pool3d

paddle.fluid.layers.pool3d(input, pool_size=-1, pool_type='max', pool_stride=1, pool_padding=0, global_pooling=False, use_cudnn=True, ceil_mode=False, name=None, exclusive=True, data_format="NCDHW")[源代码]

该OP使用上述输入参数的池化配置,为三维空间池化操作,根据 input ,池化核大小 pool_size ,池化类型 pool_type ,步长 pool_stride 和填充 pool_padding 等参数计算输出。

输入 input 和输出(Out)采用NCDHW或NDHWC格式,其中N是批大小,C是通道数,D,H和W分别是特征的深度,高度和宽度。

参数 pool_sizepool_stride 含有三个整型元素。 分别代表深度,高度和宽度维度上的参数。

输入 input 和输出(Out)的形状可能不同。

例如:

输入:
X 的形状: \((N, C, D_{in}, H_{in}, W_{in})\)
输出:
out 的形状: \((N, C, D_{out}, H_{out}, W_{out})\)

ceil_mode = false时,

\[\begin{split}D_{out} &= \frac{(D_{in} - pool\_size[0] + pad\_depth\_front + pad\_depth\_back)}{pool\_stride[0]} + 1\\ H_{out} &= \frac{(H_{in} - pool\_size[1] + pad\_height\_top + pad\_height\_bottom)}{pool\_stride[1]} + 1\\ W_{out} &= \frac{(W_{in} - pool\_size[2] + pad\_width\_left + pad\_width\_right)}{pool\_stride[2]} + 1\end{split}\]

ceil_mode = true时,

\[\begin{split}D_{out} &= \frac{(D_{in} - pool\_size[0] + pad\_depth\_front + pad\_depth\_back + pool\_stride[0] -1)}{pool\_stride[0]} + 1\\ H_{out} &= \frac{(H_{in} - pool\_size[1] + pad\_height\_top + pad\_height\_bottom + pool\_stride[1] -1)}{pool\_stride[1]} + 1\\ W_{out} &= \frac{(W_{in} - pool\_size[2] + pad\_width\_left + pad\_width\_right + pool\_stride[2] -1)}{pool\_stride[2]} + 1\end{split}\]

exclusive = false时,

\[\begin{split}dstart &= i * pool\_stride[0] - pad\_depth\_front \\ dend &= dstart + pool\_size[0] \\ hstart &= j * pool\_stride[1] - pad\_height\_top \\ hend &= hstart + pool\_size[1] \\ wstart &= k * pool\_stride[2] - pad\_width\_left \\ wend &= wstart + pool\_size[2] \\ Output(i ,j, k) &= \frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{pool\_size[0] * pool\_size[1] * pool\_size[2]}\end{split}\]

如果 exclusive = true:

\[\begin{split}dstart &= max(0, i * pool\_stride[0] - pad\_depth\_front) \\ dend &= min(D, dstart + pool\_size[0]) \\ hstart &= max(0, j * pool\_stride[1] - pad\_height\_top) \\ hend &= min(H, hstart + pool\_size[1]) \\ wstart &= max(0, k * pool\_stride[2] - pad\_width\_left) \\ wend & = min(W, wstart + pool\_size[2]) \\ Output(i ,j, k) & = \frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}\end{split}\]

如果 pool_padding = "SAME":

\[D_{out} = \frac{(D_{in} + pool\_stride[0] - 1)}{pool\_stride[0]}\]
\[H_{out} = \frac{(H_{in} + pool\_stride[1] - 1)}{pool\_stride[1]}\]
\[W_{out} = \frac{(W_{in} + pool\_stride[2] - 1)}{pool\_stride[2]}\]

如果 pool_padding = "VALID":

\[D_{out} = \frac{(D_{in} - pool\_size[0])}{pool\_stride[0]} + 1\]
\[H_{out} = \frac{(H_{in} - pool\_size[1])}{pool\_stride[1]} + 1\]
\[W_{out} = \frac{(W_{in} - pool\_size[2])}{pool\_stride[2]} + 1\]
参数:
  • input (Vairable) - 形状为 \([N, C, D, H, W]\)\([N, D, H, W, C]\) 的5-D Tensor,N是批尺寸,C是通道数,D是特征深度,H是特征高度,W是特征宽度,数据类型为float32或float64。
  • pool_size (int|list|tuple) - 池化核的大小。如果它是一个元组或列表,那么它包含三个整数值,(pool_size_Depth, pool_size_Height, pool_size_Width)。若为一个整数,则表示D,H和W维度上均为该值,比如若pool_size=2, 则池化核大小为[2,2,2]。
  • pool_type (str) - 池化类型,可以为"max"或"avg","max" 对应max-pooling, "avg" 对应average-pooling。默认值:"max"。
  • pool_stride (int|list|tuple) - 池化层的步长。如果它是一个元组或列表,那么它包含三个整数值,(pool_stride_Depth, pool_stride_Height, pool_stride_Width)。若为一个整数,则表示D,H和W维度上均为该值,比如若pool_stride=3, 则池化层步长为[3,3,3]。默认值:1。
  • pool_padding (int|list|tuple|str) - 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3个整数值:[pad_depth, pad_height, pad_width];(2)包含6个整数值:[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right];(3)包含5个二元组:当 data_format 为"NCDHW"时为[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]],当 data_format 为"NDHWC"时为[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]。若为一个整数,则表示D、H和W维度上均为该值。默认值:0。
  • global_pooling (bool)- 是否用全局池化。如果global_pooling = True,已设置的 pool_sizepool_padding 会被忽略, pool_size 将被设置为 \([D_{in}, H_{in}, W_{in}]\)pool_padding 将被设置为0。默认值:False。
  • use_cudnn (bool)- 是否使用cudnn内核。只有已安装cudnn库时才有效。默认值:True。
  • ceil_mode (bool)- 是否用ceil函数计算输出的深度、高度和宽度。计算细节可参考上述 ceil_mode = true或 ceil_mode = false 时的计算公式。默认值:False。
  • name (str,可选) – 具体用法请参见 Name ,一般无需设置。默认值:None。
  • exclusive (bool) - 是否在平均池化模式忽略填充值。计算细节可参考上述 exclusive = true或 exclusive = false 时的计算公式。默认值:True。
  • data_format (str) - 输入和输出的数据格式,可以是"NCDHW"和"NDHWC"。N是批尺寸,C是通道数,D是特征深度,H是特征高度,W是特征宽度。默认值:"NDCHW"。

