| _SupplementBase |
|
| AbstractTensor |
|
| AcceleratorHooksInterface |
|
| ActivityTypeSet |
|
| ActivityTypeSet.Iterator |
|
| Adagrad |
|
| AdagradOptions |
|
| AdagradParamState |
|
| Adam |
|
| AdamOptions |
|
| AdamParamState |
|
| AdamW |
|
| 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 |
|
| AliasDb |
|
| AliasInfo |
class AliasInfo
Data structure to hold aliasing information for an Argument.
|
| AliasInfoOptional |
|
| AliasTypeSetOptional |
|
| AllgatherOptions |
|
| Allocator |
|
| AllreduceCoalescedOptions |
|
| AllreduceOptions |
|
| AllToAllOptions |
|
| 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.
|
| AOTIModelContainerRunner |
|
| AOTIModelContainerRunnerCpu |
|
| Apply |
|
| ApproximateClockToUnixTimeConverter |
|
| ApproximateClockToUnixTimeConverter.UnixAndApproximateTimePair |
|
| ArchiveWriter |
|
| 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 |
|
| 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 |
|
| AutogradCompilerCall |
|
| AutogradContext |
Context to save information during forward that can be accessed in
backward in custom autograd operations (see torch::autograd::Function
for details).
|
| AutogradMetaFactory |
|
| AutogradMetaFactoryRegisterer |
|
| 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 |
|
| AwaitSingleElementType |
|
| AwaitType |
|
| BackendMeta |
This structure is intended to hold additional metadata of the specific device
backend.
|
| BackendMetaPtr |
|
| Backtrace |
Interface for a value that is computed on first access.
|
| BarrierOptions |
|
| 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 |
|
| BroadcastOptions |
|
| BucketAccumulator |
|
| buffer_iterator |
|
| buffer_list |
|
| BufferPolicy |
|
| BuiltinFunction |
|
| BuiltinModule |
|
| ByteArrayRef |
|
| ByteOptional |
|
| BytePointerPair |
|
| BytePointerPairOptional |
|
| BytePointerVector |
|
| BytePointerVector.Iterator |
|
| ByteVector |
|
| ByteVector.Iterator |
|
| C10dLogger |
|
| C10dLoggingData |
|
| C10FlagParser |
|
| CacheKey |
|
| CacheKeyBuffer |
|
| Call |
|
| CapsuleType |
|
| CapsuleTypePtr |
|
| CastValue |
|
| 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 |
|
| ChunkRandomDataLoader |
A dataloader for stateful datasets.
|
| ChunkRandomDataLoaderBase |
|
| ChunkRandomTensorDataLoader |
|
| ChunkRandomTensorDataLoaderBase |
|
| ChunkRecordIterator |
|
| 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 |
|
| CommHookInterface |
|
| CompilationUnit |
|
| CompiledNodeArgs |
|
| CompileTimeEmptyString |
|
| ComplexType |
|
| ComplexTypePtr |
|
| Compound |
|
| 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 |
|
| 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 |
|
| crc64_t |
|
| 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.
|
| CUDAHooksArgs |
|
| CUDAHooksInterface |
|
| CustomBatchRequest |
A base class for custom index types.
|
| CustomClassHolder |
|
| DataLoaderOptions |
Options to configure a DataLoader.
|
| DataPtr |
|
| DataPtrVector |
|
| DataPtrVector.Iterator |
|
| DDPLogger |
|
| DDPLoggingData |
|
| DebugInfoBase |
|
| DebugInfoGuard |
|
| Decl |
|
| Def |
|
| DefMaybe |
|
| DefVector |
|
| DefVector.Iterator |
|
| Delete |
|
| DeserializationStorageContext |
|
| 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.
|
| DeviceGuardImplRegistrar |
|
| DeviceObjType |
|
| DeviceObjTypePtr |
|
| DeviceOptional |
|
| DeviceTypeOptional |
|
| 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.iterator |
|
| DistanceFunction |
|
| DistributedBackend |
|
| DistributedBackend.Options |
|
| DistributedBackendOptional |
|
| DistributedBackendOptions |
|
| 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.
