The following tutorial is available on the Wallaroo Github Repository.

Wallaroo Inference Server: Resnet

This notebook is used in conjunction with the Wallaroo Inference Server Free Edition for the Computer Vision resnet 50 machine learning model. This provides a free license for performing inferences through the Computer Vision resnet 50 machine learning model. For full demonstrations of this model, see Wallaroo Edge Computer Vision Deployment Demonstration.

Prerequisites

• A deployed Wallaroo Inference Server Free Edition with one of the following options:
  • Wallaroo.AI ResNet50 Inference Server - x64
  • Wallaroo.AI ResNet50 Inference Server - Arm64
• Access via port 8080 to the Wallaroo Inference Server Free Edition.

Computer Vision Resnet Model Schemas

Inputs

The Resnet Model takes the following inputs.

Outputs

Wallaroo Inference Server API Endpoints

The following HTTPS API endpoints are available for Wallaroo Inference Server.

Pipelines Endpoint

• Endpoint: HTTPS GET /pipelines
• Returns:
  • List of pipelines with the following fields.
    • id (String): The name of the pipeline.
    • status (String): The pipeline status. Running indicates the pipeline is available for inferences.

Pipeline Endpoint Example

The following demonstrates using curl to retrieve the Pipelines endpoint. Replace the HOSTNAME with the address of your Wallaroo Inference Server.

!curl HOSTNAME:8080/pipelines

{"pipelines":[{"id":"resnet50","status":"Running"}]}

Models Endpoint

• Endpoint: GET /models
• Returns:
  • List of models with the following fields.
    • name (String): The name of the model.
    • sha (String): The sha hash of the model.
    • status (String): The model status. Running indicates the models is available for inferences.
    • version (String): The model version in UUID format.

Models Endpoint Example

The following demonstrates using curl to retrieve the Models endpoint. Replace the HOSTNAME with the address of your Wallaroo Inference Server.

!curl HOSTNAME:8080/models

{"models":[{"name":"resnet50","sha":"af16a04a6ec48ac494065d4439fe9dea590d337b9ca6dc328160ccf04a217b9c","status":"Running","version":"8131edb2-1ca7-4ef5-bb47-fbc770d60972"}]}

Inference Endpoint

• Endpoint: HTTPS POST /pipelines/resnet50

• Headers:

  • Content-Type: application/vnd.apache.arrow.file: For Apache Arrow tables.
  • Content-Type: application/json; format=pandas-records: For pandas DataFrame in record format.

• Input Parameters: DataFrame in /pipelines/hf-summarizer-standard OR Apache Arrow table in application/vnd.apache.arrow.file with the following inputs:

  • tensor (Float Required): The tensor shape is a variable array in the shape (3, {picture width}, {picture height}) float of the the normalized pixel values of the 640x480 color image.

    The following code sample is used to convert an image into a 640x480 DataFrame with the appropriate shape for the model:

    import cv2
    import torch
    import numpy as np
    
    def imageResize(image, width, height):
    
        im_pillow = np.array(image)
        image = cv2.cvtColor(im_pillow, cv2.COLOR_RGB2BGR)
    
        self.debug("Resizing to w:"+str(width) + " height:"+str(height))
        image = cv2.resize(image, (width, height)) 
        resizedImage = image.copy()
    
        # convert the image from BGR to RGB channel ordering and change the
        # image from channels last to channels first ordering
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = image.transpose((2, 0, 1))
    
        # add the batch dimension, scale the raw pixel intensities to the
        # range [0, 1], and convert the image to a floating point tensor
        image = np.expand_dims(image, axis=0)
        image = image / 255.0
        tensor = torch.FloatTensor(image)
        return tensor, resizedImage
    

• Returns:

