, 2.916]
Scales: [0.103, 0.129, 0.164]
Number of labels that don't have any matching anchor: 9

I ran your script like this:

python optimize_anchors.py --image-max-side 256 --image-min-side 256 --ratios 5 dataset.csv

But when I plug these values in the config and run the debug.py script from keras-retinanet like this:

 python keras_retinanet/bin/debug.py --image-min-side 256 --image-max-side 256 --config config.ini csv dataset.csv classes.csv

very many boxes are drawn red (much more than half of the boxes of my full dataset).

Is there a limitation for the bounds of the scales that I don't know about?
Thanks in advance

Is there a possibility to specify base sizes and strides for calculation?

MemoryError: Unable to allocate 2.62 TiB for an array with shape (1250965, 288300) and data type float64

How would I solve this problem?

Current best anchor configuration
Ratios: [0.401, 1.0, 2.497]
Scales: [0.747, 1.103, 1.398]
Number of labels that don't have any matching anchor: 56047

Current best anchor configuration
Ratios: [0.355, 1.0, 2.816]
Scales: [0.582, 1.541, 1.873]
Number of labels that don't have any matching anchor: 51863

Current best anchor configuration
Ratios: [0.29, 1.0, 3.453]
Scales: [0.931, 0.99, 1.056]
Number of labels that don't have any matching anchor: 46175

Current best anchor configuration
Ratios: [0.254, 1.0, 3.944]
Scales: [0.867, 1.148, 1.549]
Number of labels that don't have any matching anchor: 39772

Current best anchor configuration
Ratios: [0.251, 1.0, 3.99]
Scales: [0.818, 0.994, 1.783]
Number of labels that don't have any matching anchor: 39665

Current best anchor configuration
Ratios: [0.251, 1.0, 3.99]
Scales: [0.994, 1.246, 1.387]
Number of labels that don't have any matching anchor: 39424

Current best anchor configuration
Ratios: [0.251, 1.0, 3.981]
Scales: [0.853, 1.29, 1.382]
Number of labels that don't have any matching anchor: 40024

Current best anchor configuration
Ratios: [0.251, 1.0, 3.978]
Scales: [1.113, 1.289, 1.713]
Number of labels that don't have any matching anchor: 39559

Current best anchor configuration
Ratios: [0.251, 1.0, 3.991]
Scales: [0.812, 1.37, 1.965]
Number of labels that don't have any matching anchor: 39546

Current best anchor configuration
Ratios: [0.25, 1.0, 3.998]
Scales: [0.824, 1.232, 1.781]
Number of labels that don't have any matching anchor: 39590

Current best anchor configuration
Ratios: [0.25, 1.0, 3.994]
Scales: [0.605, 1.048, 1.64]
Number of labels that don't have any matching anchor: 39333

Current best anchor configuration
Ratios: [0.25, 1.0, 3.995]
Scales: [0.747, 1.004, 1.219]
Number of labels that don't have any matching anchor: 39350

this was the best anchor configuration i've got and i used --no-resize with image of size 1500*1500. how do i obtain better results without and dataset is huge to use --include-stride.

Hi,
I'm getting this error messagem when runing:

$ python anchor_optimization\optimize_anchors.py test.csv

Traceback (most recent call last):
  File "E:\anchor-optimization\anchor_optimization\optimize_anchors.py", line 11, in <module>
    from keras_retinanet.preprocessing.csv_generator import _open_for_csv
  File "C:\Users\USER\anaconda3\envs\keras_env\lib\site-packages\keras_retinanet\preprocessing\csv_generator.py", line 18, in <module>
    from .generator import Generator
  File "C:\Users\USER\anaconda3\envs\keras_env\lib\site-packages\keras_retinanet\preprocessing\generator.py", line 39, in <module>
    class Generator(keras.utils.Sequence):
AttributeError: module 'keras.utils' has no attribute 'Sequence'

Tensorflow version: 2.7.0

Any help?

Which value of --no-resize will give a better result, True of False?
It looks like that True (i.e. resizing the bounding box) is better.