mindspore.ops.adaptive_max_pool2d

mindspore.ops.adaptive_max_pool2d(input_x, output_size, return_indices=False)[source]

adaptive_max_pool2d operation.

This operator applies a 2D adaptive max pooling to an input signal composed of multiple input planes. That is, for any input size, the size of the specified output is H x W. The number of output features is equal to the number of input planes.

The input and output data format can be “NCHW” and “CHW”. N is the batch size, C is the number of channels, H is the feature height, and W is the feature width.

\[\begin{split}\begin{align} h_{start} &= floor(i * H_{in} / H_{out})\\ h_{end} &= ceil((i + 1) * H_{in} / H_{out})\\ w_{start} &= floor(j * W_{in} / W_{out})\\ w_{end} &= ceil((j + 1) * W_{in} / W_{out})\\ Output(i,j) &= {\max Input[h_{start}:h_{end}, w_{start}:w_{end}]} \end{align}\end{split}\]

Note

Ascend platform only supports float16 type for input_x.

Parameters

  • input_x (Tensor) – The input of adaptive_max_pool2d, which is a 3D or 4D tensor, with float16, float32 or float64 data type.

  • output_size (Union[int, tuple]) – The target output size is H x W. ouput_size can be a tuple, or a single H for H x H, and H and W can be int or None which means the output size is the same as the input.

  • return_indices (bool) – If return_indices is True, the indices of max value would be output. Default: False.

Returns

Tensor, with the same type as the input_x.

Shape of the output is input_x_shape[:len(input_x_shape) - len(out_shape)] + out_shape.

Raises

  • TypeError – If output_size is not int or tuple.

  • TypeError – If input_x is not a tensor.

  • TypeError – If return_indices is not a bool.

  • TypeError – If dtype of input_x is not float16, float32 or float64.

  • ValueError – If output_size is a tuple and the length of output_size is not 2.

  • ValueError – If the dimension of input_x is not NCHW or CHW.

Supported Platforms:

Ascend GPU CPU

Examples

>>> # case 1: output_size=(None, 2)
>>> input_x = Tensor(np.array([[[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
...                             [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
...                             [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]]), mindspore.float32)
>>> output = F.adaptive_max_pool2d(input_x, (None, 2))
>>> print(output)
[[[[2. 3.]
   [5. 6.]
   [8. 9.]]
  [[2. 3.]
   [5. 6.]
   [8. 9.]]
  [[2. 3.]
   [5. 6.]
   [8. 9.]]]]
>>> # case 2: output_size=2
>>> output = F.adaptive_max_pool2d(input_x, 2)
>>> print(output)
[[[[5. 6.]
   [8. 9.]]
  [[5. 6.]
   [8. 9.]]
  [[5. 6.]
   [8. 9.]]]]
>>> # case 3: output_size=(1, 2)
>>> output = F.adaptive_max_pool2d(input_x, (1, 2))
>>> print(output)
[[[[8. 9.]]
  [[8. 9.]]
  [[8. 9.]]]]