Mantieni tutto organizzato con le raccolte
Salva e classifica i contenuti in base alle tue preferenze.
#include <array_ops.h>
Estrai patches
dalle images
e inseriscile nella dimensione di output "profondità".
Riepilogo
Argomenti:
- scope: un oggetto Scope
- immagini: Tensore 4-D con forma
[batch, in_rows, in_cols, depth]
. - ksizes: la dimensione della finestra scorrevole per ciascuna dimensione delle
images
. - strides: quanto distano i centri di due patch consecutive nelle immagini. Deve essere:
[1, stride_rows, stride_cols, 1]
. - tariffe: deve essere:
[1, rate_rows, rate_cols, 1]
. Questo è il passo di input, che specifica la distanza di due campioni di patch consecutivi nell'input. Equivalente all'estrazione di patch con patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)
, seguito dal sottocampionamento spaziale di un fattore di rates
. Ciò equivale alla rate
in convoluzioni dilatate (aka Atrous). - riempimento: il tipo di algoritmo di riempimento da utilizzare.
Resi:
-
Output
: tensore 4-D con forma [batch, out_rows, out_cols, ksize_rows * ksize_cols * depth]
contenente patch di immagini con dimensioni ksize_rows x ksize_cols x depth
vettorizzate nella dimensione "profondità". Nota out_rows
e out_cols
sono le dimensioni delle patch di output.
Attributi pubblici
Funzioni pubbliche
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-07-27 UTC.
[null,null,["Ultimo aggiornamento 2025-07-27 UTC."],[],[],null,["# tensorflow::ops::ExtractImagePatches Class Reference\n\ntensorflow::ops::ExtractImagePatches\n====================================\n\n`#include \u003carray_ops.h\u003e`\n\nExtract `patches` from `images` and put them in the \"depth\" output dimension.\n\nSummary\n-------\n\nArguments:\n\n- scope: A [Scope](/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- images: 4-D [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) with shape `[batch, in_rows, in_cols, depth]`.\n- ksizes: The size of the sliding window for each dimension of `images`.\n- strides: How far the centers of two consecutive patches are in the images. Must be: `[1, stride_rows, stride_cols, 1]`.\n- rates: Must be: `[1, rate_rows, rate_cols, 1]`. This is the input stride, specifying how far two consecutive patch samples are in the input. Equivalent to extracting patches with `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by subsampling them spatially by a factor of `rates`. This is equivalent to `rate` in dilated (a.k.a. Atrous) convolutions.\n- padding: The type of padding algorithm to use.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): 4-D [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) with shape `[batch, out_rows, out_cols, ksize_rows * ksize_cols * depth]` containing image patches with size `ksize_rows x ksize_cols x depth` vectorized in the \"depth\" dimension. Note `out_rows` and `out_cols` are the dimensions of the output patches.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [ExtractImagePatches](#classtensorflow_1_1ops_1_1_extract_image_patches_1a48a27e59bf001d9d0599c4a4ad3abcf9)`(const ::`[tensorflow::Scope](/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` images, const gtl::ArraySlice\u003c int \u003e & ksizes, const gtl::ArraySlice\u003c int \u003e & strides, const gtl::ArraySlice\u003c int \u003e & rates, StringPiece padding)` ||\n\n| ### Public attributes ||\n|---------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_extract_image_patches_1a20f65de6816816f98d46af224137110d) | [Operation](/versions/r2.3/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [patches](#classtensorflow_1_1ops_1_1_extract_image_patches_1a282b671f1a0d52422cd35c75d6819ee1) | `::`[tensorflow::Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|---------------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_extract_image_patches_1a812a245b3efe85c0003da911be95b891)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_extract_image_patches_1a3dbc12d46ac43f4e5cb6868030310880)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_extract_image_patches_1a7a11be91c9fd8c6b3c5d48ae30630a18)`() const ` | ` ` ` ` |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### patches\n\n```text\n::tensorflow::Output patches\n``` \n\nPublic functions\n----------------\n\n### ExtractImagePatches\n\n```gdscript\n ExtractImagePatches(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input images,\n const gtl::ArraySlice\u003c int \u003e & ksizes,\n const gtl::ArraySlice\u003c int \u003e & strides,\n const gtl::ArraySlice\u003c int \u003e & rates,\n StringPiece padding\n)\n``` \n\n### node\n\n```gdscript\n::tensorflow::Node * node() const \n``` \n\n### operator::tensorflow::Input\n\n```gdscript\n operator::tensorflow::Input() const \n``` \n\n### operator::tensorflow::Output\n\n```gdscript\n operator::tensorflow::Output() const \n```"]]