| Package | Description |
|---|---|
| org.bytedeco.pytorch | |
| org.bytedeco.pytorch.global |
| Modifier and Type | Method and Description |
|---|---|
TensorVector |
RNNImplBase.all_weights() |
TensorVector |
GRUImplBase.all_weights() |
TensorVector |
LSTMImplBase.all_weights() |
TensorVector |
Node.apply_with_saved(TensorVector inputs,
SwapSavedVariables saved) |
TensorVector |
FunctionPreHook.apply(TensorVector grads) |
TensorVector |
Node.apply(TensorVector inputs)
Evaluates the function on the given inputs and returns the result of the
function call.
|
TensorVector |
FunctionPostHook.apply(TensorVector outputs,
TensorVector inputs) |
TensorVector |
Module.buffers() |
TensorVector |
Module.buffers(boolean recurse)
Returns the buffers of this
Module and if recurse is true, also
recursively of every submodule. |
TensorVector |
Tensor.chunk(long chunks) |
TensorVector |
Tensor.chunk(long chunks,
long dim) |
TensorVector |
Tensor.dsplit(long... indices) |
TensorVector |
Tensor.dsplit(long sections) |
TensorVector |
Tensor.dsplit(LongArrayRef indices) |
TensorVector |
PyObjectHolder.extractTensors() |
TensorVector |
AutogradContext.get_saved_variables()
Get the list of variables that were saved in
forward using
save_for_backward(). |
TensorVector |
TensorVectorOptional.get() |
TensorVector |
T_TensorTensorVector_T.get1() |
TensorVector |
T_TensorTensorVectorTensorVector_T.get1() |
static TensorVector |
T_TensorTensorVector_T.get1(T_TensorTensorVector_T container) |
static TensorVector |
T_TensorTensorVectorTensorVector_T.get1(T_TensorTensorVectorTensorVector_T container) |
TensorVector |
T_TensorTensorVectorTensorVector_T.get2() |
static TensorVector |
T_TensorTensorVectorTensorVector_T.get2(T_TensorTensorVectorTensorVector_T container) |
TensorVector |
T_TensorTensorTensorTensorVector_T.get3() |
static TensorVector |
T_TensorTensorTensorTensorVector_T.get3(T_TensorTensorTensorTensorVector_T container) |
TensorVector |
Tensor.hsplit(long... indices) |
TensorVector |
Tensor.hsplit(long sections) |
TensorVector |
Tensor.hsplit(LongArrayRef indices) |
TensorVector |
Tensor.nonzero_numpy() |
TensorVector |
Module.parameters() |
TensorVector |
Optimizer.parameters()
Provides a reference to the parameters in the first param_group this
optimizer holds.
|
TensorVector |
Module.parameters(boolean recurse)
Returns the parameters of this
Module and if recurse is true, also
recursively of every submodule. |
TensorVector |
OptimizerParamGroup.params() |
TensorVector |
TensorVector.push_back(Tensor value) |
TensorVector |
TensorVector.put(long i,
Tensor value) |
TensorVector |
TensorVector.put(Tensor... array) |
TensorVector |
TensorVector.put(Tensor value) |
TensorVector |
TensorVector.put(TensorVector x) |
TensorVector |
Tensor.split_symint(SymInt split_size) |
TensorVector |
Tensor.split_symint(SymIntArrayRef split_size) |
TensorVector |
Tensor.split_symint(SymIntArrayRef split_size,
long dim) |
TensorVector |
Tensor.split_symint(SymInt split_size,
long dim) |
TensorVector |
Tensor.split_with_sizes_symint(SymIntArrayRef split_sizes) |
TensorVector |
Tensor.split_with_sizes_symint(SymIntArrayRef split_sizes,
long dim) |
TensorVector |
Tensor.split_with_sizes(long... split_sizes) |
TensorVector |
Tensor.split_with_sizes(long[] split_sizes,
long dim) |
TensorVector |
Tensor.split_with_sizes(LongArrayRef split_sizes) |
TensorVector |
Tensor.split_with_sizes(LongArrayRef split_sizes,
long dim) |
TensorVector |
Tensor.split(long... split_size) |
TensorVector |
Tensor.split(long split_size) |
TensorVector |
Tensor.split(long[] split_size,
long dim) |
TensorVector |
Tensor.split(LongArrayRef split_size) |
TensorVector |
