Package | Description |
---|---|
org.bytedeco.pytorch | |
org.bytedeco.pytorch.global |
Modifier and Type | Method and Description |
---|---|
torch.ScalarType |
ScalarTypeArrayRef.at(long Index)
Vector compatibility
|
torch.ScalarType |
ScalarTypeVector.back() |
torch.ScalarType |
ScalarTypeArrayRef.back()
back - Get the last element.
|
torch.ScalarType |
TensorIteratorBase.common_dtype() |
torch.ScalarType |
OperandInfo.current_dtype() |
torch.ScalarType |
TensorCastValue.dtype_() |
torch.ScalarType |
TensorIteratorBase.dtype() |
torch.ScalarType |
LegacyTensorConstructor.dtype() |
torch.ScalarType |
TensorIteratorBase.dtype(int arg) |
torch.ScalarType |
ScalarTypeVector.front() |
torch.ScalarType |
ScalarTypeArrayRef.front()
front - Get the first element.
|
torch.ScalarType |
ScalarTypeOptional.get() |
torch.ScalarType[] |
ScalarTypeVector.get() |
torch.ScalarType |
ScalarTypeVector.Iterator.get() |
torch.ScalarType |
ScalarTypeVector.get(long i) |
torch.ScalarType |
ScalarTypeArrayRef.get(long Index)
\}
\name Operator Overloads
\{
|
torch.ScalarType |
TensorIteratorBase.input_dtype() |
torch.ScalarType |
TensorIteratorBase.input_dtype(int arg) |
torch.ScalarType |
ScalarTypeVector.pop_back() |
torch.ScalarType |
Quantizer.scalar_type_() |
torch.ScalarType |
Quantizer.scalar_type() |
torch.ScalarType |
TensorBase.scalar_type() |
torch.ScalarType |
VariableInfo.scalar_type() |
abstract torch.ScalarType |
AbstractTensor.scalar_type() |
torch.ScalarType |
OperandInfo.target_dtype() |
torch.ScalarType |
IValue.toScalarType() |
torch.ScalarType |
TypeMeta.toScalarType()
convert TypeMeta handles to ScalarType enum values
|
torch.ScalarType |
ArgumentInfo.type() |
torch.ScalarType |
Scalar.type() |
Modifier and Type | Method and Description |
---|---|
Tensor |
Tensor._autocast_to_reduced_precision(boolean cuda_enabled,
boolean cpu_enabled,
torch.ScalarType cuda_dtype,
torch.ScalarType cpu_dtype) |
static LegacyTensorConstructor |
LegacyTensorConstructor.create(Symbol form,
torch.ScalarType dtype,
Device device) |
static TensorType |
TensorType.createContiguous(torch.ScalarType scalar_type,
Device device,
long... sizes) |
static TensorType |
TensorType.createContiguous(torch.ScalarType scalar_type,
Device device,
LongArrayRef sizes) |
OperandInfo |
OperandInfo.current_dtype(torch.ScalarType setter) |
TensorIteratorConfig |
TensorIteratorConfig.declare_static_dtype_and_device(torch.ScalarType dtype,
Device device) |
TensorIteratorConfig |
TensorIteratorConfig.declare_static_dtype(torch.ScalarType dtype) |
TensorCastValue |
TensorCastValue.dtype_(torch.ScalarType setter) |
static TypeMeta |
TypeMeta.fromScalarType(torch.ScalarType scalar_type)
convert ScalarType enum values to TypeMeta handles
|
ScalarTypeVector.Iterator |
ScalarTypeVector.insert(ScalarTypeVector.Iterator pos,
torch.ScalarType value) |
boolean |
TypeMeta.isScalarType(torch.ScalarType scalar_type)
true if we represent ScalarType scalar_type
|
Tensor |
Tensor.norm(ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
Tensor |
Tensor.norm(ScalarOptional p,
DimnameVector dim,
boolean keepdim,
torch.ScalarType dtype) |
Tensor |
Tensor.norm(ScalarOptional p,
long[] dim,
boolean keepdim,
torch.ScalarType dtype) |
Tensor |
Tensor.norm(ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
Tensor |
Tensor.norm(ScalarOptional p,