|
| 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.
|
| DynamicLibrary |
|
| DynamoTensorArg |
|
| 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 |
|
| EnumNameValue |
|
| EnumNameValueArrayRef |
|
| EnumType |
|
| Example |
An Example from a dataset.
|
| ExampleCollation |
A transformation of a batch to a new batch.
|
| ExampleIterator |
|
| ExampleOptional |
|
| ExampleStack |
A Collation for Example<Tensor, Tensor> types that stacks all data
tensors into one tensor, and all target (label) tensors into one tensor.
|
| ExampleVector |
|
| ExampleVector.Iterator |
|
| ExampleVectorIterator |
|
| ExampleVectorOptional |
|
| ExceptionMessageValue |
|
| 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/main/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_e4m3fnuz |
|
| Float8_e4m3fnuz.from_bits_t |
|
| Float8_e5m2 |
|
| Float8_e5m2.from_bits_t |
|
| Float8_e5m2fnuz |
|
| Float8_e5m2fnuz.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 |
|
| ForceDispatchKeyGuard |
|
| ForwardADLevel |
|
| ForwardGrad |
|
| FractionalMaxPool1dOptions |
Options for a D-dimensional fractional maxpool module.
|
| 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.
|
| Func |
|
| 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 |
|
| FunctionSchemaVector.Iterator |
|
| FunctionType |
|
| FunctionValue |
|
| FunctionVector |
|
| FunctionVector.Iterator |
|
| FuncTorchTLSBase |
|
| FusionStrategy |
|
| Future |
|
| FutureArrayRef |
|
| FutureElementReference |
|
| FutureList |
|
| FutureListIterator |
|
| FutureSingleElementType |
|
| FutureType |
|
| FutureVector |
|
| FutureVector.Iterator |
|
| GatheredContext |
|
| GatherOptions |
|
| 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 |
|
| 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 |
|
| GlooDeviceVector |
|
| GlooDeviceVector.Iterator |
|
| GLUImpl |
Applies glu over a given input.
|
| GLUImplCloneable |
|
| GLUOptions |
Options for the GLU module.
|
| GradBucket |
|
| GradCallback |
|
| GradMode |
|
| Graph |
|
| graph_node_list |
|
| graph_node_list_iterator |
|
| GraphAttr |
|
| GraphExecutor |
|
| GraphExecutorImplBase |
|
| GraphExecutorState |
|
| GraphFunction |
|
| GraphFunctionCreator |
|
| GraphOptimizerEnabledGuard |
|
| GraphsAttr |
|
| GraphVector |
|
| GraphVector.Iterator |
|
| GridSampleFuncOptions |
Options for torch::nn::functional::grid_sample.
|
| GridSampleMode |
|
| GridSamplePaddingMode |
|
| GroupNormFuncOptions |
Options for torch::nn::functional::group_norm.
|
| 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 |
|
| HashIdentityIValueMap |
|
| HashIdentityIValueMap.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.
|
| HIPHooksArgs |
|
| 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 |
|
| IncludeDispatchKeyGuard |
|
| 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 |
|
| IntPair |
|
| IntSizedSmallVectorBase |
This is all the stuff common to all SmallVectors.
|
| IntType |
|
| IntTypePtr |
|
| IPUHooksArgs |
|
| IPUHooksInterface |
|
| IStreamAdapter |
|
| IValue |
IValue (Interpreter Value) is a tagged union over the types
supported by the TorchScript interpreter.
|
| IValue.CompIdentityIValues |
|
| IValue.HashIdentityIValue |
|
| IValueArrayRef |
|
| IValueOptional |
|
| IValueOptionalVector |
|
| IValueOptionalVector.Iterator |
|
| IValueSupplier |
|
| IValueVector |
|
| IValueVector.Iterator |
|
| IValueVectorConsumer |
|
| JavaBatchDataset |
|
| JavaDataset |
Abstract class for stateless datasets to be subclassed by Java user code.