  • Headers
    • Content-Type: application/json; format=pandas-records: pandas DataFrame in record format.
  • Data
    • check_failures (List[Integer]): Whether any validation checks were triggered. For more information, see Wallaroo SDK Essentials Guide: Pipeline Management: Anomaly Testing.
    • elapsed (List[Integer]): A list of time in nanoseconds for:
      • [0] The time to serialize the input.
      • [1…n] How long each step took.
    • model_name (String): The name of the model used.
    • model_version (String): The version of the model in UUID format.
    • original_data: The original input data. Returns null if the input may be too long for a proper return.
    • outputs (List): The outputs of the inference result separated by data type. The number of arrays for each field is determined by the number of detected objects.
      • Float: The bounding boxes for each detected object.
        • data (List[Float]): The bounding boxes data in the shape returned in the dim field.
        • dim (List[Integer]): The dimension shape returned in the format [number of objects, 4].
        • v (Integer): The vector shape of the data.
      • Int64: The class of each detected object.
        • data (List[Integer]): The class results in the shape of the dim field.
        • dim (List[Integer]): The dimension shape returned in the format [number of objects].
        • v (Integer): The vector shape of the data.
      • Float: The confidences of each detected object.
        • data (List[Float]): The confidence values in the shape of the dim field.
        • dim (List[Integer]): The dimension shape returned in the format [number of objects].
        • v (Integer): The vector shape of the data.
    • pipeline_name (String): The name of the pipeline.
    • shadow_data: Any shadow deployed data inferences in the same format as outputs.
    • time (Integer): The time since UNIX epoch.

Inference Endpoint Example

The following example performs an inference using the pandas DataFrame ./data/image_224x224.df.json from an image converted into a tensor for inferencing.

Updated the pipeline name based on the pipeline endpoint above.

!curl -X POST HOSTNAME:8080/pipelines/resnet50 \
    -H "Content-Type:application/json; format=pandas-records" \
    --data @./data/image_224x224.df.json

