【无标题】嘻嘻嘻嘻嘻嘻嘻_max_energy_0 = torch.max(energy, -1, keepdim=true)

class PRM(nn.Module):
 
    def __init__(self, output_chl_num, efficient=False):
        super(PRM, self).__init__()
        self.output_chl_num = output_chl_num
        self.conv_bn_relu_prm_1 = conv_bn_relu(self.output_chl_num, self.output_chl_num, kernel_size=3,
                stride=1, padding=1, has_bn=True, has_relu=True,
                efficient=efficient) 
        self.conv_bn_relu_prm_2_1 = conv_bn_relu(self.output_chl_num, self.output_chl_num, kernel_size=1,
                stride=1, padding=0, has_bn=True, has_relu=True,
                efficient=efficient)
        self.conv_bn_relu_prm_2_2 = conv_bn_relu(self.output_chl_num, self.output_chl_num, kernel_size=1,
                stride=1, padding=0, has_bn=True, has_relu=True,
                efficient=efficient)
        self.sigmoid2 = nn.Sigmoid()
        self.conv_bn_relu_prm_3_1 = conv_bn_relu(self.output_chl_num, self.output_chl_num, kernel_size=1,
                stride=1, padding=0, has_bn=True, has_relu=True,
                efficient=efficient)
        self.conv_bn_relu_prm_3_2 = conv_bn_relu(self.output_chl_num, self.output_chl_num, kernel_size=9,
                stride=1, padding=4, has_bn=True, has_relu=True,
                efficient=efficient,groups=self.output_chl_num)
        self.sigmoid3 = nn.Sigmoid()
 
    def forward(self, x):
        out = self.conv_bn_relu_prm_1(x)
        out_1 = out
        out_2 = torch.nn.functional.adaptive_avg_pool2d(out_1, (1,1))
        out_2 = self.conv_bn_relu_prm_2_1(out_2)
        out_2 = self.conv_bn_relu_prm_2_2(out_2)
        out_2 = self.sigmoid2(out_2)
        out_3 = self.conv_bn_relu_prm_3_1(out_1)
        out_3 = self.conv_bn_relu_prm_3_2(out_3)
        out_3 = self.sigmoid3(out_3)
        out = out_1.mul(1 + out_2.mul(out_3))
        return out

class CAM_Module(Module):
    """ Channel attention module"""
    def __init__(self, in_dim):
        super(CAM_Module, self).__init__()
        self.chanel_in = in_dim
 
 
        self.gamma = Parameter(torch.zeros(1))
        self.softmax  = Softmax(dim=-1)
    def forward(self,x):
        """
            inputs :
                x : input feature maps( B X C X H X W)
            returns :
                out : attention value + input feature
                attention: B X C X C
        """
        m_batchsize, C, height, width = x.size()
        proj_query = x.view(m_batchsize, C, -1)
        proj_key = x.view(m_batchsize, C, -1).permute(0, 2, 1)
        energy = torch.bmm(proj_query, proj_key)
        energy_new = torch.max(energy, -1, keepdim=True)[0].expand_as(energy)-energy
        attention = self.softmax(energy_new)
        proj_value = x.view(m_batchsize, C, -1)
 
        out = torch.bmm(attention, proj_value)
        out = out.view(m_batchsize, C, height, width)
 
        out = self.gamma*out + x
        return out