dnnl_memory_desc_t (JavaCPP Presets for DNNL 1.6.2-1.5.4 API)

java.lang.Object
- org.bytedeco.javacpp.Pointer
- - org.bytedeco.dnnl.dnnl_memory_desc_t

All Implemented Interfaces:

AutoCloseable
```
@Properties(inherit=dnnl.class)
public class dnnl_memory_desc_t
extends Pointer
```
Memory descriptor. The description is based on a number of dimensions, dimensions themselves, plus information about elements type and memory format. Additionally, contains format-specific descriptions of the data layout.

Nested Class Summary
- Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer
  Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter

Field Summary
- Fields inherited from class org.bytedeco.javacpp.Pointer
  address, capacity, limit, position

Constructor Summary

Constructors
Constructor and Description
`dnnl_memory_desc_t()` Default native constructor.
`dnnl_memory_desc_t(long size)` Native array allocator.
`dnnl_memory_desc_t(Pointer p)` Pointer cast constructor.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`int`	`data_type()` Data type of the tensor elements.
`dnnl_memory_desc_t`	`data_type(int setter)`
`LongPointer`	`dims()` Dimensions in the following order: - CNN data tensors: mini-batch, channel, spatial (`{N, C, [[D,] H,] W}`) - CNN weight tensors: group (optional), output channel, input channel, spatial (`{[G,] O, I, [[D,] H,] W}`) - RNN data tensors: time, mini-batch, channels (`{T, N, C}`) or layers, directions, states, mini-batch, channels (`{L, D, S, N, C}`) - RNN weight tensor: layers, directions, input channel, gates, output channels (`{L, D, I, G, O}`).
`dnnl_memory_extra_desc_t`	`extra()`
`dnnl_memory_desc_t`	`extra(dnnl_memory_extra_desc_t setter)`
`dnnl_blocking_desc_t`	`format_desc_blocking()` Description of the data layout for memory formats that use blocking.
`dnnl_memory_desc_t`	`format_desc_blocking(dnnl_blocking_desc_t setter)`
`dnnl_rnn_packed_desc_t`	`format_desc_rnn_packed_desc()` Tensor of packed weights for RNN.
`dnnl_memory_desc_t`	`format_desc_rnn_packed_desc(dnnl_rnn_packed_desc_t setter)`
`dnnl_wino_desc_t`	`format_desc_wino_desc()` Tensor of weights for integer 8bit winograd convolution.
`dnnl_memory_desc_t`	`format_desc_wino_desc(dnnl_wino_desc_t setter)`
`int`	`format_kind()` Memory format kind.
`dnnl_memory_desc_t`	`format_kind(int setter)`
`dnnl_memory_desc_t`	`getPointer(long i)`
`int`	`ndims()` Number of dimensions
`dnnl_memory_desc_t`	`ndims(int setter)`
`long`	`offset0()` Offset from memory origin to the current block, non-zero only in a description of a memory sub-block.
`dnnl_memory_desc_t`	`offset0(long setter)`
`LongPointer`	`padded_dims()` Size of the data including padding in each dimension.
`LongPointer`	`padded_offsets()` Per-dimension offset from the padding to actual data, the top-level tensor with offsets applied must lie within the padding area.
`dnnl_memory_desc_t`	`position(long position)`

Methods inherited from class org.bytedeco.javacpp.Pointer
address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, getPointer, getPointer, getPointer, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, sizeof, toString, totalBytes, totalCount, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - dnnl_memory_desc_t
```
public dnnl_memory_desc_t()
```
    Default native constructor.
  - dnnl_memory_desc_t
```
public dnnl_memory_desc_t(long size)
```
    Native array allocator. Access with Pointer.position(long).
  - dnnl_memory_desc_t
```
public dnnl_memory_desc_t(Pointer p)
```
    Pointer cast constructor. Invokes Pointer.Pointer(Pointer).
- Method Detail
  - position
```
public dnnl_memory_desc_t position(long position)
```
    Overrides:
    
    position in class Pointer
  - getPointer
```
public dnnl_memory_desc_t getPointer(long i)
```
    Overrides:
    
