Эта функция должна выполнять эквивалентную работу:
import tensorflow as tf
import numpy as np
def my_gather_nd(params, indices):
idx_shape = tf.shape(indices)
params_shape = tf.shape(params)
idx_dims = idx_shape[-1]
gather_shape = params_shape[idx_dims:]
params_flat = tf.reshape(params, tf.concat([[-1], gather_shape], axis=0))
axis_step = tf.cumprod(params_shape[:idx_dims], exclusive=True, reverse=True)
indices_flat = tf.reduce_sum(indices * axis_step, axis=-1)
result_flat = tf.gather(params_flat, indices_flat)
return tf.reshape(result_flat, tf.concat([idx_shape[:-1], gather_shape], axis=0))
# Test
np.random.seed(0)
with tf.Graph().as_default(), tf.Session() as sess:
params = tf.constant(np.random.rand(10, 20, 30).astype(np.float32))
indices = tf.constant(np.stack([np.random.randint(10, size=(5, 8)),
np.random.randint(20, size=(5, 8))], axis=-1))
result1, result2 = sess.run((tf.gather_nd(params, indices),
my_gather_nd(params, indices)))
print(np.allclose(result1, result2))
# True