|
| JavaDatasetBase |
|
| JavaDistributedRandomDataLoader |
|
| JavaDistributedRandomDataLoaderBase |
|
| JavaDistributedRandomTensorDataLoader |
|
| JavaDistributedRandomTensorDataLoaderBase |
|
| JavaDistributedSequentialDataLoader |
|
| JavaDistributedSequentialDataLoaderBase |
|
| JavaDistributedSequentialTensorDataLoader |
|
| JavaDistributedSequentialTensorDataLoaderBase |
|
| JavaRandomDataLoader |
|
| JavaRandomDataLoaderBase |
|
| JavaRandomTensorDataLoader |
|
| JavaRandomTensorDataLoaderBase |
|
| JavaSequentialDataLoader |
|
| JavaSequentialDataLoaderBase |
|
| JavaSequentialTensorDataLoader |
|
| JavaSequentialTensorDataLoaderBase |
|
| JavaStatefulBatchDataset |
|
| JavaStatefulDataLoader |
|
| JavaStatefulDataLoaderBase |
|
| JavaStatefulDataset |
Abstract class for stateful datasets to be subclassed by Java user code.
|
| JavaStatefulDatasetBase |
|
| JavaStatefulTensorBatchDataset |
|
| JavaStatefulTensorDataLoader |
|
| JavaStatefulTensorDataLoaderBase |
|
| JavaStatefulTensorDataset |
|
| JavaStatefulTensorDatasetBase |
|
| JavaStreamBatchDataset |
|
| JavaStreamDataLoader |
|
| JavaStreamDataLoaderBase |
|
| JavaStreamDataset |
Abstract class for stateless stream datasets to be subclassed by Java user code.
|
| JavaStreamTensorBatchDataset |
|
| JavaStreamTensorDataLoader |
|
| JavaStreamTensorDataLoaderBase |
|
| JavaStreamTensorDataset |
|
| JavaTensorBatchDataset |
|
| JavaTensorDataset |
|
| JavaTensorDatasetBase |
|
| JitModule |
|
| JitModuleApplyFunction |
|
| JitNode |
|
| JitNodeVector |
|
| JitNodeVector.Iterator |
|
| JitNodeWrap |
|
| JitObject |
|
| JitObject.Property |
|
| JitString |
|
| 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/main/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.
|
| LayerNormFuncOptions |
Options for torch::nn::functional::layer_norm.
|
| 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 |
|
| LBFGS |
|
| 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.
|
| LiftedIValueArg |
|
| LiftedIValueArgs |
|
| 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.
|
| Logger |
|
| LoggerOptional |
|
| LogSigmoidImpl |
Applies the LogSigmoid function element-wise.
|
| LogSigmoidImplCloneable |
|
| LogSoftmaxFuncOptions |
Options for torch::nn::functional::log_softmax.
|
| LogSoftmaxImpl |
Applies the LogSoftmax function element-wise.
|
| LogSoftmaxImplCloneable |
|
| LogSoftmaxOptions |
Options for the LogSoftmax module.
|
| LongArrayRef |
|
| LongArrayRefOptional |
|
| LongArrayRefVector |
|
| LongArrayRefVector.Iterator |
|
| LongElementReference |
|
| LongExpandingArrayOptional |
|
| LongList |
|
| LongListIterator |
|
| LongOptional |
|
| LongOptionalArrayRef |
|
| LongOptionalVector |
|
| LongOptionalVector.Iterator |
|
| LongSmallVectorBase |
|
| LongSmallVectorCommon |
|
| LongSmallVectorImpl |
|
| LongVaryingShape |
|
| LongVector |
|
| LongVector.Iterator |
|
| LongVectorOptional |
|
| LossClosure |
|
| 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 |
|
| LPPool3dImpl |
Applies the LPPool3d function element-wise.
|
| LPPool3dImplBase |
|
| LPPool3dImplCloneable |
|
| LPPool3dOptions |
|
| 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.
