Segformer Action

Use Segformer to get semantic segmentations

Deploys Segformer by NVIDIA Research as a DataTorch action. Currently used for internal evaluation only.

Quick Start

name: Segformer

triggers:
  # Adds a button to the annotator.
  annotatorButton:
    name: "Segformer"
    icon: brain
    # Annotator will prompt the user for 4 points before triggering the pipeline
    flow: 4-points

jobs:
  predict:
    # Properties about the trigger event can be accessed at 'event' property
    steps:
      - name: Download File
        action: datatorch/download-file@v1
        inputs:
          # Get the file id for the event that triggered this.
          fileId: ${{ event.fileId }}
          name: ${{ event.fileName }}

      - name: Predict Segmentation
        action: aoxolotl/segformer_action
        inputs:
          # Download file path from the previous action.
          imagePath: ${{ variable.path }}
          # Get the 4 points the user clicked
          points: ${{ event.flowData.points }}
          # Annotation created by the four points. We will insert the
          # segmentation into this annotation
          annotationId: ${{ event.annotationId }}

NOTE: Running Segformer for the first time will take serval minutes to complete as it needs to download the Segformer docker image. Do not exit out of your agent unless it specifically throws an error.

Action

Inputs

Name	Type	Default	Description
imagePath	string	required	Absolute path to image. This path must be in the agent directory.
points	array	required	4 points marking the most left, right, bottom and top points of the shape.
url	string	http://localhost:3445	Url for sending requests. A Segformer docker image will be spun up on this port if not found.
image	string	add3000/segformer_server	Docker image to spin up.
annotationId	string	null	Annotation to insert segmentation into. If not provided the segmentation will not be inserted.
simplify	float	1.5	Simplification tolerance applied to segmentation before importing. Set to 0 to disable. Disabling can significantly increase pipeline performance, but decrease annotator performance.

Outputs

Name	Type	Description
segmentation	array	Segmentation of points predicted by Segformer