AbstractTensor |
ActivityTypeSet |
ActivityTypeSet.Iterator |
AdagradOptions |
AdagradParamState |
AdamOptions |
AdamParamState |
AdamWOptions |
AdamWParamState |
AdaptiveAvgPool1dImpl
Applies adaptive avgpool over a 1-D input.
|
AdaptiveAvgPool1dImplBase
Base class for all (dimension-specialized) adaptive avgpool modules.
|
AdaptiveAvgPool1dImplCloneable |
AdaptiveAvgPool1dOptions
Options for a D -dimensional adaptive avgpool module.
|
AdaptiveAvgPool2dImpl
Applies adaptive avgpool over a 2-D input.
|
AdaptiveAvgPool2dImplBase |
AdaptiveAvgPool2dImplCloneable |
AdaptiveAvgPool2dOptions |
AdaptiveAvgPool3dImpl
Applies adaptive avgpool over a 3-D input.
|
AdaptiveAvgPool3dImplBase |
AdaptiveAvgPool3dImplCloneable |
AdaptiveAvgPool3dOptions |
AdaptiveLogSoftmaxWithLossImpl
Efficient softmax approximation as described in
Efficient softmax approximation for GPUs _ by Edouard Grave, Armand Joulin,
Moustapha Cissé, David Grangier, and Hervé Jégou.
|
AdaptiveLogSoftmaxWithLossImplCloneable |
AdaptiveLogSoftmaxWithLossOptions
Options for the AdaptiveLogSoftmaxWithLoss module.
|
AdaptiveMaxPool1dImpl
Applies adaptive maxpool over a 1-D input.
|
AdaptiveMaxPool1dImplBase
Base class for all (dimension-specialized) adaptive maxpool modules.
|
AdaptiveMaxPool1dImplCloneable |
AdaptiveMaxPool1dOptions
Options for a D -dimensional adaptive maxpool module.
|
AdaptiveMaxPool2dImpl
Applies adaptive maxpool over a 2-D input.
|
AdaptiveMaxPool2dImplBase |
AdaptiveMaxPool2dImplCloneable |
AdaptiveMaxPool2dOptions |
AdaptiveMaxPool3dImpl
Applies adaptive maxpool over a 3-D input.
|
AdaptiveMaxPool3dImplBase |
AdaptiveMaxPool3dImplCloneable |
AdaptiveMaxPool3dOptions |
AliasInfo
class AliasInfo
Data structure to hold aliasing information for an Argument .
|
AliasInfoOptional |
AliasTypeSetOptional |
Allocator |
AlphaDropoutFuncOptions
Options for torch::nn::functional::alpha_dropout .
|
AlphaDropoutImpl
Applies Alpha Dropout over the input.
|
AlphaDropoutImplBase |
AlphaDropoutImplCloneable |
AnomalyMetadata |
AnomalyMode |
AnyClassType |
AnyClassTypePtr |
AnyEnumType |
AnyEnumTypePtr |
AnyListType |
AnyListTypePtr |
AnyModule
Stores a type erased Module .
|
AnyModuleVector |
AnyModuleVector.Iterator |
AnyTupleType |
AnyTupleTypePtr |
AnyType |
AnyTypePtr |
AnyValue
An implementation of std::any which stores
a type erased object, whose concrete value can be retrieved at runtime by
checking if the typeid() of a requested type matches the typeid() of
the object stored.
|
Apply |
Argument |
ArgumentArrayRef
ArrayRef - Represent a constant reference to an array (0 or more elements
consecutively in memory), i.e.
|
ArgumentDef
The templated inference code creates ArgumentDef instead of Argument ,
because that can be constructed at compile time and has a much smaller
binary size than having calls to Argument constructors in the template.
|
ArgumentDefArrayRef |
ArgumentInfo |
ArgumentSpec |
ArgumentSpecCreator.Inst |
ArgumentSpecExecutionPlanMap |
ArgumentSpecExecutionPlanMap.Iterator |
ASMoutput
The output of a single invocation of an AdaptiveLogSoftmaxWithLoss
module's forward() method.
|
Assert |
Assign |
AssignList |
AssignListIterator |
AssignListMaybe |
Attribute |
attribute_iterator |
attribute_list |
AttributeList |
AttributeListIterator |
AttributePolicy |
AttributeValue |
AugAssign |
AugAssignKind |
AutoDispatchBelowADInplaceOrView |
AutoDispatchBelowAutograd |
AutoDispatchSkipFunctionalize |
AutoFwGradMode |
AutogradContext
Context to save information during forward that can be accessed in
backward in custom autograd operations (see torch::autograd::Function
for details).
|
AutogradMeta
Each Variable has one unique AutogradMeta struct, which stores autograd
metadata fields that are necessary for tracking the Variable's autograd
history.
|
AutogradMetaFactory |
AutogradMetaInterface |
AutoGradMode |
AutogradState |
AutoNonVariableTypeMode |
AvgPool1dImpl
Applies avgpool over a 1-D input.
|
AvgPool1dImplBase
Base class for all (dimension-specialized) avgpool modules.
|
AvgPool1dImplCloneable |
AvgPool1dOptions
Options for a D -dimensional avgpool module.
|
AvgPool2dImpl
Applies avgpool over a 2-D input.
|
AvgPool2dImplBase |
AvgPool2dImplCloneable |
AvgPool2dOptions |
AvgPool3dImpl
Applies avgpool over a 3-D input.
|
AvgPool3dImplBase |
AvgPool3dImplCloneable |
AvgPool3dOptions |
Await |
AwaitPtr |
AwaitSingleElementType |
BackendMeta
This structure is intended to hold additional metadata of the specific device
backend.
|
BackendMetaRef |
BatchNorm1dImpl
Applies the BatchNorm1d function.
|
BatchNorm1dImplBase
Base class for all (dimension-specialized) batchnorm modules.
|
BatchNorm1dImplBaseBase
Base class for all (dimension-specialized) batchnorm and instancenorm
modules.
|
BatchNorm1dImplCloneable |
BatchNorm2dImpl
Applies the BatchNorm2d function.
|
BatchNorm2dImplBase |
BatchNorm2dImplBaseBase |
BatchNorm2dImplCloneable |
BatchNorm3dImpl
Applies the BatchNorm3d function.
|
BatchNorm3dImplBase |
BatchNorm3dImplBaseBase |
BatchNorm3dImplCloneable |
BatchNormFuncOptions
Options for torch::nn::functional::batch_norm .
|
BatchNormOptions
Options for the BatchNorm module.
|
BatchSize
A wrapper around a batch size value, which implements the
CustomBatchRequest interface.
|
BatchSizeOptional |
BatchSizeSampler |
BCELossImpl
Creates a criterion that measures the Binary Cross Entropy
between the target and the output.
|
BCELossImplCloneable |
BCELossOptions
Options for the BCELoss module.
|
BCEWithLogitsLossImpl
This loss combines a Sigmoid layer and the BCELoss in one single
class.
|
BCEWithLogitsLossImplCloneable |
BCEWithLogitsLossOptions
Options for the BCEWithLogitsLoss module.
|
BFloat16 |
BFloat16.from_bits_t |
BFloat16ArrayRef |
BilinearImpl
Applies a billinear transformation with optional bias.
|
BilinearImplCloneable |
BilinearOptions
Options for the Bilinear module.
|
BinOp |
bits16
bits16 is an uninterpreted dtype of a tensor with 16 bits, without any
semantics defined.
