1. TiledTexture

TiledTexture

Subreport: TiledTexture_8ea2d5bf-de02-4ba0-8417-728fc669c52a_log_com.simiacryptus.ref.lang.ReferenceCountingBase

Code from TiledTexture.scala:68 executed in 0.00 seconds (0.000 gc):

    () => {
      implicit val implicitLog = log
      // First, basic configuration so we publish to our s3 site
      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, (maxResolution * Math.sqrt(magnification)).toInt).foreach(img => log.p(log.jpg(img, "Input Style")))
      val canvas = new RefAtomicReference[Tensor](null)
  
      // Generates a pretiled image (e.g. 3x3) to display
      def tiledCanvas = {
        val input = canvas.get()
        if (null == input) input else {
          val layer = new ImgTileAssemblyLayer(rowsAndCols, rowsAndCols)
          val result = layer.eval((1 to (rowsAndCols * rowsAndCols)).map(_ => input.addRef()): _*)
          input.freeRef()
          layer.freeRef()
          val tensorList = result.getData
          result.freeRef()
          val tensor = tensorList.get(0)
          tensorList.freeRef()
          tensor
        }
      }
  
      // Tiling layer used by the optimization engine.
      // Expands the canvas by a small amount, using tile wrap to draw in the expanded boundary.
      def tilingLayer(dims: Seq[Int]) = {
        val paddingX = Math.min(256, Math.max(16, dims(0) / 2))
        val paddingY = Math.min(256, Math.max(16, dims(1) / 2))
        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(() => tiledCanvas)
      try {
        // Display a pre-tiled image inside the report itself
        withMonitoredJpg(() => tiledCanvas.toImage) {
          // Display an additional, non-tiled image of the canvas
          withMonitoredJpg(() => Option(canvas.get()).map(_.toRgbImage).orNull) {
            log.subreport("Painting", (sub: NotebookOutput) => {
              paint(
                contentUrl = initUrl,
                initUrl = initUrl,
                canvas = canvas,
                network = new VisualStyleNetwork(
                  styleLayers = List(
                    // We select all the lower-level layers to achieve a good balance between speed and accuracy.
                    VGG16.VGG16_0b,
                    VGG16.VGG16_1a,
                    VGG16.VGG16_1b1,
                    VGG16.VGG16_1b2,
                    VGG16.VGG16_1c1,
                    VGG16.VGG16_1c2,
                    VGG16.VGG16_1c3
                  ),
                  styleModifiers = List(
                    // These two operators are a good combination for a vivid yet accurate style
                    new GramMatrixEnhancer(),
                    new MomentMatcher()
                  ),
                  styleUrls = Option(styleUrl),
                  magnification = magnification,
                  viewLayer = tilingLayer
                ),
                optimizer = new BasicOptimizer {
                  override val trainingMinutes: Int = 60
                  override val trainingIterations: Int = 15
                  override val maxRate = 1e9
                },
                aspect = Option(aspectRatio),
                resolutions = new GeometricSequence {
                  override val min: Double = minResolution
                  override val max: Double = maxResolution
                  override val steps = TiledTexture.this.steps
                }.toStream.map(_.round.toDouble)
              )(sub)
              null
            })
          }(log)
        }
        null
      } finally {
        registration.foreach(_.stop()(s3client, ec2client))
      }
    }

Returns:

    <function0>

Subreport: Painting