Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

Last update: Jan 5, 2023

Overview

Weighted boxes fusion

Repository contains Python implementation of several methods for ensembling boxes from object detection models:

Non-maximum Suppression (NMS)
Soft-NMS [1]
Non-maximum weighted (NMW) [2]
Weighted boxes fusion (WBF) [3] - new method which gives better results comparing to others

Requirements

Python 3.*, Numpy, Numba

Installation

pip install ensemble-boxes

Usage examples

Coordinates for boxes expected to be normalized e.g in range [0; 1]. Order: x1, y1, x2, y2.

Example of boxes ensembling for 2 models below.

First model predicts 5 boxes, second model predicts 4 boxes.
Confidence scores for each box model 1: [0.9, 0.8, 0.2, 0.4, 0.7]
Confidence scores for each box model 2: [0.5, 0.8, 0.7, 0.3]
Labels (classes) for each box model 1: [0, 1, 0, 1, 1]
Labels (classes) for each box model 2: [1, 1, 1, 0]
We set weight for 1st model to be 2, and weight for second model to be 1.
We set intersection over union for boxes to be match: iou_thr = 0.5
We skip boxes with confidence lower than skip_box_thr = 0.0001

from ensemble_boxes import *

boxes_list = [[
    [0.00, 0.51, 0.81, 0.91],
    [0.10, 0.31, 0.71, 0.61],
    [0.01, 0.32, 0.83, 0.93],
    [0.02, 0.53, 0.11, 0.94],
    [0.03, 0.24, 0.12, 0.35],
],[
    [0.04, 0.56, 0.84, 0.92],
    [0.12, 0.33, 0.72, 0.64],
    [0.38, 0.66, 0.79, 0.95],
    [0.08, 0.49, 0.21, 0.89],
]]
scores_list = [[0.9, 0.8, 0.2, 0.4, 0.7], [0.5, 0.8, 0.7, 0.3]]
labels_list = [[0, 1, 0, 1, 1], [1, 1, 1, 0]]
weights = [2, 1]

iou_thr = 0.5
skip_box_thr = 0.0001
sigma = 0.1

boxes, scores, labels = nms(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr)
boxes, scores, labels = soft_nms(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, sigma=sigma, thresh=skip_box_thr)
boxes, scores, labels = non_maximum_weighted(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr)
boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr)

Single model

If you need to apply NMS or any other method to single model predictions you can call function like that:

from ensemble_boxes import *
# Merge boxes for single model predictions
boxes, scores, labels = weighted_boxes_fusion([boxes_list], [scores_list], [labels_list], weights=None, method=method, iou_thr=iou_thr, thresh=thresh)

More examples can be found in example.py

3D version

There is support for 3D boxes in WBF method with weighted_boxes_fusion_3d function. Check example of usage in example_3d.py

Accuracy and speed comparison

Comparison was made for ensemble of 5 different object detection models predictions trained on Open Images Dataset (500 classes).

Model scores at local validation:

Model 1: mAP(0.5) 0.5164
Model 2: mAP(0.5) 0.5019
Model 3: mAP(0.5) 0.5144
Model 4: mAP(0.5) 0.5152
Model 5: mAP(0.5) 0.4910

Method	mAP(0.5) Result	Best params	Elapsed time (sec)
NMS	0.5642	IOU Thr: 0.5	47
Soft-NMS	0.5616	Sigma: 0.1, Confidence Thr: 0.001	88
NMW	0.5667	IOU Thr: 0.5	171
WBF	0.5982	IOU Thr: 0.6	249

You can download model predictions as well as ground truth labels from here: test_data.zip

Ensemble script for them is available here: example_oid.py

We also published large benchmark based on COCO dataset here.

Description of WBF method and citation

https://arxiv.org/abs/1910.13302 (updated: 2020.08)
https://authors.elsevier.com/c/1ca0dxnVK3cWY

If you find this code useful please cite:

@article{solovyev2021weighted,
  title={Weighted boxes fusion: Ensembling boxes from different object detection models},
  author={Solovyev, Roman and Wang, Weimin and Gabruseva, Tatiana},
  journal={Image and Vision Computing},
  pages={1-6},
  year={2021},
  publisher={Elsevier}
}

Comments

Add simple confidence averaging strategy
Hi, @ZFTurbo. Now, I'm reading your paper and this repo, and I might identify the cause of the issue discussed in #10. I am describing what a cause is in the following sections.

To fix this issue, I made a PR that proposes another conf_type. Note that this modification can change confidence score, but not any bbox coordinates.

I'm not an expert, so I may have missed something important. If so, please feel free to point it out. I appreciate it if you review this, and I hope this PR will contribute to the community.

Problem

The weighted_boxes_fusion can return confidence score larger than 1.0 regardless of the allows_overflow parameter.