|
bits1x8
bits1x8 is an uninterpreted dtype of a tensor with 1 bit (packed to byte
boundary), without any semantics defined.
|
bits2x4
bits2x4 is an uninterpreted dtype of a tensor with 2 bits (packed to byte
boundary), without any semantics defined.
|
bits4x2
bits4x2 is an uninterpreted dtype of a tensor with 4 bits (packed to byte
boundary), without any semantics defined.
|
bits8
bits8 is an uninterpreted dtype of a tensor with 8 bits, without any
semantics defined.
|
bitset
This is a simple bitset class with sizeof(long long int) bits.
|
Blob
\brief Blob is a general container that hosts a typed pointer.
|
Block |
BlockArrayRef |
BlockWrap |
BoolArrayRef |
BooleanElementReference |
BooleanList |
BooleanListIterator |
BoolOptional |
BoolType |
BoolTypePtr |
BoolVector |
BoolVector.Iterator |
BoolVectorOptional |
Break |
buffer_iterator |
buffer_list |
BufferPolicy |
BuiltinFunction |
ByteArrayRef |
ByteOptional |
BytePointerVector |
BytePointerVector.Iterator |
C10FlagParser |
Call |
CapsuleType |
CapsuleTypePtr |
CELUImpl
Applies celu over a given input.
|
CELUImplCloneable |
CELUOptions
Options for the CELU module.
|
ChunkBatchDataset |
ChunkBatchSharedBatchDataset |
ChunkBatchSharedTensorBatchDataset |
ChunkDataReader
Interface for chunk reader, which performs data chunking and reading of
entire chunks.
|
ChunkDataset
A stateful dataset that support hierarchical sampling and prefetching of
entre chunks.
|
ChunkDatasetOptions
Options to configure a ChunkDataset .
|
ChunkMapBatchDataset |
ChunkMapDataset |
ChunkMapTensorBatchDataset |
ChunkMapTensorDataset |
ChunkRandomDataLoaderBase |
ChunkRandomTensorDataLoaderBase |
ChunkSharedBatchDataset
A dataset that wraps another dataset in a shared pointer and implements the
BatchDataset API, delegating all calls to the shared instance.
|
ChunkSharedTensorBatchDataset |
ChunkStatefulDataset
A stateful dataset is a dataset that maintains some internal state, which
will be reset() at the beginning of each epoch.
|
ChunkStatefulTensorDataset |
ChunkTensorBatchDataset |
ChunkTensorDataReader |
ChunkTensorDataset |
ClassAttribute |
ClassDef |
ClassType |
ClassType.Property |
ClassTypePropertyOptional |
ClassValue |
ClosureValue |
Code |
CodeImpl |
CompilationUnit |
CompilationUnit.FunctionType |
CompiledNodeArgs |
CompileTimeEmptyString |
ComplexType |
ComplexTypePtr |
ConstantPad1dImpl
Applies ConstantPad over a 1-D input.
|
ConstantPad1dImplBase
Base class for all (dimension-specialized) ConstantPad modules.
|
ConstantPad1dImplCloneable |
ConstantPad1dOptions
Options for a D -dimensional ConstantPad module.
|
ConstantPad2dImpl
Applies ConstantPad over a 2-D input.
|
ConstantPad2dImplBase |
ConstantPad2dImplCloneable |
ConstantPad2dOptions |
ConstantPad3dImpl
Applies ConstantPad over a 3-D input.
|
ConstantPad3dImplBase |
ConstantPad3dImplCloneable |
ConstantPad3dOptions |
ConstantString |
ConstantStringPtr |
ConstExpr |
Context |
Continue |
Conv1dFuncOptions
Options for a D -dimensional convolution functional.
|
Conv1dImpl
Applies convolution over a 1-D input.
|
Conv1dImplBase
Base class for all (dimension-specialized) convolution modules.
|
Conv1dImplCloneable |
Conv1dOptions
Options for a D -dimensional convolution module.
|
Conv1dPadding |
Conv2dFuncOptions |
Conv2dImpl
Applies convolution over a 2-D input.
|
Conv2dImplBase |
Conv2dImplCloneable |
Conv2dOptions |
Conv2dPadding |
Conv3dFuncOptions |
Conv3dImpl
Applies convolution over a 3-D input.
|
Conv3dImplBase |
Conv3dImplCloneable |
Conv3dOptions |
Conv3dPadding |
ConvPaddingMode |
ConvTranspose1dFuncOptions
Options for a D -dimensional convolution functional.
|
ConvTranspose1dImpl
Applies the ConvTranspose1d function.
|
ConvTranspose1dImplBase
Base class for all (dimension-specialized) convolution transpose modules.
|
ConvTranspose1dImplBaseBase |
ConvTranspose1dImplCloneable |
ConvTranspose1dOptions |
ConvTranspose2dFuncOptions |
ConvTranspose2dImpl
Applies the ConvTranspose2d function.
|
ConvTranspose2dImplBase |
ConvTranspose2dImplBaseBase |
ConvTranspose2dImplCloneable |
ConvTranspose2dOptions |
ConvTranspose3dFuncOptions |
ConvTranspose3dImpl
Applies the ConvTranspose3d function.
|
ConvTranspose3dImplBase |
ConvTranspose3dImplBaseBase |
ConvTranspose3dImplCloneable |
ConvTranspose3dOptions |
CosineEmbeddingLossImpl
Creates a criterion that measures the loss given input tensors
input1 , input2 , and a Tensor label target with values 1 or
-1.
|
CosineEmbeddingLossImplCloneable |
CosineEmbeddingLossOptions
Options for the CosineEmbeddingLoss module.
|
CosineSimilarityImpl
Returns the cosine similarity between :math:x_1 and :math:x_2 , computed
along dim .
|
CosineSimilarityImplCloneable |
CosineSimilarityOptions
Options for the CosineSimilarity module.
|
CppFunction
Represents a C++ function that implements an operator.
|
CppSignature |
CppSignatureOptional |
CPUGeneratorImpl |
CrossEntropyLossImpl
Creates a criterion that computes cross entropy loss between input and
target.
|
CrossEntropyLossImplCloneable |
CrossEntropyLossOptions
Options for the CrossEntropyLoss module.
|
CrossMapLRN2dImpl
See the documentation for torch::nn::CrossMapLRN2dOptions class to learn
what constructor arguments are supported for this module.
|
CrossMapLRN2dImplCloneable |
CrossMapLRN2dOptions
Options for the CrossMapLRN2d module.
|
CTCLossImpl
The Connectionist Temporal Classification loss.
|
CTCLossImplCloneable |
CTCLossOptions
Options for the CTCLoss module.
|
CUDAHooksInterface |
CustomBatchRequest
A base class for custom index types.
|
DataLoaderOptions
Options to configure a DataLoader .
|
DataPtr |
DataPtrVector.Iterator |
DDPLoggingData |
DebugInfoBase |
Decl |
Def |
DefMaybe |
DefVector |
DefVector.Iterator |
Delete |
DetailConv1dOptions
Options for a D -dimensional convolution or convolution transpose module.
|
DetailConv2dOptions |
DetailConv3dOptions |
DetectAnomalyGuard
A RAII guard that enables Anomaly Detection Mode.
|
Device
Represents a compute device on which a tensor is located.
|
DeviceGuardImplInterface
DeviceGuardImplInterface represents the virtual interface which provides
functionality to provide an RAII class for device and stream switching,
via DeviceGuard.
