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| from typing import Optional, Sequence, Union |
|
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| import torch |
| import torch.nn as nn |
|
|
| from monai.networks.layers.factories import Pool |
| from monai.utils import ensure_tuple_rep |
|
|
|
|
| class MaxAvgPool(nn.Module): |
| """ |
| Downsample with both maxpooling and avgpooling, |
| double the channel size by concatenating the downsampled feature maps. |
| """ |
|
|
| def __init__( |
| self, |
| spatial_dims: int, |
| kernel_size: Union[Sequence[int], int], |
| stride: Optional[Union[Sequence[int], int]] = None, |
| padding: Union[Sequence[int], int] = 0, |
| ceil_mode: bool = False, |
| ) -> None: |
| """ |
| Args: |
| spatial_dims: number of spatial dimensions of the input image. |
| kernel_size: the kernel size of both pooling operations. |
| stride: the stride of the window. Default value is `kernel_size`. |
| padding: implicit zero padding to be added to both pooling operations. |
| ceil_mode: when True, will use ceil instead of floor to compute the output shape. |
| """ |
| super().__init__() |
| _params = { |
| "kernel_size": ensure_tuple_rep(kernel_size, spatial_dims), |
| "stride": None if stride is None else ensure_tuple_rep(stride, spatial_dims), |
| "padding": ensure_tuple_rep(padding, spatial_dims), |
| "ceil_mode": ceil_mode, |
| } |
| self.max_pool = Pool[Pool.MAX, spatial_dims](**_params) |
| self.avg_pool = Pool[Pool.AVG, spatial_dims](**_params) |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| """ |
| Args: |
| x: Tensor in shape (batch, channel, spatial_1[, spatial_2, ...]). |
| |
| Returns: |
| Tensor in shape (batch, 2*channel, spatial_1[, spatial_2, ...]). |
| """ |
| x_d = torch.cat([self.max_pool(x), self.avg_pool(x)], dim=1) |
| return x_d |
|
|