[{"check_failures":[],"elapsed":[27529757,325302317],"model_name":"resnet50","model_version":"8131edb2-1ca7-4ef5-bb47-fbc770d60972","original_data":null,"outputs":[{"Float":{"data":[-0.9209704995155334,-0.10495197772979736,-0.6216869950294495,-0.7639160752296448,-0.2154858112335205,-0.505703330039978,-0.7874149680137634,-0.5596786141395569,-1.1615943908691406,0.568906307220459,0.25151896476745605,-0.4426764249801636,-0.2803022563457489,-0.18332044780254364,-0.5667626857757568,-0.2607719302177429,0.8765640258789062,-0.33152544498443604,-0.09792342782020569,-0.31929707527160645,-0.03542117774486542,2.243022918701172,1.2097502946853638,0.8100910782814026,-0.7545893788337708,-0.5059118866920471,-0.0510006882250309,-0.08611194789409637,0.04777945578098297,-0.9071007370948792,-1.2397105693817139,-0.6117401123046875,-1.2649085521697998,-0.49369877576828003,0.20755505561828613,-1.5142827033996582,-0.7229145765304565,-1.4517813920974731,0.5931597352027893,-0.705878734588623,-0.49715355038642883,-0.5067124366760254,0.6836690902709961,-0.2999878525733948,-0.38008007407188416,-0.12145837396383286,-0.3811112642288208,-0.3016861081123352,-1.0309451818466187,-1.221230387687683,-0.7402653694152832,0.31255042552948,-0.2385125458240509,-0.3597520589828491,-0.611875057220459,-1.4171234369277954,-0.6953393816947937,-0.8977612257003784,-0.4419158101081848,0.1640137881040573,-0.7315932512283325,-0.7470656633377075,-0.7364163994789124,0.026501402258872986,0.1274559199810028,-0.22767779231071472,-0.08014523983001709,-0.7872171401977539,-0.24927811324596405,-0.3231212794780731,-0.7516193985939026,0.6998400092124939,-0.961872398853302,0.21504326164722443,-1.143547534942627,0.06683613359928131,-0.9779999256134033,0.10952679812908173,1.164833664894104,0.9379342198371887,0.9289938807487488,1.4498789310455322,0.08302754908800125,0.2518922686576843,-0.7904947996139526,0.06390288472175598,0.03906869888305664,-0.05500852316617966,-0.4420778453350067,0.5122808218002319,-0.5220428109169006,0.43826937675476074,1.7422548532485962,-0.5095168352127075,-0.030511990189552307,-0.4733806848526001,-0.40053123235702515,-1.2809487581253052,0.5619056224822998,0.43158140778541565,-0.3192000091075897,-0.5592494010925293,-0.9030879139900208,-0.46101677417755127,-1.1964744329452515,-1.8386478424072266,-1.268721103668213,-0.4987451434135437,-0.9467898607254028,-0.7961544990539551,-1.4408328533172607,1.5324277877807617,0.45404139161109924,0.11730155348777771,-0.41721677780151367,-1.6324219703674316,-1.3549885749816895,0.5803042650222778,-1.0710985660552979,-0.60124671459198,-0.6479066610336304,-1.2841085195541382,-0.7957830429077148,-1.401261806488037,-0.8226445913314819,-0.39799076318740845,-0.33132264018058777,1.0660581588745117,1.3188652992248535,0.5740012526512146,-0.5056800246238708,-0.35661759972572327,0.13007494807243347,-0.47445982694625854,0.19196167588233948,0.21153435111045837,-0.9289034008979797,-0.37291449308395386,0.2660676836967468,0.41866201162338257,-0.19892796874046326,0.7431018352508545,0.18201318383216858,0.4720017910003662,0.5242726802825928,0.21621166169643402,1.60371732711792,0.8835126161575317,-0.12380070984363556,-0.026944845914840698,0.009989067912101746,-0.4380689859390259,0.11144248396158218,0.1770261973142624,-0.4518960118293762,-0.780663013458252,-0.5365955233573914,-0.2538329064846039,-0.8964980840682983,-0.7351016402244568,-0.562610924243927,-0.3931219279766083,-0.8018279075622559,-0.38880228996276855,-0.26731815934181213,-0.8326029777526855,-0.8142890334129333,-0.8159071207046509,-0.553368091583252,-0.88005131483078,-1.0833899974822998,-0.6795664429664612,0.05457449331879616,-0.20487120747566223,-0.5509709119796753,-0.6473774313926697,-0.8227453827857971,-0.48676371574401855,-0.17701402306556702,-0.6249679327011108,-1.0367640256881714,-0.18442967534065247,-0.7315160036087036,-0.26268064975738525,-0.653117299079895,-0.15912845730781555,-1.0511419773101807,-0.11766250431537628,-0.5304980278015137,-0.6946197748184204,0.9431405663490295,-0.16277898848056793,-1.259573221206665,-0.2701641023159027,-0.22112299501895905,0.022292666137218475,-1.0969599485397339,-0.940108597278595,-1.1559655666351318,-0.47618287801742554,-0.3831486999988556,-1.4605140686035156,-0.1628812998533249,-0.27183040976524353,-0.9461800456047058,-1.0246052742004395,-0.8583532571792603,-0.4901716709136963,-0.5817583203315735,-0.37399306893348694,-0.6668270826339722,-0.8859307169914246,-0.7769283056259155,-0.6553910970687866,-0.46439623832702637,-1.036147117614746,-0.4044029414653778,-0.794582188129425,-1.0624295473098755,-1.026174783706665,-0.21227572858333588,-0.5376724004745483,-0.11686201393604279,-0.08071712404489517,-0.6508125066757202,-1.112339735031128,-1.0137107372283936,-0.8185564279556274,-0.18649427592754364,-0.41967037320137024,-0.7899032831192017,-0.4784793257713318,-0.30983883142471313,-0.8027055263519287,-0.4974499046802521,-0.2866147458553314,-0.39197778701782227,-0.6406401991844177,-0.29926228523254395,-0.2155657261610031,-0.42895907163619995,-0.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