AdaptiveAvgPool2D

class paddle.nn. AdaptiveAvgPool2D ( output_size, data_format='NCHW', name=None ) [source]

This operation applies 2D adaptive avg pooling on input tensor. The h and w dimensions of the output tensor are determined by the parameter output_size.

For avg adaptive pool2d:

\[ \begin{align}\begin{aligned}hstart &= floor(i * H_{in} / H_{out})\\hend &= ceil((i + 1) * H_{in} / H_{out})\\wstart &= floor(j * W_{in} / W_{out})\\wend &= ceil((j + 1) * W_{in} / W_{out})\\Output(i ,j) &= \frac{\sum Input[hstart:hend, wstart:wend]}{(hend - hstart) * (wend - wstart)}\end{aligned}\end{align} \]
Parameters
  • output_size (int|list|tuple) – The pool kernel size. If pool kernel size is a tuple or list, it must contain two element, (H, W). H and W can be either a int, or None which means the size will be the same as that of the input.

  • data_format (str, optional) – The data format of the input and output data. An optional string from: “NCHW”, “NHWC”. The default is “NCHW”. When it is “NCHW”, the data is stored in the order of: [batch_size, input_channels, input_height, input_width].

  • name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

Shape:
  • x(Tensor): The input tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type can be float32, float64.

  • output(Tensor): The output tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type is same as input x.

Returns

A callable object of AdaptiveAvgPool2D.

Examples

# adaptive avg pool2d
# suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
# output shape is [N, C, m, n], adaptive pool divide H and W dimensions
# of input data into m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive avg pool performs calculations as follow:
#
#     for i in range(m):
#         for j in range(n):
#             hstart = floor(i * H / m)
#             hend = ceil((i + 1) * H / m)
#             wstart = floor(i * W / n)
#             wend = ceil((i + 1) * W / n)
#             output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
#
import paddle
import numpy as np

input_data = np.random.rand(2, 3, 32, 32)
x = paddle.to_tensor(input_data)
# x.shape is [2, 3, 32, 32]
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=3)
pool_out = adaptive_avg_pool(x = x)
# pool_out.shape is [2, 3, 3, 3]
forward ( x )

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments

extra_repr ( )

Extra representation of this layer, you can have custom implementation of your own layer.