Pooling
- class complextorch.nn.modules.pooling.CVAdaptiveAvgPool1d(output_size: int | Tuple[int])
1-D Complex-Valued Adaptive Average Pooling
Applies adaptive average pooling using torch.nn.AdaptiveAvgPool1d to the real and imaginary parts of the input tensor separately.
Implements the following operation:
\[G(\mathbf{z}) = \texttt{AdaptiveAvgPool1d}(\mathbf{x}) + j \texttt{AdaptiveAvgPool1d}(\mathbf{y}),\]where \(\mathbf{z} = \mathbf{x} + j\mathbf{y}\)
- forward(input: CVTensor) CVTensor
Applies adaptive average pooling using torch.nn.AdaptiveAvgPool1d to the real and imaginary parts of the input tensor separately.
- class complextorch.nn.modules.pooling.CVAdaptiveAvgPool2d(output_size)
2-D Complex-Valued Adaptive Average Pooling
Applies adaptive average pooling using torch.nn.AdaptiveAvgPool2d to the real and imaginary parts of the input tensor separately.
Implements the following operation:
\[G(\mathbf{z}) = \texttt{AdaptiveAvgPool2d}(\mathbf{x}) + j \texttt{AdaptiveAvgPool2d}(\mathbf{y}),\]where \(\mathbf{z} = \mathbf{x} + j\mathbf{y}\)
- forward(input: CVTensor) CVTensor
Applies adaptive average pooling using torch.nn.AdaptiveAvgPool2d to the real and imaginary parts of the input tensor separately.
- class complextorch.nn.modules.pooling.CVAdaptiveAvgPool3d(output_size)
3-D Complex-Valued Adaptive Average Pooling
Applies adaptive average pooling using torch.nn.AdaptiveAvgPool3d to the real and imaginary parts of the input tensor separately.
Implements the following operation:
\[G(\mathbf{z}) = \texttt{AdaptiveAvgPool3d}(\mathbf{x}) + j \texttt{AdaptiveAvgPool3d}(\mathbf{y}),\]where \(\mathbf{z} = \mathbf{x} + j\mathbf{y}\)
- forward(input: CVTensor) CVTensor
Applies adaptive average pooling using torch.nn.AdaptiveAvgPool3d to the real and imaginary parts of the input tensor separately.