- paddle.nn.functional. adaptive_max_pool1d ( x, output_size, return_mask=False, name=None )
This API implements adaptive max pooling 1d operation. See more details in api_nn_pooling_AdaptiveMaxPool1d .
x (Tensor) – The input tensor of pooling 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.
output_size (int) – The pool kernel size. The value should be an integer.
return_mask (bool) – If true, the index of max pooling point will be returned along with outputs. It cannot be set in average pooling type. Default False.
name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.
- The output tensor of adaptive pooling result. The data type is same
as input tensor.
- Return type
ValueError – ‘output_size’ should be an integer.
# max adaptive pool1d # suppose input data in shape of [N, C, L], `output_size` is m or [m], # output shape is [N, C, m], adaptive pool divide L dimension # of input data into m grids averagely and performs poolings in each # grid to get output. # adaptive max pool performs calculations as follow: # # for i in range(m): # lstart = floor(i * L / m) # lend = ceil((i + 1) * L / m) # output[:, :, i] = max(input[:, :, lstart: lend]) # import paddle import paddle.nn.functional as F import numpy as np data = paddle.to_tensor(np.random.uniform(-1, 1, [1, 3, 32]).astype(np.float32)) pool_out = F.adaptive_max_pool1d(data, output_size=16) # pool_out shape: [1, 3, 16]) pool_out, indices = F.adaptive_max_pool1d(data, output_size=16, return_mask=True) # pool_out shape: [1, 3, 16] indices shape: [1, 3, 16]