Code from AnimatedStyleTransfer.scala:67 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()))
try {
paintEveryOther(contentUrl, initUrl, canvases, (1 to frames).map(f => f.toString -> {
new VisualStyleContentNetwork(
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),
contentLayers = List(
VGG16.VGG16_1b2
),
contentModifiers = List(
new ContentMatcher()
),
magnification = magnification
)
}).toList, new BasicOptimizer {
override val trainingMinutes: Int = 60
override val trainingIterations: Int = 30
override val maxRate = 1e9
}, x => new PipelineNetwork(1), new GeometricSequence {
override val min: Double = minResolution
override val max: Double = maxResolution
override val steps = AnimatedStyleTransfer.this.steps
}.toStream.map(_.round.toDouble): _*)
null
} finally {
registration.foreach(_.stop()(s3client, ec2client))
}
}
<function0>