Tensor.split(LongArrayRef split_size,
long dim) |
TensorVector |
Tensor.split(long split_size,
long dim) |
TensorVector |
Tensor.tensor_split_symint(SymInt sections) |
TensorVector |
Tensor.tensor_split_symint(SymIntArrayRef indices) |
TensorVector |
Tensor.tensor_split_symint(SymIntArrayRef indices,
long dim) |
TensorVector |
Tensor.tensor_split_symint(SymInt sections,
long dim) |
TensorVector |
Tensor.tensor_split(long... indices) |
TensorVector |
Tensor.tensor_split(long sections) |
TensorVector |
Tensor.tensor_split(long[] indices,
long dim) |
TensorVector |
Tensor.tensor_split(LongArrayRef indices) |
TensorVector |
Tensor.tensor_split(LongArrayRef indices,
long dim) |
TensorVector |
Tensor.tensor_split(long sections,
long dim) |
TensorVector |
Tensor.tensor_split(Tensor tensor_indices_or_sections) |
TensorVector |
Tensor.tensor_split(Tensor tensor_indices_or_sections,
long dim) |
TensorVector |
Pickler.tensorData() |
TensorVector |
IValue.toTensorVector() |
TensorVector |
JitNode.ts(Symbol name) |
TensorVector |
Tensor.unbind() |
TensorVector |
Tensor.unbind(Dimname dim) |
TensorVector |
Tensor.unbind(long dim) |
TensorVector |
Tensor.unsafe_chunk(long chunks) |
TensorVector |
Tensor.unsafe_chunk(long chunks,
long dim) |
TensorVector |
Tensor.unsafe_split_symint(SymInt split_size) |
TensorVector |
Tensor.unsafe_split_symint(SymInt split_size,
long dim) |
TensorVector |
Tensor.unsafe_split_with_sizes_symint(SymIntArrayRef split_sizes) |
TensorVector |
Tensor.unsafe_split_with_sizes_symint(SymIntArrayRef split_sizes,
long dim) |
TensorVector |
Tensor.unsafe_split_with_sizes(long... split_sizes) |
TensorVector |
Tensor.unsafe_split_with_sizes(long[] split_sizes,
long dim) |
TensorVector |
Tensor.unsafe_split_with_sizes(LongArrayRef split_sizes) |
TensorVector |
Tensor.unsafe_split_with_sizes(LongArrayRef split_sizes,
long dim) |
TensorVector |
Tensor.unsafe_split(long split_size) |
TensorVector |
Tensor.unsafe_split(long split_size,
long dim) |
TensorVector |
StringTensorDict.values()
Returns a newly allocated vector and copies all values from this
OrderedDict into the vector. |
TensorVector |
ParameterDictImpl.values()
Return the Values in the dict
|
TensorVector |
TensorArrayRef.vec()
\}
\name Expensive Operations
\{
|
TensorVector |
TensorList.vec() |
TensorVector |
Tensor.vsplit(long... indices) |
TensorVector |
Tensor.vsplit(long sections) |
TensorVector |
Tensor.vsplit(LongArrayRef indices) |
| Modifier and Type | Method and Description |
|---|---|
void |
Tensor.__dispatch__backward(TensorVector inputs) |
void |
Tensor.__dispatch__backward(TensorVector inputs,
TensorOptional gradient,
BoolOptional retain_graph,
boolean create_graph) |
void |
VariableHooksInterface._backward(Tensor arg0,
TensorVector arg1,
TensorOptional arg2,
BoolOptional arg3,
boolean arg4) |
void |
Tensor._backward(TensorVector inputs,
TensorOptional gradient,
BoolOptional keep_graph,
boolean create_graph) |
void |
Optimizer.add_parameters(TensorVector parameters)
Adds the given vector of parameters to the optimizer's parameter list.
|
TensorVector |
Node.apply_with_saved(TensorVector inputs,
SwapSavedVariables saved) |
TensorVector |
FunctionPreHook.apply(TensorVector grads) |
TensorVector |
Node.apply(TensorVector inputs)
Evaluates the function on the given inputs and returns the result of the
function call.
|
TensorVector |
FunctionPostHook.apply(TensorVector outputs,
TensorVector inputs) |
void |
AutogradContext.mark_dirty(TensorVector inputs)
Marks variables in the list as modified in an in-place operation.
|
void |
AutogradContext.mark_non_differentiable(TensorVector outputs)
Marks outputs in the list as not requiring gradients.
