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[MXNET-531] NeuralStyle Example for Scala #11621

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[MXNET-531] NeuralStyle Example for Scala #11621

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b30a719
add initial neuralstyle and test coverage
lanking520 Jul 9, 2018
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Add two more test and README
lanking520 Jul 9, 2018
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kill comments
lanking520 Jul 9, 2018
777e9a3
patch on memory leaks fix
lanking520 Jul 10, 2018
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fix formatting issues
lanking520 Jul 10, 2018
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lanking520 Jul 10, 2018
16862eb
disable the Gan example for now
lanking520 Jul 11, 2018
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add ignore method
lanking520 Jul 23, 2018
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add new download scheme to match the changes
lanking520 Jul 30, 2018
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139 changes: 61 additions & 78 deletions scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/ModelVgg19.scala
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Expand Up @@ -17,92 +17,73 @@

package org.apache.mxnetexamples.neuralstyle

import org.apache.mxnet.Context
import org.apache.mxnet.Executor
import org.apache.mxnet.NDArray
import org.apache.mxnet.Symbol
import org.apache.mxnet.Shape
import org.apache.mxnet.{Context, Executor, NDArray, Shape, Symbol}

/**
* Definition for the neuralstyle network and initialize it with pretrained weight
* @author Depeng Liang
*/
* Definition for the neuralstyle network and initialize it with pretrained weight
*/
object ModelVgg19 {
case class ConvExecutor(executor: Executor, data: NDArray, dataGrad: NDArray,
style: Array[NDArray], content: NDArray, argDict: Map[String, NDArray])
style: Array[NDArray], content: NDArray, argDict: Map[String, NDArray])

def ConvRelu(data : Symbol, convName : String, reluName : String,
numFilter : Int, kernel : (Int, Int) = (3, 3),
stride : (Int, Int) = (1, 1)) : Symbol = {
val conv = Symbol.api.Convolution(data = Some(data), num_filter = numFilter,
pad = Some(Shape(1, 1)), kernel = Shape(kernel._1, kernel._2),
stride = Some(Shape(stride._1, stride._2)), no_bias = Some(false),
workspace = Some(1024), name = convName)
val relu = Symbol.api.relu(data = Some(conv), name = reluName)
conv.dispose()
relu
}

def getSymbol: (Symbol, Symbol) = {
getVggSymbol()
}

def getVggSymbol(prefix: String = "", contentOnly: Boolean = false): (Symbol, Symbol) = {
// declare symbol
val data = Symbol.Variable("data")
val conv1_1 = Symbol.Convolution("conv1_1")()(Map("data" -> data , "num_filter" -> 64,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu1_1 = Symbol.Activation("relu1_1")()(Map("data" -> conv1_1 , "act_type" -> "relu"))
val conv1_2 = Symbol.Convolution("conv1_2")()(Map("data" -> relu1_1 , "num_filter" -> 64,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu1_2 = Symbol.Activation("relu1_2")()(Map("data" -> conv1_2 , "act_type" -> "relu"))
val pool1 = Symbol.Pooling("pool1")()(Map("data" -> relu1_2 , "pad" -> "(0,0)",
"kernel" -> "(2,2)", "stride" -> "(2,2)", "pool_type" -> "avg"))
val conv2_1 = Symbol.Convolution("conv2_1")()(Map("data" -> pool1 , "num_filter" -> 128,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu2_1 = Symbol.Activation("relu2_1")()(Map("data" -> conv2_1 , "act_type" -> "relu"))
val conv2_2 = Symbol.Convolution("conv2_2")()(Map("data" -> relu2_1 , "num_filter" -> 128,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu2_2 = Symbol.Activation("relu2_2")()(Map("data" -> conv2_2 , "act_type" -> "relu"))
val pool2 = Symbol.Pooling("pool2")()(Map("data" -> relu2_2 , "pad" -> "(0,0)",
"kernel" -> "(2,2)", "stride" -> "(2,2)", "pool_type" -> "avg"))
val conv3_1 = Symbol.Convolution("conv3_1")()(Map("data" -> pool2 , "num_filter" -> 256,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu3_1 = Symbol.Activation("relu3_1")()(Map("data" -> conv3_1 , "act_type" -> "relu"))
val conv3_2 = Symbol.Convolution("conv3_2")()(Map("data" -> relu3_1 , "num_filter" -> 256,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu3_2 = Symbol.Activation("'relu3_2")()(Map("data" -> conv3_2 , "act_type" -> "relu"))
val conv3_3 = Symbol.Convolution("conv3_3")()(Map("data" -> relu3_2 , "num_filter" -> 256,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu3_3 = Symbol.Activation("relu3_3")()(Map("data" -> conv3_3 , "act_type" -> "relu"))
val conv3_4 = Symbol.Convolution("conv3_4")()(Map("data" -> relu3_3 , "num_filter" -> 256,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu3_4 = Symbol.Activation("relu3_4")()(Map("data" -> conv3_4 , "act_type" -> "relu"))
val pool3 = Symbol.Pooling("pool3")()(Map("data" -> relu3_4 , "pad" -> "(0,0)",
"kernel" -> "(2,2)", "stride" -> "(2,2)", "pool_type" -> "avg"))
val conv4_1 = Symbol.Convolution("conv4_1")()(Map("data" -> pool3 , "num_filter" -> 512,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu4_1 = Symbol.Activation("relu4_1")()(Map("data" -> conv4_1 , "act_type" -> "relu"))
val conv4_2 = Symbol.Convolution("conv4_2")()(Map("data" -> relu4_1 , "num_filter" -> 512,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu4_2 = Symbol.Activation("relu4_2")()(Map("data" -> conv4_2 , "act_type" -> "relu"))
val conv4_3 = Symbol.Convolution("conv4_3")()(Map("data" -> relu4_2 , "num_filter" -> 512,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu4_3 = Symbol.Activation("relu4_3")()(Map("data" -> conv4_3 , "act_type" -> "relu"))
val conv4_4 = Symbol.Convolution("conv4_4")()(Map("data" -> relu4_3 , "num_filter" -> 512,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu4_4 = Symbol.Activation("relu4_4")()(Map("data" -> conv4_4 , "act_type" -> "relu"))
val pool4 = Symbol.Pooling("pool4")()(Map("data" -> relu4_4 , "pad" -> "(0,0)",
"kernel" -> "(2,2)", "stride" -> "(2,2)", "pool_type" -> "avg"))
val conv5_1 = Symbol.Convolution("conv5_1")()(Map("data" -> pool4 , "num_filter" -> 512,
"pad" -> "(1,1)", "kernel" -> "(3,3)", "stride" -> "(1,1)",
"no_bias" -> false, "workspace" -> 1024))
val relu5_1 = Symbol.Activation("relu5_1")()(Map("data" -> conv5_1 , "act_type" -> "relu"))
val data = Symbol.Variable(s"${prefix}data")

