Explore CVAT serverless implementation, and design our knative implementation #735
Comments
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CVAT kicks off the serverless piece with an HTTP request (trigger)
CVAT deploys the functions for Nuclio with
Nuclio, for k8s, converts the code into an image, CRD in k8s, and relevant resources in k8s (ingress, deployment, services)
CVAT has
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@rushtehrani This is the response for the "models" page in CVAT. Some comments on the fields:
The request is simple [
{
"id": "openvino.dextr",
"kind": "interactor",
"labels": [],
"state": "ready",
"description": "Deep Extreme Cut",
"framework": "openvino",
"name": "DEXTR"
},
{
"id": "openvino.omz.intel.person-reidentification-retail-0300",
"kind": "reid",
"labels": [],
"state": "ready",
"description": "Person reidentification model for a general scenario",
"framework": "openvino",
"name": "Person reidentification"
},
{
"id": "openvino.omz.intel.text-detection-0004",
"kind": "detector",
"labels": [
"text"
],
"state": "ready",
"description": "Text detector based on PixelLink architecture with MobileNetV2-like as a backbone for indoor/outdoor scenes.",
"framework": "openvino",
"name": "Text detection v4"
},
{
"id": "openvino.omz.public.faster_rcnn_inception_v2_coco",
"kind": "detector",
"labels": [
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic_light",
"fire_hydrant",
"stop_sign",
"parking_meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports_ball",
"kite",
"baseball_bat",
"baseball_glove",
"skateboard",
"surfboard",
"tennis_racket",
"bottle",
"wine_glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot_dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted_plant",
"bed",
"dining_table",
"toilet",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell_phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy_bear",
"hair_drier",
"toothbrush"
],
"state": "ready",
"description": "Faster RCNN inception v2 COCO via Intel OpenVINO toolkit",
"framework": "openvino",
"name": "Faster RCNN"
},
{
"id": "openvino.omz.public.mask_rcnn_inception_resnet_v2_atrous_coco",
"kind": "detector",
"labels": [
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic_light",
"fire_hydrant",
"stop_sign",
"parking_meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports_ball",
"kite",
"baseball_bat",
"baseball_glove",
"skateboard",
"surfboard",
"tennis_racket",
"bottle",
"wine_glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot_dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted_plant",
"bed",
"dining_table",
"toilet",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell_phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy_bear",
"hair_drier",
"toothbrush"
],
"state": "ready",
"description": "Mask RCNN inception resnet v2 COCO via Intel OpenVINO",
"framework": "openvino",
"name": "Mask RCNN"
},
{
"id": "openvino.omz.public.yolo-v3-tf",
"kind": "detector",
"labels": [
"person",
"bicycle",
"car",
"motorbike",
"aeroplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports ball",
"kite",
"baseball bat",
"baseball glove",
"skateboard",
"surfboard",
"tennis racket",
"bottle",
"wine glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot dog",
"pizza",
"donut",
"cake",
"chair",
"sofa",
"pottedplant",
"bed",
"diningtable",
"toilet",
"tvmonitor",
"laptop",
"mouse",
"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush"
],
"state": "ready",
"description": "YOLO v3 via Intel OpenVINO",
"framework": "openvino",
"name": "YOLO v3"
},
{
"id": "openvino.omz.semantic-segmentation-adas-0001",
"kind": "detector",
"labels": [
"road",
"sidewalk",
"building",
"wall",
"fence",
"pole",
"traffic light",
"traffic sign",
"vegetation",
"terrain",
"sky",
"person",
"rider",
"car",
"truck",
"bus",
"train",
"motorcycle",
"bicycle",
"ego-vehicle",
"background"
],
"state": "ready",
"description": "Segmentation network to classify each pixel into typical 20 classes for ADAS",
"framework": "openvino",
"name": "Semantic segmentation for ADAS"
},
{
"id": "pth.foolwood.siammask",
"kind": "tracker",
"labels": [],
"state": "ready",
"description": "Fast Online Object Tracking and Segmentation",
"framework": "pytorch",
"name": "SiamMask"
},
{
"id": "tf.faster_rcnn_inception_v2_coco",
"kind": "detector",
"labels": [
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic_light",
"fire_hydrant",
"stop_sign",
"parking_meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports_ball",
"kite",
"baseball_bat",
"baseball_glove",
"skateboard",
"surfboard",