|
TensorVectorOptional |
TensorVectorOptional.put(TensorVector value) |
TensorArrayRefOptional |
TensorArrayRefOptional.put(TensorVector value) |
TensorVector |
TensorVector.put(TensorVector x) |
void |
AutogradContext.save_for_backward(TensorVector to_save)
Saves the list of variables for a future call to
backward. |
JitNode |
JitNode.ts_(Symbol name,
TensorVector v) |
| Modifier and Type | Method and Description |
|---|---|
static TensorVector |
torch.align_tensors(TensorArrayRef tensors) |
static TensorVector |
torch.align_tensors(TensorVector tensors) |
static TensorVector |
torch.atleast_1d(TensorArrayRef tensors) |
static TensorVector |
torch.atleast_1d(TensorVector tensors) |
static TensorVector |
torch.atleast_2d(TensorArrayRef tensors) |
static TensorVector |
torch.atleast_2d(TensorVector tensors) |
static TensorVector |
torch.atleast_3d(TensorArrayRef tensors) |
static TensorVector |
torch.atleast_3d(TensorVector tensors) |
static TensorVector |
torch.broadcast_tensors(TensorArrayRef tensors) |
static TensorVector |
torch.broadcast_tensors(TensorVector tensors) |
static TensorVector |
torch.chunk(Tensor self,
long chunks) |
static TensorVector |
torch.chunk(Tensor self,
long chunks,
long dim) |
static TensorVector |
torch.dequantize(TensorArrayRef tensors) |
static TensorVector |
torch.dequantize(TensorVector tensors) |
static TensorVector |
torch.dsplit(Tensor self,
long... indices) |
static TensorVector |
torch.dsplit(Tensor self,
long sections) |
static TensorVector |
torch.dsplit(Tensor self,
LongArrayRef indices) |
static TensorVector |
torch.expand_outplace(TensorArrayRef to_expand) |
static TensorVector |
torch.expand_outplace(TensorVector to_expand) |
static TensorVector |
torch.grad(TensorVector outputs,
TensorVector inputs) |
static TensorVector |
torch.grad(TensorVector outputs,
TensorVector inputs,
TensorVector grad_outputs,
BoolOptional retain_graph,
boolean create_graph,
boolean allow_unused)
Computes and returns the sum of gradients of outputs with respect to the
inputs.
|
static TensorVector |
torch.gradient(Tensor self) |
static TensorVector |
torch.gradient(Tensor self,
long... dim) |
static TensorVector |
torch.gradient(Tensor self,
long[] dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
LongArrayRef dim) |
static TensorVector |
torch.gradient(Tensor self,
LongArrayRef dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
long... dim) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
long[] dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
LongArrayRef dim) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
LongArrayRef dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
LongOptional dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
Scalar spacing,
long... dim) |
static TensorVector |
torch.gradient(Tensor self,
Scalar spacing,
long[] dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
Scalar spacing,
LongArrayRef dim) |
static TensorVector |
torch.gradient(Tensor self,
Scalar spacing,
LongArrayRef dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
ScalarOptional spacing,
LongOptional dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing,
LongArrayRef dim) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing,
LongArrayRef dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing,
LongOptional dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
TensorVector spacing) |
static TensorVector |
torch.gradient(Tensor self,
TensorVector spacing,
long... dim) |
static TensorVector |
torch.gradient(Tensor self,
TensorVector spacing,
long[] dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
TensorVector spacing,
LongOptional dim,
long edge_order) |
static TensorVector |
torch.hsplit(Tensor self,
long... indices) |
static TensorVector |
torch.hsplit(Tensor self,
long sections) |
static TensorVector |
torch.hsplit(Tensor self,
LongArrayRef indices) |
static TensorVector |
torch.meshgrid(TensorArrayRef tensors) |
static TensorVector |
torch.meshgrid(TensorArrayRef tensors,
BytePointer indexing) |
static TensorVector |
torch.meshgrid(TensorVector tensors) |
static TensorVector |
torch.meshgrid(TensorVector tensors,
String indexing) |
static TensorVector |
torch.nonzero_numpy(Tensor self) |
static TensorVector |
torch.quantize_per_tensor(TensorArrayRef tensors,
Tensor scales,
Tensor zero_points,
torch.ScalarType dtype) |
static TensorVector |
torch.quantize_per_tensor(TensorVector tensors,
Tensor scales,
Tensor zero_points,
torch.ScalarType dtype) |
static TensorVector |
torch.split_copy_symint(Tensor self,
SymInt split_size) |
static TensorVector |
torch.split_copy_symint(Tensor self,
SymInt split_size,
long dim) |
static TensorVector |
torch.split_copy(Tensor self,
long split_size) |
static TensorVector |
torch.split_copy(Tensor self,
long split_size,
long dim) |
static TensorVector |
torch.split_symint(Tensor self,
SymInt split_size) |
static TensorVector |
