From df567469ed904e2efb409fa918cc9285fe5a6e03 Mon Sep 17 00:00:00 2001 From: Dominic Jack Date: Tue, 5 Feb 2019 13:07:24 +1000 Subject: [PATCH] requested changes --- tensorflow_datasets/video/moving_sequence.py | 8 +++++--- tensorflow_datasets/video/moving_sequence_test.py | 9 ++++++--- 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/tensorflow_datasets/video/moving_sequence.py b/tensorflow_datasets/video/moving_sequence.py index ba36c51c0a5..18d7582d203 100644 --- a/tensorflow_datasets/video/moving_sequence.py +++ b/tensorflow_datasets/video/moving_sequence.py @@ -108,6 +108,7 @@ def image_as_moving_sequence( import tensorflow as tf import tensorflow_datasets as tfds from tensorflow_datasets.video import moving_sequence + tf.compat.v1.enable_eager_execution() def animate(sequence): import numpy as np @@ -199,9 +200,10 @@ def map_fn(image, label): total_padding = output_size - image_shape[:2] # cond = tf.assert_greater(total_padding, -1) - # if not tf.executing_eagerly(): - # with tf.control_dependencies([cond]): - # total_padding = tf.identity(total_padding) + cond = tf.compat.v1.assert_greater(total_padding, -1) + if not tf.executing_eagerly(): + with tf.control_dependencies([cond]): + total_padding = tf.identity(total_padding) sequence_pad_lefts = tf.cast( tf.math.round(trajectory * tf.cast(total_padding, tf.float32)), tf.int32) diff --git a/tensorflow_datasets/video/moving_sequence_test.py b/tensorflow_datasets/video/moving_sequence_test.py index 9c46edcc0b7..148b14e96c1 100644 --- a/tensorflow_datasets/video/moving_sequence_test.py +++ b/tensorflow_datasets/video/moving_sequence_test.py @@ -7,6 +7,7 @@ import tensorflow as tf from tensorflow_datasets.core import test_utils import tensorflow_datasets.video.moving_sequence as ms +tf.compat.v1.enable_eager_execution() class MovingSequenceTest(tf.test.TestCase): @@ -29,15 +30,17 @@ def test_images_as_moving_sequence(self): sequence = tf.cast(sequence.image_sequence, tf.float32) self.assertAllEqual( - tf.reduce_sum(sequence, axis=(1, 2, 3)), - tf.fill((sequence_length,), tf.reduce_sum(tf.cast(image, tf.float32)))) + self.evaluate(tf.reduce_sum(sequence, axis=(1, 2, 3))), + self.evaluate( + tf.fill( + (sequence_length,), tf.reduce_sum(tf.cast(image, tf.float32))))) for i, full_image in enumerate(tf.unstack(sequence, axis=0)): j = i // 2 subimage = full_image[i:i+h, j:j+w] n_true = tf.reduce_sum(subimage) # allow for pixel rounding errors in each dimension - self.assertAllEqual(n_true >= (h-1)*(w-1), True) + self.assertTrue(self.evaluate(n_true) >= (h-1)*(w-1)) if __name__ == '__main__':