torchlayers.pooling module

class torchlayers.pooling.AvgPool(kernel_size: int = 2, stride: int = None, padding: int = 0, ceil_mode: bool = False, count_include_pad: bool = True)

Perform avg operation across first torch.Tensor dimension.

Depending on shape of passed torch.Tensor either torch.nn.AvgPool1D, torch.nn.AvgPool2D or torch.nn.AvgPool3D pooling will be used for 3D, 4D and 5D shape respectively (batch included).

Default value for kernel_size (2) was added.

Parameters

• kernel_size (int, optional) – The size of the window. Default: 2

• stride (int, optional) – The stride of the window. Default value is kernel_size

• padding (int, oprtional) – Implicit zero padding to be added on both sides. Default: 0

• ceil_mode (bool, opriontal) – When True, will use ceil instead of floor to compute the output shape. Default: True

• count_include_pad (bool, optional) – When True, will include the zero-padding in the averaging. Default: True

Returns

Same shape as input with values pooled.

Return type

torch.Tensor

class torchlayers.pooling.GlobalAvgPool

Perform mean operation across first torch.Tensor dimension.

Usually used after last convolution layer to get mean of pixels from each channel.

Depending on shape of passed torch.Tensor either 1D, 2D or 3D pooling will be used for 3D, 4D and 5D shape respectively (batch included).

Returns

2D tensor (batch, features)

Return type

torch.Tensor

class torchlayers.pooling.GlobalMaxPool

Perform max operation across first torch.Tensor dimension.

Usually used after last convolution layer to get pixels of maximum value from each channel.

Depending on shape of passed torch.Tensor either 1D, 2D or 3D pooling will be used for 3D, 4D and 5D shape respectively (batch included).

Returns

2D tensor (batch, features)

Return type

torch.Tensor

class torchlayers.pooling.MaxPool(kernel_size: int = 2, stride: int = None, padding: int = 0, dilation: int = 1, return_indices: bool = False, ceil_mode: bool = False)

Perform max operation across first torch.Tensor dimension.

Depending on shape of passed torch.Tensor either torch.nn.MaxPool1D, torch.nn.MaxPool2D or torch.nn.MaxPool3D pooling will be used for 3D, 4D and 5D shape respectively (batch included).

Default value for kernel_size (2) was added.

Parameters

• kernel_size (int, optional) – The size of the window to take a max over. Default: 2

• stride (int, optional) – The stride of the window. Default value is kernel_size

• padding (int, optional) – Implicit zero padding to be added on both sides. Default: 0

• dilation (int) – Parameter controlling the stride of elements in the window. Default: 1

• return_indices (bool, optional) – If True, will return the max indices along with the outputs. Useful for torch.nn.MaxUnpool later. Default: False

• ceil_mode (bool, optional) – When True, will use ceil instead of floor to compute the output shape. Default: False

Returns

Same shape as input with values pooled.

Return type

torch.Tensor