|
DeviceObjType |
DeviceObjTypePtr |
DeviceOptional |
DeviceTypeSet |
DeviceTypeSet.Iterator |
DictComp |
DictLiteral |
DictType |
Dimname |
DimnameArrayRef |
DimnameListOptional |
DimnameOptional |
DimnameVector |
DimnameVector.Iterator |
DimVector |
DimVectorInferExpandGeometryResult |
DimVectorOptional |
DisabledStr |
DisablePythonDispatcher |
DisableRecordFunctionGuard |
Dispatcher
Top-level dispatch interface for dispatching via the dynamic dispatcher.
|
DispatchKeyExtractor
An instance of DispatchKeyExtractor knows how to get a dispatch key given
a list of arguments for an operator call.
|
DispatchKeyOptional |
DispatchKeySet |
DispatchKeySet.Full |
DispatchKeySet.FullAfter |
DispatchKeySet.iterator |
DispatchKeySet.Raw |
DistributedRandomSampler
Select samples randomly.
|
DistributedSampler
A Sampler that selects a subset of indices to sample from and defines a
sampling behavior.
|
DistributedSequentialSampler
Select samples sequentially.
|
DLDevice_ |
DontIncreaseRefcount |
Dots |
DoubleArrayRef |
DoubleArrayRefOptional |
DoubleComplex |
DoubleComplexArrayRef |
DoubleComplexElementReference |
DoubleComplexList
An object of this class stores a list of values of type T.
|
DoubleComplexListIterator |
DoubleElementReference |
DoubleExpandingArrayOptional |
DoubleList |
DoubleListIterator |
DoubleOptional |
DoubleVector |
DoubleVector.Iterator |
DoubleVectorOptional |
Dropout2dImpl
Applies dropout over a 2-D input.
|
Dropout2dImplBase |
Dropout2dImplCloneable |
Dropout3dImpl
Applies dropout over a 3-D input.
|
Dropout3dImplBase |
Dropout3dImplCloneable |
DropoutFuncOptions
Options for torch::nn::functional::dropout .
|
DropoutImpl
Applies dropout over a 1-D input.
|
DropoutImplBase |
DropoutImplCloneable |
DropoutOptions
Options for the Dropout module.
|
Edge
Represents a particular input of a function.
|
EdgeVector |
EdgeVector.Iterator |
EllipsisIndexType |
ELUImpl
Applies elu over a given input.
|
ELUImplCloneable |
ELUOptions
Options for the ELU module.
|
EmbeddingBagFromPretrainedOptions
Options for the EmbeddingBag::from_pretrained function.
|
EmbeddingBagFuncOptions
Options for torch::nn::functional::embedding_bag .
|
EmbeddingBagImpl
Computes sums or means of 'bags' of embeddings, without instantiating the
intermediate embeddings.
|
EmbeddingBagImplCloneable |
EmbeddingBagMode |
EmbeddingBagOptions
Options for the EmbeddingBag module.
|
EmbeddingFromPretrainedOptions
Options for the Embedding::from_pretrained function.
|
EmbeddingFuncOptions
Options for torch::nn::functional::embedding .
|
EmbeddingImpl
Performs a lookup in a fixed size embedding table.
|
EmbeddingImplCloneable |
EmbeddingOptions
Options for the Embedding module.
|
EnabledStr |
EnableProfilingGuard |
EnumHolder |
EnumHolderPtr |
EnumNameValue |
EnumNameValueArrayRef |
EnumType |
Error
The primary ATen error class.
|
ErrorReport |
Example
An Example from a dataset.
|
ExampleCollation
A transformation of a batch to a new batch.
|
ExampleIterator |
ExampleOptional |
ExampleStack |
ExampleVector |
ExampleVector.Iterator |
ExampleVectorIterator |
ExampleVectorOptional |
ExceptionValue |
ExecutionPlan |
ExecutorExecutionModeOptional |
ExperimentalConfig |
Expr |
ExprList |
ExprListIterator |
ExprMaybe |
ExprStmt |
ExtraFilesMap |
ExtraFilesMap.Iterator |
FanModeType |
FeatureAlphaDropoutFuncOptions
Options for torch::nn::functional::feature_alpha_dropout .
|
FeatureAlphaDropoutImpl
See the documentation for torch::nn::FeatureAlphaDropoutOptions class to
learn what constructor arguments are supported for this module.
|
FeatureAlphaDropoutImplBase |
FeatureAlphaDropoutImplCloneable |
FileLineFunc |
FlattenImpl
A placeholder for Flatten operator
See https://pytorch.org/docs/master/generated/torch.nn.Flatten.html to learn
about the exact behavior of this module.
|
FlattenImplCloneable |
FlattenOptions
Options for the Flatten module.
|
Float8_e4m3fn |
Float8_e4m3fn.from_bits_t |
Float8_e5m2 |
Float8_e5m2.from_bits_t |
FloatArrayRef |
FloatComplex |
FloatComplexArrayRef |
FloatOptional |
FloatType |
FloatTypePtr |
FoldImpl
Applies fold over a 3-D input.
|
FoldImplCloneable |
FoldOptions
Options for the Fold module.
|
For |
ForwardADLevel |
ForwardGrad |
FractionalMaxPool2dImpl
Applies fractional maxpool over a 2-D input.
|
FractionalMaxPool2dImplCloneable |
FractionalMaxPool2dOptions |
FractionalMaxPool3dImpl
Applies fractional maxpool over a 3-D input.
|
FractionalMaxPool3dImplCloneable |
FractionalMaxPool3dOptions |
FullDataLoaderOptions
Like DataLoaderOptions , but without any unconfigured state.
|
Function |
FunctionalityOffsetAndMask |
FunctionCrossMapLRN2d
To use custom autograd operations, implement a Function subclass with
static forward and backward functions:
forward can take as many arguments as you want and should return either a
variable list or a Variable.
|
FunctionPostHook |
FunctionPostHookVector |
FunctionPostHookVector.Iterator |
FunctionPreHook |
FunctionPreHookVector |
FunctionPreHookVector.Iterator |
FunctionSchema |
FunctionSchemaOptional |
FunctionSchemaVector.Iterator |
FunctionType |
FunctionVector |
FunctionVector.Iterator |
FuncTorchTLSBase |
FusionStrategy |
Future |
Future.FutureError |
FuturePtr |
FuturePtrArrayRef |
FuturePtrElementReference |
FuturePtrList |
FuturePtrListIterator |
FutureSingleElementType |
GatheredContext |
GELUImpl
Applies gelu over a given input.
|
GELUImplCloneable |
GELUOptions
Options for the GELU module.
|
Generator
Note [Acquire lock when using random generators]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Generator and its derived classes are NOT thread-safe.
|
GeneratorImpl |
GeneratorImplPtr |
GeneratorOptional |
GeneratorType |
GeneratorTypePtr |
GenericDict
An object of this class stores a map from Key to Value.
|
GenericDictEntryRef
A reference to an entry in the Dict.
|
GenericDictIterator |
GenericElementReference |
GenericList |
GenericListIterator |
Global |
GLUImpl
Applies glu over a given input.
|
GLUImplCloneable |
GLUOptions
Options for the GLU module.
|
GradMode |
Graph |
graph_node_list |
graph_node_list_iterator |
GraphExecutor |
GraphExecutorState |
GraphFunction |
GraphOptimizerEnabledGuard |
GraphVector |
GraphVector.Iterator |
GridSampleFuncOptions
Options for torch::nn::functional::grid_sample .
