adaptive_avg_pool2d

paddle.nn.functional. adaptive_avg_pool2d ( x, output_size, data_format='NCHW', name=None ) [source]

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: .. math:

System Message: ERROR/3 (/usr/local/lib/python3.8/site-packages/paddle/nn/functional/pooling.py:docstring of paddle.nn.functional.pooling.adaptive_avg_pool2d, line 6)

Unexpected indentation.

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) &=

System Message: WARNING/2 (/usr/local/lib/python3.8/site-packages/paddle/nn/functional/pooling.py:docstring of paddle.nn.functional.pooling.adaptive_avg_pool2d, line 11)

Block quote ends without a blank line; unexpected unindent.

rac{sum Input[hstart:hend, wstart:wend]}{(hend - hstart) * (wend - wstart)}

Args:
x (Tensor): The input tensor of adaptive avg pool2d operator, which is a 4-D tensor.

The data type can be float32 or float64.

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): 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.

Returns:

Tensor: The output tensor of avg adaptive pool2d result. The data type is same as input tensor.

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

x = paddle.rand([2, 3, 32, 32])
# x.shape is [2, 3, 32, 32]
out = paddle.nn.functional.adaptive_avg_pool2d(
                x = x,
                output_size=[3, 3])
# out.shape is [2, 3, 3, 3]