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Update stdlib, more helpers
Signed-off-by: Ryan Nett <rnett@calpoly.edu>
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4 files changed

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lines changed

‎tensorflow-core-kotlin/pom.xml‎

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@@ -38,7 +38,7 @@
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<dependencies>
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<dependency>
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<groupId>org.jetbrains.kotlin</groupId>
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<artifactId>kotlin-stdlib</artifactId>
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<artifactId>kotlin-stdlib-jdk8</artifactId>
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<version>${kotlin.version}</version>
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</dependency>
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</dependencies>

‎tensorflow-core-kotlin/tensorflow-core-kotlin-api/src/main/kotlin/org/tensorflow/ExecutionEnvironmentHelpers.kt‎

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package org.tensorflow
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import org.tensorflow.EagerSession.DevicePlacementPolicy
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import org.tensorflow.proto.framework.ConfigProto
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import kotlin.contracts.InvocationKind
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import kotlin.contracts.contract
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/**
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* Construct a TensorFlow [Graph] and run [block] on it.
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*/
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public inline fun <R> Graph(block: Graph.() -> R): R {
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contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
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return Graph().use{
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it.run(block)
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}
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}
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/**
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* Construct a new session with the associated {@link Graph} and configuration options, and run [block] on it.
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*
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* @param g The {@link Graph} the created Session will operate on.
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* @param config Configuration parameters for the session specified as a [ConfigProto](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)
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* protocol buffer.
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* @throws IllegalArgumentException if the config is not a valid serialization of the ConfigProto
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* protocol buffer.
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*/
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public inline fun <R> Graph.withSession(config: ConfigProto? = null, block: (Session) -> R): R {
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contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
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return Session(this, config).use(block)
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}
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/**
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* An environment for executing TensorFlow operations eagerly.
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*
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* Eager execution is an imperative programming environment that evaluates operations
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* immediately, without building graphs. Operations return concrete values instead of constructing a
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* computational graph to run later, as with {@link Graph}s and {@link Session}s.
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*
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* This makes it easy to develop with TensorFlow and debug models, as it behaves more like a
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* standard programming library.
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*
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* Instances of a {@code EagerSession} are thread-safe.
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*
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* @param options The options for this session.
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* @see EagerSession.Options
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*/
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public inline fun <R> EagerSession(
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options: EagerSession.Options? = null,
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block: EagerSession.() -> R
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): R {
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contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
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val ses = options?.build() ?: EagerSession.create()
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return ses.use(block)
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}
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/**
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* An environment for executing TensorFlow operations eagerly.
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*
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* Eager execution is an imperative programming environment that evaluates operations
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* immediately, without building graphs. Operations return concrete values instead of constructing a
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* computational graph to run later, as with {@link Graph}s and {@link Session}s.
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*
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* This makes it easy to develop with TensorFlow and debug models, as it behaves more like a
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* standard programming library.
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*
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* Instances of a {@code EagerSession} are thread-safe.
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*
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* @param config The session configuration to use. See [EagerSession.Options.config] and [ConfigProto].
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* @param async Whether to return from op methods before the outputs have been calculated. See [EagerSession.Options.async].
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* @param devicePlacementPolicy How to handle tensors on different devices. See [EagerSession.Options.devicePlacementPolicy].
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* @see EagerSession.Options
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*/
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public inline fun <R> EagerSession(
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config: ConfigProto? = null,
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async: Boolean = false,
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devicePlacementPolicy: DevicePlacementPolicy = DevicePlacementPolicy.SILENT,
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block: EagerSession.() -> R
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): R {
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contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
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val options = EagerSession.options()
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.config(config)
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.async(async)
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.devicePlacementPolicy(devicePlacementPolicy)
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return EagerSession(options, block)
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}
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/**
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* Executed [block] in the default eager session, creating it if necessary.
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*
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* To configure the default session, use [EagerSession.initDefault].
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*/
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public fun <R> withDefaultEagerSession(block: EagerSession.() -> R): R {
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contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
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return EagerSession.getDefault().use(block)
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}

‎tensorflow-core-kotlin/tensorflow-core-kotlin-api/src/main/kotlin/org/tensorflow/ndarray/NDArayUtils.kt‎

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package org.tensorflow.ndarray
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/**
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* Convert the [Shape] to a List.
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*/
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public fun Shape.toList(): List<Long> = asArray().toList()
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/**
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* Get the size at [index].
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*/
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public operator fun Shape.get(index: Int): Long = this.size(index)

‎tensorflow-core-kotlin/tensorflow-core-kotlin-api/src/main/kotlin/org/tensorflow/op/kotlin/OpsHelpers.kt‎

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package org.tensorflow.op.kotlin
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import org.tensorflow.DataType
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import org.tensorflow.ExecutionEnvironment
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import org.tensorflow.ndarray.Shape
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import org.tensorflow.op.JavaOps
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import org.tensorflow.op.Op
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import org.tensorflow.op.Ops
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import org.tensorflow.op.core.Placeholder
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import org.tensorflow.types.family.TType
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import kotlin.contracts.ExperimentalContracts
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import kotlin.contracts.InvocationKind
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import kotlin.contracts.contract
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*
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* @see {@link Scope#withSubScope(String)}
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*/
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public fun <R> KotlinOps.withSubScope(childScopeName: String, block: KotlinOps.() -> R): R {
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public inline fun <R> KotlinOps.withSubScope(childScopeName: String, block: KotlinOps.() -> R): R {
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contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
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return withSubScope(childScopeName).run(block)
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}
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*/
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public val ExecutionEnvironment.tf: KotlinOps get() = JavaOps.create(this).kotlin
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//TODO we could have tf that gets itself from ExecutionEnvironment.default(). I think this will be too error prone to be worth doing
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//TODO we could have tf that gets itself from ExecutionEnvironment.default(). I think this will be too error prone to be worth doing
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//public fun <T: TType> Ops.placeholder(dtype: DataType<T>, vararg shape: Long): Placeholder<T> = placeholder(dtype, Shape.of(*shape))

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