node-red-contrib-tf-model

This is a Node-RED custom node which is used to load tensorflow models and perform inference. Currently, it only supports Web friendly JSON format model which mainly used by Tensorflow.js. SavedModel format will be added soon.

Installation

Prerequisite

This module requires @tensorflow/tfjs-node as a peer dependency. You need to install it within Node-RED manually. Tensorflow.js on Node.js (@tensorflow/tfjs-node or @tensorflow/tfjs-node-gpu), depend on the Tensorflow shared libraries. Putting Tensorflow.js as the dependency of custom Node-RED node may run into a situation that multiple custom nodes install multiple tfjs-node module as their dependencies. While loading multiple Tensorflow shared libraries in the same process, the process would abort by hitting protobuf assertion.

Install @tensorflow/tfjs-node:

npm install @tensorflow/tfjs-node

Note:

If you are planning to run this node-red-contrib-tf-model Node on a Jetson Nano or Raspberry Pi 4, note that @tensorflow/tfjs-node does not yet support ARM64 or ARM32 architectures. Here are the instructions to install @tensorflow/tfjs-node on Jetson Nano and Raspberry Pi 4:

The version must to be equal or greater then 1.5.1 (up to 1.7.0)
Run the command to install tfjs-node
```
npm install @tensorflow/tfjs-node@1.x.y
```
Please append the version you need. i.e @tensorflow/tfjs-node@1.5.1. You will see error messages while downloading/installing/compiling the shared libraries and node binding. But the JavaScript libraries are installed.
Find the tfjs-node package directory on your local system. Usually it would be under node_modules/@tensorflow/tfjs-node from the directory you run the npm install command. And switch to that directory.
```
cd node_modules/@tensorflow/tfjs-node
```

For Jetson Nano, you need to provide a file named custom-binary.json under scripts directory with the following contents:

{
  "tf-lib": "https://s3.us.cloud-object-storage.appdomain.cloud/tfjs-cos/libtensorflow-gpu-linux-arm64-1.15.0.tar.gz"
}

For Raspberry Pi4, you need to provide a file named custom-binary.json under scripts directory with the following contents:

{
  "tf-lib": "https://s3.us.cloud-object-storage.appdomain.cloud/tfjs-cos/libtensorflow-cpu-linux-arm-1.15.0.tar.gz"
}

Run the following command under node_module/@tensorflow/tfjs-node directory:
```
npm install
```
It will download the pre-compiled tensorflow shared libraries binaries and build the node binding.

Install this module:

Once you have installed the peer dependency, you can install this module:

npm install node-red-contrib-tf-model

Usage

You can see the tf-model node in the Models category, like this:

Then you can use tf-model node in your flow. It only needs one property: Model URL.

The Model URL should point to a Tensorflow.js model which is in web friendly format. Typically, it should be a model JSON file. After you specify the Model URL and deploy the flow, it will fetch the model files, including shard files, and store them in ${HOME}/.node-red/tf-model directory. You can also use a model from the local file system, for example: file:///home/mymodel/model.json. The new node will load the model and maintain the cache entry. You can specify the Output Node name when running model inference. By default, it uses that last node as the output node.

Data Format

When performing the inference of a Tensorflow.js model, you need to pass the corresponding msg.payload to tf-model node. The msg.payload would be a tf.NamedTensorMap object containing all the needed features in tf.Tensor data type for the model. When a model is loaded, the input node list is output to the console. It may help you to build the input named map for the model. By default, the prediction runs through the whole model graph and returns the final output of that last node. You can use Output Node to specify a different node as output node. After model prediction, results are passed to the next node in msg.payload. It could be a tf.Tensor or tf.Tensor[].

Examples

We provide some example flows under the examples folder. They may help you to understand the usage of tf-model node and other Node-RED custom node we provide.

moeiscool / node-red-contrib-tf-model