1. BigTexture
  2. Resolution 200.0
  3. Resolution 400.0
  4. Resolution 800.0
  5. Resolution 1200.0
  6. Resolution 2191.0

BigTexture

Creates a large texture based on a style using:
  1. Random plasma initialization
  2. Standard VGG19 layers
  3. Operators constraining and enhancing style
  4. Progressive resolution increase
  5. View layer to enforce tiling

Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase

BigTexture.scala:64 executed in 0.00 seconds (0.000 gc):

    () => {
      implicit val implicitLog = log
      // First, basic configuration so we publish to our s3 site
      if(Option(s3bucket).filter(!_.isEmpty).isDefined)
        log.setArchiveHome(URI.create(s"s3://$s3bucket/$className/${log.getId}/"))
      log.onComplete(() => upload(log): Unit)
      // Fetch image (user upload prompt) and display a rescaled copy
      loadImages(log, styleUrl, 1200).foreach(img => log.p(log.jpg(img, "Input Style")))
      val canvas = new RefAtomicReference[Tensor](null)
  
      // Tiling layer used by the optimization engine.
      // Expands the canvas by a small amount, using tile wrap to draw in the expanded boundary.
      val min_padding = 64
      val max_padding = 256
      val border_factor = 1.0
  
      def viewLayer(dims: Seq[Int]): ImgViewLayer = {
        val paddingX = Math.min(max_padding, Math.max(min_padding, dims(0) * border_factor)).toInt
        val paddingY = Math.min(max_padding, Math.max(min_padding, dims(1) * border_factor)).toInt
        val layer = new ImgViewLayer(dims(0) + paddingX, dims(1) + paddingY, true)
        layer.setOffsetX(-paddingX / 2)
        layer.setOffsetY(-paddingY / 2)
        layer
      }
  
      // Execute the main process while registered with the site index
      val registration = registerWithIndexJPG(() => canvas.get())
      try {
        withMonitoredJpg(() => Option(canvas.get()).map(tensor => {
          val image = tensor.toRgbImage
          tensor.freeRef()
          image
        }).orNull) {
          paint(
            contentUrl = initUrl,
            initUrl = initUrl,
            canvas = canvas.addRef(),
            network = new VisualStyleNetwork(
              styleLayers = List(
                // We select all the lower-level layers to achieve a good balance between speed and accuracy.
                VGG19.VGG19_0b,
                VGG19.VGG19_1a,
                VGG19.VGG19_1b1,
                VGG19.VGG19_1b2,
                VGG19.VGG19_1c1,
                VGG19.VGG19_1c2,
                VGG19.VGG19_1c3,
                VGG19.VGG19_1c4,
                VGG19.VGG19_1d1,
                VGG19.VGG19_1d2,
                VGG19.VGG19_1d3,
                VGG19.VGG19_1d4
              ),
              styleModifiers = List(
                // These two operators are a good combination for a vivid yet accurate style
                new GramMatrixEnhancer().setMinMax(-5,5).scale(5),
                new MomentMatcher()
              ),
              styleUrls = Seq(styleUrl),
              magnification = 1,
              viewLayer = viewLayer
            ),
            optimizer = new BasicOptimizer {
              override val trainingMinutes: Int = 60
              override val trainingIterations: Int = 20
              override val maxRate = 1e9
            },
            aspect = Option(aspectRatio),
            resolutions = new GeometricSequence {
              override val min: Double = 200
              override val max: Double = 800
              override val steps = 3
            }.toStream.map(_.round.toDouble)
          )
          paint(
            contentUrl = initUrl,
            initUrl = initUrl,
            canvas = canvas.addRef(),
            network = new VisualStyleNetwork(
              styleLayers = List(
                // We select all the lower-level layers to achieve a good balance between speed and accuracy.
                VGG19.VGG19_1b1,
                VGG19.VGG19_1b2,
                VGG19.VGG19_1c1,
                VGG19.VGG19_1c2,
                VGG19.VGG19_1c3,
                VGG19.VGG19_1c4
              ),
              styleModifiers = List(
                new GramMatrixEnhancer().setMinMax(-5,5).scale(5),
                new GramMatrixMatcher()
              ),
              styleUrls = Seq(styleUrl),
              magnification = 1,
              viewLayer = viewLayer
            ),
            optimizer = new BasicOptimizer {
              override val trainingMinutes: Int = 90
              override val trainingIterations: Int = 10
              override val maxRate = 1e9
            },
            aspect = Option(aspectRatio),
            resolutions = new GeometricSequence {
              override val min: Double = 1200
              override val max: Double = 4000
              override val steps = 3
            }.toStream.map(_.round.toDouble)
          )
        }
        null
      } finally {
        registration.foreach(_.stop()(s3client, ec2client))
        canvas.freeRef()
      }
    }

Returns

    { }

Input Style

Input Style

Resolution 200.0

Subreport: Optimization

Resolution 400.0

Subreport: Optimization

Resolution 800.0

Subreport: Optimization

Resolution 1200.0

Subreport: Optimization

Resolution 2191.0

Subreport: Optimization