AttributeError: module 'tensorflow' has no attribute 'py_func'

Question

I'm trying to run a code on ubuntu that uses tensorflow, it gives me the error:

AttributeError: module 'tensorflow' has no attribute 'py_func'

how can I fix it?
the relevant part of the code:

for i in range(cfg.num_layers):
  neighbour_idx = tf.py_func(DP.knn_search, [batch_xyz, batch_xyz, cfg.k_n], tf.int32)
  sub_points = batch_xyz[:, :tf.shape(batch_xyz)[1] // cfg.sub_sampling_ratio[i], :]
  pool_i = neighbour_idx[:, :tf.shape(batch_xyz)[1] // cfg.sub_sampling_ratio[i], :]
  up_i = tf.py_func(DP.knn_search, [sub_points, batch_xyz, 1], tf.int32)
  input_points.append(batch_xyz)
  input_neighbors.append(neighbour_idx)
  input_pools.append(pool_i)
  input_up_samples.append(up_i)
  batch_xyz = sub_points

input_list = input_points + input_neighbors + input_pools + input_up_samples
input_list += [batch_features, batch_labels, batch_pc_idx, batch_cloud_idx]

It can't get the tf.py_func.

PS: I tried adding tf.compat.v1.py_func and it didn't work.

Answer 1

tf.py_func is designed for Tensorflow 1.

In Tensorflow 2,tf.numpy_function is a near-exact replacement, just drop the stateful argument (all tf.numpy_function calls are considered stateful). It is compatible with eager execution and tf.function.

tf.py_function is a close but not an exact replacement, passing TensorFlow tensors to the wrapped function instead of NumPy arrays, which provides gradients and can take advantage of accelerators.