torch.split_symint(Tensor self,
SymIntArrayRef split_size) |
static TensorVector |
torch.split_symint(Tensor self,
SymIntArrayRef split_size,
long dim) |
static TensorVector |
torch.split_symint(Tensor self,
SymInt split_size,
long dim) |
static TensorVector |
torch.split_with_sizes_copy_symint(Tensor self,
SymIntArrayRef split_sizes) |
static TensorVector |
torch.split_with_sizes_copy_symint(Tensor self,
SymIntArrayRef split_sizes,
long dim) |
static TensorVector |
torch.split_with_sizes_copy(Tensor self,
long... split_sizes) |
static TensorVector |
torch.split_with_sizes_copy(Tensor self,
long[] split_sizes,
long dim) |
static TensorVector |
torch.split_with_sizes_copy(Tensor self,
LongArrayRef split_sizes) |
static TensorVector |
torch.split_with_sizes_copy(Tensor self,
LongArrayRef split_sizes,
long dim) |
static TensorVector |
torch.split_with_sizes_symint(Tensor self,
SymIntArrayRef split_sizes) |
static TensorVector |
torch.split_with_sizes_symint(Tensor self,
SymIntArrayRef split_sizes,
long dim) |
static TensorVector |
torch.split_with_sizes(Tensor self,
long... split_sizes) |
static TensorVector |
torch.split_with_sizes(Tensor self,
long[] split_sizes,
long dim) |
static TensorVector |
torch.split_with_sizes(Tensor self,
LongArrayRef split_sizes) |
static TensorVector |
torch.split_with_sizes(Tensor self,
LongArrayRef split_sizes,
long dim) |
static TensorVector |
torch.split(Tensor self,
long... split_size) |
static TensorVector |
torch.split(Tensor self,
long split_size) |
static TensorVector |
torch.split(Tensor self,
long[] split_size,
long dim) |
static TensorVector |
torch.split(Tensor self,
LongArrayRef split_size) |
static TensorVector |
torch.split(Tensor self,
LongArrayRef split_size,
long dim) |
static TensorVector |
torch.split(Tensor self,
long split_size,
long dim) |
static TensorVector |
torch.tensor_split_symint(Tensor self,
SymInt sections) |
static TensorVector |
torch.tensor_split_symint(Tensor self,
SymIntArrayRef indices) |
static TensorVector |
torch.tensor_split_symint(Tensor self,
SymIntArrayRef indices,
long dim) |
static TensorVector |
torch.tensor_split_symint(Tensor self,
SymInt sections,
long dim) |
static TensorVector |
torch.tensor_split(Tensor self,
long... indices) |
static TensorVector |
torch.tensor_split(Tensor self,
long sections) |
static TensorVector |
torch.tensor_split(Tensor self,
long[] indices,
long dim) |
static TensorVector |
torch.tensor_split(Tensor self,
LongArrayRef indices) |
static TensorVector |
torch.tensor_split(Tensor self,
LongArrayRef indices,
long dim) |
static TensorVector |
torch.tensor_split(Tensor self,
long sections,
long dim) |
static TensorVector |
torch.tensor_split(Tensor self,
Tensor tensor_indices_or_sections) |
static TensorVector |
torch.tensor_split(Tensor self,
Tensor tensor_indices_or_sections,
long dim) |
static TensorVector |
torch.unbind_copy(Tensor self) |
static TensorVector |
torch.unbind_copy(Tensor self,
long dim) |
static TensorVector |
torch.unbind(Tensor self) |
static TensorVector |
torch.unbind(Tensor self,
Dimname dim) |
static TensorVector |
torch.unbind(Tensor self,
long dim) |
static TensorVector |
torch.unflatten_dense_tensors(Tensor flat,
TensorArrayRef tensors) |
static TensorVector |
torch.unflatten_dense_tensors(Tensor flat,
TensorVector tensors) |
static TensorVector |
torch.unsafe_chunk(Tensor self,
long chunks) |
static TensorVector |
torch.unsafe_chunk(Tensor self,
long chunks,
long dim) |
static TensorVector |
torch.unsafe_split_symint(Tensor self,
SymInt split_size) |
static TensorVector |
torch.unsafe_split_symint(Tensor self,
SymInt split_size,
long dim) |
static TensorVector |
torch.unsafe_split_with_sizes_symint(Tensor self,
SymIntArrayRef split_sizes) |
static TensorVector |
torch.unsafe_split_with_sizes_symint(Tensor self,
SymIntArrayRef split_sizes,
long dim) |
static TensorVector |
torch.unsafe_split_with_sizes(Tensor self,
long... split_sizes) |
static TensorVector |
torch.unsafe_split_with_sizes(Tensor self,
long[] split_sizes,
long dim) |
static TensorVector |
torch.unsafe_split_with_sizes(Tensor self,
LongArrayRef split_sizes) |
static TensorVector |
torch.unsafe_split_with_sizes(Tensor self,
LongArrayRef split_sizes,
long dim) |
static TensorVector |
torch.unsafe_split(Tensor self,
long split_size) |
static TensorVector |
torch.unsafe_split(Tensor self,
long split_size,
long dim) |
static TensorVector |
torch.vsplit(Tensor self,
long... indices) |
static TensorVector |
torch.vsplit(Tensor self,
long sections) |
static TensorVector |
torch.vsplit(Tensor self,
LongArrayRef indices) |
static TensorVector |
torch.where(Tensor condition) |
| Modifier and Type | Method and Description |
|---|---|
static TensorVector |
torch.align_tensors(TensorVector tensors) |
static boolean |
torch.any_variable_requires_grad(TensorVector variables)
Return true if any of the variables in the list require a gradient.