|
| MagicMethod |
|
| MAIAHooksArgs |
|
| MAIAHooksInterface |
|
| 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 |
|
| MaxUnpool1dFuncOptions |
Options for a D-dimensional maxunpool functional.
|
| 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.
|
| MaxUnpool2dFuncOptions |
|
| MaxUnpool2dImpl |
Applies maxunpool over a 2-D input.
|
| MaxUnpool2dImplBase |
|
| MaxUnpool2dImplCloneable |
|
| MaxUnpool2dOptions |
|
| MaxUnpool3dFuncOptions |
|
| MaxUnpool3dImpl |
Applies maxunpool over a 3-D input.
|
| MaxUnpool3dImplBase |
|
| MaxUnpool3dImplCloneable |
|
| MaxUnpool3dOptions |
|
| MemCopyFunction |
|
| MemoryFormatOptional |
|
| MemoryFormattEnumerationType |
|
| MemoryFormatType |
|
| MemoryReportingInfoBase |
|
| MetaBase |
|
| MetadataLogger |
|
| Method |
|
| MethodOptional |
|
| MethodValue |
|
| MishImpl |
Applies mish over a given input.
|
| MishImplCloneable |
|
| MNIST |
The MNIST dataset.
|
| 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.
|
| MNISTRandomDataLoader |
A dataloader for stateless datasets.
|
| MNISTRandomDataLoaderBase |
|
| MobileCode |
|
| Module |
The base class for all modules in PyTorch.
|
| module_iterator |
|
| module_list |
|
| ModuleApplyFunction |
|
| ModuleDictImpl |
An OrderedDict of Modules that registers its elements by their keys.
|
| 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 |
|
| ModuleListImpl |
A list of Modules that registers its elements.
|
| ModuleListImplCloneable |
|
| ModulePolicy |
|
| MPSHooksArgs |
|
| 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 |
|
| MultiheadAttentionForwardFuncOptions |
Options for torch::nn::functional::multi_head_attention_forward
|
| 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.
|
| MzZipReaderIterWrapper |
|
| 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 |
|
| NamedModuleApplyFunction |
|
| NamedSharedModuleApplyFunction |
|
| NamedTensor |
|
| NamedTensorMeta |
|
| 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 |
|
| NCCLPreMulSumSupplement |
|
| NestedTensorImpl |
|
| NLLLossImpl |
The negative log likelihood loss.
|
| NLLLossImplCloneable |
|
| NLLLossOptions |
Options for the NLLLoss module.
|
| Node |
|
| Node.undefined_input |
|
| NodeCall |
|
| NodeCalls |
|
| NodeNodeCallMap |
|
| NodeNodeCallMap.Iterator |
|
| NodeSet |
|
| NodeSet.Iterator |
|
| NoGradGuard |
|
| NoNamesGuard |
|
| NoneType |
|
| NoneTypePtr |
|
| Nonlinearity |
|
| NormalizeFuncOptions |
Options for torch::nn::functional::normalize.
|
| NoTarget |
|
| NoTF32Guard |
|
| NumberType |
|
| NumberTypePtr |
|
| Obj |
|
| ObjLoader |
|
| OperandInfo |
|
| Operation |
|
| OperationCreator |
|
| 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.
|
| OptionalStreamGuard |
An OptionalStreamGuard is an RAII class that sets a device to some value on
initialization, and resets the device to its original value on destruction.
|
| OptionalType |
|
| OutputArchive |
|
| PackedSequence |
Holds the data and list of batch_sizes of a packed sequence.
|
| PaddingMode |
|
| PadFuncOptions |
Options for torch::nn::functional::pad.
|
| 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 |
|
| ParameterDictImpl |
|
| ParameterDictImplCloneable |
|
| ParameterListImpl |
|
| ParameterListImplCloneable |
|
| ParameterPolicy |
|
| ParamList |
|
| ParamListIterator |
|
| Pass |
|
| Pickler |
|
| PickleReader |
|
| PickleWriter |
|
| 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.