|
GridSampleMode |
GridSamplePaddingMode |
GroupNormImpl
Applies Group Normalization over a mini-batch of inputs as described in
the paper Group Normalization _ .
|
GroupNormImplCloneable |
GroupNormOptions
Options for the GroupNorm module.
|
GRUCellImpl
A gated recurrent unit (GRU) cell.
|
GRUCellImplBase |
GRUCellImplCloneable |
GRUCellOptions
Options for the GRUCell module.
|
GRUImpl
A multi-layer gated recurrent unit (GRU) module.
|
GRUImplBase |
GRUImplCloneable |
GRUOptions
Options for the GRU module.
|
GumbelSoftmaxFuncOptions
Options for torch::nn::functional::gumbel_softmax .
|
Half |
Half.from_bits_t |
HalfArrayRef |
HalfComplex |
HardshrinkImpl
Applies the hard shrinkage function element-wise.
|
HardshrinkImplCloneable |
HardshrinkOptions
Options for the Hardshrink module.
|
HardtanhImpl
Applies the HardTanh function element-wise.
|
HardtanhImplCloneable |
HardtanhOptions
Options for the Hardtanh module.
|
HashAliasedIValueMap |
HashAliasedIValueMap.Iterator |
HashAliasedIValues |
HashAliasedIValues.Iterator |
HermeticPyObjectTLS |
HingeEmbeddingLossImpl
Creates a criterion that measures the loss given an input tensor :math:x
and a labels tensor :math:y (containing 1 or -1).
|
HingeEmbeddingLossImplCloneable |
HingeEmbeddingLossOptions
Options for the HingeEmbeddingLoss module.
|
HIPHooksInterface |
HuberLossImpl
Creates a criterion that uses a squared term if the absolute
element-wise error falls below delta and a delta-scaled L1 term otherwise.
|
HuberLossImplCloneable |
HuberLossOptions
Options for the HuberLoss module.
|
Ident |
IdentityImpl
A placeholder identity operator that is argument-insensitive.
|
IdentityImplCloneable |
IdentList |
IdentListIterator |
If |
IMethod |
InferenceMode |
InferredType |
InlinedCallStack |
InlinedCallStackOptional |
InputArchive
A recursive representation of tensors that can be deserialized from a file
or stream.
|
InstanceNorm1dImpl
Applies the InstanceNorm1d function.
|
InstanceNorm1dImplBase
Base class for all (dimension-specialized) instance norm modules
|
InstanceNorm1dImplBaseBase |
InstanceNorm1dImplCloneable |
InstanceNorm2dImpl
Applies the InstanceNorm2d function.
|
InstanceNorm2dImplBase |
InstanceNorm2dImplBaseBase |
InstanceNorm2dImplCloneable |
InstanceNorm3dImpl
Applies the InstanceNorm3d function.
|
InstanceNorm3dImplBase |
InstanceNorm3dImplBaseBase |
InstanceNorm3dImplCloneable |
InstanceNormFuncOptions
Options for torch::nn::functional::instance_norm .
|
InstanceNormOptions
Options for the InstanceNorm module.
|
Instruction |
IntArrayRef |
InterfaceType |
InterpolateFuncOptions
Options for torch::nn::functional::interpolate .
|
InterpolateMode |
IntOptional |
IntSizedSmallVectorBase
This is all the stuff common to all SmallVectors.
|
IntType |
IntTypePtr |
IValue
IValue (Interpreter Value) is a tagged union over the types
supported by the TorchScript interpreter.
|
IValueArrayRef |
IValueOptional |
IValueOptionalVector |
IValueOptionalVector.Iterator |
IValueVector |
IValueVector.Iterator |
JavaBatchDataset |
JavaDataset
Abstract class for stateless datasets to be subclassed by Java user code.
|
JavaDatasetBase |
JavaDistributedRandomDataLoaderBase |
JavaDistributedRandomTensorDataLoaderBase |
JavaDistributedSequentialDataLoaderBase |
JavaDistributedSequentialTensorDataLoaderBase |
JavaRandomDataLoaderBase |
JavaRandomTensorDataLoaderBase |
JavaSequentialDataLoaderBase |
JavaSequentialTensorDataLoaderBase |
JavaStatefulBatchDataset |
JavaStatefulDataLoaderBase |
JavaStatefulDataset
Abstract class for stateful datasets to be subclassed by Java user code.
|
JavaStatefulDatasetBase |
JavaStatefulTensorBatchDataset |
JavaStatefulTensorDataLoaderBase |
JavaStatefulTensorDataset |
JavaStatefulTensorDatasetBase |
JavaStreamBatchDataset |
JavaStreamDataLoaderBase |
JavaStreamDataset
Abstract class for stateless stream datasets to be subclassed by Java user code.
|
JavaStreamTensorBatchDataset |
JavaStreamTensorDataLoaderBase |
JavaStreamTensorDataset |
JavaTensorBatchDataset |
JavaTensorDataset |
JavaTensorDatasetBase |
JitModule |
JitNode |
JitNodeVector |
JitNodeVector.Iterator |
JitNodeWrap |
JitObject |
JitObject.Property |
kArea |
kBatchMean |
kBicubic |
kBilinear |
kBorder |
kCircular |
kConstant |
kConv1D |
kConv2D |
kConv3D |
kConvTranspose1D |
kConvTranspose2D |
kConvTranspose3D |
KernelFunction
KernelFunction is similar to std::function but stores a kernel function.
|
kFanIn |
kFanOut |
kGELU |
kGRU |
KLDivLossImpl
The Kullback-Leibler divergence loss measure
See https://pytorch.org/docs/master/nn.html#torch.nn.KLDivLoss to learn
about the exact behavior of this module.
|
KLDivLossImplCloneable |
KLDivLossOptions
Options for the KLDivLoss module.
|
KLDivLossReduction |
kLeakyReLU |
kLinear |
kLSTM |
kMax |
kMean |
kMish |
kNearest |
kNearestExact |
kNone |
kReflect |
kReflection |
kReLU |
kReplicate |
kRNN_RELU |
kRNN_TANH |
kSame |
kSigmoid |
kSiLU |
kSum |
kTanh |
kTrilinear |
kValid |
kZeros |
L1LossImpl
Creates a criterion that measures the mean absolute error (MAE) between each
element in the input : math :x and target : y .
|
L1LossImplCloneable |
L1LossOptions
Options for the L1Loss module.
|
LayerNormImpl
Applies Layer Normalization over a mini-batch of inputs as described in
the paper Layer Normalization _ .
|
LayerNormImplCloneable |
LayerNormOptions
Options for the LayerNorm module.
|
LayoutEnumerationType |
LayoutOptional |
LayoutType |
LayoutTypePtr |
LBFGSOptions |
LBFGSParamState |
LeakyReLUImpl
Applies the LeakyReLU function element-wise.
|
LeakyReLUImplCloneable |
LeakyReLUOptions
Options for the LeakyReLU module.
|
LegacyTensorConstructor |
Library
This object provides the API for defining operators and providing
implementations at dispatch keys.
|
Library.Kind
\private
Which type of macro produced this Library
|
LinearImpl
Applies a linear transformation with optional bias.
|
LinearImplCloneable |
LinearOptions
Options for the Linear module.
|
ListComp |
ListLiteral |
ListSingleElementType |
ListType |
LocalDispatchKeySet |
LocalResponseNormImpl
Applies local response normalization over an input signal composed
of several input planes, where channels occupy the second dimension.
|
LocalResponseNormImplCloneable |
LocalResponseNormOptions
Options for the LocalResponseNorm module.