val relu1_1 = ConvRelu(data, s"${prefix}conv1_1", s"${prefix}relu1_1", 64)
val relu1_2 = ConvRelu(relu1_1, s"${prefix}conv1_2", s"${prefix}relu1_2", 64)
val pool1 = Symbol.api.Pooling(data = Some(relu1_2), pad = Some(Shape(0, 0)),
kernel = Some(Shape(2, 2)), stride = Some(Shape(2, 2)), pool_type = Some("avg"),
name = s"${prefix}pool1")

val relu2_1 = ConvRelu(pool1, s"${prefix}conv2_1", s"${prefix}relu2_1", 128)
val relu2_2 = ConvRelu(relu2_1, s"${prefix}conv2_2", s"${prefix}relu2_2", 128)
val pool2 = Symbol.api.Pooling(data = Some(relu2_2), pad = Some(Shape(0, 0)),
kernel = Some(Shape(2, 2)), stride = Some(Shape(2, 2)), pool_type = Some("avg"),
name = s"${prefix}pool2")

val relu3_1 = ConvRelu(pool2, s"${prefix}conv3_1", s"${prefix}relu3_1", 256)
val relu3_2 = ConvRelu(relu3_1, s"${prefix}conv3_2", s"${prefix}relu3_2", 256)
val relu3_3 = ConvRelu(relu3_2, s"${prefix}conv3_3", s"${prefix}relu3_3", 256)
val relu3_4 = ConvRelu(relu3_3, s"${prefix}conv3_4", s"${prefix}relu3_4", 256)
val pool3 = Symbol.api.Pooling(data = Some(relu3_4), pad = Some(Shape(0, 0)),
kernel = Some(Shape(2, 2)), stride = Some(Shape(2, 2)), pool_type = Some("avg"),
name = s"${prefix}pool3")

val relu4_1 = ConvRelu(pool3, s"${prefix}conv4_1", s"${prefix}relu4_1", 512)
val relu4_2 = ConvRelu(relu4_1, s"${prefix}conv4_2", s"${prefix}relu4_2", 512)
val relu4_3 = ConvRelu(relu4_2, s"${prefix}conv4_3", s"${prefix}relu4_3", 512)
val relu4_4 = ConvRelu(relu4_3, s"${prefix}conv4_4", s"${prefix}relu4_4", 512)
val pool4 = Symbol.api.Pooling(data = Some(relu4_4), pad = Some(Shape(0, 0)),
kernel = Some(Shape(2, 2)), stride = Some(Shape(2, 2)), pool_type = Some("avg"),
name = s"${prefix}pool4")

val relu5_1 = ConvRelu(pool4, s"${prefix}conv5_1", s"${prefix}relu5_1", 512)

// style and content layers
val style = Symbol.Group(relu1_1, relu2_1, relu3_1, relu4_1, relu5_1)
val style = if (contentOnly) null else Symbol.Group(relu1_1, relu2_1, relu3_1, relu4_1, relu5_1)
val content = Symbol.Group(relu4_2)
(style, content)
}

def getExecutor(style: Symbol, content: Symbol, modelPath: String,
inputSize: (Int, Int), ctx: Context): ConvExecutor = {
inputSize: (Int, Int), ctx: Context): ConvExecutor = {
val out = Symbol.Group(style, content)
// make executor
val (argShapes, outputShapes, auxShapes) = out.inferShape(
Expand All @@ -116,15 +97,17 @@ object ModelVgg19 {
val key = s"arg:$name"
if (pretrained.contains(key)) argDict(name).set(pretrained(key))
}
pretrained.foreach(ele => ele._2.dispose())
val executor = out.bind(ctx, argDict, gradDict)
out.dispose()
val outArray = executor.outputs
ConvExecutor(executor = executor,
data = argDict("data"),
dataGrad = gradDict("data"),
style = outArray.take(outArray.length - 1),
content = outArray(outArray.length - 1),
argDict = argDict)
}
data = argDict("data"),
dataGrad = gradDict("data"),
style = outArray.take(outArray.length - 1),
content = outArray(outArray.length - 1),
argDict = argDict)
}

def getModel(modelPath: String, inputSize: (Int, Int), ctx: Context): ConvExecutor = {
val (style, content) = getSymbol
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