Tensorflow1.14 no gradients providerd for any variables

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computer-vision

tensorflow

I am using tf1.14 to make a loss function, which project the predicted pose on a 2D image. And then I use the projected point to get the distance transform as loss, I have finished the loss function, but when start training the optimizer gives a error massage. I posted my code here and error, please give some advise...

def transform_pt(v, q, t, cam, Ds_gt):#placeholder_get_pointsds # v: 3d point # q: quarternion (4d vector) = w + xi+ yj + zk
q = tf.convert_to_tensor(q)
t = tf.convert_to_tensor(t)

loss = None
Ds = None
w, x, y, z = tf.split(tf.gather(q, 0), num_or_size_splits=4, axis=-1)
t_0, t_1, t_2 = tf.split(tf.gather(t, 0), num_or_size_splits=3, axis=-1)


for i in range(100):
    # v_0, v_1, v_2 = tf.split(v[i], num_or_size_splits=3, axis=-1)
    # w, x, y, z = tf.split(q[i], num_or_size_splits=4, axis=-1)
    # t_0, t_1, t_2 = tf.split(t[i], num_or_size_splits=3, axis=-1)

    v_0, v_1, v_2 = tf.split(tf.gather(v, i), num_or_size_splits=3, axis=-1)
    # w, x, y, z = tf.split(tf.gather(q, 0), num_or_size_splits=4, axis=-1)
    # t_0, t_1, t_2 = tf.split(tf.gather(t, 0), num_or_size_splits=3, axis=-1)

    a1 = tf.multiply(tf.multiply(w, w), v_0)
    a2 = tf.multiply(tf.multiply(tf.multiply(2.0,y),w),v_2)

    a3 = tf.multiply(tf.multiply(tf.multiply(2.0,z),w),v_1)
    a4 = tf.multiply(tf.multiply(x,x),v_0)
    a5 = tf.multiply(tf.multiply(tf.multiply(2.0,y),x),v_1)
    a6 = tf.multiply(tf.multiply(tf.multiply(2.0,z),x),v_2)
    a7 = tf.multiply(tf.multiply(z,z),v_0)
    a8 = tf.multiply(tf.multiply(y,y),v_0)
    a = tf.subtract(tf.subtract(tf.add(tf.add(tf.add(tf.subtract(tf.add(a1,a2),a3),a4),a5),a6),a7),a8)
    #a = w*w*v[0] + 2*y*w*v[2] - 2*z*w*v[1] + x*x*v[0] + 2*y*x*v[1] + 2*z*x*v[2] - z*z*v[0] - y*y*v[0]


    b1 = tf.multiply(tf.multiply(tf.multiply(2.0,x),y),v_0)
    b2 = tf.multiply(tf.multiply(y,y),v_1)
    b3 = tf.multiply(tf.multiply(tf.multiply(2.0,x),y),v_2)
    b4 = tf.multiply(tf.multiply(tf.multiply(2.0, w), z), v_0)
    b5 = tf.multiply(tf.multiply(z,z),v_1)
    b6 = tf.multiply(tf.multiply(w,w),v_1)
    b7 = tf.multiply(tf.multiply(tf.multiply(2.0, x), w), v_2)
    b8 = tf.multiply(tf.multiply(x,x),v_1)
    #b = tf.add(b1,b2)
    b = tf.subtract(tf.subtract(tf.add(tf.subtract(tf.add(tf.add(tf.add(b1,b2),b3),b4),b5),b6),b7),b8)

    c1 = tf.multiply(tf.multiply(tf.multiply(2.0,x),z),v_0)
    c2 = tf.multiply(tf.multiply(tf.multiply(2.0,y),z),v_1)
    c3 = tf.multiply(tf.multiply(z, z), v_2)
    c4 = tf.multiply(tf.multiply(tf.multiply(2.0,w),y),v_0)
    c5 = tf.multiply(tf.multiply(y,y),v_2)
    c6 = tf.multiply(tf.multiply(tf.multiply(2.0,w),x),v_1)
    c7 = tf.multiply(tf.multiply(x,x),v_2)
    c8 = tf.multiply(tf.multiply(w,w),v_2)
    c = tf.add(tf.subtract(tf.add(tf.subtract(tf.subtract(tf.add(tf.add(c1,c2),c3),c4),c5),c6),c7),c8)

    at = tf.add(a,t_0)
    bt = tf.add(b,t_1)
    ct = tf.add(c,t_2)

    k = tf.concat([at, bt, ct], axis=-1)
    l = tf.expand_dims(k,0)
    


    #res = project2(l, cam)
    res = proj(l, cam)
    out = tf.gather(res, 0)
    k = tf.gather(out, 0)
    m = tf.gather(out, 1)

    k= tf.cast(k, dtype=tf.int32)
    m = tf.cast(m, dtype = tf.int32)
    res_ds = tf.gather(Ds_gt, k)
    res_ds = tf.gather(res_ds, m)
    #res = tf.reduce_sum(tf.gather(res, tf.cast(y, dtype=tf.int32)))

    if Ds is None:
        Ds = res_ds
        #print("guo")
    else:
        Ds += res_ds
Ds = tf.reduce_sum(Ds)
print("Ds_shape",tf.shape(Ds))
try:
    if loss is None:
        loss = Ds
    else:
        loss += Ds
        #loss += Ds_gt[x, y]
    

except:
    pass

return loss

Error message: Traceback (most recent call last):

ValueError: No gradients provided for any variable, check your graph for ops that do not support gradients, between variables ["<tf.variable 'stem="" basic_conv2d="" conv2d="" kernel:0'="" shape="(3," 3,="" 32)="" dtype="float32">", "<tf.variable 'stem="" basic_conv2d="" conv2d="" bias:0'="" shape="(32,)" dtype="float32">", "<tf.variable 'stem="" basic_conv2d="" batch_normalization="" gamma:0'="" shape="(32,)" dtype="float32">", .

Process finished with exit code 1

What I have tried:

I want to find the where the problem is, it seems because i get a loss without the result of network. If i multiply or subtract the result of network, the error is dissapear.like

Quote:

Ds = tf.reduce_sum(Ds * (q * t))