squeeze_and_excitation.py

"""
Squeeze and Excitation Module
*****************************

Collection of squeeze and excitation classes where each can be inserted as a block into a neural network architechture

    1. `Channel Squeeze and Excitation <https://arxiv.org/abs/1709.01507>`_
    2. `Spatial Squeeze and Excitation <https://arxiv.org/abs/1803.02579>`_
    3. `Channel and Spatial Squeeze and Excitation <https://arxiv.org/abs/1803.02579>`_

"""

from enum import Enum

import torch
import torch.nn as nn
import torch.nn.functional as F


class ChannelSELayer(nn.Module):
    """
    Re-implementation of Squeeze-and-Excitation (SE) block described in:
        *Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*

    """

    def __init__(self, num_channels, reduction_ratio=2):
        """

        :param num_channels: No of input channels
        :param reduction_ratio: By how much should the num_channels should be reduced
        """
        super(ChannelSELayer, self).__init__()
        num_channels_reduced = num_channels // reduction_ratio
        self.reduction_ratio = reduction_ratio
        self.fc1 = nn.Linear(num_channels, num_channels_reduced, bias=True)
        self.fc2 = nn.Linear(num_channels_reduced, num_channels, bias=True)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_tensor):
        """

        :param input_tensor: X, shape = (batch_size, num_channels, H, W)
        :return: output tensor
        """
        batch_size, num_channels, H, W = input_tensor.size()
        # Average along each channel
        squeeze_tensor = input_tensor.view(batch_size, num_channels, -1).mean(dim=2)

        # channel excitation
        fc_out_1 = self.relu(self.fc1(squeeze_tensor))
        fc_out_2 = self.sigmoid(self.fc2(fc_out_1))

        a, b = squeeze_tensor.size()
        output_tensor = torch.mul(input_tensor, fc_out_2.view(a, b, 1, 1))
        return output_tensor


class SpatialSELayer(nn.Module):
    """
    Re-implementation of SE block -- squeezing spatially and exciting channel-wise described in:
        *Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, MICCAI 2018*
    """

    def __init__(self, num_channels):
        """

        :param num_channels: No of input channels
        """
        super(SpatialSELayer, self).__init__()
        self.conv = nn.Conv2d(num_channels, 1, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_tensor, weights=None):
        """

        :param weights: weights for few shot learning
        :param input_tensor: X, shape = (batch_size, num_channels, H, W)
        :return: output_tensor
        """
        # spatial squeeze
        batch_size, channel, a, b = input_tensor.size()

        if weights is not None:
            weights = torch.mean(weights, dim=0)
            weights = weights.view(1, channel, 1, 1)
            out = F.conv2d(input_tensor, weights)
        else:
            out = self.conv(input_tensor)
        squeeze_tensor = self.sigmoid(out)

        # spatial excitation
        # print(input_tensor.size(), squeeze_tensor.size())
        squeeze_tensor = squeeze_tensor.view(batch_size, 1, a, b)
        output_tensor = torch.mul(input_tensor, squeeze_tensor)
        # output_tensor = torch.mul(input_tensor, squeeze_tensor)
        return output_tensor


class ChannelSpatialSELayer(nn.Module):
    """
    Re-implementation of concurrent spatial and channel squeeze & excitation:
        *Roy et al., Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks, MICCAI 2018, arXiv:1803.02579*
    """

    def __init__(self, num_channels, reduction_ratio=2):
        """

        :param num_channels: No of input channels
        :param reduction_ratio: By how much should the num_channels should be reduced
        """
        super(ChannelSpatialSELayer, self).__init__()
        self.cSE = ChannelSELayer(num_channels, reduction_ratio)
        self.sSE = SpatialSELayer(num_channels)

    def forward(self, input_tensor):
        """

        :param input_tensor: X, shape = (batch_size, num_channels, H, W)
        :return: output_tensor
        """
        output_tensor = torch.max(self.cSE(input_tensor), self.sSE(input_tensor))
        return output_tensor


class SELayer(Enum):
    """
    Enum restricting the type of SE Blockes available. So that type checking can be adding when adding these blockes to
    a neural network::

        if self.se_block_type == se.SELayer.CSE.value:
            self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])

        elif self.se_block_type == se.SELayer.SSE.value:
            self.SELayer = se.SpatialSELayer(params['num_filters'])

        elif self.se_block_type == se.SELayer.CSSE.value:
            self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
    """
    NONE = 'NONE'
    CSE = 'CSE'
    SSE = 'SSE'
    CSSE = 'CSSE'