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docs/torchvision/torchvision.models/resnet/Bottleneck

Bottleneck

torchvision.models.resnet.Bottleneck
See theBottleneck companion object
class Bottleneck[D <: BFloat16 | Float32 | Float64](inplanes: Int, planes: Int, stride: Int, downsample: Option[TensorModule[D]], groups: Int, baseWidth: Int, dilation: Int, normLayer: Int => HasWeight[D] & TensorModule[D])(implicit evidence$4: Default[D]) extends TensorModule[D]

Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) while original implementation places the stride at the first 1x1 convolution(self.conv1) according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. This variant is also known as ResNet V1.5 and improves accuracy according to https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

Attributes

Companion
object
Source
resnet.scala
Graph
Supertypes
trait TensorModule[D]
trait Tensor[D] => Tensor[D]
class Module
class Object
trait Matchable
class Any
Show all

Members list

Value members

Concrete methods

def apply(x: Tensor[D]): Tensor[D]

Attributes

Source
resnet.scala
override def toString(): String

Returns a string representation of the object.

Returns a string representation of the object.

The default representation is platform dependent.

Attributes

Returns

a string representation of the object.

Definition Classes
Source
resnet.scala

Inherited methods

def andThen[A](g: Tensor[D] => A): T1 => A

Attributes

Inherited from:
Function1
def apply(fn: Module => Unit): Module.this.type

Attributes

Inherited from:
Module
Source
Module.scala
def compose[A](g: A => Tensor[D]): A => R

Attributes

Inherited from:
Function1
def eval(): Unit

Attributes

Inherited from:
Module
Source
Module.scala
def hasBias(): Boolean

Attributes

Inherited from:
Module
Source
Module.scala
def isTraining: Boolean

Attributes

Inherited from:
Module
Source
Module.scala
def load(inputArchive: InputArchive): Unit

Attributes

Inherited from:
Module
Source
Module.scala
def loadStateDict(stateDict: Map[String, Tensor[DType]]): Unit

Attributes

Inherited from:
Module
Source
Module.scala
def modules: Seq[Module]

Attributes

Inherited from:
Module
Source
Module.scala
def modules(recurse: Boolean): Seq[Module]

Attributes

Inherited from:
Module
Source
Module.scala
def namedBuffers(recurse: Boolean): SeqMap[String, Tensor[_]]

Attributes

Inherited from:
Module
Source
Module.scala
def namedChildren: SeqMap[String, Module]

Attributes

Inherited from:
Module
Source
Module.scala
def namedModules: SeqMap[String, Module]

Attributes

Inherited from:
Module
Source
Module.scala
def namedParameters(recurse: Boolean): SeqMap[String, Tensor[_]]

Attributes

Inherited from:
Module
Source
Module.scala
def parameters(recurse: Boolean): Seq[Tensor[_]]

Attributes

Inherited from:
Module
Source
Module.scala
def parameters: Seq[Tensor[_]]

Attributes

Inherited from:
Module
Source
Module.scala
def register[M <: Module](child: M, n: String)(using name: Name): M

Attributes

Inherited from:
Module
Source
Module.scala
def registerBuffer[D <: DType](name: String, tensor: Tensor[D]): Tensor[D]

Adds a buffer to the module.

Adds a buffer to the module.

Attributes

Inherited from:
Module
Source
Module.scala
def registerBuffer[D <: DType](t: Tensor[D], n: String)(using name: Name): Tensor[D]

Attributes

Inherited from:
Module
Source
Module.scala
def registerModule[M <: Module](child: M, n: String)(using name: Name): M

Attributes

Inherited from:
Module
Source
Module.scala
def registerParameter[D <: DType](t: Tensor[D], requiresGrad: Boolean, n: String)(using name: Name): Tensor[D]

Attributes

Inherited from:
Module
Source
Module.scala
def save(outputArchive: OutputArchive): Unit

Attributes

Inherited from:
Module
Source
Module.scala
def summarize: String

Attributes

Inherited from:
Module
Source
Module.scala
def to(device: Device): Module.this.type

Attributes

Inherited from:
Module
Source
Module.scala
def train(on: Boolean): Unit

Attributes

Inherited from:
Module
Source
Module.scala

Concrete fields

val bn1: HasWeight[D] & TensorModule[D]

Attributes

Source
resnet.scala
val bn2: HasWeight[D] & TensorModule[D]

Attributes

Source
resnet.scala
val bn3: HasWeight[D] & TensorModule[D]

Attributes

Source
resnet.scala
val conv1: Conv2d[D]

Attributes

Source
resnet.scala
val conv2: Conv2d[D]

Attributes

Source
resnet.scala
val conv3: Conv2d[D]

Attributes

Source
resnet.scala
val relu: ReLU[D]

Attributes

Source
resnet.scala
val width: Int

Attributes

Source
resnet.scala
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