espnet2.spk.pooling.abs_pooling.AbsPooling

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class espnet2.spk.pooling.abs_pooling.AbsPooling(*args, **kwargs)

Bases: Module, ABC

Abstract base class for pooling layers in the ESPnet2 speaker processing

module.

This class inherits from torch.nn.Module and serves as a blueprint for creating various pooling strategies. It defines the essential methods that must be implemented by any concrete pooling class.

None

• Parameters:None
• Returns: None
• Yields: None
• Raises:
  • NotImplementedError – If the derived class does not implement the required
  • methods. –

######### Examples

class CustomPooling(AbsPooling): : def forward(self, input: torch.Tensor) -> torch.Tensor: : # Implement the pooling logic here pass <br/> def output_size(self) -> int: : # Return the size of the output tensor return 0

NOTE

This class should not be instantiated directly.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

abstract forward(input: Tensor) → Tensor

Abstract method to define the forward pass for the AbsPooling class.

This method must be implemented by subclasses of AbsPooling to specify how the pooling operation should be performed on the input tensor.

• Parameters:input (torch.Tensor) – The input tensor on which the pooling operation will be applied.
• Returns: The output tensor after applying the pooling operation.
• Return type: torch.Tensor
• Raises:NotImplementedError – If this method is not implemented by a subclass.

######### Examples

class MaxPooling(AbsPooling): : def forward(self, input: torch.Tensor) -> torch.Tensor: : return torch.max(input, dim=1)[0]

pooling_layer = MaxPooling() output = pooling_layer.forward(torch.randn(1, 3, 32, 32)) print(output.shape) # Example output shape after pooling

abstract output_size() → int

Abstract base class for pooling layers in PyTorch.

This class defines the interface for pooling layers that can be implemented by derived classes. It includes an abstract method for the forward pass and an abstract method for determining the output size of the pooling operation.

None

• Parameters:None
• Returns: None
• Yields: None
• Raises:NotImplementedError – If the method is not implemented in a derived class.

######### Examples

To create a custom pooling layer, inherit from this class and implement the forward and output_size methods:

class CustomPooling(AbsPooling): : def forward(self, input: torch.Tensor) -> torch.Tensor: : # Custom pooling logic return pooled_output <br/> def output_size(self) -> int: : # Logic to calculate output size return calculated_output_size

NOTE

This is an abstract base class and cannot be instantiated directly.