The ivtmetrics library provides a Python implementation of metrics for benchmarking surgical action triplet detection and recognition.
The following are available with ivtmetrics:
- Recognition Evaluation: Provides AP metrics to measure the performance of a model on action triplet recognition.
- Detection Evaluation: Supports Intersection over Union distances measure of the triplet localization with respect to the instruments.
- Flexible Analysis: (1) Supports for switching between frame-wise to video-wise averaging of the AP. (2) Supports disentangle prediction and obtained filtered performance for the various components of the triplets as well as their association performances at various levels.
To install ivtmetrics use pip
pip install ivtmetrics
conda install -c nwoye ivtmetrics
Python 3.5-3.9 and numpy and scikit-learn are required.
The metrics have been aligned with what is reported by CholecT50 benchmark. ivtmetrics can be imported in the following way:
import ivtmetrics
The metrics implement both recognition and detection evaluation. The metrics internally implement a disentangle function to help filter the triplet components as well as triplet different levels of association.
Recognition ivtmetrics can be used in the following ways:
metric = ivtmetrics.Recognition(num_class)
This takes an argument num_class
which is default to 100
The following function are possible with the Recognition
class:
Name | Description |
---|---|
update(targets, predictions ) |
takes in a (batch of) vector predictions and their corresponding groundtruth. vector size must match num_class in the class initialization. |
video_end() | Call to make the end of one video sequence. |
reset() | Reset current records. Useful during training and can be called at the begining of each epoch to avoid overlapping epoch performances. |
reset_global() | Reset all records. Useful for switching between training/validation/testing or can be called at the begining of new experiment. |
compute_AP(component, ignore_null ) |
Obtain the average precision on the fly. This gives the AP only on examples cases after the last reset() call. Useful for epoch performance during training. |
compute_video_AP(component, ignore_null ) |
(RECOMMENDED) compute video-wise AP performance as used in CholecT50 benchmarks. |
compute_global_AP(component, ignore_null ) |
compute frame-wise AP performance for all seen samples. |
topK(k, component ) |
Obtain top K performance on action triplet recognition for all seen examples. args k can be any int between 1-99. k = [5,10,15,20] have been used in benchmark papers. |
topClass(k, component ) |
Obtain top K recognized classes on action triplet recognition for all seen examples. args k can be any int between 1-99. k = 10 have been used in benchmark papers. |
- args
component
can be any of the following ('i', 'v', 't', 'iv', 'it','ivt') to compute performance for (instrument, verb, target, instrument-verb, instrument-target, instrument-verb-target) respectively. default is 'ivt' for triplets. - args
ignore_null
(optional, default=False): to ignore null triplet classes in the evaluation. This option is enabled in CholecTriplet2021 challenge. - the output is a
dict
with keys("AP", "mAP") for per-class and mean AP respectively.
import ivtmetrics
recognize = ivtmetrics.Recognition(num_class=100)
network = MyModel(...) # your model here
# training
for epoch in number-of-epochs:
recognize.reset()
for images, labels in dataloader(...): # your data loader
predictions = network(image)
recognize.update(labels, predictions)
results_i = recognize.compute_AP('i')
print("instrument per class AP", results_i["AP"])
print("instrument mean AP", results_i["mAP"])
results_ivt = recognize.compute_AP('ivt')
print("triplet mean AP", results_ivt["mAP"])
# evaluation
recognize.reset_global()
for video in videos:
for images, labels in dataloader(video, ..): # your data loader
predictions = network(image)
recognize.update(labels, predictions)
recognize.video_end()
results_i = recognize.compute_video_AP('i')
print("instrument per class AP", results_i["AP"])
print("instrument mean AP", results_i["mAP"])
results_it = recognize.compute_video_AP('it')
print("instrument-target mean AP", results_it["mAP"])
results_ivt = recognize.compute_video_AP('ivt')
print("triplet mean AP", results_ivt["mAP"])
Any nan
value in results is for classes with no occurrence in the data sample.
