AdaptiveMaxPool3D

paddle.nn. AdaptiveMaxPool3D ( output_size, return_mask=False, name=None )

该算子根据输入 x , output_size 等参数对一个输入Tensor计算3D的自适应最大池化。输入和输出都是5-D Tensor, 默认是以 NCDHW 格式表示的,其中 N 是 batch size, C 是通道数, DHW 分别是输入特征的深度,高度,宽度.

计算公式如下:

\[ \begin{align}\begin{aligned}dstart &= floor(i * D_{in} / D_{out})\\dend &= ceil((i + 1) * D_{in} / D_{out})\\hstart &= floor(j * H_{in} / H_{out})\\hend &= ceil((j + 1) * H_{in} / H_{out})\\wstart &= floor(k * W_{in} / W_{out})\\wend &= ceil((k + 1) * W_{in} / W_{out})\\Output(i ,j, k) &= max(Input[dstart:dend, hstart:hend, wstart:wend])\end{aligned}\end{align} \]

参数

  • output_size (int|list|tuple): 算子输出特征图的高宽长大小,其数据类型为int,list或tuple。

  • return_mask (bool): 如果设置为True,则会与输出一起返回最大值的索引,默认为False。

  • name (str,可选): 操作的名称(可选,默认值为None)。更多信息请参见 Name

形状

  • x (Tensor): 默认形状为(批大小,通道数,输出特征深度,高度,宽度),即NCDHW格式的5-D Tensor。 其数据类型为float32或者float64。

  • output (Tensor): 默认形状为(批大小,通道数,输出特征深度,高度,宽度),即NCDHW格式的5-D Tensor。 其数据类型与输入x相同。

返回

计算AdaptiveMaxPool3D的可调用对象

代码示例

# adaptive max pool3d
# suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
# of input data into l * m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive max pool performs calculations as follow:
#
#     for i in range(l):
#         for j in range(m):
#             for k in range(n):
#                 dstart = floor(i * D / l)
#                 dend = ceil((i + 1) * D / l)
#                 hstart = floor(j * H / m)
#                 hend = ceil((j + 1) * H / m)
#                 wstart = floor(k * W / n)
#                 wend = ceil((k + 1) * W / n)
#                 output[:, :, i, j, k] =
#                     max(input[:, :, dstart:dend, hstart: hend, wstart: wend])

import paddle
x = paddle.rand((2, 3, 8, 32, 32))
pool = paddle.nn.AdaptiveMaxPool3D(output_size=4)
out = pool(x)
print(out.shape)
# out shape: [2, 3, 4, 4, 4]
pool = paddle.nn.AdaptiveMaxPool3D(output_size=3, return_mask=True)
out, indices = pool(x)
print(out.shape)
print(indices.shape)
# out shape: [2, 3, 3, 3, 3], indices shape: [2, 3, 3, 3, 3]