Migrate tensorflow_graphics/nn to Python 3.

PiperOrigin-RevId: 361949734

tensorflow_graphics/nn/layer/tests/graph_convolution_test.py

@@ -13,8 +13,14 @@
 # limitations under the License.
 """Tests for the graph convolution layers."""
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 from absl.testing import parameterized
 import numpy as np
+from six.moves import range
+from six.moves import zip
 import tensorflow as tf
 
 import tensorflow_graphics.nn.layer.graph_convolution as gc_layer
@@ -170,7 +176,7 @@ def test_feature_steered_convolution_layer_training(self):
       for _ in range(num_training_iterations):
         grads = tape.gradient(loss, trainable_variables)
         tf.compat.v1.train.GradientDescentOptimizer(1e-4).apply_gradients(
-            zip(grads, trainable_variables))
+            list(zip(grads, trainable_variables)))
     else:
       output = gc_layer.feature_steered_convolution_layer(
           data=data,
@@ -288,7 +294,8 @@ def test_dynamic_graph_convolution_keras_layer_training(self, reduction):
       for _ in range(num_training_iterations):
         grads = tape.gradient(loss, trainable_variables)
         tf.compat.v1.train.GradientDescentOptimizer(1e-4).apply_gradients(
-            zip(grads, trainable_variables))
+            list(zip(grads, trainable_variables)))
+
 
 if __name__ == "__main__":
   test_case.main()

tensorflow_graphics/nn/metric/intersection_over_union.py

@@ -17,6 +17,7 @@
 from __future__ import division
 from __future__ import print_function
 
+from six.moves import range
 import tensorflow as tf
 
 from tensorflow_graphics.util import asserts
@@ -66,12 +67,12 @@ def evaluate(ground_truth_labels,
     predicted_labels = asserts.assert_binary(predicted_labels)
 
     sum_ground_truth = tf.math.reduce_sum(
-        input_tensor=ground_truth_labels, axis=range(-grid_size, 0))
+        input_tensor=ground_truth_labels, axis=list(range(-grid_size, 0)))
     sum_predictions = tf.math.reduce_sum(
-        input_tensor=predicted_labels, axis=range(-grid_size, 0))
+        input_tensor=predicted_labels, axis=list(range(-grid_size, 0)))
     intersection = tf.math.reduce_sum(
         input_tensor=ground_truth_labels * predicted_labels,
-        axis=range(-grid_size, 0))
+        axis=list(range(-grid_size, 0)))
     union = sum_ground_truth + sum_predictions - intersection
 
     return tf.where(