VAE_HF.py 文件源码

def forward(self, v, z):
        '''
        :param v: batch_size (B) x latent_size (L)
        :param z: batch_size (B) x latent_size (L)
        :return: z_new = z - 2* v v_T / norm(v,2) * z
        '''
        # v * v_T
        vvT = torch.bmm( v.unsqueeze(2), v.unsqueeze(1) )  # v * v_T : batch_dot( B x L x 1 * B x 1 x L ) = B x L x L
        # v * v_T * z
        vvTz = torch.bmm( vvT, z.unsqueeze(2) ).squeeze(2) # A * z : batchdot( B x L x L * B x L x 1 ).squeeze(2) = (B x L x 1).squeeze(2) = B x L
        # calculate norm ||v||^2
        norm_sq = torch.sum( v * v, 1 ) # calculate norm-2 for each row : B x 1
        norm_sq = norm_sq.expand( norm_sq.size(0), v.size(1) ) # expand sizes : B x L
        # calculate new z
        z_new = z - 2 * vvTz / norm_sq # z - 2 * v * v_T  * z / norm2(v)
        return z_new