TensorflowJS based hooks for react.js
npm install react-tensorflow![Typed with TypeScript][typescript]
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> A library of React hooks and HOCs written in Typescript to use Tensorflow models in your application! 🤖ðŸ§
Demo application with Code Examples
``sh`
yarn add react-tensorflow
`sh`
npm i react-tensorflow -S
- react >=16.8.0
- @tensorflow/tfjs >=2.0.0
`tsx
import { useModel } from 'react-tensorflow'
const MyModelComponent = () => {
const model = useModel({ modelUrl: ${PATH_TO_MODEL} })
// ...do something with the model
return null
}
`
`tsx`
useModel({
model?: any,
modelUrl?: string,
layers?: boolean,
onLoadCallback?: (model: GraphModel | LayersModel | null) => void
}): GraphModel | LayersModel | null
If model or modelUrl is omitted useModel will look to find the ModelProvider as it's contextlayers
for returning the model. When loading a model with this hook, the boolean is passed iftf.loadLayersModel
your TF model should be loaded with the function otherwise it is assumed thetf.loadGraphModel
model should be loaded with . If a model is loaded with modelUrl and anonLoadCallback function is provided, it will be called with the loaded model. This function is
intended to be used as a warm up function that could look like this –
`js`
(model) => {
const zeroTensor = tf.zeros([1, 300, 300, 3], 'int32');
const result = await model.executeAsync(zeroTensor) as tf.Tensor[];
await Promise.all(result.map(t => t.data()));
result.map(t => t.dispose());
zeroTensor.dispose();
}
`tsx`
Wraps the children in a React Provider to be consumed by Context's in either the useModel hookwithModel
or HOC. The props passed to this provider are the same as the documented props foruseModel.
`tsx`
withModel(Component: React.ComponentType): JSX.Element
Wraps the provided component in a React Context, passing the model give to the provider as a prop.
`tsx`
useWebcam (options?: {
width?: number
height?: number
facingMode?: string
}): [React.MutableRefObject
Provides a ref to be used on a video element, the hook then returns a tensor with shape
[1, width, height, 3] where the width and height are either dictated by the element's width
& height or the provided argument documented above. The options argument while documented
above can infact take all the properties of the MediaStreamConstraints.
> :point_right: All the following hooks use useModel under the hood, therefore accepting any of the args
> passed to useModel :point_left:
`tsx`
usePrediction (options?: {
predictConfig?: {},
useExecute?: boolean = false,
outputName?: string,
predictionFunction?: string,
modelUrl?: string,
layers?: boolean,
}): [(data: tf.Tensor) => void, tf.Tensor | tf.Tensor[] | tf.NamedTensorMap | null]
Provides a function to set the data you want to use to create a prediction. The data must be
in the form of a tensor. It then returns a new tensor as the prediction using either the model
set with the ModelProvider component or by passing a modelUrl as an argument as it usesuseModel under the hood. You can then perform different actions such as normalizing the datausePrediction
for to classify the original input. By default uses .predict, if you want to.executeAsync
force the use of set useExecute: true and if you want to use a custom predictpredictionFunction
function, pass it's name via the key. If you're using a LayersModel yououtputName
must set .
:no_entry_sign: Using a @tensorflow/tfjs-models model with this hook will cause typescript
errors if the model predicition method is called or will simply return null because the model
either does not have an executeAsync or predict function or it does, and it has not returned a
Tensor. :no_entry_sign:
> :point_right: All the following hooks use usePrediction under the hood, therefore accepting any of the args
> passed to usePrediction :point_left:
`tsx`
useClassifer(options?: {
classes?: {},
returns?: number,
modelUrl?: string,
layers?: boolean,
}): [(data: tf.Tensor) => void, Array<{class: number, probability: number}> | Array<{class: string, probability: number}> | null]
Returns a function to set the data which must be in the form of a tensor. After prediction has
been made, returns an array of classifications (be default, the array will have length 5).
If the classes argument is provided, the class key in the returned array will be the class at
the index of the prediction.
`tsx`
useObjectDetect(options?:{
returns?: number,
minConfidence?: number,
classes?: {},
width?: number,
height?: number,
modelUrl?: string,
layers?: boolean,
}): [(data: tf.Tensor) => void, Array<{class: number, probability: number, boundingBox: number[]}> | Array<{class: string, probability: number, boundingBox: number[]}> | null]
Returns an array with index 0 being a function to set the data. This data must be in the form
of a tensor. After a prediction has been made, returns an array of objects detected. Both height
and of the media must be provided to recieve values inside bounding box which will be an[left, top, width, height]
array of four numbers – . minConfidence must be a value between 0 - 1.string`.
The hook then returns an array of detected objects (by default, the array will be length 5). If the
classes argument is provided, the class key in the returned array will be the class at the index of
the prediction as a
Contributions are very welcome and wanted.
Before submitting a new pull request, please make sure:
1. Consider if the pull-request should be going to the master branch or the latest release branch.
2. If merging to master, you have updated the package.json version.
3. You report your changes into the CHANGELOG file.
4. make sure you run the test and build script before submitting your merge request.
5. make sure you've added the documentation of your changes.
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