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【Hackathon 6th No.16】为 Paddle 新增 LPPool1D / LPPool2D API #6639
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Asthestarsfalll
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May 8, 2024
- 【Hackathon 6th No.16】为 Paddle 新增 LPPool1D / LPPool2D API -part Paddle#63544
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-6639.paddle-docs-preview.paddlepaddle.org.cn/documentation/docs/zh/api/index_cn.html |
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在 paddle.nn.Overview 加一下描述
docs/api/paddle/nn/LPPool2D_cn.rst
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- **norm_type** (int|float):幂平均池化的指数,不可以为 0。 | ||
- **kernel_size** (int|list|tuple):池化核大小。如果它是一个元组或列表,它必须包含两个整数值,(pool_size_Height, pool_size_Width)。若为一个整数,则它的平方值将作为池化核大小,比如若 pool_size=2,则池化核大小为 2x2。 | ||
- **stride** (int|list|tuple,可选):池化层的步长。如果它是一个元组或列表,它将包含两个整数,(pool_stride_Height, pool_stride_Width)。若为一个整数,则表示 H 和 W 维度上 stride 均为该值。默认值为 None,这时会使用 kernel_size 作为 stride。 | ||
- **padding** (str|int|list|tuple,可选) 池化填充。如果它是一个字符串,可以是"VALID"或者"SAME",表示填充算法,计算细节可参考上述 pool_padding = "SAME"或 pool_padding = "VALID" 时的计算公式。如果它是一个元组或列表,它可以有 3 种格式:(1)包含 2 个整数值:[pad_height, pad_width];(2)包含 4 个整数值:[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right];(3)包含 4 个二元组:当 data_format 为"NCHW"时为 [[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]],当 data_format 为"NHWC"时为[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]。若为一个整数,则表示 H 和 W 维度上均为该值。默认值:0。 |
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计算细节可参考上述 pool_padding = "SAME"或 pool_padding = "VALID" 时的计算公式。
上述的 pool_padding 是啥
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文档直接修改自AvgPool,,,然而找不到这个东西
input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)^{norm\_type})^{1 / norm\_type} | ||
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参数 |
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这边的参数的一些描述也和英文对齐吧,比如 pool_size_Height
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这里应该和上面的属性保持一致吧
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这里应该和上面的属性保持一致吧
属性里写的好像也是 kernal_size
?
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- kernel_size: :math:`ksize`
kernel_size应该是参数名,后面的是属性吧?
input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)^{norm\_type})^{1 / norm\_type} | ||
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参数 |
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这里应该和上面的属性保持一致吧
属性里写的好像也是 kernal_size
?
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LGTM,修复一下CI错误