torch's Introduction

VIAM PYTORCH ML MODEL

in progress

This is a Viam module providing a mlmodel service for PyTorch model

Getting started

Installation with `pip install`

pip install -r requirements.txt

Configure your `mlmodel:torch-cpu` vision service

Note

Before configuring your vision service, you must create a robot.

Navigate to the Config tab of your robot’s page in the Viam app. Click on the Services subtab and click Create service. Select the Vision type, then select the deepface_identification model. Enter a name for your service and click Create.

Example

{
    "modules": [
    {
      "executable_path": "/Users/robinin/torch-infer/torch/run.sh",
      "name": "mymodel",
      "type": "local"
    }
  ],
  "services": [
    {
      "name": "torch",
      "type": "mlmodel",
      "model": "viam:mlmodel:torch-cpu",
      "attributes": {
        "model_path": "examples/resnet_18/resnet-18.pt", 
        "label_path": "examples/resnet_18/labels.txt", 
      }
    }
  ]
}

Attributes description

The following attributes are available to configure your module:

Name	Type	Inclusion	Default	Description
`model_path`	string	Required		Path to standalone model file
`label_path`	string	Optional		Path to file with class labels.

Methods

`infer()`

infer(input_tensors: Dict[str, NDArray], *, timeout: Optional[float]) -> Dict[str, NDArray]

Example

my_model = MLModelClient.from_robot(robot, "torch")
input_image = np.array(Image.open(path_to_input_image), dtype=np.float32)
input_image = np.transpose(input_image, (2, 0, 1))  # channel first
input_image = np.expand_dims(input_image, axis=0)  # batch dim
input_tensor = dict()
input_tensor["input"] = input_image
output = await my_model.infer(input_tensor)
print(f"output.shape is {output['output'].shape}")