oneflow.nn.MaxPool1d

class oneflow.nn.MaxPool1d(kernel_size: Union[int, Tuple[int]], stride: Optional[Union[int, Tuple[int]]] = None, padding: Union[int, Tuple[int]] = 0, dilation: Union[int, Tuple[int]] = 1, return_indices: bool = False, ceil_mode: bool = False)

Applies a 1D max pooling over an input signal composed of several input planes.

The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.MaxPool1d.html.

In the simplest case, the output value of the layer with input size \((N, C, L)\) and output \((N, C, L_{out})\) can be precisely described as:

\[out(N_i, C_j, k) = \max_{m=0, \ldots, \text{kernel\_size} - 1} input(N_i, C_j, stride \times k + m)\]

If padding is non-zero, then the input is implicitly padded with minimum value on both sides for padding number of points. dilation is the stride between the elements within the sliding window. This link has a nice visualization of the pooling parameters.

Note

When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.

Parameters

• kernel_size – The size of the sliding window, must be > 0.

• stride – The stride of the sliding window, must be > 0. Default value is kernel_size.

• padding – Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.

• dilation – The stride between elements within a sliding window, must be > 0.

• return_indices – If True, will return the argmax along with the max values.

• ceil_mode – If True, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.

Shape:

• Input: \((N, C, L_{in})\)

• Output: \((N, C, L_{out})\), where

  \[L_{out} = \left\lfloor \frac{L_{in} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel_size} - 1) - 1}{\text{stride}} + 1\right\rfloor\]

For example:

import oneflow as flow
import numpy as np

of_maxpool1d = flow.nn.MaxPool1d(kernel_size=3, padding=1, stride=1)
x = flow.Tensor(np.random.randn(1, 4, 4))
y = of_maxpool1d(x)
y.shape
oneflow.Size([1, 4, 4])

__init__(kernel_size: Union[int, Tuple[int]], stride: Optional[Union[int, Tuple[int]]] = None, padding: Union[int, Tuple[int]] = 0, dilation: Union[int, Tuple[int]] = 1, return_indices: bool = False, ceil_mode: bool = False)

Initialize self. See help(type(self)) for accurate signature.

Methods

__call__(*args, **kwargs)	Call self as a function.
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattr__(name)
__getattribute__(name, /)	Return getattr(self, name).
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(kernel_size[, stride, padding, …])	Initialize self.
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_apply(fn[, applied_dict])
_get_name()
_load_from_state_dict(state_dict, prefix, …)
_named_members(get_members_fn[, prefix, recurse])
_save_to_state_dict(destination, prefix, …)
_shallow_repr()
add_module(name, module)	Adds a child module to the current module.
apply(fn)	Applies fn recursively to every submodule (as returned by .children()) as well as self.
buffers([recurse])	Returns an iterator over module buffers.
children()	Returns an iterator over immediate children modules.
cpu()	Moves all model parameters and buffers to the CPU.
cuda([device])	Moves all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Sets the module in evaluation mode.
extra_repr()	Set the extra representation of the module
float()	Casts all floating point parameters and buffers to float datatype.
forward(x)
half()	Casts all floating point parameters and buffers to half datatype.
load_state_dict(state_dict[, strict])	Copies parameters and buffers from state_dict into this module and its descendants.
modules()	Returns an iterator over all modules in the network.
named_buffers([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Returns an iterator over module parameters.
register_buffer(name, tensor[, persistent])	Adds a buffer to the module.
register_forward_hook(hook)	Registers a forward hook on the module.
register_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_parameter(name, param)	Adds a parameter to the module.
state_dict([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
to([device])	Moves the parameters and buffers.
to_consistent(*args, **kwargs)	This interface is no longer available, please use oneflow.nn.Module.to_global() instead.
to_global([placement, sbp])	Convert the parameters and buffers to global.
train([mode])	Sets the module in training mode.
zero_grad([set_to_none])	Sets gradients of all model parameters to zero.