Table Of Contents
Table Of Contents

MaxPool2D

class mxnet.gluon.nn.MaxPool2D(pool_size=(2, 2), strides=None, padding=0, layout='NCHW', ceil_mode=False, **kwargs)[source]

Max pooling operation for two dimensional (spatial) data.

Parameters:
  • pool_size (int or list/tuple of 2 ints,) – Size of the max pooling windows.
  • strides (int, list/tuple of 2 ints, or None.) – Factor by which to downscale. E.g. 2 will halve the input size. If None, it will default to pool_size.
  • padding (int or list/tuple of 2 ints,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.
  • layout (str, default 'NCHW') – Dimension ordering of data and weight. Only supports ‘NCHW’ layout for now. ‘N’, ‘C’, ‘H’, ‘W’ stands for batch, channel, height, and width dimensions respectively. padding is applied on ‘H’ and ‘W’ dimension.
  • ceil_mode (bool, default False) – When True, will use ceil instead of floor to compute the output shape.
Inputs:
  • data: 4D input tensor with shape (batch_size, in_channels, height, width) when layout is NCHW. For other layouts shape is permuted accordingly.
Outputs:
  • out: 4D output tensor with shape (batch_size, channels, out_height, out_width) when layout is NCHW. out_height and out_width are calculated as:

    out_height = floor((height+2*padding[0]-pool_size[0])/strides[0])+1
    out_width = floor((width+2*padding[1]-pool_size[1])/strides[1])+1
    

    When ceil_mode is True, ceil will be used instead of floor in this equation.

__init__(pool_size=(2, 2), strides=None, padding=0, layout='NCHW', ceil_mode=False, **kwargs)[source]

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

Methods

__init__([pool_size, strides, padding, …]) Initialize self.
apply(fn) Applies fn recursively to every child block as well as self.
cast(dtype) Cast this Block to use another data type.
collect_params([select]) Returns a ParameterDict containing this Block and all of its children’s Parameters(default), also can returns the select ParameterDict which match some given regular expressions.
export(path[, epoch]) Export HybridBlock to json format that can be loaded by SymbolBlock.imports, mxnet.mod.Module or the C++ interface.
forward(x, *args) Defines the forward computation.
hybrid_forward(F, x) Overrides to construct symbolic graph for this Block.
hybridize([active]) Activates or deactivates HybridBlock s recursively.
infer_shape(*args) Infers shape of Parameters from inputs.
infer_type(*args) Infers data type of Parameters from inputs.
initialize([init, ctx, verbose, force_reinit]) Initializes Parameter s of this Block and its children.
load_parameters(filename[, ctx, …]) Load parameters from file previously saved by save_parameters.
load_params(filename[, ctx, allow_missing, …]) [Deprecated] Please use load_parameters.
name_scope() Returns a name space object managing a child Block and parameter names.
register_child(block[, name]) Registers block as a child of self.
register_forward_hook(hook) Registers a forward hook on the block.
register_forward_pre_hook(hook) Registers a forward pre-hook on the block.
save_parameters(filename) Save parameters to file.
save_params(filename) [Deprecated] Please use save_parameters.
summary(*inputs) Print the summary of the model’s output and parameters.

Attributes

name Name of this Block, without ‘_’ in the end.
params Returns this Block’s parameter dictionary (does not include its children’s parameters).
prefix Prefix of this Block.