pytorch_lite

flutter package to help run pytorch lite models classification and YoloV5 and YoloV8.

example for Classification

example for Object detection

Usage

preparing the model

classification

import torch
from torch.utils.mobile_optimizer import optimize_for_mobile


model = torch.load('model_scripted.pt',map_location="cpu")
model.eval()
example = torch.rand(1, 3, 224, 224)
traced_script_module = torch.jit.trace(model, example)
optimized_traced_model = optimize_for_mobile(traced_script_module)
optimized_traced_model._save_for_lite_interpreter("model.pt")

object detection (yolov5)

!python export.py --weights "the weights of your model" --include torchscript --img 640 --optimize

example

!python export.py --weights yolov5s.pt --include torchscript --img 640 --optimize

object detection (yolov8)

!yolo mode=export model="your model" format=torchscript optimize

example

!yolo mode=export model=yolov8s.pt format=torchscript optimize

Installation

To use this plugin, add pytorch_lite as a dependency in your pubspec.yaml file.

Create a assets folder with your pytorch model and labels if needed. Modify pubspec.yaml accordingly.

assets:
  - assets/models/model_classification.pt
  - assets/labels_classification.txt
  - assets/models/model_objectDetection.torchscript
  - assets/labels_objectDetection.txt

Run flutter pub get

For release

Go to android/app/build.gradle
Add those next lines in the release config

shrinkResources false
minifyEnabled false

example

    buildTypes {
        release {
            shrinkResources false
            minifyEnabled false
            // TODO: Add your own signing config for the release build.
            // Signing with the debug keys for now, so `flutter run --release` works.
            signingConfig signingConfigs.debug
        }
    }

Import the library

import 'package:pytorch_lite/pytorch_lite.dart';

Load model

Either classification model:

ClassificationModel classificationModel= await PytorchLite.loadClassificationModel(
          "assets/models/model_classification.pt", 224, 224,
          labelPath: "assets/labels/label_classification_imageNet.txt");

Or objectDetection model:

ModelObjectDetection objectModel = await PytorchLite.loadObjectDetectionModel(
          "assets/models/yolov5s.torchscript", 80, 640, 640,
          labelPath: "assets/labels/labels_objectDetection_Coco.txt",
          objectDetectionModelType: ObjectDetectionModelType.yolov5);

Get classification prediction as label

String imagePrediction = await classificationModel.getImagePrediction(await File(image.path).readAsBytes());

Get classification prediction as label from camera image

String imagePrediction = await _objectModel.getCameraImagePrediction(
        cameraImage,
        rotation, // check example for rotation values
        );

Get classification prediction as raw output layer

List<double>? predictionList = await _imageModel!.getImagePredictionList(
      await File(image.path).readAsBytes(),
    );

Get classification prediction as raw output layer from camera image

List<double>? predictionList = await _imageModel!.getCameraImagePredictionList(
        cameraImage,
        rotation, // check example for rotation values
    );

Get classification prediction as Probabilities (incase model is not using softmax)

List<double>? predictionListProbabilities = await _imageModel!.getImagePredictionListProbabilities(
      await File(image.path).readAsBytes(),
    );

Get classification prediction as Probabilities (incase model is not using softmax)

List<double>? predictionListProbabilities = await _imageModel!.getCameraPredictionListProbabilities(
        cameraImage,
        rotation, // check example for rotation values
    );

Get object detection prediction for an image

 List<ResultObjectDetection> objDetect = await _objectModel.getImagePrediction(await File(image.path).readAsBytes(),
        minimumScore: 0.1, IOUThershold: 0.3);

Get object detection prediction from camera image

 List<ResultObjectDetection> objDetect = await _objectModel.getCameraImagePrediction(
        cameraImage,
        rotation, // check example for rotation values
        minimumScore: 0.1, IOUThershold: 0.3);

Get render boxes with image

objectModel.renderBoxesOnImage(_image!, objDetect)

Image prediction for an image with custom mean and std

final mean = [0.5, 0.5, 0.5];
final std = [0.5, 0.5, 0.5];
String prediction = await classificationModel
        .getImagePrediction(image, mean: mean, std: std);

quangthaizota / pytorchlite Goto Github PK

pytorchlite's Introduction

pytorch_lite

example for Classification

example for Object detection

Usage

preparing the model

Installation

For release

Import the library

Load model

Get classification prediction as label

Get classification prediction as label from camera image

Get classification prediction as raw output layer

Get classification prediction as raw output layer from camera image

Get classification prediction as Probabilities (incase model is not using softmax)

Get classification prediction as Probabilities (incase model is not using softmax)

Get object detection prediction for an image

Get object detection prediction from camera image

Get render boxes with image

Image prediction for an image with custom mean and std

pytorchlite's People

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