python - 如何修复签名中 Tensorflow 1.14.0rc 中的 'strided slice assignment are not compatible with expected types'？

Question

我正在尝试在tensorflow 1.14.0中实现滑动DFT算法并使用tf.function，这样我就不必太担心控制流，但是我遇到了问题。当我尝试将变量的一个元素分配给该变量中的另一个元素时，我收到有关跨步切片分配中的不兼容类型的错误。

我尝试过使用 tf.scatter update、tf allocate 以及仅使用典型的切片分配，但是这些都不起作用。

    @tf.function
    def sdft_func(self,input_tensor):
        for i in range(self.N_t):
        #retrieving variables so that I have direct access to it
        #instead of getting access to the read tensor
        _, _, self.in_s = self.get_variables()
        last = self.in_s[self.N_t-1]
        for j in range(self.N_t,0,-1):
            _, _, self.in_s = self.get_variables()
            val = self.in_s[j-1]
            #The line below gives the error
            self.in_s = self.in_s[j].assign(val)
            print(self.in_s)

我收到的错误如下:

TypeError: In op 'strided_slice_1/_assign', input types ([tf.complex64, tf.int32, tf.int32, tf.int32, tf.complex64]) are not compatible with expected types ([tf.complex64_ref, tf.int32, tf.int32, tf.int32, tf.complex64])

提前谢谢您!

Answer 1

看来我缩小了问题范围，因为它与尝试在 tf.function 函数内部设置 tf.complex64 变量有关。因此，为了克服这个问题，我简单地抽象了该操作，以便在 tf.function 函数之外完成变量设置。解决方法见下文:

    def sdft_func(self,input_tensor):

        @tf.function
        def func(input_tensor,N_t,in_s,coeffs,freqs):
            in_s = tf.identity(in_s)
            coeffs = tf.identity(coeffs)
            freqs = tf.identity(freqs)


            for i in range(N_t):
                last = in_s[self.N_t-1]

                in_s = in_s[:-1]
                new_val = tf.expand_dims(tf.complex(input_tensor[i],
                                tf.cast(0.0,dtype=tf.float32)),0)

                in_s = tf.concat([new_val,in_s],axis=0)
                    delta = in_s[0] - last

                freqs_2 = tf.TensorArray(tf.complex64,size=self.N)

                for j in range(self.N_t):
                    freqs_2 = freqs_2.write(j,(freqs[j]+delta)*coeffs[j])
                freqs = freqs_2.stack()
                freqs.set_shape([self.N])

            return freqs,in_s

        new_freqs, new_in_s = func(input_tensor,self.N_t,
                self.in_s,self.coeffs,self.freqs)

        self.in_s = self.in_s.assign(new_in_s)
        self.freqs = self.freqs.assign(new_freqs)