    getPointer in class Pointer
  - ndims
```
public int ndims()
```
    Number of dimensions
  - ndims
```
public dnnl_memory_desc_t ndims(int setter)
```
  - dims
```
@MemberGetter
 @Cast(value="int64_t*")
public LongPointer dims()
```
    Dimensions in the following order: - CNN data tensors: mini-batch, channel, spatial ({N, C, [[D,] H,] W}) - CNN weight tensors: group (optional), output channel, input channel, spatial ({[G,] O, I, [[D,] H,] W}) - RNN data tensors: time, mini-batch, channels ({T, N, C}) or layers, directions, states, mini-batch, channels ({L, D, S, N, C}) - RNN weight tensor: layers, directions, input channel, gates, output channels ({L, D, I, G, O}). \note The order of dimensions does not depend on the memory format, so whether the data is laid out in #dnnl_nchw or #dnnl_nhwc the dims for 4D CN data tensor would be {N, C, H, W}.
  - data_type
```
@Cast(value="dnnl_data_type_t")
public int data_type()
```
    Data type of the tensor elements.
  - data_type
```
public dnnl_memory_desc_t data_type(int setter)
```
  - padded_dims
```
@MemberGetter
 @Cast(value="int64_t*")
public LongPointer padded_dims()
```
    Size of the data including padding in each dimension.
  - padded_offsets
```
@MemberGetter
 @Cast(value="int64_t*")
public LongPointer padded_offsets()
```
    Per-dimension offset from the padding to actual data, the top-level tensor with offsets applied must lie within the padding area.
  - offset0
```
@Cast(value="dnnl_dim_t")
public long offset0()
```
    Offset from memory origin to the current block, non-zero only in a description of a memory sub-block.
  - offset0
```
public dnnl_memory_desc_t offset0(long setter)
```
  - format_kind
```
@Cast(value="dnnl_format_kind_t")
public int format_kind()
```
    Memory format kind.
  - format_kind
```
public dnnl_memory_desc_t format_kind(int setter)
```
  - format_desc_blocking
```
@Name(value="format_desc.blocking")
 @ByRef
public dnnl_blocking_desc_t format_desc_blocking()
```
    Description of the data layout for memory formats that use blocking.
  - format_desc_blocking
```
public dnnl_memory_desc_t format_desc_blocking(dnnl_blocking_desc_t setter)
```
  - format_desc_wino_desc
```
@Name(value="format_desc.wino_desc")
 @ByRef
public dnnl_wino_desc_t format_desc_wino_desc()
```
    Tensor of weights for integer 8bit winograd convolution.
  - format_desc_wino_desc
```
public dnnl_memory_desc_t format_desc_wino_desc(dnnl_wino_desc_t setter)
```
  - format_desc_rnn_packed_desc
```
@Name(value="format_desc.rnn_packed_desc")
 @ByRef
public dnnl_rnn_packed_desc_t format_desc_rnn_packed_desc()
```
    Tensor of packed weights for RNN.
  - format_desc_rnn_packed_desc
```
public dnnl_memory_desc_t format_desc_rnn_packed_desc(dnnl_rnn_packed_desc_t setter)
```
  - extra
```
@ByRef
public dnnl_memory_extra_desc_t extra()
```
  - extra
```
public dnnl_memory_desc_t extra(dnnl_memory_extra_desc_t setter)
```

Class dnnl_memory_desc_t

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

Constructor Summary

Method Summary

Methods inherited from class org.bytedeco.javacpp.Pointer

Methods inherited from class java.lang.Object

Constructor Detail

dnnl_memory_desc_t

dnnl_memory_desc_t

dnnl_memory_desc_t

Method Detail

position

getPointer

ndims

ndims

dims

data_type

data_type

padded_dims

padded_offsets

offset0

offset0

format_kind

format_kind

format_desc_blocking

format_desc_blocking

format_desc_wino_desc

format_desc_wino_desc

format_desc_rnn_packed_desc

format_desc_rnn_packed_desc

extra

extra