Package | Description |
---|---|
org.bytedeco.pytorch | |
org.bytedeco.pytorch.global |
Modifier and Type | Method and Description |
---|---|
TensorArrayRef |
TensorArrayRefOptional.get() |
TensorArrayRef |
TensorArrayRef.getPointer(long i) |
TensorArrayRef |
TensorArrayRef.position(long position) |
TensorArrayRef |
TensorArrayRef.slice(long N)
slice(n) - Chop off the first N elements of the array.
|
TensorArrayRef |
TensorArrayRef.slice(long N,
long M)
slice(n, m) - Take M elements of the array starting at element N
|
Modifier and Type | Method and Description |
---|---|
void |
Tensor.__dispatch__backward(TensorArrayRef inputs) |
void |
Tensor.__dispatch__backward(TensorArrayRef inputs,
TensorOptional gradient,
BoolOptional retain_graph,
boolean create_graph)
This function can be used to set the value of the forward grad.
|
void |
Tensor._backward(TensorArrayRef inputs,
TensorOptional gradient,
BoolOptional keep_graph,
boolean create_graph) |
void |
VariableHooksInterface._backward(Tensor arg0,
TensorArrayRef arg1,
TensorOptional arg2,
BoolOptional arg3,
boolean arg4) |
TensorArrayRefOptional |
TensorArrayRefOptional.put(TensorArrayRef value) |
Constructor and Description |
---|
TensorArrayRefOptional(TensorArrayRef value) |
TensorList(TensorArrayRef initial_values)
Constructs a list with some initial values.
|
Modifier and Type | Method and Description |
---|---|
static TensorVector |
torch.align_tensors(TensorArrayRef tensors) |
static Tensor |
torch.as_nested_tensor(TensorArrayRef list) |
static Tensor |
torch.as_nested_tensor(TensorArrayRef list,
ScalarTypeOptional dtype,
DeviceOptional device)
As Nested Tensor
See
https://pytorch.org/docs/master/nested.html#torch.nested.as_nested_tensor
|
static TensorVector |
torch.atleast_1d(TensorArrayRef tensors) |
static TensorVector |
torch.atleast_2d(TensorArrayRef tensors) |
static TensorVector |
torch.atleast_3d(TensorArrayRef tensors) |
static Tensor |
torch.block_diag_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.block_diag_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.block_diag(TensorArrayRef tensors) |
static TensorVector |
torch.broadcast_tensors(TensorArrayRef tensors) |
static Tensor |
torch.cartesian_prod(TensorArrayRef tensors) |
static Tensor |
torch.cat_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.cat_out(Tensor out,
TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.cat_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.cat_outf(TensorArrayRef tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.cat_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch.cat(TensorArrayRef tensors) |
static Tensor |
torch.cat(TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.cat(TensorArrayRef tensors,
long dim) |
static Tensor |
torch.chain_matmul_out(Tensor out,
TensorArrayRef matrices) |
static Tensor |
torch.chain_matmul_outf(TensorArrayRef matrices,
Tensor out) |
static Tensor |
torch.chain_matmul(TensorArrayRef matrices) |
static void |
torch.checkBackend(BytePointer c,
TensorArrayRef t,
torch.Backend backend) |
static void |
torch.checkDeviceType(BytePointer c,
TensorArrayRef tensors,
torch.DeviceType device_type) |
static TensorImplVector |
torch.checked_dense_tensor_list_unwrap(TensorArrayRef tensors,
BytePointer name,
int pos,
torch.DeviceType device_type,
torch.ScalarType scalar_type) |
static void |
torch.checkLayout(BytePointer c,
TensorArrayRef tensors,
torch.Layout layout) |
static Tensor |
torch.column_stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.column_stack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.column_stack(TensorArrayRef tensors) |
static Tensor |
torch.concat_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.concat_out(Tensor out,
TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.concat_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.concat_outf(TensorArrayRef tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.concat_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch.concat(TensorArrayRef tensors) |
static Tensor |
torch.concat(TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.concat(TensorArrayRef tensors,
long dim) |
static Tensor |
torch.concatenate_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.concatenate_out(Tensor out,
TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.concatenate_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.concatenate_outf(TensorArrayRef tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.concatenate_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch.concatenate(TensorArrayRef tensors) |
static Tensor |
torch.concatenate(TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.concatenate(TensorArrayRef tensors,
long dim) |
static void |
torch.dequantize_out(TensorArrayRef out,
TensorArrayRef tensors) |
static void |
torch.dequantize_outf(TensorArrayRef tensors,
TensorArrayRef out) |
static TensorVector |
torch.dequantize(TensorArrayRef tensors) |
static DeviceOptional |
torch.device_of(TensorArrayRef t)
Return the Device of a TensorList, if the list is non-empty and
the first Tensor is defined.
