MaxPool1d() in PyTorch

This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

*Memos:

My post explains Pooling Layer.
My post explains requires_grad.

MaxPool1d() can get the 2D or 3D tensor of the one or more values computed by 1D max pooling from the 2D or 3D tensor of one or more elements as shown below:

*Memos:

The 1st argument for initialization is kernel_size(Required-Type:int or tuple or list of int). *It must be 1 <= x.
The 2nd argument for initialization is stride(Optional-Default:kernel_size-Type:int or tuple or list of int). *It must be 1 <= x.
The 3rd argument for initialization is padding(Optional-Default:0-Type:int or tuple or list of int). *It must be 0 <= x.
The 4th argument for initialization is dilation(Optional-Default:1-Type:int or tuple or list of int). *It must be 1 <= x.
The 5th argument for initialization is return_indices(Optional-Default:False-Type:bool).
The 6th argument for initialization is ceil_mode(Optional-Default:False-Type:bool).
The 1st argument is input(Required-Type:tensor of float).
The tensor's requires_grad which is False by default is not set to True by MaxPool1d().

import torch
from torch import nn

tensor1 = torch.tensor([[8., -3., 0., 1., 5., -2.]])

tensor1.requires_grad
# False

maxpool1d = nn.MaxPool1d(kernel_size=1)
tensor2 = maxpool1d(input=tensor1)
tensor2
# tensor([[8., -3., 0., 1., 5., -2.]])

tensor2.requires_grad
# False

maxpool1d
# MaxPool1d(kernel_size=1, stride=1, padding=0, dilation=1, ceil_mode=False)

maxpool1d.kernel_size
# 1

maxpool1d.stride
# 1

maxpool1d.padding
# 0

maxpool1d.dilation
# 1

maxpool1d.return_indices
# False

maxpool1d.ceil_mode
# False

maxpool1d = nn.MaxPool1d(kernel_size=1, stride=None, padding=0, 
                         dilation=1, return_indices=False, ceil_mode=False)
maxpool1d(input=tensor1)
# tensor([[8., -3., 0., 1., 5., -2.]])

maxpool1d = nn.MaxPool1d(kernel_size=2, return_indices=True)
maxpool1d(input=tensor1)
# (tensor([[8., 1., 5.]]), tensor([[0, 3, 4]]))

maxpool1d = nn.MaxPool1d(kernel_size=3, return_indices=True)
maxpool1d(input=tensor1)
# (tensor([[8., 5.]]), tensor([[0, 4]]))

maxpool1d = nn.MaxPool1d(kernel_size=4, return_indices=True)
maxpool1d(input=tensor1)
# (tensor([[8.]]), tensor([[0]]))

maxpool1d = nn.MaxPool1d(kernel_size=5, return_indices=True)
maxpool1d(input=tensor1)
# (tensor([[8.]]), tensor([[0]]))

maxpool1d = nn.MaxPool1d(kernel_size=6, return_indices=True)
maxpool1d(input=tensor1)
# (tensor([[8.]]), tensor([[0]]))

my_tensor = torch.tensor([[8., -3., 0.],
                          [1., 5., -2.]])
maxpool1d = nn.MaxPool1d(kernel_size=1, return_indices=True)
maxpool1d(input=my_tensor)
# (tensor([[8., -3., 0.],
#          [1., 5., -2.]]),
#  tensor([[0, 1, 2],
#          [0, 1, 2]]))

maxpool1d = nn.MaxPool1d(kernel_size=2, return_indices=True)
maxpool1d(input=my_tensor)
# (tensor([[8.],
#          [5.]]),
#  tensor([[0],
#          [1]]))

maxpool1d = nn.MaxPool1d(kernel_size=3, return_indices=True)
maxpool1d(input=my_tensor)
# (tensor([[8.],
#          [5.]]),
#  tensor([[0],
#          [1]]))

my_tensor = torch.tensor([[8.], [-3.], [0.], [1.], [5.], [-2.]])

maxpool1d = nn.MaxPool1d(kernel_size=1, return_indices=True)
maxpool1d(input=my_tensor)
# (tensor([[8.], [-3.], [0.], [1.], [5.], [-2.]]),
#  tensor([[0], [0], [0], [0], [0], [0]]))

maxpool1d = nn.MaxPool1d(kernel_size=1, return_indices=True)
maxpool1d(input=my_tensor)
# (tensor([[8.], [-3.], [0.], [1.], [5.], [-2.]]),
#  tensor([[0], [0], [0], [0], [0], [0]]))

my_tensor = torch.tensor([[[8.], [-3.], [0.]],
                          [[1.], [5.], [-2.]]])
maxpool1d = nn.MaxPool1d(kernel_size=1, return_indices=True)
maxpool1d(input=my_tensor)
# (tensor([[[8.], [-3.], [0.]],
#          [[1.], [5.], [-2.]]]),
#  tensor([[[0], [0], [0]],
#          [[0], [0], [0]]]))

This content originally appeared on DEV Community and was authored by Super Kai (Kazuya Ito)

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