Monitoring Progress

It can be useful to monitor the progress of an upscale operation, particularly for larger images or heavier models. UpscalerJS provides an easy way to do so.

Specifying a progress Callback Function

caution

progress will only be called if a patchSize is set during an upscale. Read more about patch sizes here.

We can pass a callback function to the upscale method that will be called upon on any progress operations:

import Upscaler from 'upscaler'
import image from '/path/to/image.png'

const upscaler = new Upscaler()

upscaler.upscale(image, {
  progress: (percent) => {
    console.log(`${percent * 100}% of image has been processed`)
  }
})

Callback Function

In addition to returning the percentage of operation completed, the progress callback can accept a few positional other arguments. The full list of arguments is:

• percent - The percentage of the upscale operation completed
• imageSlice - The patch of image being operated upon
• row - The row being operated upon
• col - The column being operated upon

By default, imageSlice will be in the format specified by output - e.g., if we've specified an output of tensor, we'll receive a tensor back in the progress function.

To change this, we can specify the format we wish to receive in progress with progressOutput:

upscaler.upscale(image, {
  output: 'tensor',
  progressOutput: 'base64',
  progress: (percent, slice) => {
    // our slice will now be a base64 src, even though the response
    // from upscale will be a tensor
    console.log(slice) 
  }
})

As in other operations, when receiving a tensor in the progress callback, we are responsible for disposing of that tensor.

upscaler.upscale(image, {
  output: 'tensor',
  progress: (percent, slice) => {
    console.log(slice) 

    // Now that we're done with our tensor, dispose of it
    slice.dispose()
  }
})

Next, we can learn how to cancel an inflight request.