This is a reproducable example.

from ensemble_boxes import * boxes_list = [[ [0.1, 0.1, 0.2, 0.2], [0.1, 0.1, 0.2, 0.2], ],[ [0.3, 0.3, 0.4, 0.4], ]] scores_list = [[1.0, 1.0], [0.5]] labels_list = [[0, 0], [0]] weights = [2, 1] iou_thr = 0.5 skip_box_thr = 0.0001 sigma = 0.1 boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr, allows_overflow=True) print(scores) # [1.33333337 0.16666667] boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr, allows_overflow=False) print(scores) # [1.33333337 0.16666667]

Why?

According to the paper^[1], the confidence score is given by the eq.(1) and (5). Combining the two equations, we can get the following equation.

$C = \frac{\sum_i^TC_i}{T} \frac{min(W, T))}{W}.$

C = \frac{\sum_i^TC_i}{T} \frac{min(W, T))}{W}.

Since the current implementation has taken into account the weights, we should rewrite the equation. If we expand min(...) part explicitly, the resulting equation looks like this:

$\begin{align*} C &= \frac{\sum_i^T w_i C_i}{T} \frac{min(W, T))}{W}, \\ &= \begin{cases} \frac{\sum_i^T w_i c_i}{T}, & W \le T \\ \frac{\sum_i^T w_i c_i}{W}, & W > T \end{cases} \end{align*}$

\begin{align*} C &= \frac{\sum_i^T w_i C_i}{T} \frac{min(W, T))}{W}, \\ &= \begin{cases} \frac{\sum_i^T w_i c_i}{T}, & W \le T \\ \frac{\sum_i^T w_i c_i}{W}, & W > T \end{cases} \end{align*},

where the w_i is a weight per box, and the W denotes the total weights over the models (weight.sum()).

The last equation implies that the result is unbounded if w_i's are not unbounded. In the above case, we can calculate C = 2*1 + 2*1 / 3 = 1.33..., (W = 3, T = 2). This is why the result can be larger than 1.0.

A proposal to fix

In this PR, I propose the following simple weighted average.

$C = \frac{\sum_i^Tw_iC_i}{\sum_i^T{w_i}}$

C = \frac{\sum_i^Tw_iC_i}{\sum_i^T{w_i}}

Note that the denominator is a weights sum over the boxes of a cluster (not over the models weight.sum()). Apparently, this is bounded in [0, 1] if the input scores are bounded in [0, 1].

Note that using the normalized weight (to make sure weight.sum == 1) does not fix the issue. If you use normalized weight, the resulting scores will be underestimated by dividing T or W.

This is a weighted_avg version example.

boxes_list = [[ [0.1, 0.1, 0.2, 0.2], [0.1, 0.1, 0.2, 0.2], ],[ [0.3, 0.3, 0.4, 0.4], ]] scores_list = [[1.0, 1.0], [0.5]] labels_list = [[0, 0], [0]] weights = [2, 1] iou_thr = 0.5 skip_box_thr = 0.0001 sigma = 0.1 boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr, allows_overflow=False, conf_type='weighted_avg') print(scores) # [1. 0.5] ## use other scores scores_list = [[0.8, 1.0], [0.5]] boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr, allows_overflow=False, conf_type='weighted_avg') print(scores) # [0.89999998 0.5]

Performance

Sorry, I did not conduct any performance experiments. So, I have no idea how much of an impact this fix will have on performance at this point.

[1] https://arxiv.org/abs/1910.13302
opened by i-aki-y 8
How to find the optimal hyper-parameters in the ensembling process

Hello, I would like to ask how to find the best parameters combination, such as the model importances, IOU threshold when performing the ensemble process? Now I got prediction results from different models and try to ensemble these predictions, but I cannot know how to find the optimal parameters. Any suggestions will be helpful! Thank you in advance.

opened by 123dddd 6

run NMS on empty bounding box from one of ensembled models

Hi, I tried to ensemble 5 models. if there is no bounding box detected from one of 5 models, the nms gives error as follow :

ValueError                                Traceback (most recent call last)
Input In [39], in <cell line: 14>()
     12 lebar = dict_pred[img.split('\\')[-1].split('.')[0]]['image_width']
     13 tinggi = dict_pred[img.split('\\')[-1].split('.')[0]]['image_height']
---> 14 boxes, scores, labels = nms_method(bbox_list, conf_list, cat_list, method=2, weights=weights, iou_thr=0.3, sigma=0.05, thresh=0.001)

