mxnet.gluon.nn.AvgPool3D¶

class mxnet.gluon.nn.AvgPool3D(pool_size=(2, 2, 2), strides=None, padding=0, ceil_mode=False, layout='NCDHW', count_include_pad=True, **kwargs)[source]¶

Average pooling operation for 3D data (spatial or spatio-temporal).

Parameters

pool_size (int or list/tuple of 3 ints,) – Size of the max pooling windows.
strides (int, list/tuple of 3 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 3 ints,) – If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.
layout (str, default 'NCDHW') – Dimension ordering of data and weight. Can be ‘NCDHW’, ‘NDHWC’, etc. ‘N’, ‘C’, ‘H’, ‘W’, ‘D’ stands for batch, channel, height, width and depth dimensions respectively. padding is applied on ‘D’, ‘H’ and ‘W’ dimension.
ceil_mode (bool, default False) – When True, will use ceil instead of floor to compute the output shape.
count_include_pad (bool, default True) – When ‘False’, will exclude padding elements when computing the average value.

Inputs:

data: 5D input tensor with shape (batch_size, in_channels, depth, height, width) when layout is NCDHW. For other layouts shape is permuted accordingly.

Outputs:

out: 5D output tensor with shape (batch_size, channels, out_depth, out_height, out_width) when layout is NCDHW. out_depth, out_height and out_width are calculated as:
```
out_depth = floor((depth+2*padding[0]-pool_size[0])/strides[0])+1
out_height = floor((height+2*padding[1]-pool_size[1])/strides[1])+1
out_width = floor((width+2*padding[2]-pool_size[2])/strides[2])+1
```
When ceil_mode is True, ceil will be used instead of floor in this equation.

__init__(pool_size=(2, 2, 2), strides=None, padding=0, ceil_mode=False, layout='NCDHW', count_include_pad=True, **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`.