|
LogSigmoidImpl
Applies the LogSigmoid function element-wise.
|
LogSigmoidImplCloneable |
LogSoftmaxImpl
Applies the LogSoftmax function element-wise.
|
LogSoftmaxImplCloneable |
LogSoftmaxOptions
Options for the LogSoftmax module.
|
LongArrayRef |
LongArrayRefOptional |
LongElementReference |
LongExpandingArrayOptional |
LongList |
LongListIterator |
LongOptional |
LongOptionalArrayRef |
LongOptionalVector |
LongOptionalVector.Iterator |
LongSmallVectorBase |
LongSmallVectorCommon |
LongSmallVectorImpl |
LongVaryingShape |
LongVector |
LongVector.Iterator |
LongVectorOptional |
LossReduction |
LPPool1dImpl
Applies the LPPool1d function element-wise.
|
LPPool1dImplBase
Base class for all (dimension-specialized) lppool modules.
|
LPPool1dImplCloneable |
LPPool1dOptions
Options for a D -dimensional lppool module.
|
LPPool2dImpl
Applies the LPPool2d function element-wise.
|
LPPool2dImplBase |
LPPool2dImplCloneable |
LPPool2dOptions |
LRScheduler |
LSTMCellImpl
A long short-term memory (LSTM) cell.
|
LSTMCellImplBase |
LSTMCellImplCloneable |
LSTMCellOptions
Options for the LSTMCell module.
|
LSTMImpl
A multi-layer long-short-term-memory (LSTM) module.
|
LSTMImplBase |
LSTMImplCloneable |
LSTMOptions
Options for the LSTM module.
|
MarginRankingLossImpl
Creates a criterion that measures the loss given
inputs :math:x1 , :math:x2 , two 1D mini-batch Tensors ,
and a label 1D mini-batch tensor :math:y (containing 1 or -1).
|
MarginRankingLossImplCloneable |
MarginRankingLossOptions
Options for the MarginRankingLoss module.
|
MatchedSchema |
MatchTypeReturn |
MaxPool1dImpl
Applies maxpool over a 1-D input.
|
MaxPool1dImplBase
Base class for all (dimension-specialized) maxpool modules.
|
MaxPool1dImplCloneable |
MaxPool1dOptions
Options for a D -dimensional maxpool module.
|
MaxPool2dImpl
Applies maxpool over a 2-D input.
|
MaxPool2dImplBase |
MaxPool2dImplCloneable |
MaxPool2dOptions |
MaxPool3dImpl
Applies maxpool over a 3-D input.
|
MaxPool3dImplBase |
MaxPool3dImplCloneable |
MaxPool3dOptions |
MaxUnpool1dImpl
Applies maxunpool over a 1-D input.
|
MaxUnpool1dImplBase
Base class for all (dimension-specialized) maxunpool modules.
|
MaxUnpool1dImplCloneable |
MaxUnpool1dOptions
Options for a D -dimensional maxunpool module.
|
MaxUnpool2dImpl
Applies maxunpool over a 2-D input.
|
MaxUnpool2dImplBase |
MaxUnpool2dImplCloneable |
MaxUnpool2dOptions |
MaxUnpool3dImpl
Applies maxunpool over a 3-D input.
|
MaxUnpool3dImplBase |
MaxUnpool3dImplCloneable |
MaxUnpool3dOptions |
MemoryFormatOptional |
MemoryFormattEnumerationType |
MetaBase |
Method |
MethodOptional |
MishImpl
Applies mish over a given input.
|
MishImplCloneable |
MNIST
The MNIST dataset.
|
MNIST.Mode
The mode in which the dataset is loaded.
|
MNISTBatchDataset
A dataset that can yield data only in batches.
|
MNISTDataset
A dataset that can yield data in batches, or as individual examples.
|
MNISTMapBatchDataset |
MNISTMapDataset
A MapDataset is a dataset that applies a transform to a source dataset.
|
MNISTRandomDataLoaderBase |
Module
The base class for all modules in PyTorch.
|
module_iterator |
module_list |
ModuleDictImplCloneable
The clone() method in the base Module class does not have knowledge of
the concrete runtime type of its subclasses.
|
ModuleInstanceInfo
ModuleInstanceInfo is a structure to include the module type and instance
name.
|
ModuleInstanceInfoOptional |
ModuleListImplCloneable |
ModulePolicy |
MPSHooksInterface |
MSELossImpl
Creates a criterion that measures the mean squared error (squared L2 norm)
between each element in the input :math:x and target :math:y .
|
MSELossImplCloneable |
MSELossOptions
Options for the MSELoss module.
|
mt19937_data_pod
mt19937_data_pod is used to get POD data in and out
of mt19937_engine.
|
mt19937_engine |
MTIAHooksInterface |
MultiheadAttentionImpl
Applies the MultiheadAttention function element-wise.
|
MultiheadAttentionImplCloneable |
MultiheadAttentionOptions
Options for the MultiheadAttention module.
|
MultiLabelMarginLossImpl
Creates a criterion that optimizes a multi-class multi-classification
hinge loss (margin-based loss) between input :math:x (a 2D mini-batch
Tensor ) and output :math:y (which is a 2D Tensor of target class
indices).
|
MultiLabelMarginLossImplCloneable |
MultiLabelMarginLossOptions
Options for the MultiLabelMarginLoss module.
|
MultiLabelSoftMarginLossImpl
Creates a criterion that optimizes a multi-label one-versus-all
loss based on max-entropy, between input :math:x and target :math:y of
size :math:(N, C) .
|
MultiLabelSoftMarginLossImplCloneable |
MultiLabelSoftMarginLossOptions
Options for the MultiLabelSoftMarginLoss module.
|
MultiMarginLossImpl
Creates a criterion that optimizes a multi-class classification hinge
loss (margin-based loss) between input :math:x (a 2D mini-batch Tensor )
and output :math:y (which is a 1D tensor of target class indices, :math:0
\leq y \leq \text{x.size}(1)-1 ).
|
MultiMarginLossImplCloneable |
MultiMarginLossOptions
Options for the MultiMarginLoss module.
|
named_attribute_iterator |
named_attribute_list |
named_buffer_iterator |
named_buffer_list |
named_module_iterator |
named_module_list |
named_parameter_iterator |
named_parameter_list |
NamedIValue |
NamedJitModule |
NamedTensor |
NamedTensorMeta |
NamedTensorMeta.HAS_NON_WILDCARD |
NamedTensorMetaInterface |
NamedTupleConstructor |
NamedType |
NamedValue
A value with optional extra name and location information.
|
NamedValueArrayRef |
NamedValueOptional |
NameMangler
class NameMangler
Utility to mangle qualified names in order to make them unique.
|
NamesMode |
NativeResolver |
NLLLossImpl
The negative log likelihood loss.
|
NLLLossImplCloneable |
NLLLossOptions
Options for the NLLLoss module.
|
Node |
Node.undefined_input |
NodeSet |
NodeSet.Iterator |
NoGradGuard |
NoNamesGuard |
NoneType |
NoneTypePtr |
Nonlinearity |
NormalizeFuncOptions
Options for torch::nn::functional::normalize .
|
NoTF32Guard |
NumberType |
NumberTypePtr |
OperandInfo |
Operation |
Operator |
OperatorHandle
This is a handle to an operator schema registered with the dispatcher.
|
OperatorHandleOptional |
OperatorKernel
Inherit from OperatorKernel to implement a c10 kernel.