File ~\Anaconda3\envs\SiT\lib\site-packages\ensemble_boxes\ensemble_boxes_nms.py:187, in nms_method(boxes, scores, labels, method, iou_thr, sigma, thresh, weights)
    184             scores[i] = (np.array(scores[i]) * weights[i]) / weights.sum()
    186 # We concatenate everything
--> 187 boxes = np.concatenate(boxes)
    188 scores = np.concatenate(scores)
    189 labels = np.concatenate(labels)

File <__array_function__ internals>:5, in concatenate(*args, **kwargs)

ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 3 has 1 dimension(s)

If I used wbf method, it runs succesfully. is there any solution to run nms in this situation like wbf does. please advise

thank you regards

opened by ramdhan1989 5

Incorrect allows_overflow=False mode

Example:

from ensemble_boxes import *

weigths = [0.2, 0.2, 0.2, 0.2, 0.2]
pred_boxes = []
pred_scores = []
pred_labels = []
for _ in range(5):
    pred_boxes.append([[0.    , 0.    , 0.0001, 0.0001]])
    pred_scores.append([1.])
    pred_labels.append([0])

pred_boxes, pred_scores, pred_labels = weighted_boxes_fusion(
    pred_boxes,
    pred_scores,
    pred_labels,
    weights=weigths,
    iou_thr=0.4,
    skip_box_thr=0.,
    allows_overflow=False
)
print(pred_scores)

Actual result: score [0.2] Expected result: score [1]

Probably we need to change the line weighted_boxes[i][1] = weighted_boxes[i][1] * min(weights.sum(), len(clustered_boxes)) / weights.sum() -> weighted_boxes[i][1] = weighted_boxes[i][1] * min(len(weights), len(clustered_boxes)) / weights.sum()

opened by Sergey-Zlobin 5

Strange behavior of weighted_boxes_fusion

@ZFTurbo

boxes = [
    [[410, 464, 354, 410],
    [511, 89, 470, 32],
    [503, 213, 447, 166],
    [300, 444, 252, 391],
    [290, 184, 234, 133]],
    
    [[354, 412, 409, 463],
    [447, 165, 504, 212],
    [251, 392, 299, 444],
    [470, 35, 511, 90],
    [187, 316, 240, 378]],

    [[447, 166, 503, 213],
    [355, 412, 411, 464],
    [470, 33, 511, 88],
    [251, 391, 300, 444],
    [234, 132, 289, 184]],

    [[251, 391, 300, 445],
    [354, 412, 410, 463],
    [447, 166, 503, 212],
    [235, 134, 289, 184],
    [191, 316, 239, 380]],

    [[410, 465, 355, 412],
    [511, 88, 470, 33],
    [504, 213, 448, 167],
    [299, 444, 251, 392],
    [289, 185, 236, 133]]
]

scores = [
    [0.893,0.886,0.865,0.864,0.801],
    [0.915,0.881,0.873,0.852,0.844],
    [0.896, 0.895,0.860,0.855,0.801],
    [0.900,0.889,0.861,0.838,0.831],
    [0.897,0.877, 0.877,0.861,0.820],
]

labels = [
    [1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1]
]

wbf_boxes, wbf_scores, wbf_labels = ensemble_boxes.ensemble_boxes_wbf.weighted_boxes_fusion(boxes, scores, labels, weights=None, iou_thr=0.43, skip_box_thr=0.44)

wbf_boxes.shape
>>> (16, 4)

I think we see excess wbf_boxes, it is very strange. Picture with demonstration:

Без названия (1)

Python 3.7.6 numpy==1.18.1 ensemble_boxes==1.0.1

opened by shonenkov 5

Why does my ensemble still have so many repetitive (IoU > thresh I set) bboxes?

Thank you for your code.

However, when I use this method, I have so many repetitive bboxes. Do you have some advices?

Code is following: boxes, score12, label = example_nms_2_models(boxes_list, labels_list, scores_list, draw_image=False, method=3, iou_thr=0.5, thresh=0.1, sigma=0.5)

I just use nms method because the limitation of running time. An example of results is following: (Pdb) boxes array([[0.73385417, 0.34259259, 0.81041667, 0.45648148], [0.74930206, 0.36653722, 0.79911662, 0.45000624]]) The labels are same. Obviously, the IoU of these two boxes should be over than 0.5. Looks like NMS doen not work in ensemble. Right?

opened by RainbowSun11Q2H 5
Speed up find_matching_box bottleneck

When using weighted boxes fusion with a couple thousand detections it quickly becomes very slow requiring significantly longer than the inference. The bottleneck turned out to be the find_matching_box function which is called n*n times for n detections. Vectorisation of this function with numpy speeds up the function by a factor of around 100 and makes the weighted boxes fusion time negligible next to the inference time.

