[Docathon][Add CN Doc No.24-25] #6360
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感谢你贡献飞桨文档，文档预览构建中，Docs-New 跑完后即可预览，预览链接：http://preview-pr-6360.paddle-docs-preview.paddlepaddle.org.cn/documentation/docs/zh/api/index_cn.html |
26 tasks
Xreki
reviewed
Jan 15, 2024
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| .. py:function:: paddle.sparse.nn.functional.conv2d(x, weight, bias=None, stride=1, padding=0, dilation=1, groups=1, data_format='NHWC', name=None) | ||
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| 稀疏二维卷积层（sparse convolution2d），根据输入、卷积核、步长（stride）、填充（padding）、空洞大小（dilations）一组参数计算得到输出特征层大小。输入和输出是 NHWC 格式，其中 N 是批尺寸，H 是特征的高度，W 是特征层宽度，C 是通道数。如果 bias_attr 不为 False，卷积计算会添加偏置项。 |
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空洞大小（dilations）一组参数->空洞大小（dilations）、组参数（groups）- 计算得到的是
输出特征，而不是输出特征层大小 multidimensional SparseCooTensors信息遗漏了
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multidimensional SparseCooTensors信息遗漏了
遗漏信息补充： 输入（Input）和输出（Output）是形状为[N,H,W,C]的多维稀疏坐标格式张量（SparseCooTensors）。
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| 稀疏二维卷积层（sparse convolution2d），根据输入、卷积核、步长（stride）、填充（padding）、空洞大小（dilations）一组参数计算得到输出特征层大小。输入和输出是 NHWC 格式，其中 N 是批尺寸，H 是特征的高度，W 是特征层宽度，C 是通道数。如果 bias_attr 不为 False，卷积计算会添加偏置项。 | ||
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| 对于每个输入 X，方程为： |
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| - :math:`X` ：输入值，NHWC 格式的 Tensor | ||
| - :math:`W` ：卷积核值，MCDHW 格式的 Tensor |
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| **稀疏二维卷积层** | ||
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| 二维稀疏卷积层（Sparse convolution2d layer）根据输入、卷积核、步长（stride）、填充（padding）、空洞大小（dilations）一组参数计算得到输出特征层大小。 |
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整体请参考conv2d的review comment检查一遍
Co-authored-by: zachary sun <70642955+sunzhongkai588@users.noreply.github.com>
Done @sunzhongkai588 |
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中文文档添加任务
#6193
新增中文文档：
英文文档链接：
@Xreki @sunzhongkai588