ZoomingRotor.scala:112 executed in 0.01 seconds (0.000 gc):
val outerMask = innerMask.map(x => 1 - x)
var style: VisualNetwork = new VisualStyleNetwork(
styleLayers = List(
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(
new MomentMatcher().scale(Math.pow(2, -enhancementCoeff * 2)),
new GramMatrixEnhancer().setMinMax(-5, 5).scale(Math.pow(2, enhancementCoeff * 2))
).map(_.withMask(outerMask.addRef())),
styleUrls = styleUrl,
magnification = magnification,
viewLayer = dims => getKaleidoscope(dims.toArray)
)
if (innerCoeff > 0) style = style.asInstanceOf[VisualStyleNetwork].withContent(
contentLayers = List(
VGG19.VGG19_0a
), contentModifiers = List(
new ContentMatcher().withMask(innerMask.addRef()).scale(innerCoeff)
))
style
Returns
VisualStyleNetwork(List(VGG19_1b2, VGG19_1c1, VGG19_1c2, VGG19_1c3, VGG19_1c4, VGG19_1d1, VGG19_1d2, VGG19_1d3, VGG19_1d4),List(com.simiacryptus.mindseye.art.VisualModifier$5@b91d030, com.simiacryptus.mindseye.art.VisualModifier$5@485d1271),List(),WrappedArray(http://examples.deepartist.org/TextureTiledRotor/427d8b22-5cbd-478b-ad84-055d8c146216/etc/26db2c3d-6f29-4895-8053-684646ee92b1.jpg, http://examples.deepartist.org/TextureTiledRotor/427d8b22-5cbd-478b-ad84-055d8c146216/etc/722a3c1e-0be9-4e2b-93c0-e74acbb1fc6e.jpg, http://examples.deepartist.org/TextureTiledRotor/427d8b22-5cbd-478b-ad84-055d8c146216/etc/54f22692-5df5-4f35-aa2a-d0a62956dc28.jpg, http://examples.deepartist.org/TextureTiledRotor/427d8b22-5cbd-478b-ad84-055d8c146216/etc/2d1d77df-c88b-4445-b737-4bd646392cee.jpg, http://examples.deepartist.org/TextureTiledRotor/427d8b22-5cbd-478b-ad84-055d8c146216/etc/7baf8cf5-60b6-4c43-b69b-0c70d93fe354.jpg),Float,com.simiacryptus.mindseye.art.examples.ZoomingRotor$$Lambda$447/324245816@2f4473cc,1400,64,1,10000,5.0E8,2.0)
ZoomingRotor.scala:94 executed in 0.00 seconds (0.000 gc):
new BasicOptimizer {
override val trainingMinutes: Int = 90
override val trainingIterations: Int = 10
override val maxRate = 1e9
override def trustRegion(layer: Layer): TrustRegion = null
override def lineSearchFactory: LineSearchStrategy = {
super.lineSearchFactory
}
override def renderingNetwork(dims: Seq[Int]) = getKaleidoscope(dims.toArray)
}
Returns
{
"trainingMinutes" : 90,
"trainingIterations" : 10,
"maxRate" : 1.0E9
}