|
static Tensor |
torch.applySlicing(Tensor self,
TensorIndexArrayRef indices,
TensorVector outIndices,
boolean disable_slice_optimization,
Device self_device,
SymIntArrayRefOptional self_sizes) |
static Tensor |
torch.applySlicing(Tensor self,
TensorIndexVector indices,
TensorVector outIndices,
boolean disable_slice_optimization,
Device self_device,
SymIntArrayRefOptional self_sizes) |
static Tensor |
torch.as_nested_tensor(TensorVector list) |
static Tensor |
torch.as_nested_tensor(TensorVector list,
ScalarTypeOptional dtype,
DeviceOptional device) |
static TensorVector |
torch.atleast_1d(TensorVector tensors) |
static TensorVector |
torch.atleast_2d(TensorVector tensors) |
static TensorVector |
torch.atleast_3d(TensorVector tensors) |
static void |
torch.backward(TensorVector tensors) |
static void |
torch.backward(TensorVector tensors,
TensorVector grad_tensors,
BoolOptional retain_graph,
boolean create_graph,
TensorVector inputs)
Computes the sum of gradients of given tensors with respect to graph leaves.
|
static Tensor |
torch.block_diag_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.block_diag_outf(TensorVector tensors,
Tensor out) |
static Tensor |
torch.block_diag(TensorVector tensors) |
static TensorVector |
torch.broadcast_tensors(TensorVector tensors) |
static Tensor |
torch.cartesian_prod(TensorVector tensors) |
static Tensor |
torch.cat_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.cat_out(Tensor out,
TensorVector tensors,
Dimname dim) |
static Tensor |
torch.cat_out(Tensor out,
TensorVector tensors,
long dim) |
static Tensor |
torch.cat_outf(TensorVector tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.cat_outf(TensorVector tensors,
long dim,
Tensor out) |
static Tensor |
torch.cat(TensorVector tensors) |
static Tensor |
torch.cat(TensorVector tensors,
Dimname dim) |
static Tensor |
torch.cat(TensorVector tensors,
long dim) |
static Tensor |
torch.chain_matmul_out(Tensor out,
TensorVector matrices) |
static Tensor |
torch.chain_matmul_outf(TensorVector matrices,
Tensor out) |
static Tensor |
torch.chain_matmul(TensorVector matrices) |
static void |
torch.checkBackend(String c,
TensorVector t,
torch.Backend backend) |
static void |
torch.checkDeviceType(String c,
TensorVector tensors,
torch.DeviceType device_type) |
static TensorImplVector |
torch.checked_dense_tensor_list_unwrap(TensorVector tensors,
String name,
int pos,
byte device_type,
torch.ScalarType scalar_type) |
static void |
torch.checkLayout(String c,
TensorVector tensors,
torch.Layout layout) |
static double |
torch.clip_grad_norm_(TensorVector parameters,
double max_norm) |
static double |
torch.clip_grad_norm_(TensorVector parameters,
double max_norm,
double norm_type,
boolean error_if_nonfinite)
A
ModuleHolder subclass for UpsampleImpl. |
static void |
torch.clip_grad_value_(TensorVector parameters,
double clip_value) |
static Tensor |
torch.column_stack_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.column_stack_outf(TensorVector tensors,
Tensor out) |
static Tensor |
torch.column_stack(TensorVector tensors) |
static Tensor |
torch.concat_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.concat_out(Tensor out,
TensorVector tensors,
Dimname dim) |
static Tensor |
torch.concat_out(Tensor out,
TensorVector tensors,
long dim) |
static Tensor |
torch.concat_outf(TensorVector tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.concat_outf(TensorVector tensors,
long dim,
Tensor out) |
static Tensor |
torch.concat(TensorVector tensors) |
static Tensor |
torch.concat(TensorVector tensors,
Dimname dim) |
static Tensor |
torch.concat(TensorVector tensors,
long dim) |
static Tensor |
torch.concatenate_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.concatenate_out(Tensor out,
TensorVector tensors,
Dimname dim) |
static Tensor |
torch.concatenate_out(Tensor out,
TensorVector tensors,
long dim) |
static Tensor |