|
| PlacementConsumer |
|
| PlacementCopier |
|
| PlacementDeleteContext |
|
| PODLocalDispatchKeySet |
|
| PointerConsumer |
|
| PointerPair |
|
| PointerPairOptional |
|
| PointerSupplier |
|
| PoissonNLLLossImpl |
Negative log likelihood loss with Poisson distribution of target.
|
| PoissonNLLLossImplCloneable |
|
| PoissonNLLLossOptions |
Options for the PoissonNLLLoss module.
|
| PostAccumulateGradHook |
|
| PrefixStore |
|
| PReLUImpl |
Applies the PReLU function element-wise.
|
| PReLUImplCloneable |
|
| PReLUOptions |
Options for the PReLU module.
|
| pretty_tree |
|
| PrintValue |
|
| PrivateUse1HooksArgs |
|
| PrivateUse1HooksInterface |
|
| ProcessGroup |
|
| ProcessGroup.Options |
|
| ProcessGroupCppCommHookInterface |
|
| ProcessGroupGloo |
|
| ProcessGroupGloo.AsyncWork |
|
| ProcessGroupGloo.GlooStore |
|
| ProcessGroupGloo.Options |
|
| ProcessGroupGloo.RecvWork |
|
| ProcessGroupGloo.SendWork |
|
| ProfileIValueOp |
|
| ProfilerConfig |
|
| Property |
|
| PropertyList |
|
| PropertyListIterator |
|
| PropertyListMaybe |
|
| PropertyVector |
|
| PropertyVector.Iterator |
|
| PyInterpreter |
|
| PyInterpreterVTable |
|
| PyObject_TorchDispatchMode |
|
| PyObject_TorchDispatchModeOptional |
|
| PyObjectHolder |
|
| PyObjectType |
|
| PyObjectTypePtr |
|
| PythonDispatcherTLS |
|
| PythonOp |
|
| PythonTorchFunctionTLS |
|
| PyTorchStreamReader |
|
| QEngineVector |
|
| QEngineVector.Iterator |
|
| qint32 |
qint32 is for signed 32 bit quantized Tensors
|
| qint8 |
This is the data type for quantized Tensors.
|
| QSchemeType |
|
| QSchemeTypePtr |
|
| QTensorImpl |
|
| QualifiedName |
|
| QualifiedNameOptional |
|
| Quantizer |
Quantizer is the class for storing all the information
that's necessary to perform quantize and dequantize
operation.
|
| 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.
|
| RangeValue |
|
| ReadAdapterInterface |
|
| ReadAdapterInterfaceVector |
|
| ReadAdapterInterfaceVector.Iterator |
|
| Reader |
|
| RecordFunction |
|
| RecordFunctionCallbacksEntry |
|
| RecordFunctionGuard |
|
| RecordFunctionHandleIntList |
|
| RecordFunctionHandleIntList.Iterator |
|
| RecordFunctionHandleIntPair |
|
| RecordFunctionTLS |
|
| ReduceLROnPlateauScheduler |
|
| ReduceOp |
|
| ReduceOptions |
|
| Reducer |
|
| ReduceScatterOptions |
|
| 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 |
|
| Result |
|
| Return |
|
| RMSprop |
|
| 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.
|
| ROCmBackwardPassGuard |
|
| RRefInterface |
|
| RRefSingleElementType |
|
| RRefType |
|
| RReLUFuncOptions |
Options for torch::nn::functional::rrelu.
|
| RReLUImpl |
Applies the RReLU function element-wise.
|
| RReLUImplCloneable |
|
| RReLUOptions |
Options for the RReLU module.
|
| SafePyHandle |
|
| SafePyObject |
|
| SafePyObjectOptional |
|
| Sampler |
A Sampler is an object that yields an index with which to access a
dataset.
|
| SavedTensorDefaultHooks |
|
| SavedTensorDefaultHooksTLS |
|
| SavedVariableHooks |
|
| Scalar |
Scalar represents a 0-dimensional tensor which contains a single element.