|
OperatorName |
OperatorNameOptional |
OperatorSet |
OperatorVector |
OperatorVector.Iterator |
OpRegistrationListener
Implement this interface and register your instance with the dispatcher
to get notified when operators are registered or deregistered with
the dispatcher.
|
Optimizer |
OptimizerCloneableAdagradOptions |
OptimizerCloneableAdagradParamState |
OptimizerCloneableAdamOptions |
OptimizerCloneableAdamParamState |
OptimizerCloneableAdamWOptions |
OptimizerCloneableAdamWParamState |
OptimizerCloneableLBFGSOptions |
OptimizerCloneableLBFGSParamState |
OptimizerCloneableRMSpropOptions |
OptimizerCloneableRMSpropParamState |
OptimizerCloneableSGDOptions |
OptimizerCloneableSGDParamState |
OptimizerOptions |
OptimizerParamGroup
Stores parameters in the param_group and stores a pointer to the
OptimizerOptions
|
OptimizerParamGroupVector |
OptimizerParamGroupVector.Iterator |
OptimizerParamState |
OptionalDeviceGuard
A OptionalDeviceGuard is an RAII class that sets a device to some value on
initialization, and resets the device to its original value on destruction.
|
OptionalType |
ORTHooksInterface |
OutputArchive |
PackedSequence
Holds the data and list of batch_sizes of a packed sequence.
|
PaddingMode |
PairwiseDistanceImpl
Returns the batchwise pairwise distance between vectors :math:v_1 ,
:math:v_2 using the p-norm.
|
PairwiseDistanceImplCloneable |
PairwiseDistanceOptions
Options for the PairwiseDistance module.
|
Param |
parameter_iterator |
parameter_list |
ParameterDictImplCloneable |
ParameterListImplCloneable |
ParameterPolicy |
ParamList |
ParamListIterator |
Pass |
PixelShuffleImpl
Rearranges elements in a tensor of shape :math:(*, C \times r^2, H, W)
to a tensor of shape :math:(*, C, H \times r, W \times r) , where r is an
upscale factor.
|
PixelShuffleImplCloneable |
PixelShuffleOptions
Options for the PixelShuffle module.
|
PixelUnshuffleImpl
Reverses the PixelShuffle operation by rearranging elements in a tensor of
shape :math:(*, C, H \times r, W \times r) to a tensor of shape :math:(*,
C \times r^2, H, W) , where r is a downscale factor.
|
PixelUnshuffleImplCloneable |
PixelUnshuffleOptions
Options for the PixelUnshuffle module.
|
PlacementDeleteContext |
PODLocalDispatchKeySet |
PointerPair |
PointerPairOptional |
PoissonNLLLossImpl
Negative log likelihood loss with Poisson distribution of target.
|
PoissonNLLLossImplCloneable |
PoissonNLLLossOptions
Options for the PoissonNLLLoss module.
|
PostAccumulateGradHook |
PReLUImpl
Applies the PReLU function element-wise.
|
PReLUImplCloneable |
PReLUOptions
Options for the PReLU module.
|
pretty_tree |
PrintValue |
PrivateUse1HooksInterface |
ProfilerConfig |
Property |
PropertyList |
PropertyListIterator |
PropertyListMaybe |
PropertyVector |
PropertyVector.Iterator |
PyInterpreter |
PyInterpreterVTable |
PyObjectHolder |
PyObjectHolderPtr |
PyObjectType |
PyObjectTypePtr |
PythonDispatcherTLS |
PythonTorchFunctionTLS |
QEngineVector |
QEngineVector.Iterator |
qint32
qint32 is for signed 32 bit quantized Tensors
|
qint8
This is the data type for quantized Tensors.
|
QSchemeType |
QSchemeTypePtr |
QualifiedName |
QualifiedNameOptional |
Quantizer
Quantizer is the class for storing all the information
that's necessary to perform quantize and dequantize
operation.
|
QuantizerPtr |
QuantizerType |
QuantizerTypePtr |
quint2x4
quint2x4 is for un-signed 2 bit quantized Tensors that are packed to byte
boundary.
|
quint4x2
quint4x2 is for un-signed 4 bit quantized Tensors that are packed to byte
boundary.
|
quint8
quint8 is for unsigned 8 bit quantized Tensors
|
Raise |
RandomSampler
A Sampler that returns random indices.
|
ReadAdapterInterface |
RecordFunction |
RecordFunctionCallbacksEntry |
RecordFunctionGuard |
RecordFunctionHandleIntList |
RecordFunctionHandleIntList.Iterator |
RecordFunctionHandleIntPair |
RecordFunctionTLS |
ReflectionPad1dImpl
Applies ReflectionPad over a 1-D input.
|
ReflectionPad1dImplBase
Base class for all (dimension-specialized) ReflectionPad modules.
|
ReflectionPad1dImplCloneable |
ReflectionPad1dOptions
Options for a D -dimensional ReflectionPad module.
|
ReflectionPad2dImpl
Applies ReflectionPad over a 2-D input.
|
ReflectionPad2dImplBase |
ReflectionPad2dImplCloneable |
ReflectionPad2dOptions |
ReflectionPad3dImpl
Applies ReflectionPad over a 3-D input.
|
ReflectionPad3dImplBase |
ReflectionPad3dImplCloneable |
ReflectionPad3dOptions |
RegisterOperators
An instance of this class handles the registration for one or more operators.
|
RegistrationHandleRAII |
ReLU6Impl
Applies the ReLU6 function element-wise.
|
ReLU6ImplCloneable |
ReLU6Options
Options for the ReLU6 module.
|
ReLUImpl
Applies the ReLU function element-wise.
|
ReLUImplCloneable |
ReLUOptions
Options for the ReLU module.
|
ReplicationPad1dImpl
Applies ReplicationPad over a 1-D input.
|
ReplicationPad1dImplBase
Base class for all (dimension-specialized) ReplicationPad modules.
|
ReplicationPad1dImplCloneable |
ReplicationPad1dOptions
Options for a D -dimensional ReplicationPad module.
|
ReplicationPad2dImpl
Applies ReplicationPad over a 2-D input.
|
ReplicationPad2dImplBase |
ReplicationPad2dImplCloneable |
ReplicationPad2dOptions |
ReplicationPad3dImpl
Applies ReplicationPad over a 3-D input.
|
ReplicationPad3dImplBase |
ReplicationPad3dImplCloneable |
ReplicationPad3dOptions |
Resolver
class Resolver
Represents an "outer environment" in which we an look up names and return
a corresponding SugaredValue.
|
ResolverVector |
ResolverVector.Iterator |
Return |
RMSpropOptions |
RMSpropParamState |
RNNBaseMode |
RNNCellImpl
An Elman RNN cell with tanh or ReLU non-linearity.
|
RNNCellImplBase
Base class for all RNNCell implementations (intended for code sharing).
|
RNNCellImplCloneable |
RNNCellOptions
Options for the RNNCell module.
|
RNNCellOptionsBase
Common options for RNNCell, LSTMCell and GRUCell modules
|
RNNImpl
A multi-layer Elman RNN module with Tanh or ReLU activation.
|
RNNImplBase
Base class for all RNN implementations (intended for code sharing).
|
RNNImplCloneable |
RNNNonlinearity |
RNNOptions
Options for the RNN module.
|
RNNOptionsBase
Common options for RNN, LSTM and GRU modules.
|
RRefInterface |
RRefInterfacePtr |
RRefSingleElementType |
RReLUFuncOptions
Options for torch::nn::functional::rrelu .