torch.ScalarType dtype) |
ScalarTypeVector |
ScalarTypeVector.push_back(torch.ScalarType value) |
ScalarTypeVector |
ScalarTypeVector.put(long i,
torch.ScalarType value) |
ScalarTypeVector |
ScalarTypeVector.put(torch.ScalarType... array) |
ScalarTypeOptional |
ScalarTypeOptional.put(torch.ScalarType value) |
ScalarTypeVector |
ScalarTypeVector.put(torch.ScalarType value) |
TypeMeta |
TypeMeta.put(torch.ScalarType scalar_type) |
VariableInfo |
VariableInfo.scalar_type(torch.ScalarType setter) |
OperandInfo |
OperandInfo.target_dtype(torch.ScalarType setter) |
void |
JitModule.to(Device device,
torch.ScalarType dtype) |
Tensor |
Tensor.to(Device device,
torch.ScalarType dtype) |
void |
RNNImplBase.to(Device device,
torch.ScalarType dtype) |
void |
GRUImplBase.to(Device device,
torch.ScalarType dtype) |
void |
LSTMImplBase.to(Device device,
torch.ScalarType dtype) |
void |
Module.to(Device device,
torch.ScalarType dtype,
boolean non_blocking)
Recursively casts all parameters to the given
dtype and device . |
void |
JitModule.to(Device device,
torch.ScalarType dtype,
boolean non_blocking)
Recursively casts all parameters to the given
dtype and device . |
void |
RNNImplBase.to(Device device,
torch.ScalarType dtype,
boolean non_blocking)
Overrides
nn::Module::to() to call flatten_parameters() after the
original operation. |
void |
GRUImplBase.to(Device device,
torch.ScalarType dtype,
boolean non_blocking)
Overrides
nn::Module::to() to call flatten_parameters() after the
original operation. |
void |
LSTMImplBase.to(Device device,
torch.ScalarType dtype,
boolean non_blocking)
Overrides
nn::Module::to() to call flatten_parameters() after the
original operation. |
Tensor |
Tensor.to(Device device,
torch.ScalarType dtype,
boolean non_blocking,
boolean copy,
MemoryFormatOptional memory_format) |
void |
JitModule.to(torch.ScalarType dtype) |
Tensor |
Tensor.to(torch.ScalarType dtype) |
void |
RNNImplBase.to(torch.ScalarType dtype) |
void |
GRUImplBase.to(torch.ScalarType dtype) |
void |
LSTMImplBase.to(torch.ScalarType dtype) |
void |
Module.to(torch.ScalarType dtype,
boolean non_blocking)
Recursively casts all parameters to the given dtype.
|
void |
JitModule.to(torch.ScalarType dtype,
boolean non_blocking)
Recursively casts all parameters to the given dtype.
|
void |
RNNImplBase.to(torch.ScalarType dtype,
boolean non_blocking) |
void |
GRUImplBase.to(torch.ScalarType dtype,
boolean non_blocking) |
void |
LSTMImplBase.to(torch.ScalarType dtype,
boolean non_blocking) |
Tensor |
Tensor.to(torch.ScalarType dtype,
boolean non_blocking,
boolean copy,
MemoryFormatOptional memory_format) |
Tensor |
Tensor.toType(torch.ScalarType t) |
Tensor |
Tensor.view(torch.ScalarType dtype) |
Constructor and Description |
---|
IValue(torch.ScalarType t) |
LegacyTensorConstructor(Symbol form,
torch.ScalarType dtype,
Device device) |
RandomSampler(long size,
torch.ScalarType index_dtype)
Constructs a
RandomSampler with a size and dtype for the stored indices. |
ScalarTypeOptional(torch.ScalarType value) |
ScalarTypeVector(torch.ScalarType... array) |
ScalarTypeVector(torch.ScalarType value) |
TensorCastValue(torch.ScalarType type,
NamedValue self) |
TensorOptions(torch.ScalarType dtype)
legacy constructor to support ScalarType
|
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