|
| ScalarArrayRef |
|
| ScalarOptional |
|
| ScalarTypeArrayRef |
|
| ScalarTypeEnumerationType |
|
| ScalarTypeOptional |
|
| ScalarTypeType |
|
| ScalarTypeTypePtr |
|
| ScalarTypeVector |
|
| ScalarTypeVector.Iterator |
|
| ScatterOptions |
|
| SchemaArgument |
struct SchemaArgument
Structure used to represent arguments or returns for a schema.
|
| SchemaInfo |
class SchemaInfo
FunctionSchema wrapper that publicizes argument value specific operator
behavior (mutation, aliasing, special cases, etc...)
|
| 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.
|
| SequentialImpl |
A list of Modules that acts as a Module itself.
|
| SequentialImplCloneable |
|
| SequentialSampler |
A Sampler that returns indices sequentially.
|
| SGD |
|
| SGDOptions |
|
| SGDParamState |
|
| ShapeSymbol |
|
| ShapeSymbolVector |
|
| ShapeSymbolVector.Iterator |
|
| ShapeSymbolVectorOptional |
|
| SharedClassTypeVector |
|
| SharedClassTypeVector.Iterator |
|
| SharedModuleApplyFunction |
|
| SharedModuleVector |
|
| SharedModuleVector.Iterator |
|
| SharedParserData |
|
| SharedSugaredValueVector |
|
| SharedSugaredValueVector.Iterator |
|
| SharedType |
|
| ShortArrayRef |
|
| ShortSet |
|
| ShortSet.Iterator |
|
| SigmoidImpl |
Applies sigmoid over a given input.
|
| SigmoidImplCloneable |
|
| SiLUImpl |
Applies silu over a given input.
|
| SiLUImplCloneable |
|
| SimpleSelf |
|
| SimpleValue |
|
| SingletonTypePtr |
|
| SizeInput |
|
| SizesAndStrides |
|
| SizeTArrayRef |
|
| SizeTMatchedSchemaPair |
|
| SizeTOptional |
|
| SizeTStringMap |
|
| SizeTStringMap.Iterator |
|
| SizeTSupplier |
|
| SizeTVector |
|
| SizeTVector.Iterator |
|
| SizeTVectorOptional |
|
| SizeTVectorVector |
|
| SizeTVectorVector.Iterator |
|
| Slice |
|
| SliceExpr |
|
| SliceValue |
|
| 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 |
|
| SoftmaxFuncOptions |
Options for torch::nn::functional::softmax.
|
| SoftmaxImpl |
Applies the Softmax function.
|
| SoftmaxImplCloneable |
|
| SoftmaxOptions |
Options for the Softmax module.
|
| SoftminFuncOptions |
Options for torch::nn::functional::softmin.
|
| 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 |
|
| 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 |
|
| StackTraceFetcher |
|
| Starred |
|
| StashTorchDispatchModeGuard |
|
| StashTorchDispatchStackGuard |
|
| StepLR |
|
| Stmt |
|
| StmtList |
|
| StmtListIterator |
|
| Storage |
|
| Storage.unsafe_borrow_t |
|
| Storage.use_byte_size_t |
|
| StorageImpl |
|
| StorageImpl.use_byte_size_t |
|
| StorageType |
|
| StorageTypePtr |
|
| Store |
|
| StoreTimeoutGuard |
|
| Stream |
A stream is a software mechanism used to synchronize launched kernels
without requiring explicit synchronizations between kernels.