torch.concatenate_outf(TensorVector tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.concatenate_outf(TensorVector tensors,
long dim,
Tensor out) |
static Tensor |
torch.concatenate(TensorVector tensors) |
static Tensor |
torch.concatenate(TensorVector tensors,
Dimname dim) |
static Tensor |
torch.concatenate(TensorVector tensors,
long dim) |
static void |
torch.dequantize_out(TensorVector out,
TensorVector tensors) |
static void |
torch.dequantize_outf(TensorVector tensors,
TensorVector out) |
static TensorVector |
torch.dequantize(TensorVector tensors) |
static DeviceOptional |
torch.device_of(TensorVector t) |
static Tensor |
torch.dispatch_index_put_(Tensor self,
TensorVector indices,
Tensor value) |
static Tensor |
torch.dispatch_index(Tensor self,
TensorVector indices) |
static Tensor |
torch.dstack_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.dstack_outf(TensorVector tensors,
Tensor out) |
static Tensor |
torch.dstack(TensorVector tensors) |
static Tensor |
torch.einsum(String equation,
TensorVector tensors) |
static Tensor |
torch.einsum(String equation,
TensorVector tensors,
long... path) |
static TensorVector |
torch.expand_outplace(TensorVector to_expand) |
static Tensor |
torch.flatten_dense_tensors(TensorVector tensors) |
static TensorVector |
torch.grad(TensorVector outputs,
TensorVector inputs) |
static TensorVector |
torch.grad(TensorVector outputs,
TensorVector inputs,
TensorVector grad_outputs,
BoolOptional retain_graph,
boolean create_graph,
boolean allow_unused)
Computes and returns the sum of gradients of outputs with respect to the
inputs.
|
static TensorVector |
torch.gradient(Tensor self,
TensorVector spacing) |
static TensorVector |
torch.gradient(Tensor self,
TensorVector spacing,
long... dim) |
static TensorVector |
torch.gradient(Tensor self,
TensorVector spacing,
long[] dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
TensorVector spacing,
LongOptional dim,
long edge_order) |
static T_TensorTensor_T |
torch.gru(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.gru(Tensor input,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
long[] dim_ptr,
long[] specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
SymIntArrayRefOptional prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongBuffer dim_ptr,
LongBuffer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
SymIntArrayRefOptional prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongPointer dim_ptr,
LongPointer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
SymIntArrayRefOptional prev_dim_result_sizes) |
static boolean |
torch.has_names(TensorVector tensors) |
static T_TensorTensorVector_T |
torch.histogramdd(Tensor self,
TensorVector bins) |
static T_TensorTensorVector_T |
torch.histogramdd(Tensor self,
TensorVector bins,
double[] range,
TensorOptional weight,
boolean density) |
static Tensor |
torch.hstack_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.hstack_outf(TensorVector tensors,
Tensor out) |
static Tensor |
torch.hstack(TensorVector tensors) |
static Tensor |
torch.linalg_multi_dot_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.linalg_multi_dot_outf(TensorVector tensors,
Tensor out) |
static Tensor |
torch.linalg_multi_dot(TensorVector tensors) |
static T_TensorTensor_T |
torch.lstm_cell(Tensor input,
TensorVector hx,
Tensor w_ih,
Tensor w_hh) |
static T_TensorTensor_T |
torch.lstm_cell(Tensor input,
TensorVector hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static void |
torch.lstm_mps_backward_out(Tensor out0,
TensorVector out1,
TensorVector out2,
TensorOptional grad_y,
TensorOptional grad_hy,
TensorOptional grad_cy,
Tensor z_state,
Tensor cell_state_fwd,
Tensor input,
Tensor layersOutputs,
TensorVector hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static void |
torch.lstm_mps_backward_outf(TensorOptional grad_y,
TensorOptional grad_hy,
TensorOptional grad_cy,
Tensor z_state,
Tensor cell_state_fwd,
Tensor input,
Tensor layersOutputs,
TensorVector hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first,