opened by bartonp2 4

Strange result if boxes are intersected inside one model

Example:

from ensemble_boxes import *

boxes_list = [[
    [0.1, 0.1, 0.2, 0.2],
    [0.1, 0.1, 0.2, 0.2],
], [
    [0.3, 0.3, 0.4, 0.4],
]]
scores_list = [[1.0, 1.0], [0.5]]
labels_list = [[0, 0], [0]]
weights = [2, 1]

iou_thr = 0.5
skip_box_thr = 0.0001

boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr,
                                              skip_box_thr=skip_box_thr, allows_overflow=False
                                              )
print(scores)

Actual result: [1.33333337 0.16666667] Expected result: scores are <= 1.

opened by Sergey-Zlobin 2

Sould the "break" change to "continue"

https://github.com/ZFTurbo/Weighted-Boxes-Fusion/blob/d48402e487dcdf9a5b4e519ed0ef254f233c3fd6/ensemble_boxes/ensemble_boxes_wbf.py#L35

Hi I do not see why this should break,if one prediction is bad,we should just ignore instead of jumping out of loop unless this list is sorted?

Thanks!

opened by EvanAlbee 2

Incorrect conf_type='max' mode

Example:

from ensemble_boxes import *

boxes_list = [[
    [0.1, 0.1, 0.2, 0.2],
], [
    [0.1, 0.1, 0.2, 0.2],
]]
scores_list = [[1], [1]]
labels_list = [[0], [0]]
weights = [1, 3]

iou_thr = 0.5
skip_box_thr = 0.0001

boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr,
                                              skip_box_thr=skip_box_thr, allows_overflow=True, conf_type='max'
                                              )
print(scores)

Actual result: [1.5] Expected result: [1]

opened by Sergey-Zlobin 1

I've found typo in your title!

Set of methods to ensemble boxes from object detection models, inlcuding implementation of "Weighted boxes fusion (WBF)" method.

inlcuding -> including

Thank you!

opened by Bluespen 1

2d bbox wbf compute result error

from ensemble_boxes import *

box_list = [[[0.3186783194541931, 0.7567034912109375, 0.3458101451396942, 0.8225753173828125],
             [0.2893345057964325, 0.7414910888671875, 0.30395767092704773, 0.82741845703125],
             [0.08234874159097672, 0.7192938232421875, 0.10110706090927124, 0.7978226318359375],
             [0.03901633247733116, 0.6389908447265625, 0.08065500855445862, 0.794277587890625],
             [0.10205534100532532, 0.7005220947265625, 0.3009164333343506, 0.8869298095703125],
             [0.013241510838270187, 0.7410292358398437, 0.4873046875, 0.9892860717773437],
             [0.20103125274181366, 0.6678619384765625, 0.2735580503940582, 0.8197706298828125],
             [0.2807173728942871, 0.748069091796875, 0.34884029626846313, 0.8265133056640624]],
            [[499.8333263993263, 0.7574194946289062, 499.8582033813, 0.8213037719726562],
             [499.80139984190464, 0.744067626953125, 499.8154309540987, 0.83922900390625],
             [499.79503624141216, 0.7509530639648437, 499.86309084296227, 0.8289489135742187],
             [499.52070143818855, 0.7752119750976563, 500.0, 0.9840308227539063],
             [499.5126953125, 0.5068761901855469, 499.98771003447473, 0.74507958984375]],
            [[0.31953710317611694, 0.758130859375, 0.34634509682655334, 0.821462158203125],
             [0.2890157401561737, 0.7444622192382813, 0.3044307231903076, 0.8266027221679687],
             [0.008551719598472118, 0.4673032836914062, 0.36901113390922546, 0.7606300659179688],
             [0.0, 0.4736612548828125, 0.4354751706123352, 0.738594970703125],
             [0.004452536813914776, 0.7197838134765625, 0.4873046875, 1.0818572998046876],
             [0.0, 0.7836503295898437, 0.4873046875, 0.9610354614257812],
             [0.0, 0.4444543762207031, 0.4644381105899811, 0.7153197021484375],
             [0.0, 0.4595667419433594, 0.45853325724601746, 0.745251220703125]],
            [[0.31933701038360596, 0.7575816040039063, 0.3459422290325165, 0.8217849731445312],
             [0.2892712652683258, 0.742080322265625, 0.3039073348045349, 0.82669189453125],
             [0.08082691580057144, 0.7194573364257812, 0.10114574432373047, 0.7929706420898438],
             [0.014002567157149315, 0.73406103515625, 0.4873046875, 1.049856201171875],
             [0.0058621931821107864, 0.485712646484375, 0.39766108989715576, 0.765383544921875],
             [0.0058621931821107864, 0.485712646484375, 0.39766108989715576, 0.765383544921875],
             [0.0010756775736808777, 0.7905551147460937, 0.4873046875, 0.9734806518554687]],
            [[0.3187095522880554, 0.7571224365234375, 0.34541282057762146, 0.82177685546875],
             [0.2895776629447937, 0.7409693603515625, 0.3036201298236847, 0.8277974853515625],
             [0.09533613175153732, 0.6792424926757813, 0.2974565327167511, 0.8962586059570312],
             [0.08239483088254929, 0.720238525390625, 0.10080346465110779, 0.7970889892578125],
             [0.03715477138757706, 0.6399656372070313, 0.08126747608184814, 0.7963726196289063],
             [0.11268552392721176, 0.6827256469726563, 0.12404713779687881, 0.7254093627929687],
             [0.13349150121212006, 0.6368695678710937, 0.14964361488819122, 0.6750675659179688],
             [0.15557613968849182, 0.6638201904296875, 0.28306907415390015, 0.858079833984375],
             [0.01832456886768341, 0.7675958251953126, 0.47474271059036255, 0.944915283203125],
             [0.4468492865562439, 0.6279349975585937, 0.4620358347892761, 0.6685007934570313]],
            [[0.3194415271282196, 0.7588555908203125, 0.3447743058204651, 0.8150806884765625],
             [0.0741792619228363, 0.6543595581054688, 0.3116285800933838, 0.9023289184570312],
             [0.08234858512878418, 0.7222454223632813, 0.10103598982095718, 0.7950789184570313],
             [0.13459675014019012, 0.6382381591796875, 0.1480436772108078, 0.6662626953125],
             [0.28921717405319214, 0.738470703125, 0.3036784827709198, 0.8197808837890626],
             [0.007326365448534489, 0.7826550903320313, 0.4597592055797577, 0.9532874145507813],
             [0.0, 0.6689840087890625, 0.0, 0.70613916015625]]]

