main.py 文件源码

def read_attention(self, x, x_hat, h_dec_prev):
        Fx, Fy, gamma = self.attn_window("read", h_dec_prev)
        # we have the parameters for a patch of gaussian filters. apply them.
        def filter_img(img, Fx, Fy, gamma):
            Fxt = tf.transpose(Fx, perm=[0,2,1])
            img = tf.reshape(img, [-1, self.img_size, self.img_size])
            # Apply the gaussian patches:
            # keep in mind: horiz = imgsize = verts (they are all the image size)
            # keep in mind: attn = height/length of attention patches
            # allfilters = [attn, vert] * [imgsize,imgsize] * [horiz, attn]
            # we have batches, so the full batch_matmul equation looks like:
            # [1, 1, vert] * [batchsize,imgsize,imgsize] * [1, horiz, 1]
            glimpse = tf.batch_matmul(Fy, tf.batch_matmul(img, Fxt))
            glimpse = tf.reshape(glimpse, [-1, self.attention_n**2])
            # finally scale this glimpse w/ the gamma parameter
            return glimpse * tf.reshape(gamma, [-1, 1])
        x = filter_img(x, Fx, Fy, gamma)
        x_hat = filter_img(x_hat, Fx, Fy, gamma)
        return tf.concat(1, [x, x_hat])

    # encode an attention patch