Tensor out0,
TensorVector out1,
TensorVector out2) |
static T_TensorTensorVectorTensorVector_T |
torch.lstm_mps_backward(TensorOptional grad_y,
TensorOptional grad_hy,
TensorOptional grad_cy,
Tensor z_state,
Tensor cell_state_fwd,
Tensor input,
Tensor layersOutputs,
TensorVector hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensorTensor_T |
torch.lstm(Tensor data,
Tensor batch_sizes,
TensorVector hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensorTensor_T |
torch.lstm(Tensor input,
TensorVector hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static long |
torch.maybe_wrap_dim(long dim,
TensorVector tensors) |
static TensorVector |
torch.meshgrid(TensorVector tensors) |
static TensorVector |
torch.meshgrid(TensorVector tensors,
String indexing) |
static void |
torch.miopen_rnn_backward_out(Tensor out0,
Tensor out1,
Tensor out2,
TensorVector out3,
Tensor input,
TensorVector weight,
long weight_stride0,
Tensor weight_buf,
Tensor hx,
TensorOptional cx,
Tensor output,
TensorOptional grad_output,
TensorOptional grad_hy,
TensorOptional grad_cy,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state,
Tensor reserve,
BoolPointer output_mask) |
static void |
torch.miopen_rnn_backward_outf(Tensor input,
TensorVector weight,
long weight_stride0,
Tensor weight_buf,
Tensor hx,
TensorOptional cx,
Tensor output,
TensorOptional grad_output,
TensorOptional grad_hy,
TensorOptional grad_cy,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state,
Tensor reserve,
BoolPointer output_mask,
Tensor out0,
Tensor out1,
Tensor out2,
TensorVector out3) |
static T_TensorTensorTensorTensorVector_T |
torch.miopen_rnn_backward(Tensor input,
TensorVector weight,
long weight_stride0,
Tensor weight_buf,
Tensor hx,
TensorOptional cx,
Tensor output,
TensorOptional grad_output,
TensorOptional grad_hy,
TensorOptional grad_cy,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state,
Tensor reserve,
BoolPointer output_mask) |
static T_TensorTensorTensorTensorTensor_T |
torch.miopen_rnn_out(Tensor out0,
Tensor out1,
Tensor out2,
Tensor out3,
Tensor out4,
Tensor input,
TensorVector weight,
long weight_stride0,
Tensor hx,
TensorOptional cx,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state) |
static T_TensorTensorTensorTensorTensor_T |
torch.miopen_rnn_outf(Tensor input,
TensorVector weight,
long weight_stride0,
Tensor hx,
TensorOptional cx,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state,
Tensor out0,
Tensor out1,
Tensor out2,
Tensor out3,
Tensor out4) |
static T_TensorTensorTensorTensorTensor_T |
torch.miopen_rnn(Tensor input,
TensorVector weight,
long weight_stride0,
Tensor hx,
TensorOptional cx,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state) |
static Tensor |
torch.multi_dot_out(TensorVector tensors,
Tensor result) |
static Tensor |
torch.multi_dot(TensorVector tensors) |
static Tensor |
torch.nested_tensor(TensorVector nested_tensor_data) |
static Tensor |
torch.nested_tensor(TensorVector nested_tensor_data,
TensorOptions options) |
static PackedSequence |
torch.pack_sequence(TensorVector sequences) |
static PackedSequence |
torch.pack_sequence(TensorVector sequences,
boolean enforce_sorted) |
static Tensor |
torch.pad_sequence(TensorVector sequences) |
static Tensor |
torch.pad_sequence(TensorVector sequences,
boolean batch_first,
double padding_value) |
static Tensor |
torch.parameters_to_vector(TensorVector parameters) |
static BytePointer |
torch.pickle(IValue ivalue,
TensorVector tensor_table)
Save a
torch::IValue in a format compatible with Python's pickle module
If present, tensor_table is a pointer to a table in which tensors that
are contained within ivalue are stored, and the bytes returned by the
pickler will only include references to these tensors in the table. |
static void |
torch.pickle(PickleWriter writer,
IValue ivalue,
TensorVector tensor_table)
Pickle an IValue by calling a function to handle writing the data.
