| 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 |