"tennis_racket",
"bottle",
"wine_glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot_dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted_plant",
"bed",
"dining_table",
"toilet",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell_phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy_bear",
"hair_drier",
"toothbrush"
],
"state": "ready",
"description": "Faster RCNN from Tensorflow Object Detection API",
"framework": "tensorflow",
"name": "Faster RCNN via Tensorflow"
},
{
"id": "tf.matterport.mask_rcnn",
"kind": "detector",
"labels": [
"BG",
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic_light",
"fire_hydrant",
"stop_sign",
"parking_meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports_ball",
"kite",
"baseball_bat",
"baseball_glove",
"skateboard",
"surfboard",
"tennis_racket",
"bottle",
"wine_glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot_dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted_plant",
"bed",
"dining_table",
"toilet",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell_phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy_bear",
"hair_drier",
"toothbrush"
],
"state": "ready",
"description": "An implementation of Mask RCNN on Python 3, Keras, and TensorFlow.\n",
"framework": "tensorflow",
"name": "Mask RCNN via Tensorflow"
}
] |
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@aleksandrmelnikov how are things like labels, kind, framework, etc stored? Are they actually coming from the wrapper code for each model? |
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They are actually stored via They store the information via metadata in the spec, and they load it inside Example yaml: metadata:
name: tf.matterport.mask_rcnn
namespace: cvat
annotations:
name: Mask RCNN via Tensorflow
type: detector
framework: tensorflow
spec: |
[
{ "id": 0, "name": "BG" },
{ "id": 1, "name": "person" },
{ "id": 2, "name": "bicycle" },
{ "id": 3, "name": "car" },
{ "id": 4, "name": "motorcycle" },
{ "id": 5, "name": "airplane" },
{ "id": 6, "name": "bus" },
{ "id": 7, "name": "train" },
{ "id": 8, "name": "truck" },
{ "id": 9, "name": "boat" },
{ "id": 10, "name": "traffic_light" },
{ "id": 11, "name": "fire_hydrant" },
{ "id": 12, "name": "stop_sign" },
{ "id": 13, "name": "parking_meter" },
{ "id": 14, "name": "bench" },
{ "id": 15, "name": "bird" },
{ "id": 16, "name": "cat" },
{ "id": 17, "name": "dog" },
{ "id": 18, "name": "horse" },
{ "id": 19, "name": "sheep" },
{ "id": 20, "name": "cow" },
{ "id": 21, "name": "elephant" },
{ "id": 22, "name": "bear" },
{ "id": 23, "name": "zebra" },
{ "id": 24, "name": "giraffe" },
{ "id": 25, "name": "backpack" },
{ "id": 26, "name": "umbrella" },
{ "id": 27, "name": "handbag" },
{ "id": 28, "name": "tie" },
{ "id": 29, "name": "suitcase" },
{ "id": 30, "name": "frisbee" },
{ "id": 31, "name": "skis" },
{ "id": 32, "name": "snowboard" },
{ "id": 33, "name": "sports_ball" },
{ "id": 34, "name": "kite" },
{ "id": 35, "name": "baseball_bat" },
{ "id": 36, "name": "baseball_glove" },
{ "id": 37, "name": "skateboard" },
{ "id": 38, "name": "surfboard" },
{ "id": 39, "name": "tennis_racket" },
{ "id": 40, "name": "bottle" },
{ "id": 41, "name": "wine_glass" },
{ "id": 42, "name": "cup" },
{ "id": 43, "name": "fork" },
{ "id": 44, "name": "knife" },
{ "id": 45, "name": "spoon" },
{ "id": 46, "name": "bowl" },
{ "id": 47, "name": "banana" },
{ "id": 48, "name": "apple" },
{ "id": 49, "name": "sandwich" },
{ "id": 50, "name": "orange" },
{ "id": 51, "name": "broccoli" },
{ "id": 52, "name": "carrot" },
{ "id": 53, "name": "hot_dog" },
{ "id": 54, "name": "pizza" },
{ "id": 55, "name": "donut" },
{ "id": 56, "name": "cake" },
{ "id": 57, "name": "chair" },
{ "id": 58, "name": "couch" },
{ "id": 59, "name": "potted_plant" },
{ "id": 60, "name": "bed" },
{ "id": 61, "name": "dining_table" },
{ "id": 62, "name": "toilet" },
{ "id": 63, "name": "tv" },
{ "id": 64, "name": "laptop" },
{ "id": 65, "name": "mouse" },
{ "id": 66, "name": "remote" },
{ "id": 67, "name": "keyboard" },
{ "id": 68, "name": "cell_phone" },
{ "id": 69, "name": "microwave" },
{ "id": 70, "name": "oven" },
{ "id": 71, "name": "toaster" },
{ "id": 72, "name": "sink" },
{ "id": 73, "name": "refrigerator" },
{ "id": 74, "name": "book" },
{ "id": 75, "name": "clock" },
{ "id": 76, "name": "vase" },
{ "id": 77, "name": "scissors" },
{ "id": 78, "name": "teddy_bear" },
{ "id": 79, "name": "hair_drier" },
{ "id": 80, "name": "toothbrush" }
]
spec:
description: |
An implementation of Mask RCNN on Python 3, Keras, and TensorFlow.