score_list = [[0.83423, 0.79483235, 0.6388512, 0.18261029, 0.14935824, 0.088351235, 0.08091657, 0.07652432],
              [0.879186, 0.60111153, 0.14815152, 0.14187415, 0.063758574],
              [0.5681963, 0.4344975, 0.31340924, 0.21414337, 0.14648718, 0.07502777, 0.06623978, 0.06619557],
              [0.7660579, 0.6813378, 0.24239238, 0.11751874, 0.11032326, 0.10366057, 0.074862376],
              [0.8430377, 0.82931787, 0.7683554, 0.6823021, 0.28933194, 0.18235974, 0.17097037, 0.09869627, 0.08994506, 0.079594925],
              [0.78075427, 0.7309819, 0.6295794, 0.48739368, 0.39831838, 0.073874675, 0.059466254]]

label_list = [[6, 6, 6, 7, 7, 9, 7, 6],
              [6, 6, 6, 9, 11],
              [6, 6, 13, 11, 13, 9, 8, 0],
              [6, 6, 6, 13, 11, 13, 9],
              [6, 6, 7, 6, 7, 6, 6, 7, 9, 6],
              [6, 7, 6, 6, 6, 9, 6]]

weights = [1, 1, 1, 1, 1, 1]
box_list, score_list, label_list = weighted_boxes_fusion(box_list, score_list, label_list,
                                                         weights=weights, iou_thr=0.5,
                                                         skip_box_thr=0.1)
print(box_list)
print(score_list)
print(label_list)

and the result is:

[[0.31910411 0.75763094 0.34561539 0.82052845]
[0.28932601 0.7415092  0.30388758 0.82627851]
[0.08219483 0.72045314 0.10099649 0.79627055]
[0.08656451 0.67013788 0.3040534  0.89810479]
[0.13430972 0.63788271 0.14845915 0.66854924]
[0.03787507 0.63958848 0.0810305  0.79556197]
[0.00788325 0.47187883 0.37613192 0.76181149]
[0.00199323 0.47775888 0.42261785 0.74770349]
[0.00870361 0.72613913 0.48730466 1.        ]
[0.11268552 0.68272565 0.12404714 0.72540936]]
[0.63204601 0.52305063 0.36552083 0.2747826  0.10972734 0.07865704
0.06951164 0.05407777 0.04400099 0.03039329]
[ 6.  6.  6.  7.  6.  7. 13. 11. 13.  6.]

The low confidence box that lower than 0.1 (skip_box_thr) not be filtrated.

opened by VariableXX 0

Single instance NMS

I'm after NMS over classes, but by default most implementations just do within class. Can't work out if over classes is implemented by this tool / repo?

See yolo discussion https://github.com/ultralytics/yolov5/issues/2162

(struggling to work with that implementation, not obvious what the format of the input predictions is)

opened by GeorgePearse 0
label list on SSD

Hello, I'm building an ensemble object detection model with two SSD head detectors. In this implementation the labels of the predicted model are stored with the corresponding conf scores and the bbox coordinates.

