Code from AnimatedRotor.scala:73 executed in 0.00 seconds (0.000 gc):
() => {
implicit val _ = log
// First, basic configuration so we publish to our s3 site
log.setArchiveHome(URI.create(s"s3://$s3bucket/${getClass.getSimpleName.stripSuffix("$")}/${log.getId}/"))
log.onComplete(() => upload(log): Unit)
// Fetch input images (user upload prompts) and display rescaled copies
log.p(log.jpg(ImageArtUtil.load(log, styleUrl, (maxResolution * Math.sqrt(magnification)).toInt), "Input Style"))
log.p(log.jpg(ImageArtUtil.load(log, contentUrl, maxResolution), "Input Content"))
val canvases = (1 to frames).map(_ => new AtomicReference[Tensor](null)).toList
// Execute the main process while registered with the site index
val registration = registerWithIndexGIF_Cyclic(canvases.map(_.get()).map(t=>{
val kaleidoscope = getKaleidoscope(t.getDimensions)
val transformed = kaleidoscope.eval(t).getDataAndFree.getAndFree(0)
kaleidoscope.freeRef()
transformed
}))
try {
paintEveryOther(
contentUrl,
initUrl,
canvases,
(1 to frames).map(f => f.toString -> {
new VisualStyleNetwork(
styleLayers = List(
// We select all the lower-level layers to achieve a good balance between speed and accuracy.
VGG16.VGG16_0,
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()
),
styleUrl = List(styleUrl),
magnification = magnification,
// Our optimization network should be built with a kaliedoscope appended to the canvas
viewLayer = dims => getKaleidoscope(dims.toArray)
)
}).toList,
new BasicOptimizer {
override val trainingMinutes: Int = 60
override val trainingIterations: Int = 30
override val maxRate = 1e9
// The canvas result should be processed by the kaliedoscope
override def renderingNetwork(dims: Seq[Int]) = getKaleidoscope(dims.toArray).copyPipeline()
// By default, we use a trust region that constrains the canvas pixel values from 0-256.
// This conflicts with using a kaliedoscope, whose output is bounded and input may be out of that range.
override def trustRegion(layer: Layer): TrustRegion = null
},
(dims: Seq[Int]) => getKaleidoscope(dims.toArray).copyPipeline(),
new GeometricSequence {
override val min: Double = minResolution
override val max: Double = maxResolution
override val steps = AnimatedRotor.this.steps
}.toStream.map(_.round.toDouble): _*)
null
} finally {
registration.foreach(_.stop()(s3client, ec2client))
}
}
<function0>
