oneflow.nn.AdaptiveMaxPool2d¶
-
class
oneflow.nn.
AdaptiveMaxPool2d
(output_size, return_indices: bool = False)¶ Applies a 2D adaptive max pooling over an input signal composed of several input planes.
The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.AdaptiveMaxPool2d.html.
The output is of size \(H_{out} \times W_{out}\), for any input size. The number of output features is equal to the number of input planes.
- Parameters
output_size – the target output size of the image of the form \(H_{out} \times W_{out}\). Can be a tuple \((H_{out}, W_{out})\) or a single \(H_{out}\) for a square image \(H_{out} \times H_{out}\). \(H_{out}\) and \(W_{out}\) should be a
int
.return_indices – if
True
, will return the indices along with the outputs. Default:False
- Shape:
Input: \((N, C, H_{in}, W_{in})\).
Output: \((N, C, H_{out}, W_{out})\), where \((H_{out}, W_{out})=\text{output_size}\).
Examples:
>>> import oneflow as flow >>> import oneflow.nn as nn >>> # target output size of 5x7 >>> m = nn.AdaptiveMaxPool2d((5,7)) >>> input = flow.randn(1, 64, 8, 9) >>> output = m(input) >>> print(output.shape) oneflow.Size([1, 64, 5, 7]) >>> # target output size of 7x7 (square) >>> m = nn.AdaptiveMaxPool2d(7) >>> input = flow.randn(1, 64, 10, 9) >>> output = m(input) >>> print(output.shape) oneflow.Size([1, 64, 7, 7])