|
| 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 |
|
| StringConsumer |
|
| StringCordView |
|
| StringCordView.Iterator |
|
| StringGenericListDict |
|
| StringGenericListDictIterator |
|
| StringIntMap |
|
| StringIntMap.Iterator |
|
| StringIValueMap |
|
| StringIValueMap.Iterator |
|
| StringLiteral |
|
| StringLongMap |
|
| StringLongMap.Iterator |
|
| StringLongVector |
|
| StringMapper |
|
| StringOptional |
|
| StringPair |
|
| StringScalarTypeMap |
|
| StringScalarTypeMap.Iterator |
|
| StringSet |
|
| StringSet.Iterator |
|
| StringSharedModuleDict |
|
| StringSharedModuleDictItem |
|
| StringSharedModuleDictItemVector |
|
| StringSharedModuleDictItemVector.Iterator |
|
| StringSharedModulePair |
|
| StringSharedModuleVector |
|
| StringSizeTMap |
|
| StringSizeTMap.Iterator |
|
| StringStringMap |
|
| StringStringMap.Iterator |
|
| StringSupplier |
|
| StringTensorDict |
An ordered dictionary implementation, akin to Python's OrderedDict.
|
| StringTensorDictItem |
|
| StringTensorDictItemVector |
|
| StringTensorDictItemVector.Iterator |
|
| StringTensorMap |
|
| StringTensorMap.Iterator |
|
| StringTensorPair |
|
| StringTensorVector |
|
| StringType |
|
| StringTypePtr |
|
| StringValueMap |
|
| StringValueMap.Iterator |
|
| StringVector |
|
| StringVector.Iterator |
|
| StringVectorOptional |
|
| StringViewOptional |
|
| StringViewVector |
|
| StringViewVector.Iterator |
|
| StringViewVectorOptional |
|
| StrongTypePtr |
|
| Subscript |
|
| SugaredEnumClass |
|
| SugaredTupleValue |
|
| SugaredValue |
|
| SwapSavedVariables |
|
| Symbol |
|
| SymbolArrayRef |
|
| SymbolicShape |
|
| SymbolicShapeMeta |
|
| SymbolSet |
|
| SymbolSet.Iterator |
|
| SymbolVector |
|
| SymbolVector.Iterator |
|
| SymBool |
|
| SymBoolType |
|
| SymDimVector |
This is a 'vector' (really, a variable-sized array), optimized
for the case when the array is small.
|
| SymDimVectorOptional |
|
| SymFloat |
|
| SymFloatType |
|
| SymInt |
|
| SymIntArrayRef |
|
| SymIntArrayRefOptional |
|
| SymIntOptional |
|
| SymIntOptionalVector |
|
| SymIntOptionalVector.Iterator |
|
| 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.
|
| SymIntType |
|
| SymIntVector |
|
| SymIntVector.Iterator |
|
| SymNode |
|
| SymNodeArrayRef |
|
| SymNodeVector |
|
| SymNodeVector.Iterator |
|
| T_DataPtrSizeT_T |
|
| T_IntInt_T |
|
| T_LongLong_T |
|
| T_PackedSequenceT_TensorTensor_T_T |
|
| T_PackedSequenceTensor_T |
|
| T_PyObject_TorchDispatchModeTorchDispatchModeKey_T |
|
| T_SafePyObjectTorchDispatchModeKey_T |
|
| T_SizeTVectorVectorSizeTVector_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 |
|
| TagVector |
|
| TagVector.Iterator |
|
| TanhImpl |
Applies Tanh over a given input.
|
| TanhImplCloneable |
|
| TanhshrinkImpl |
Applies Tanhshrink over a given input.
|
| TanhshrinkImplCloneable |
|
| Tensor |
|
| TensorArg |
|
| TensorArgArrayRef |
|
| TensorArgs |
|
| TensorArrayRef |
|
| TensorArrayRefOptional |
|
| TensorBase |
|
| TensorBaseMaybeOwned |
|
| TensorBatchDataset |
|
| TensorCastValue |
|
| TensorDataset |
A dataset of tensors.
|
| TensorDatasetBase |
|
| TensorDeque |
|
| TensorDeque.Iterator |
|
| TensorElementReference |
|
| TensorExample |
|
| TensorExampleCollation |
|
| TensorExampleIterator |
|
| TensorExampleOptional |
|
| TensorExampleStack |
A Collation for Example<Tensor, NoTarget> types that stacks all data
tensors into one tensor.
