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Description
i am using this issue to provide info on the data i use when benchmarking wsinfer in qupath.
image: TCGA-3C-AALI-01Z-00-DX1.F6E9A5DF-D8FB-45CF-B4BD-C6B76294C291.svs (link to image for download on Genomic Data Commons)
geojson representation of the square region. the region has an area of 100 millimeters squared. (the geojson coordinates are in pixels, and it is the equivalent of 100 mm2 in the whole slide image).
i will update this issue with my running times for a 12th gen i5 cpu and an nvidia 2080ti gpu, both in windows 11.
here are running times.... on an i5-12600K, it took 6 minutes 37 seconds. on an NVIDIA RTX 2080Ti, it took 40 seconds. please see other environment details below. this was using the WSI and ROI in this issue.
- os: windows 11
- qupath v0.4.4
- wsinfer extension v0.2.1
- model "breast-tumor-resnet34.tcga-brca"
- ROI of 100 mm^2 (see geojson below)
- openslide loader (i think -- i'm not sure how to change this to bioformats)
{
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"id": "9724d4e3-cd6d-4630-a474-4d71e35db065",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
25471,
25869
],
[
65471,
25869
],
[
65471,
65869
],
[
25471,
65869
],
[
25471,
25869
]
]
]
},
"properties": {
"objectType": "annotation"
}
}
]
}
here is a groovy script i was using to measure the runtime:
import groovy.time.TimeCategory
// Set device (cpu, gpu, or mps)
qupath.ext.wsinfer.ui.WSInferPrefs.deviceProperty().setValue("gpu");
// Set number of parallel workers
qupath.ext.wsinfer.ui.WSInferPrefs.numWorkersProperty().setValue(8);
// ---
selectAnnotations()
def timeStart = new Date()
qupath.ext.wsinfer.WSInfer.runInference("kaczmarj/breast-tumor-resnet34.tcga-brca")
def timeStop = new Date()
duration = TimeCategory.minus(timeStop, timeStart)
println duration