|
static Tensor |
torch.dstack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.dstack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.dstack(TensorArrayRef tensors) |
static Tensor |
torch.einsum(BytePointer equation,
TensorArrayRef tensors) |
static Tensor |
torch.einsum(BytePointer equation,
TensorArrayRef tensors,
LongArrayRefOptional path) |
static TensorVector |
torch.expand_outplace(TensorArrayRef to_expand) |
static Tensor |
torch.flatten_dense_tensors(TensorArrayRef tensors) |
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 T_TensorTensor_T |
torch.gru(Tensor input,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensor_T |
torch.gru(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static boolean |
torch.has_names(TensorArrayRef tensors) |
static T_TensorTensorVector_T |
torch.histogramdd(Tensor self,
TensorArrayRef bins) |
static T_TensorTensorVector_T |
torch.histogramdd(Tensor self,
TensorArrayRef bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density) |
static Tensor |
torch.hstack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.hstack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.hstack(TensorArrayRef tensors) |
static Tensor |
torch.linalg_multi_dot_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.linalg_multi_dot_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.linalg_multi_dot(TensorArrayRef tensors) |
static T_TensorTensor_T |
torch.lstm_cell(Tensor input,
TensorArrayRef hx,
Tensor w_ih,
Tensor w_hh) |
static T_TensorTensor_T |
torch.lstm_cell(Tensor input,
TensorArrayRef hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static void |
torch.lstm_mps_backward_out(Tensor out0,
TensorArrayRef out1,
TensorArrayRef out2,
TensorOptional grad_y,
TensorOptional grad_hy,
TensorOptional grad_cy,
Tensor z_state,
Tensor cell_state_fwd,
Tensor input,
Tensor layersOutputs,
TensorArrayRef hx,
TensorArrayRef 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,
TensorArrayRef hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first,
Tensor out0,
TensorArrayRef out1,
TensorArrayRef 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,
TensorArrayRef hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensorTensor_T |
torch.lstm(Tensor input,
TensorArrayRef hx,
TensorArrayRef 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,
TensorArrayRef hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static long |
torch.maybe_wrap_dim(long dim,
TensorArrayRef tensors) |
static TensorVector |
torch.meshgrid(TensorArrayRef tensors) |
static TensorVector |
torch.meshgrid(TensorArrayRef tensors,
BytePointer indexing) |
static void |
torch.miopen_rnn_backward_out(Tensor out0,
Tensor out1,
Tensor out2,
TensorArrayRef out3,
Tensor input,
TensorArrayRef 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,
LongArrayRef batch_sizes,
TensorOptional dropout_state,
Tensor reserve,
BoolPointer output_mask) |
static void |
torch.miopen_rnn_backward_outf(Tensor input,
TensorArrayRef 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,
LongArrayRef batch_sizes,
TensorOptional dropout_state,
Tensor reserve,
BoolPointer output_mask,
Tensor out0,
Tensor out1,
Tensor out2,
TensorArrayRef out3) |
static T_TensorTensorTensorTensorVector_T |
torch.miopen_rnn_backward(Tensor input,
TensorArrayRef 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,
LongArrayRef 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,
TensorArrayRef 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,
LongArrayRef batch_sizes,
TensorOptional dropout_state) |
static T_TensorTensorTensorTensorTensor_T |
torch.miopen_rnn_outf(Tensor input,
TensorArrayRef 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,
LongArrayRef batch_sizes,
TensorOptional dropout_state,
Tensor out0,
Tensor out1,
Tensor out2,
Tensor out3,
Tensor out4) |
static T_TensorTensorTensorTensorTensor_T |
torch.miopen_rnn(Tensor input,
TensorArrayRef 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,
LongArrayRef batch_sizes,
TensorOptional dropout_state) |
static Tensor |
torch.multi_dot_out(TensorArrayRef tensors,
Tensor result) |
static Tensor |
torch.multi_dot(TensorArrayRef tensors) |
static Tensor |
torch.nested_tensor(TensorArrayRef nested_tensor_data) |
static Tensor |
torch.nested_tensor(TensorArrayRef nested_tensor_data,
TensorOptions options)
Nested tensor
See
https://pytorch.org/docs/master/nested.html#torch.nested.nested_tensor
|
static PackedSequence |
torch.pack_sequence(TensorArrayRef sequences) |
static PackedSequence |
torch.pack_sequence(TensorArrayRef sequences,
boolean enforce_sorted)
Packs a list of variable length Tensors
sequences should be a list of Tensors of size L x *, where
L is
the length of a sequence and * is any number of trailing dimensions,
including zero. |
static Tensor |
torch.pad_sequence(TensorArrayRef sequences) |
static Tensor |
torch.pad_sequence(TensorArrayRef sequences,
boolean batch_first,
double padding_value) |
static void |
torch.quantize_per_tensor_out(TensorArrayRef out,
TensorArrayRef tensors,
Tensor scales,
