Subreport: Logs for com.simiacryptus.ref.lang.ReferenceCountingBase
SmoothStyle.scala:65 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 input images (user upload prompts) and display a rescaled copies
ImageArtUtil.loadImages(log, styleUrl, 600).foreach(img => log.p(log.jpg(img, "Input Style")))
log.p(log.jpg(ImageArtUtil.loadImage(log, contentUrl, 600), "Input Content"))
val canvas = new RefAtomicReference[Tensor](null)
// Execute the main process while registered with the site index
val registration = registerWithIndexJPG(() => canvas.get())
try {
withMonitoredJpg(() => {
val tensor = canvas.get()
if (tensor == null) null
else tensor.toImage
}) {
paint(
contentUrl = contentUrl,
initFn = content => {
val fastPhotoStyleTransfer = FastPhotoStyleTransfer.fromZip(new ZipFile(Util.cacheFile(new URI(
"https://simiacryptus.s3-us-west-2.amazonaws.com/photo_wct.zip"))))
val style = ImageArtUtil.loadImages(log, styleUrl, 500).map(Tensor.fromRGB(_)).head
val wctRestyled = fastPhotoStyleTransfer.photoWCT(style, content.addRef())
fastPhotoStyleTransfer.freeRef()
val topology = new SearchRadiusTopology(content.addRef())
topology.setSelfRef(true)
topology.setVerbose(true)
var affinity = new RelativeAffinity(content, topology)
affinity.setContrast(20)
affinity.setGraphPower1(2)
affinity.setMixing(0.1)
//val wrapper = affinity.wrap((graphEdges, innerResult) => adjust(graphEdges, innerResult, degree(innerResult), 0.2))
val operator = solver.solve(topology, affinity, 1e-4)
val tensor = operator.apply(wctRestyled)
operator.freeRef()
tensor
},
canvas = canvas.addRef(),
network = new VisualStyleContentNetwork(
styleLayers = List(
//VGG16.VGG16_1a,
//VGG16.VGG16_1b1,
//VGG16.VGG16_1b2,
VGG16.VGG16_1c1,
VGG16.VGG16_1c2,
VGG16.VGG16_1c3,
VGG16.VGG16_1d1,
VGG16.VGG16_1d2,
VGG16.VGG16_1d3,
//VGG16.VGG16_1e1,
//VGG16.VGG16_1e2,
//VGG16.VGG16_1e3
).flatMap(x => List(
x, x.prependAvgPool(2)
)),
styleModifiers = List(
//new GramMatrixEnhancer().setMinMax(-0.5, 0.5),
new MomentMatcher()
),
styleUrls = Option(styleUrl),
contentLayers = List(
VGG16.VGG16_1b1,
VGG16.VGG16_1c2
//VGG16.VGG16_1c3
),
contentModifiers = List(
new ContentMatcher().scale(1e1)
),
magnification = 1
),
optimizer = new BasicOptimizer {
override val trainingMinutes: Int = 90
override val trainingIterations: Int = 20
override val maxRate = 1e9
}, resolutions = new GeometricSequence {
override val min: Double = 600
override val max: Double = 1200
override val steps = 2
}.toStream.map(_.round.toDouble))
paint(
contentUrl = contentUrl,
initFn = x => x,
canvas = canvas.addRef(),
network = new VisualStyleContentNetwork(
styleLayers = List(
//VGG16.VGG16_1a,
//VGG16.VGG16_1b1,
//VGG16.VGG16_1b2,
VGG16.VGG16_1c1,
VGG16.VGG16_1c2,
VGG16.VGG16_1c3,
VGG16.VGG16_1d1,
VGG16.VGG16_1d2,
VGG16.VGG16_1d3
),
styleModifiers = List(
//new GramMatrixEnhancer().setMinMax(-0.5, 0.5),
new GramMatrixMatcher()
),
styleUrls = Option(styleUrl),
contentLayers = List(
VGG16.VGG16_1a,
VGG16.VGG16_1b2
).map(_.prependAvgPool(2).appendMaxPool(2)),
contentModifiers = List(
new ContentMatcher().scale(1e0)
),
magnification = 1
),
optimizer = new BasicOptimizer {
override val trainingMinutes: Int = 180
override val trainingIterations: Int = 10
override val maxRate = 1e9
},
resolutions = new GeometricSequence {
override val min: Double = 1600
override val max: Double = 2400
override val steps = 2
}.toStream.map(_.round.toDouble))
}
null
} finally {
canvas.freeRef()
registration.foreach(_.stop()(s3client, ec2client))
}
}
Returns
{ }
