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


class BAM(nn.Module):
    """ Basic self-attention module
    """

    def __init__(self, in_dim, ds=8, activation=nn.ReLU):
        super(BAM, self).__init__()
        self.chanel_in = in_dim
        self.key_channel = self.chanel_in //8
        self.activation = activation
        self.ds = ds  #
        self.pool = nn.AvgPool2d(self.ds)
        print('ds: ',ds)
        self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.gamma = nn.Parameter(torch.zeros(1))

        self.softmax = nn.Softmax(dim=-1)  #

    def forward(self, input):
        """
            inputs :
                x : input feature maps( B X C X W X H)
            returns :
                out : self attention value + input feature
                attention: B X N X N (N is Width*Height)
        """
        x = self.pool(input)
        m_batchsize, C, width, height = x.size()
        proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1)  # B X C X (N)/(ds*ds)
        proj_key = self.key_conv(x).view(m_batchsize, -1, width * height)  # B X C x (*W*H)/(ds*ds)
        energy = torch.bmm(proj_query, proj_key)  # transpose check
        energy = (self.key_channel**-.5) * energy

        attention = self.softmax(energy)  # BX (N) X (N)/(ds*ds)/(ds*ds)

        proj_value = self.value_conv(x).view(m_batchsize, -1, width * height)  # B X C X N

        out = torch.bmm(proj_value, attention.permute(0, 2, 1))
        out = out.view(m_batchsize, C, width, height)

        out = F.interpolate(out, [width*self.ds,height*self.ds])
        out = out + input

        return out