|
| TensorExampleVector |
|
| TensorExampleVector.Iterator |
|
| TensorExampleVectorIterator |
|
| TensorExampleVectorOptional |
|
| TensorGeometry |
|
| TensorGeometryArg |
|
| TensorIdGetter |
|
| 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.LongIdentity |
|
| TensorImpl.SymIntIdentity |
|
| 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.
|
| TensorMapper |
|
| 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.
|
| TensorTensorDict |
|
| TensorTensorDictIterator |
|
| TensorTensorHook |
|
| TensorTensorRefHook |
|
| TensorType |
|
| TensorVector |
|
| TensorVector.Iterator |
|
| TensorVectorOptional |
|
| TernaryIf |
|
| ThreadIdGuard |
|
| ThreadLocalDebugInfo |
|
| ThreadLocalPythonObjects |
|
| ThreadLocalState |
|
| ThreadLocalStateGuard |
|
| ThresholdImpl |
Applies the Threshold function element-wise.
|
| ThresholdImplCloneable |
|
| ThresholdOptions |
Options for the Threshold module.
|
| Timer |
|
| Token |
|
| TorchDispatchModeTLS |
|
| TraceableFunction |
See Node::is_traceable() for definition.
|
| TraceState |
|
| 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 |
|
| TreeStringMap |
|
| TreeStringMap.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 |
|
| TupleType |
|
| Type |
|
| type_index |
|
| Type.TypePtr |
|
| TypeArrayRef |
|
| TypeEnv |
|
| TypeEnv.Iterator |
|
| TypeIdentifier |
A type id is a unique id for a given C++ type.
|
| TypeMapper |
|
| 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 |
|
| TypeParser |
|
| TypePrinter |
|
| TypePtrOptional |
|
| TypeRenamer |
|
| TypeResolver |
|
| TypeSupplier |
|
| TypeVector |
|
| TypeVector.Iterator |
|
| UnaryOp |
|
| UndefinedTensorImpl |
|
| UnflattenImpl |
A placeholder for unflatten operator
See https://pytorch.org/docs/main/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 |
|
| Unpickler |
|
| 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 |
|
| ValueMapper |
|
| ValueOptional |
|
| ValueValueMap |
|
| ValueValueMap.Iterator |
|
| ValueVector |
|
| ValueVector.Iterator |
|
| ValueWrap |
|
| Var |
|
| VariableHooksInterface |
|
| VariableInfo |
|
| VariableVersion |
|
| VarMaybe |
|
| VoidTensorHook |
|
| WarnAlways |
|
| Warning |
|
| Warning.DeprecationWarning |
|
| Warning.UserWarning |
|
| WarningHandler |
|
| WarningHandlerGuard |
|
| WarningVariant |
|
| WeakIValue |
|
| WeakOrStrongCompilationUnit |
|
| WeakOrStrongTypePtr |
|
| WeakStorageVector |
|
| WeakStorageVector.Iterator |
|
| WeakStorageVectorOptional |
|
| WeakTypePtr |
|
| While |
|
| With |
|
| WithItem |
|
| WithItemList |
|
| WithItemListIterator |
|
| Work |
|
| WorkInfo |
|
| WorkInfoConsumer |
|
| WriteableTensorData |
|
| XPUHooksArgs |
|
| XPUHooksInterface |
|
| ZeroPad1dImpl |
|
| ZeroPad1dImplBase |
Base class for all (dimension-specialized) ZeroPad modules.
|
| ZeroPad1dImplCloneable |
|
| ZeroPad1dOptions |
|
| ZeroPad2dImpl |
|
| ZeroPad2dImplBase |
|
| ZeroPad2dImplCloneable |
|
| ZeroPad2dOptions |
|
| ZeroPad3dImpl |
|
| ZeroPad3dImplBase |
|
| ZeroPad3dImplCloneable |
|
| ZeroPad3dOptions |
|