In SSD, though, the model doesn't produce the corresponding label during the detection.

Is there any way to make sense this implementation when using SSD as the detector or does anyone have any input on this matter? Looking forward for sharing!

opened by ksmdnl 1
Building wheel for llvmlite ... error
hi there, maybe this is not a issue but an error that I faced when I install ensemble_boxes with the following command:

pip install ensemble_boxes

My environment is Jetson Xavier:

Ubuntu:18.04 LTS

ARMv8 Processor rev 0 (v8l) × 6

NVIDIA Tegra Xavier (nvgpu)/integrated

Conda 4.10.3

To reproduce: 1 create a conda environment named pytorch1 2 install pytorch via the following link https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048 In my case, I used the python 3.6 and pytorch v1.10.0 3 After successful installed use 'pip install ensemble_boxes'

4 No error if I use python 3.8 and with cpu version pytorch.

Error occurs:

(pytorch1) user@GTW-XNX-C1P0IUL1:~/Github/$ pip install ensemble_boxes Collecting ensemble_boxes Using cached ensemble_boxes-1.0.8-py3-none-any.whl (21 kB) Collecting numba Using cached numba-0.53.1-cp36-cp36m-linux_aarch64.whl Requirement already satisfied: numpy in /home/user/anaconda3/envs/pytorch1/lib/python3.6/site-packages (from ensemble_boxes) (1.19.5) Requirement already satisfied: pandas in /home/user/anaconda3/envs/pytorch1/lib/python3.6/site-packages (from ensemble_boxes) (1.1.5) Collecting llvmlite<0.37,>=0.36.0rc1 Using cached llvmlite-0.36.0.tar.gz (126 kB) Preparing metadata (setup.py) ... done Requirement already satisfied: setuptools in /home/user/anaconda3/envs/pytorch1/lib/python3.6/site-packages (from numba->ensemble_boxes) (59.5.0) Requirement already satisfied: python-dateutil>=2.7.3 in /home/user/anaconda3/envs/pytorch1/lib/python3.6/site-packages (from pandas->ensemble_boxes) (2.8.2) Requirement already satisfied: pytz>=2017.2 in /home/user/anaconda3/envs/pytorch1/lib/python3.6/site-packages (from pandas->ensemble_boxes) (2021.3) Requirement already satisfied: six>=1.5 in /home/user/anaconda3/envs/pytorch1/lib/python3.6/site-packages (from python-dateutil>=2.7.3->pandas->ensemble_boxes) (1.16.0) Building wheels for collected packages: llvmlite Building wheel for llvmlite (setup.py) ... error ERROR: Command errored out with exit status 1: command: /home/user/anaconda3/envs/pytorch1/bin/python3.6 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/setup.py'"'"'; file='"'"'/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(file) if os.path.exists(file) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' bdist_wheel -d /tmp/pip-wheel-lykc90s6 cwd: /tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ Complete output (11 lines): running bdist_wheel /home/user/anaconda3/envs/pytorch1/bin/python3.6 /tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ffi/build.py LLVM version... Traceback (most recent call last): File "/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ffi/build.py", line 220, in main() File "/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ffi/build.py", line 210, in main main_posix('linux', '.so') File "/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ffi/build.py", line 134, in main_posix raise RuntimeError(msg) from None RuntimeError: Could not find a llvm-config binary. There are a number of reasons this could occur, please see: https://llvmlite.readthedocs.io/en/latest/admin-guide/install.html#using-pip for help. error: command '/home/user/anaconda3/envs/pytorch1/bin/python3.6' failed with exit status 1

ERROR: Failed building wheel for llvmlite Running setup.py clean for llvmlite Failed to build llvmlite Installing collected packages: llvmlite, numba, ensemble-boxes Running setup.py install for llvmlite ... error ERROR: Command errored out with exit status 1: command: /home/user/anaconda3/envs/pytorch1/bin/python3.6 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/setup.py'"'"'; file='"'"'/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(file) if os.path.exists(file) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record /tmp/pip-record-hho726u9/install-record.txt --single-version-externally-managed --compile --install-headers /home/user/anaconda3/envs/pytorch1/include/python3.6m/llvmlite cwd: /tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ Complete output (16 lines): running install /home/user/anaconda3/envs/pytorch1/lib/python3.6/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools. setuptools.SetuptoolsDeprecationWarning, running build got version from file /tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/llvmlite/_version.py {'version': '0.36.0', 'full': 'e6bb8d137d922bec8beeb01a237254778759becd'} running build_ext /home/user/anaconda3/envs/pytorch1/bin/python3.6 /tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ffi/build.py LLVM version... Traceback (most recent call last): File "/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ffi/build.py", line 220, in main() File "/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ffi/build.py", line 210, in main main_posix('linux', '.so') File "/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/ffi/build.py", line 134, in main_posix raise RuntimeError(msg) from None RuntimeError: Could not find a llvm-config binary. There are a number of reasons this could occur, please see: https://llvmlite.readthedocs.io/en/latest/admin-guide/install.html#using-pip for help. error: command '/home/user/anaconda3/envs/pytorch1/bin/python3.6' failed with exit status 1 ---------------------------------------- ERROR: Command errored out with exit status 1: /home/user/anaconda3/envs/pytorch1/bin/python3.6 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/setup.py'"'"'; file='"'"'/tmp/pip-install-v9ddsi1d/llvmlite_c662f38a09a547d3aac9a4c30baebe97/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(file) if os.path.exists(file) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record /tmp/pip-record-hho726u9/install-record.txt --single-version-externally-managed --compile --install-headers /home/user/anaconda3/envs/pytorch1/include/python3.6m/llvmlite Check the logs for full command output.
opened by jeffenhuang 1