|
static void |
torch.quantize_per_tensor_out(TensorVector out,
TensorVector tensors,
Tensor scales,
Tensor zero_points,
torch.ScalarType dtype) |
static void |
torch.quantize_per_tensor_outf(TensorVector tensors,
Tensor scales,
Tensor zero_points,
torch.ScalarType dtype,
TensorVector out) |
static TensorVector |
torch.quantize_per_tensor(TensorVector tensors,
Tensor scales,
Tensor zero_points,
torch.ScalarType dtype) |
static T_TensorTensor_T |
torch.quantized_lstm_cell(Tensor input,
TensorVector hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static void |
torch.recordTensorIndex(Tensor tensor,
TensorVector outIndices,
long[] dim_ptr) |
static void |
torch.recordTensorIndex(Tensor tensor,
TensorVector outIndices,
LongBuffer dim_ptr) |
static void |
torch.recordTensorIndex(Tensor tensor,
TensorVector outIndices,
LongPointer dim_ptr) |
static T_TensorTensor_T |
torch.rnn_relu(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.rnn_relu(Tensor input,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensor_T |
torch.rnn_tanh(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.rnn_tanh(Tensor input,
Tensor hx,
TensorVector params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static Tensor |
torch.row_stack_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.row_stack_outf(TensorVector tensors,
Tensor out) |
static Tensor |
torch.row_stack(TensorVector tensors) |
static void |
torch.split_copy_out(TensorVector out,
Tensor self,
long split_size) |
static void |
torch.split_copy_out(TensorVector out,
Tensor self,
long split_size,
long dim) |
static void |
torch.split_copy_outf(Tensor self,
long split_size,
long dim,
TensorVector out) |
static void |
torch.split_copy_symint_out(TensorVector out,
Tensor self,
SymInt split_size) |
static void |
torch.split_copy_symint_out(TensorVector out,
Tensor self,
SymInt split_size,
long dim) |
static void |
torch.split_copy_symint_outf(Tensor self,
SymInt split_size,
long dim,
TensorVector out) |
static void |
torch.split_with_sizes_copy_out(TensorVector out,
Tensor self,
long... split_sizes) |
static void |
torch.split_with_sizes_copy_out(TensorVector out,
Tensor self,
long[] split_sizes,
long dim) |
static void |
torch.split_with_sizes_copy_outf(Tensor self,
long[] split_sizes,
long dim,
TensorVector out) |
static void |
torch.split_with_sizes_copy_symint_out(TensorVector out,
Tensor self,
SymIntArrayRef split_sizes) |
static void |
torch.split_with_sizes_copy_symint_out(TensorVector out,
Tensor self,
SymIntArrayRef split_sizes,
long dim) |
static void |
torch.split_with_sizes_copy_symint_outf(Tensor self,
SymIntArrayRef split_sizes,
long dim,
TensorVector out) |
static Tensor |
torch.stack_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.stack_out(Tensor out,
TensorVector tensors,
long dim) |
static Tensor |
torch.stack_outf(TensorVector tensors,
long dim,
Tensor out) |
static Tensor |
torch.stack(TensorVector tensors) |
static Tensor |
torch.stack(TensorVector tensors,
long dim) |
static TensorOptionalVector |
torch.to_optional(TensorVector output) |
static TensorOptionalList |
torch.typeConvertIndices(Tensor arg0,
TensorVector indices) |
static void |
torch.unbind_copy_out(TensorVector out,
Tensor self) |
static void |
torch.unbind_copy_out(TensorVector out,
Tensor self,
long dim) |
static void |
torch.unbind_copy_outf(Tensor self,
long dim,
TensorVector out) |
static TensorVector |
torch.unflatten_dense_tensors(Tensor flat,
TensorVector tensors) |
static IValue |
torch.unpickle(PickleReader reader,
TypeResolver type_resolver,
TensorVector tensor_table) |
static IValue |
torch.unpickle(PickleReader reader,
TypeResolver type_resolver,
TensorVector tensor_table,
TypeParser type_parser) |
static IValue |
torch.unpickle(String data,
long size,
TypeResolver type_resolver,
TensorVector tensor_table,
TypeParser type_parser) |
static void |
torch.unsafe_split_out(TensorVector out,
Tensor self,
long split_size) |
static void |
torch.unsafe_split_out(TensorVector out,
Tensor self,
long split_size,
long dim) |
static void |
torch.unsafe_split_outf(Tensor self,
long split_size,
long dim,
TensorVector out) |
static void |
torch.unsafe_split_symint_out(TensorVector out,
Tensor self,
SymInt split_size) |
static void |
torch.unsafe_split_symint_out(TensorVector out,
Tensor self,
SymInt split_size,
long dim) |
static void |
torch.unsafe_split_symint_outf(Tensor self,
SymInt split_size,
long dim,
TensorVector out) |
static void |
torch.unsafe_split_with_sizes_out(TensorVector out,
Tensor self,
long... split_sizes) |
static void |
torch.unsafe_split_with_sizes_out(TensorVector out,
Tensor self,
long[] split_sizes,
long dim) |
static void |
torch.unsafe_split_with_sizes_outf(Tensor self,
long[] split_sizes,
long dim,
TensorVector out) |
static void |
torch.unsafe_split_with_sizes_symint_out(TensorVector out,
Tensor self,
SymIntArrayRef split_sizes) |
static void |
torch.unsafe_split_with_sizes_symint_out(TensorVector out,
Tensor self,
SymIntArrayRef split_sizes,
long dim) |
static void |
torch.unsafe_split_with_sizes_symint_outf(Tensor self,
SymIntArrayRef split_sizes,
long dim,
TensorVector out) |
static void |
torch.vector_to_parameters(Tensor vec,
TensorVector parameters) |
static Tensor |
torch.vstack_out(Tensor out,
TensorVector tensors) |
static Tensor |
torch.vstack_outf(TensorVector tensors,
Tensor out) |
static Tensor |
torch.vstack(TensorVector tensors) |
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