Upgrading tf.contrib.slim manually to tf 2.0

Question

I have a problem with my python code that uses tf.contrib.slim functionalities and no longer works after upgrading to tensorflow to 2.0.

How can I upgrade the following to tf 2.0:

import tensorflow.contrib.slim as slim
import tensorflow.contrib.slim.nets

# ...

net = slim.conv2d(
    inp, 
    dim,
    [3, 3],
    rate=1,
    normalizer_fn=slim.layer_norm,
    activation_fn=lrelu,
    scope='g_' + str(width) + '_conv1') 

Thanks.

Answer 1

You can use tf_slim package instead (https://github.com/google-research/tf-slim)

However, the push request updating the package to TF2 is still pending. So you should install from the branch pip install git+https://github.com/adrianc-a/tf-slim.git@remove_contrib

Answer 2

That might do it:

net = tf.keras.layers.Conv2D(filters=dim, kernel_size=3, name='g_' + str(width) + '_conv1')(inp)
net = tf.keras.layers.BatchNormalization()(net)
net = tf.keras.layers.LeakyReLU()(net)

Using slim, there's an option to mention which batch normalization and activation you want to have after the convolution layer. You can't do it in 2.0, so you need to specify exactly which normalization and activation you want in 2 different layers.