Compatibility for Instance Segmentation

I'm not actually suggesting to do wbf or nms on masks but I think those methods should be compatible with masks for instance segmentation. I haven't tried any methods in this repository for instance segmentation yet but I think it makes sense to get more refined masks with wbf and nms.

I'm currently doing this by adding masks argument to prepare_boxes and nms_method functions.

def prepare_boxes(boxes, scores, labels, masks=None):
    result_boxes = boxes.copy()

    cond = (result_boxes < 0)
    cond_sum = cond.astype(np.int32).sum()
    if cond_sum > 0:
        print('Warning. Fixed {} boxes coordinates < 0'.format(cond_sum))
        result_boxes[cond] = 0

    cond = (result_boxes > 1)
    cond_sum = cond.astype(np.int32).sum()
    if cond_sum > 0:
        print('Warning. Fixed {} boxes coordinates > 1. Check that your boxes was normalized at [0, 1]'.format(cond_sum))
        result_boxes[cond] = 1

    boxes1 = result_boxes.copy()
    result_boxes[:, 0] = np.min(boxes1[:, [0, 2]], axis=1)
    result_boxes[:, 2] = np.max(boxes1[:, [0, 2]], axis=1)
    result_boxes[:, 1] = np.min(boxes1[:, [1, 3]], axis=1)
    result_boxes[:, 3] = np.max(boxes1[:, [1, 3]], axis=1)

    area = (result_boxes[:, 2] - result_boxes[:, 0]) * (result_boxes[:, 3] - result_boxes[:, 1])
    cond = (area == 0)
    cond_sum = cond.astype(np.int32).sum()
    if cond_sum > 0:
        print('Warning. Removed {} boxes with zero area!'.format(cond_sum))
        result_boxes = result_boxes[area > 0]
        scores = scores[area > 0]
        labels = labels[area > 0]
        if masks is not None:
            masks = masks[area > 0]

    return result_boxes, scores, labels, masks

def nms_method(boxes, scores, labels, masks=None, method=3, iou_thr=0.5, sigma=0.5, thresh=0.001, weights=None):
    """
    :param boxes: list of boxes predictions from each model, each box is 4 numbers. 
    It has 3 dimensions (models_number, model_preds, 4)
    Order of boxes: x1, y1, x2, y2. We expect float normalized coordinates [0; 1] 
    :param scores: list of scores for each model 
    :param labels: list of labels for each model
    :param method: 1 - linear soft-NMS, 2 - gaussian soft-NMS, 3 - standard NMS
    :param iou_thr: IoU value for boxes to be a match 
    :param sigma: Sigma value for SoftNMS
    :param thresh: threshold for boxes to keep (important for SoftNMS)
    :param weights: list of weights for each model. Default: None, which means weight == 1 for each model

    :return: boxes: boxes coordinates (Order of boxes: x1, y1, x2, y2). 
    :return: scores: confidence scores
    :return: labels: boxes labels
    """

    # If weights are specified
    if weights is not None:
        if len(boxes) != len(weights):
            print('Incorrect number of weights: {}. Must be: {}. Skip it'.format(len(weights), len(boxes)))
        else:
            weights = np.array(weights)
            for i in range(len(weights)):
                scores[i] = (np.array(scores[i]) * weights[i]) / weights.sum()

    # We concatenate everything
    boxes = np.concatenate(boxes)
    scores = np.concatenate(scores)
    labels = np.concatenate(labels)
    if masks is not None:
        masks = np.concatenate(masks)

    # Fix coordinates and removed zero area boxes
    boxes, scores, labels, masks = prepare_boxes(boxes, scores, labels, masks)

