# Source code for monai.networks.blocks.downsample

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Sequence, Union
import torch
import torch.nn as nn
from monai.networks.layers.factories import Pool
from monai.utils import ensure_tuple_rep
[docs]class MaxAvgPool(nn.Module):
"""
Downsample with both maxpooling and avgpooling,
double the channel size by concatenating the downsampled feature maps.
"""
[docs] 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)
[docs] 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, ...]).
"""
return torch.cat([self.max_pool(x), self.avg_pool(x)], dim=1)