max_unpool1d

paddle.nn.functional. max_unpool1d ( x, indices, kernel_size, stride=None, padding=0, data_format='NCL', output_size=None, name=None ) [source]

This API implements max unpooling 1d opereation. max_unpool1d accepts the output of max_pool1d as input, including the indices of the maximum value and calculate the partial inverse. All non-maximum values ​​are set to zero.

  • Input: \((N, C, L_{in})\)

  • Output: \((N, C, L_{out})\), where

\[L_{out} = (L_{in} - 1) * stride - 2 * padding + kernel\_size\]

or as given by output_size in the call operator.

Parameters

  • x (Tensor) – The input tensor of unpooling operator which is a 3-D tensor with shape [N, C, L]. The format of input tensor is “NCL”, where N is batch size, C is the number of channels, L is the length of the feature. The data type is float32 or float64.

  • indices (Tensor) – The indices given out by maxpooling1d which is a 3-D tensor with shape [N, C, L]. The format of input tensor is “NCL” , where N is batch size, C is the number of channels, L is the length of the featuree. The data type is float32 or float64.

  • kernel_size (int|list|tuple) – The unpool kernel size. If unpool kernel size is a tuple or list, it must contain an integer.

  • stride (int|list|tuple) – The unpool stride size. If unpool stride size is a tuple or list, it must contain an integer.

  • padding (int | tuple) – Padding that was added to the input.

  • output_size (list|tuple, optional) – The target output size. If output_size is not specified, the actual output shape will be automatically calculated by (input_shape, kernel_size, stride, padding).

  • data_format (string) – The data format of the input and output data. The default is “NCL”. When it is “NCL”, the data is stored in the order of: [batch_size, input_channels, input_length].

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

Returns

The output tensor of unpooling result.

Return type

Tensor

Examples

import paddle
import paddle.nn.functional as F

data = paddle.rand(shape=[1, 3, 16])
pool_out, indices = F.max_pool1d(data, kernel_size=2, stride=2, padding=0, return_mask=True)
# pool_out shape: [1, 3, 8],  indices shape: [1, 3, 8]
unpool_out = F.max_unpool1d(pool_out, indices, kernel_size=2, padding=0)
# unpool_out shape: [1, 3, 16]