|
RReLUImpl
Applies the RReLU function element-wise.
|
RReLUImplCloneable |
RReLUOptions
Options for the RReLU module.
|
SafePyHandle |
SafePyObject |
Sampler
A Sampler is an object that yields an index with which to access a
dataset.
|
SavedTensorDefaultHooks |
SavedTensorDefaultHooksTLS |
Scalar
Scalar represents a 0-dimensional tensor which contains a single element.
|
ScalarArrayRef |
ScalarOptional |
ScalarTypeArrayRef |
ScalarTypeEnumerationType |
ScalarTypeOptional |
ScalarTypeType |
ScalarTypeTypePtr |
ScalarTypeVector |
ScalarTypeVector.Iterator |
SchemaArgument
struct SchemaArgument
Structure used to represent arguments or returns for a schema.
|
Scope |
ScopeOptional |
ScriptTypeParser
class ScriptTypeParser
Parses expressions in our typed AST format (TreeView) into types and
typenames.
|
Select |
Self |
SELUImpl
Applies the selu function element-wise.
|
SELUImplCloneable |
SELUOptions
Options for the SELU module.
|
SequentialImplCloneable |
SequentialSampler
A Sampler that returns indices sequentially.
|
SGDOptions |
SGDParamState |
ShapeSymbol |
ShapeSymbolVector |
ShapeSymbolVector.Iterator |
ShapeSymbolVectorOptional |
SharedClassTypeVector |
SharedClassTypeVector.Iterator |
SharedModuleVector |
SharedModuleVector.Iterator |
SharedParserData |
SharedSugaredValueVector |
SharedSugaredValueVector.Iterator |
SharedType |
ShortArrayRef |
SigmoidImpl
Applies sigmoid over a given input.
|
SigmoidImplCloneable |
SiLUImpl
Applies silu over a given input.
|
SiLUImplCloneable |
SingletonTypePtr |
SizesAndStrides |
SizeTArrayRef |
SizeTMatchedSchemaPair |
SizeTOptional |
SizeTVector |
SizeTVector.Iterator |
SizeTVectorOptional |
Slice |
SliceExpr |
SlotCursor |
SmoothL1LossImpl
Creates a criterion that uses a squared term if the absolute
element-wise error falls below beta and an L1 term otherwise.
|
SmoothL1LossImplCloneable |
SmoothL1LossOptions
Options for the SmoothL1Loss module.
|
SoftMarginLossImpl
Creates a criterion that optimizes a two-class classification
logistic loss between input tensor :math:x and target tensor :math:y
(containing 1 or -1).
|
SoftMarginLossImplCloneable |
SoftMarginLossOptions
Options for the SoftMarginLoss module.
|
Softmax2dImpl
Applies the Softmax2d function element-wise.
|
Softmax2dImplCloneable |
SoftmaxImpl
Applies the Softmax function.
|
SoftmaxImplCloneable |
SoftmaxOptions
Options for the Softmax module.
|
SoftminImpl
Applies the Softmin function element-wise.
|
SoftminImplCloneable |
SoftminOptions
Options for the Softmin module.
|
SoftplusImpl
Applies softplus over a given input.
|
SoftplusImplCloneable |
SoftplusOptions
Options for the Softplus module.
|
SoftshrinkImpl
Applies the soft shrinkage function element-wise.
|
SoftshrinkImplCloneable |
SoftshrinkOptions
Options for the Softshrink module.
|
SoftsignImpl
Applies Softsign over a given input.
|
SoftsignImplCloneable |
Source |
Source.CopiesString |
SourceLocation
Represents a location in source code (for debugging).
|
SourceRange |
SourceRangeHasher |
SourceRangeOptional |
SourceRangeUnpickler |
SpecialFormValue |
SplitUntil32Bit
A container-like struct that acts as if it contains splits of a
TensorIterator that can use 32-bit indexing.
|
SplitUntil32Bit.iterator |
StackEntry |
Starred |
Stmt |
StmtList |
StmtListIterator |
Storage |
Storage.use_byte_size_t |
StorageImpl |
StorageImpl.use_byte_size_t |
StorageImplPtr |
StorageType |
StorageTypePtr |
Stream
A stream is a software mechanism used to synchronize launched kernels
without requiring explicit synchronizations between kernels.
|
Stream.Default |
Stream.Unsafe |
StreamData3 |
StreamObjType |
StreamObjTypePtr |
StreamOptional |
StreamSampler
A sampler for (potentially infinite) streams of data.
|
Stride |
StrideArrayRef |
StrideOptional |
StrideVaryingShape |
StrideVector |
StrideVector.Iterator |
StrideVectorOptional |
StringAnyModuleDict |
StringAnyModuleDictItem |
StringAnyModuleDictItemVector |
StringAnyModuleDictItemVector.Iterator |
StringAnyModulePair |
StringAnyModuleVector |
StringArrayRef |
StringBoolMap |
StringBoolMap.Iterator |
StringCordView |
StringCordView.Iterator |
StringGenericListDict |
StringIValueMap |
StringIValueMap.Iterator |
StringLiteral |
StringLongMap |
StringLongMap.Iterator |
StringLongVector |
StringOptional |
StringSet |
StringSet.Iterator |
StringSharedModuleDict |
StringSharedModuleDictItem |
StringSharedModuleDictItemVector |
StringSharedModuleDictItemVector.Iterator |
StringSharedModulePair |
StringSharedModuleVector |
StringSizeTMap |
StringSizeTMap.Iterator |
StringStringMap |
StringStringMap.Iterator |
StringTensorDict
An ordered dictionary implementation, akin to Python's OrderedDict .
|
StringTensorDictItem |
StringTensorDictItemVector |
StringTensorDictItemVector.Iterator |
StringTensorPair |
StringTensorVector |
StringType |
StringTypePtr |
StringValueMap |
StringValueMap.Iterator |
StringVector |
StringVector.Iterator |
StringVectorOptional |
StringView |
StringViewOptional |
StringViewVector |
StringViewVector.Iterator |
StringViewVectorOptional |
StrongTypePtr |
Subscript |
SugaredTupleValue |
SugaredValue |
SwapSavedVariables |
Symbol |
SymbolArrayRef |
SymbolicShape |
SymbolicShapeMeta |
SymbolSet |
SymbolSet.Iterator |
SymbolVector |
SymbolVector.Iterator |
SymBool |
SymDimVector
This is a 'vector' (really, a variable-sized array), optimized
for the case when the array is small.
|
SymDimVectorOptional |
SymFloat |
SymInt |
SymInt.Unchecked |
SymIntArrayRef |
SymIntArrayRefOptional |
SymIntOptional |
SymIntSmallVectorBase
SmallVectorTemplateBase - This is where we put
method implementations that are designed to work with non-trivial T's.
|
SymIntSmallVectorCommon
This is the part of SmallVectorTemplateBase which does not depend on whether
the type T is a POD.
|
SymIntSmallVectorImpl
This class consists of common code factored out of the SmallVector class to
reduce code duplication based on the SmallVector 'N' template parameter.