Detection ivtmetrics can be used in the following ways:
metric = ivtmetrics.Detection(num_class, num_tool)
This takes an argument num_class
which is default to 100
and num_tool
which is default to 6
The following function are possible with the Detection
class:
Name | Description |
---|---|
update(targets, predictions, format ) |
input: takes in a (batch of) list/dict predictions and their corresponding groundtruth. Each frame prediction/groundtruth can be either as a list of list or list of dict . (more details below). |
video_end() | Call to make the end of one video sequence. |
reset() | Reset current records. Useful during training and can be called at the begining of each epoch to avoid overlapping epoch performances. |
reset_global() | Reset all records. Useful for switching between training/validation/testing or can be called at the begining of new experiment. |
compute_AP(component ) |
Obtain the average precision on the fly. This gives the AP only on examples cases after the last reset() call. Useful for epoch performance during training. |
compute_video_AP(component ) |
(RECOMMENDED) compute video-wise AP performance as used in CholecT50 benchmarks. |
compute_global_AP(component ) |
compute frame-wise AP performance for all seen samples. |
-
list of list format: [[tripletID, toolID, toolProbs, x, y, w, h], [tripletID, toolID, toolProbs, x, y, w, h], ...], where:
tripletID
= triplet unique identitytoolID
= instrument unique identitytoolProbs
= instrument detection confidencex
= bounding box x1 coordiantey
= bounding box y1 coordinatew
= width of the boxh
= height of the box- The [x,y,w,h] are scaled between 0..1
-
list of dict format: [{"triplet":tripletID, "instrument":[toolID, toolProbs, x, y, w, h]}, {"triplet":tripletID, "instrument":[toolID, toolProbs, x, y, w, h]}, ...].
-
format
args describes the input format with either of the values ("list", "dict") -
component
can be any of the following ('i', 'v', 't', 'iv', 'it','ivt') to compute performance for (instrument, verb, target, instrument-verb, instrument-target, instrument-verb-target) respectively, default is 'ivt' for triplets.<
- the output is a
dict
with keys("AP", "mAP", "Rec", "mRec", "Pre", "mPre") for per-class AP, mean AP, per-class Recall, mean Recall, per-class Precision and mean Precision respectively.
import ivtmetrics
detect = ivtmetrics.Detection(num_class=100)
network = MyModel(...) # your model here
# training
format = "list"
for epoch in number of epochs:
for images, labels in dataloader(...): # your data loader
predictions = network(image)
labels, predictions = formatYourLabels(labels, predictions)
detect.update(labels, predictions, format=format)
results_i = detect.compute_AP('i')
print("instrument per class AP", results_i["AP"])
print("instrument mean AP", results_i["mAP"])
results_ivt = detect.compute_AP('ivt')
print("triplet mean AP", results_ivt["mAP"])
detect.reset()
# evaluation
format = "dict"
for video in videos:
for images, labels in dataloader(video, ..): # your data loader
predictions = network(image)
labels, predictions = formatYourLabels(labels, predictions)
detect.update(labels, predictions, format=format)
detect.video_end()
results_ivt = detect.compute_video_AP('ivt')
print("triplet mean AP", results_ivt["mAP"])
print("triplet mean recall", results_ivt["mRec"])
print("triplet mean precision", results_ivt["mPre"])
Any nan
value in results is for classes with no occurrence in the data sample.
Although, the Detection()
and Recognition()
classes uses the Disentangle()
internally, this function can still be used independently for component filtering in the following ways:
filter = ivtmetrics.Disentangle()
Afterwards, each of the component's predictions/labels can be filtered from the main triplet's predictions/labels as follows:
i_labels = filter.extract(inputs=ivt_labels, component="i")
v_preds = filter.extract(inputs=ivt_preds, component="v")
t_preds = filter.extract(inputs=ivt_preds, component="t")
iv_labels = filter.extract(inputs=ivt_labels, component="iv")
it_labels = filter.extract(inputs=ivt_labels, component="it")
coming soon ..
If you use this metrics in your project or research, please consider citing the associated publication:
@article{nwoye2022data,
title={Data Splits and Metrics for Benchmarking Methods on Surgical Action Triplet Datasets},
author={Nwoye, Chinedu Innocent and Padoy, Nicolas},
journal={arXiv preprint arXiv:2204.05235},
year={2022}
}
- Nwoye, C. I., Yu, T., Gonzalez, C., Seeliger, B., Mascagni, P., Mutter, D., ... & Padoy, N. (2021). Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos. arXiv preprint arXiv:2109.03223.
- Nwoye, C. I., Gonzalez, C., Yu, T., Mascagni, P., Mutter, D., Marescaux, J., & Padoy, N. (2020, October). Recognition of instrument-tissue interactions in endoscopic videos via action triplets. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 364-374). Springer, Cham.
- http://camma.u-strasbg.fr/datasets
- https://cholectriplet2022.grand-challenge.org
- https://cholectriplet2021.grand-challenge.org
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