Tensor zero_points,
torch.ScalarType dtype) |
static void |
torch.quantize_per_tensor_outf(TensorArrayRef tensors,
Tensor scales,
Tensor zero_points,
torch.ScalarType dtype,
TensorArrayRef out) |
static TensorVector |
torch.quantize_per_tensor(TensorArrayRef tensors,
Tensor scales,
Tensor zero_points,
torch.ScalarType dtype) |
static T_TensorTensor_T |
torch.quantized_lstm_cell(Tensor input,
TensorArrayRef 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 T_TensorTensor_T |
torch.rnn_relu(Tensor input,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static T_TensorTensor_T |
torch.rnn_relu(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static T_TensorTensor_T |
torch.rnn_tanh(Tensor input,
Tensor hx,
TensorArrayRef 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,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static Tensor |
torch.row_stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.row_stack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.row_stack(TensorArrayRef tensors) |
static void |
torch.split_copy_out(TensorArrayRef out,
Tensor self,
long split_size) |
static void |
torch.split_copy_out(TensorArrayRef out,
Tensor self,
long split_size,
long dim) |
static void |
torch.split_copy_outf(Tensor self,
long split_size,
long dim,
TensorArrayRef out) |
static void |
torch.split_copy_symint_out(TensorArrayRef out,
Tensor self,
SymInt split_size) |
static void |
torch.split_copy_symint_out(TensorArrayRef out,
Tensor self,
SymInt split_size,
long dim) |
static void |
torch.split_copy_symint_outf(Tensor self,
SymInt split_size,
long dim,
TensorArrayRef out) |
static void |
torch.split_with_sizes_copy_out(TensorArrayRef out,
Tensor self,
LongArrayRef split_sizes) |
static void |
torch.split_with_sizes_copy_out(TensorArrayRef out,
Tensor self,
LongArrayRef split_sizes,
long dim) |
static void |
torch.split_with_sizes_copy_outf(Tensor self,
LongArrayRef split_sizes,
long dim,
TensorArrayRef out) |
static void |
torch.split_with_sizes_copy_symint_out(TensorArrayRef out,
Tensor self,
SymIntArrayRef split_sizes) |
static void |
torch.split_with_sizes_copy_symint_out(TensorArrayRef out,
Tensor self,
SymIntArrayRef split_sizes,
long dim) |
static void |
torch.split_with_sizes_copy_symint_outf(Tensor self,
SymIntArrayRef split_sizes,
long dim,
TensorArrayRef out) |
static Tensor |
torch.stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.stack_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.stack_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch.stack(TensorArrayRef tensors) |
static Tensor |
torch.stack(TensorArrayRef tensors,
long dim) |
static void |
torch.unbind_copy_out(TensorArrayRef out,
Tensor self) |
static void |
torch.unbind_copy_out(TensorArrayRef out,
Tensor self,
long dim) |
static void |
torch.unbind_copy_outf(Tensor self,
long dim,
TensorArrayRef out) |
static TensorVector |
torch.unflatten_dense_tensors(Tensor flat,
TensorArrayRef tensors) |
static IValue |
torch.unpickle(BytePointer data,
long size,
TypeResolver type_resolver,
TensorArrayRef tensor_table,
TypeParser type_parser)
Decode a chunk of memory containing pickled data into its
torch::IValue s. |
static IValue |
torch.unpickle(PickleReader reader,
TypeResolver type_resolver,
TensorArrayRef tensor_table) |
static IValue |
torch.unpickle(PickleReader reader,
TypeResolver type_resolver,
TensorArrayRef tensor_table,
TypeParser type_parser)
reader is a function that takes in a size to read from some pickled
binary. |
static void |
torch.unsafe_split_out(TensorArrayRef out,
Tensor self,
long split_size) |
static void |
torch.unsafe_split_out(TensorArrayRef out,
Tensor self,
long split_size,
long dim) |
static void |
torch.unsafe_split_outf(Tensor self,
long split_size,
long dim,
TensorArrayRef out) |
static void |
torch.unsafe_split_symint_out(TensorArrayRef out,
Tensor self,
SymInt split_size) |
static void |
torch.unsafe_split_symint_out(TensorArrayRef out,
Tensor self,
SymInt split_size,
long dim) |
static void |
torch.unsafe_split_symint_outf(Tensor self,
SymInt split_size,
long dim,
TensorArrayRef out) |
static void |
torch.unsafe_split_with_sizes_out(TensorArrayRef out,
Tensor self,
LongArrayRef split_sizes) |
static void |
torch.unsafe_split_with_sizes_out(TensorArrayRef out,
Tensor self,
LongArrayRef split_sizes,
long dim) |
static void |
torch.unsafe_split_with_sizes_outf(Tensor self,
LongArrayRef split_sizes,
long dim,
TensorArrayRef out) |
static void |
torch.unsafe_split_with_sizes_symint_out(TensorArrayRef out,
Tensor self,
SymIntArrayRef split_sizes) |
static void |
torch.unsafe_split_with_sizes_symint_out(TensorArrayRef out,
Tensor self,
SymIntArrayRef split_sizes,
long dim) |
static void |
torch.unsafe_split_with_sizes_symint_outf(Tensor self,
SymIntArrayRef split_sizes,
long dim,
TensorArrayRef out) |
static Tensor |
torch.vstack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.vstack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.vstack(TensorArrayRef tensors) |
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