runtime: "python:3.6"
handler: main:handler
eventTimeout: 30s
env:
- name: MASK_RCNN_DIR
value: /opt/nuclio/Mask_RCNN
build:
image: cvat/tf.matterport.mask_rcnn
baseImage: tensorflow/tensorflow:2.1.0-py3
directives:
postCopy:
- kind: WORKDIR
value: /opt/nuclio
- kind: RUN
value: apt update && apt install --no-install-recommends -y git curl libsm6 libxext6 libxrender-dev
- kind: RUN
value: git clone https://github.com/matterport/Mask_RCNN.git
- kind: RUN
value: curl -L https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 -o Mask_RCNN/mask_rcnn_coco.h5
- kind: RUN
value: pip3 install -r Mask_RCNN/requirements.txt
- kind: RUN
value: pip3 install pycocotools tensorflow==1.13.1 keras==2.1.0 pillow pyyaml
triggers:
myHttpTrigger:
maxWorkers: 2
kind: "http"
workerAvailabilityTimeoutMilliseconds: 10000
attributes:
maxRequestBodySize: 33554432 # 32MB
platform:
attributes:
restartPolicy:
name: always
maximumRetryCount: 3 |
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Example structure: |
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These were investigated from create new task -> actions -> automatic annotation Kick off serverless function. /* Request */
{
"threshold": 0.5,
"max_distance": 50,
"task": 1,
"function": "openvino.omz.intel.person-reidentification-retail-0300"
}
/* Response */
{
"id": "6dd018f2-64fa-4c8f-948b-121c92be4bd1",
"function": {
"id": "openvino.omz.intel.person-reidentification-retail-0300",
"threshold": 0.5,
"task": 1
},
"status": "queued",
"progress": 0,
"enqueued": "2020-11-18T19:05:49.161191",
"started": null,
"ended": null,
"exc_info": null
}Status and progress check on the serverless execution. {
"id": "6dd018f2-64fa-4c8f-948b-121c92be4bd1",
"function": {
"id": "openvino.omz.intel.person-reidentification-retail-0300",
"threshold": 0.5,
"task": 1
},
"status": "finished", //queued, started, finished
"progress": 100, //starts with 0, goes to 100
"enqueued": "2020-11-18T19:05:49.161191",
"started": "2020-11-18T19:05:49.174984",
"ended": "2020-11-18T19:05:49.313751",
"exc_info": null
}Cancelling a serverless execution Returns 204 No Content |
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Recording request and responses for annotation kicked off from annotation page (of a given task). Request Body {
"mapping": {
"person": "person",
"car": "car"
},
"cleanup": false, //deletes task data from database
"frame": 0,
"task": 1
}Body {
"mapping": {
"text": "person"
},
"cleanup": false,
"frame": 0,
"task": 1
}Response [
{
"confidence": null,
"label": "person",
"points": [
496,
132,
531,
132,
531,
142,
496,
142
],
"type": "polygon"
},
{
"confidence": null,
"label": "person",
"points": [
528,
210,
555,
210,
555,
224,
528,
224
],
"type": "polygon"
},
//...continues...
]Request Body {
"mapping": {
"person": "person",
"car": "car"
},
"cleanup": false,
"frame": 0,
"task": 1
}Response [
{
"confidence": "0.99303883",
"label": "person",
"points": [
1176.3516235351562,
374.1847229003906,
1280.0,
699.4732475280762
],
"type": "rectangle"
},
{
"confidence": "0.98096424",
"label": "person",
"points": [
83.2307243347168,
359.85846519470215,
198.68478775024414,
662.8982162475586
],
"type": "rectangle"
}
//...
]Request {
"mapping": {
"person": "person",
"car": "car"
},
"cleanup": false,
"frame": 0,
"task": 1
}Response [
{
"confidence": "0.99985194",
"label": "person",
"points": [
61,
344,
217,
670
],
"type": "rectangle"
},
{
"confidence": "0.9997302",
"label": "person",
"points": [
849,
354,
1061,
720
],
"type": "rectangle"
}
//...
]Request POST http://localhost:8080/api/v1/lambda/functions/openvino-omz-semantic-segmentation-adas-0001
{
"mapping": {
"person": "person",
"car": "car"
},
"cleanup": false,
"frame": 0,
"task": 1
}Response [
{
"confidence": null,
"label": "person",
"points": [
0.0,
323.37236285124135,
6.0,
324.0474398505345,
15.0,
312.00384387108494,
20.0,
311.0982009090489,
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322.080764686252,
40.0,
314.0945640011426,
50.0,
315.0491316160951,
60.0,
328.34740633562683,
73.04631569391837,
321.0,
80.0,
311.1112883342544,
90.0,
320.3486430694041,
94.0,
307.0201630502398,
105.0,
310.0227706337829,
110.0,
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120.0,
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132.494829009255,
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150.0,
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156.98935181762772,
315.0,
159.0,
331.0842018812,
172.0,
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Explore CVAT implementation cvat-ai/cvat#1767 and https://github.com/openvinotoolkit/cvat/tree/e7585b8ce900cec2c9dd6ef2dc1092e6567780b1/cvat/apps/lambda_manager so we can design our Knative implementation in a way that it is easily pluggable into CVAT.
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