    # Run NMS independently for each label
    unique_labels = np.unique(labels)
    final_boxes = []
    final_scores = []
    final_labels = []
    if masks is not None:
        final_masks = []

    for l in unique_labels:
        condition = (labels == l)
        boxes_by_label = boxes[condition]
        scores_by_label = scores[condition]
        labels_by_label = np.array([l] * len(boxes_by_label))
        if masks is not None:
            masks_by_label = masks[condition]

        if method != 3:
            keep = cpu_soft_nms_float(boxes_by_label.copy(), scores_by_label.copy(), Nt=iou_thr, sigma=sigma, thresh=thresh, method=method)
        else:
            # Use faster function
            keep = nms_float_fast(boxes_by_label, scores_by_label, thresh=iou_thr)

        final_boxes.append(boxes_by_label[keep])
        final_scores.append(scores_by_label[keep])
        final_labels.append(labels_by_label[keep])
        if masks is not None:
            final_masks.append(masks_by_label[keep])
    final_boxes = np.concatenate(final_boxes)
    final_scores = np.concatenate(final_scores)
    final_labels = np.concatenate(final_labels)
    final_masks = np.concatenate(final_masks)

    return final_boxes, final_scores, final_labels, final_masks

def nms(boxes, scores, labels, masks=None, iou_thr=0.5, weights=None):
    """
    Short call for standard NMS 
    
    :param boxes: 
    :param scores: 
    :param labels: 
    :param iou_thr: 
    :param weights: 
    :return: 
    """
    return nms_method(boxes, scores, labels, masks, method=3, iou_thr=iou_thr, weights=weights)


def soft_nms(boxes, scores, labels, masks=None, method=2, iou_thr=0.5, sigma=0.5, thresh=0.001, weights=None):
    """
    Short call for Soft-NMS
     
    :param boxes: 
    :param scores: 
    :param labels: 
    :param method: 
    :param iou_thr: 
    :param sigma: 
    :param thresh: 
    :param weights: 
    :return: 
    """
    return nms_method(boxes, scores, labels, masks, method=method, iou_thr=iou_thr, sigma=sigma, thresh=thresh, weights=weights)

I know it looks like error prone with this fast work around but do you think this feature deserves a pull request? @ZFTurbo

opened by gunesevitan 1

Releases(v1.0.8)

v1.0.8(Mar 24, 2022)

Benchmark files for NLP problem for testing WBF 1D case.
Source code(tar.gz)
Source code(zip)
benchmark_nlp.zip(16.03 MB)
v1.0.5(Jul 13, 2020)

Benchmark
Source code(tar.gz)
Source code(zip)
benchmark.zip(298.63 MB)
v1.0.4(Jun 25, 2020)
Added many input data checks in all methods: set to 0.0 coords < 0, set to 1.0 coords > 1, swap cases where x2 < x1 and y2 < y1, remove zero area boxes.

Added numba @jit(nopython=True) for critical functions (NMS, NMW and WBF). Speed up around x2 times (tested on example_oid.py).

Added support for 3D boxes with function weighted_boxes_fusion_3d

Source code(tar.gz)
Source code(zip)
v1.0.1(Jun 3, 2020)

Fixed bug with incorrect work of WBF and NMW algorithms if provided boxes was unsorted and skip_box_thr was larger than 0.0.
Source code(tar.gz)
Source code(zip)
v1.0.0(Oct 31, 2019)

Source code(tar.gz)
Source code(zip)
v1.0(Oct 27, 2019)

Source code(tar.gz)
Source code(zip)
test_data.zip(76.94 MB)

Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

Related tags

Overview

Weighted boxes fusion

Requirements

Installation

Usage examples

Single model

3D version

Accuracy and speed comparison

Description of WBF method and citation

Comments

Problem

Why?

A proposal to fix

Performance

Releases(v1.0.8)

v1.0.8(Mar 24, 2022)

v1.0.5(Jul 13, 2020)

v1.0.4(Jun 25, 2020)

v1.0.1(Jun 3, 2020)

v1.0.0(Oct 31, 2019)

v1.0(Oct 27, 2019)

Owner

ML-Ensemble – high performance ensemble learning

zeus is a Python implementation of the Ensemble Slice Sampling method.

A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.

Automatically download the cwru data set, and then divide it into training data set and test data set

Federated Learning - Including common test models for federated learning, like CNN, Resnet18 and lstm, controlled by different parser

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

A collection of pre-trained StyleGAN2 models trained on different datasets at different resolution.

Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

PyTorch implementation of the end-to-end coreference resolution model with different higher-order inference methods.

An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

A PyTorch-based open-source framework that provides methods for improving the weakly annotated data and allows researchers to efficiently develop and compare their own methods.

Intrusion Detection System using ensemble learning (machine learning)

A general and strong 3D object detection codebase that supports more methods, datasets and tools (debugging, recording and analysis).

Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection

Yolo object detection - Yolo object detection with python