|
SymIntVector |
SymIntVector.Iterator |
SymNode |
SymNodeArrayRef |
SymNodeImpl |
T_DataPtrSizeT_T |
T_IntInt_T |
T_LongLong_T |
T_PackedSequenceT_TensorTensor_T_T |
T_PackedSequenceTensor_T |
T_StringSizeTSizeT_T |
T_StringSizeTSizeT_TOptional |
T_TensorMaybeOwnedTensorMaybeOwned_T |
T_TensorMaybeOwnedTensorMaybeOwnedTensorMaybeOwned_T |
T_TensorT_TensorTensor_T_T |
T_TensorTensor_T |
T_TensorTensor_TOptional |
T_TensorTensorDoubleLong_T |
T_TensorTensorTensor_T |
T_TensorTensorTensorTensor_T |
T_TensorTensorTensorTensorTensor_T |
T_TensorTensorTensorTensorTensorTensorTensor_T |
T_TensorTensorTensorTensorVector_T |
T_TensorTensorVector_T |
T_TensorTensorVectorTensorVector_T |
T_TypePtrLong_T |
T_TypePtrLong_TOptional |
TagArrayRef |
TanhImpl
Applies Tanh over a given input.
|
TanhImplCloneable |
TanhshrinkImpl
Applies Tanhshrink over a given input.
|
TanhshrinkImplCloneable |
Tensor |
TensorArg |
TensorArgArrayRef |
TensorArrayRef |
TensorArrayRefOptional |
TensorBase |
TensorBaseMaybeOwned |
TensorBatchDataset |
TensorCastValue |
TensorDataset
A dataset of tensors.
|
TensorDatasetBase |
TensorDeque |
TensorDeque.Iterator |
TensorElementReference |
TensorExample |
TensorExampleCollation |
TensorExampleIterator |
TensorExampleOptional |
TensorExampleStack |
TensorExampleVector |
TensorExampleVector.Iterator |
TensorExampleVectorIterator |
TensorExampleVectorOptional |
TensorGeometry |
TensorGeometryArg |
TensorImpl
The low-level representation of a tensor, which contains a pointer
to a storage (which contains the actual data) and metadata (e.g., sizes and
strides) describing this particular view of the data as a tensor.
|
TensorImpl.ImplType |
TensorImpl.LongIdentity |
TensorImpl.SizesStridesPolicy |
TensorImpl.SymIntIdentity |
TensorImplPtr |
TensorImplSet |
TensorImplSet.Iterator |
TensorImplVector |
TensorImplVector.Iterator |
TensorIndex |
TensorIndexArrayRef |
TensorIndexVector |
TensorIndexVector.Iterator |
TensorIterator |
TensorIteratorBase |
TensorIteratorConfig |
TensorList |
TensorListIterator |
TensorMaker
Provides a fluent API to construct tensors from external data.
|
TensorMaybeOwned
A smart pointer around either a borrowed or owned T.
|
TensorName |
TensorNames |
TensorOptional |
TensorOptionalArrayRef |
TensorOptionalElementReference |
TensorOptionalList |
TensorOptionalListIterator |
TensorOptionalVector |
TensorOptionalVector.Iterator |
TensorOptions
A class to encapsulate construction axes of an Tensor.
|
TensorType |
TensorVector |
TensorVector.Iterator |
TensorVectorOptional |
TernaryIf |
ThreadLocalDebugInfo |
ThreadLocalPythonObjects |
ThreadLocalState |
ThresholdImpl
Applies the Threshold function element-wise.
|
ThresholdImplCloneable |
ThresholdOptions
Options for the Threshold module.
|
Token |
TorchDispatchModeTLS |
TransformerActivation |
TransformerDecoderImpl
TransformerDecoder is a stack of N decoder layers.
|
TransformerDecoderImplCloneable |
TransformerDecoderLayerImpl
TransformerDecoderLayer is made up of self-attn, multi-head-attn and
feedforward network.
|
TransformerDecoderLayerImplCloneable |
TransformerDecoderLayerOptions
Options for the TransformerDecoderLayer module.
|
TransformerDecoderOptions
Options for the TransformerDecoder module.
|
TransformerEncoderImpl
TransformerEncoder module.
|
TransformerEncoderImplCloneable |
TransformerEncoderLayerImpl
TransformerEncoderLayer module.
|
TransformerEncoderLayerImplCloneable |
TransformerEncoderLayerOptions
Options for the TransformerEncoderLayer
Example:
|
TransformerEncoderOptions
Options for the TransformerEncoder
Example:
|
TransformerImpl
A transformer model.
|
TransformerImplCloneable |
TransformerOptions
Options for the Transformer module
Example:
|
Tree |
TreeRef |
TreeRefStringMap |
TreeRefStringMap.Iterator |
TreeView |
TripletMarginLossImpl
Creates a criterion that measures the triplet loss given an input
tensors :math:x1 , :math:x2 , :math:x3 and a margin with a value greater
than :math:0 .
|
TripletMarginLossImplCloneable |
TripletMarginLossOptions
Options for the TripletMarginLoss module.
|
TripletMarginWithDistanceLossImpl
Creates a criterion that measures the triplet loss given input
tensors :math:a , :math:p , and :math:n (representing anchor,
positive, and negative examples, respectively); and a nonnegative,
real-valued function
("distance function") used to compute the relationships between the anchor
and positive example ("positive distance") and the anchor and negative
example ("negative distance").
|
TripletMarginWithDistanceLossImplCloneable |
TripletMarginWithDistanceLossOptions
Options for the TripletMarginWithDistanceLoss module.
|
Tuple |
TupleElements |
TupleLiteral |
TuplePtr |
TupleType |
Type |
type_index |
Type.TypePtr |
TypeArrayRef |
TypeEnv |
TypeEnv.Iterator |
TypeIdentifier
A type id is a unique id for a given C++ type.
|
TypeMeta
TypeMeta is a thin class that allows us to store the type of a container such
as a blob, or the data type of a tensor, with a unique run-time id.
|
TypeMetaOptional |
TypePtrOptional |
TypeVector |
TypeVector.Iterator |
UnaryOp |
UnflattenImpl
A placeholder for unflatten operator
See https://pytorch.org/docs/master/generated/torch.nn.Unflatten.html to
learn about the exact behavior of this module.
|
UnflattenImplCloneable |
UnflattenOptions
Options for the Unflatten module.
|
UnfoldImpl
Applies unfold over a 4-D input.
|
UnfoldImplCloneable |
UnfoldOptions
Options for the Unfold module.
|
UnionType |
UniqueVoidPtr |
UpsampleImpl
Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D
(volumetric) data.
|
UpsampleImplCloneable |
UpsampleMode |
UpsampleOptions
Options for the Upsample module.
|
Use |
Value |
ValueArrayRef |
ValueOptional |
ValueValueMap |
ValueValueMap.Iterator |
ValueVector |
ValueVector.Iterator |
ValueWrap |
Var |
VariableInfo |
VariableVersion |
VariableVersion.Disabled |
VarMaybe |
WarnAlways |
Warning |
WarningHandler |
WeakIValue |
WeakOrStrongCompilationUnit |
WeakOrStrongTypePtr |
WeakStorage |
WeakStorageVector |
WeakStorageVector.Iterator |
WeakStorageVectorOptional |
WeakTypePtr |
While |
With |
WithItem |
WithItemList |
WithItemListIterator |
WriteableTensorData |
XPUHooksInterface |
ZeroPad1dImpl |
ZeroPad1dImplBase
Base class for all (dimension-specialized) ZeroPad modules.
|
ZeroPad1dImplCloneable |
ZeroPad1dOptions |
ZeroPad2dImpl |
ZeroPad2dImplBase |
ZeroPad2dImplCloneable |
ZeroPad2dOptions |
ZeroPad3dImpl |
ZeroPad3dImplBase |
ZeroPad3dImplCloneable |
ZeroPad3dOptions |