返回: 5-D Tensor,数据类型与 input 一致。

返回类型:Variable。

抛出异常:
  • ValueError - 如果 pool_type 既不是"max"也不是"avg"。
  • ValueError - 如果 global_pooling 为False并且 pool_size 为-1。
  • ValueError - 如果 use_cudnn 不是bool值。
  • ValueError - 如果 data_format 既不是"NCHW"也不是"NHWC"。
  • ValueError - 如果 pool_padding 是字符串,既不是"SAME"也不是"VALID"。
  • ValueError - 如果 pool_padding 含有5个二元组,与批尺寸对应维度的值不为0或者与通道对应维度的值不为0。

代码示例

import paddle.fluid as fluid
data_NCDHW = fluid.layers.data(
    name='data', shape=[2, 3, 8, 8, 8], dtype='float32', append_batch_size=False)

data_NDHWC = fluid.layers.data(
    name='data', shape=[2, 8, 8, 8, 3], dtype='float32', append_batch_size=False)

# example 1:
# ceil_mode = False
out_1 = fluid.layers.pool3d(
              input=data_NCDHW, # shape: [2, 3, 8, 8, 8]
              pool_size=[3,3,3],
              pool_type='avg',
              pool_stride=[3,3,3],
              pool_padding=[2,2,1], # it is same as pool_padding = [2,2,2,2,1,1]
              global_pooling=False,
              ceil_mode=False,
              exclusive=True,
              data_format="NCDHW")
# shape of out_1: [2, 3, 4, 4, 3]

# example 2:
# ceil_mode = True (different from example 1)
out_2 = fluid.layers.pool3d(
              input=data_NCDHW,
              pool_size=[3,3,3],
              pool_type='avg',
              pool_stride=[3,3,3],
              pool_padding=[[0,0], [0,0], [2,2], [2,2], [1,1]], # it is same as pool_padding = [2,2,2,2,1,1]
              global_pooling=False,
              ceil_mode=True,
              exclusive=True,
              data_format="NCDHW")
# shape of out_2: [2, 3, 4, 4, 4] which is different from out_1

# example 3:
# pool_padding = "SAME" (different from example 1)
out_3 = fluid.layers.pool3d(
              input=data_NCDHW,
              pool_size=[3,3,3],
              pool_type='avg',
              pool_stride=[3,3,3],
              pool_padding="SAME",
              global_pooling=False,
              ceil_mode=False,
              exclusive=True,
              data_format="NCDHW")
# shape of out_3: [2, 3, 3, 3, 3] which is different from out_1

# example 4:
# pool_padding = "VALID" (different from example 1)
out_4 = fluid.layers.pool3d(
              input=data_NCDHW,
              pool_size=[3,3,3],
              pool_type='avg',
              pool_stride=[3,3,3],
              pool_padding="VALID",
              global_pooling=False,
              ceil_mode=False,
              exclusive=True,
              data_format="NCDHW")
# shape of out_4: [2, 3, 2, 2, 2] which is different from out_1

# example 5:
# global_pooling = True (different from example 1)
# It will be set pool_size = [8,8,8] and pool_padding = [0,0,0] actually.
out_5 = fluid.layers.pool3d(
              input=data_NCDHW,
              pool_size=[3,3,3],
              pool_type='avg',
              pool_stride=[3,3,3],
              pool_padding=[2,2,1],
              global_pooling=True,
              ceil_mode=False,
              exclusive=True,
              data_format="NCDHW")
# shape of out_5: [2, 3, 1, 1, 1] which is different from out_1

# example 6:
# data_format = "NDHWC" (different from example 1)
out_6 = fluid.layers.pool3d(
              input=data_NHWC, # shape: [2, 8, 8, 8, 3]
              pool_size=[3,3,3],
              pool_type='avg',
              pool_stride=[3,3,3],
              pool_padding=[2,2,1],
              global_pooling=False,
              ceil_mode=False,
              exclusive=True,
              data_format="NDHWC")
# shape of out_6: [2, 4, 4, 3, 3] which is different from out_1