def testComputeShape(self):
shape = core_layers.Flatten().compute_output_shape((1, 2, 3, 2))
self.assertEqual(shape.as_list(), [1, 12])
shape = core_layers.Flatten().compute_output_shape((None, 3, 2))
self.assertEqual(shape.as_list(), [None, 6])
shape = core_layers.Flatten().compute_output_shape((None, 3, None))
self.assertEqual(shape.as_list(), [None, None])
def testFlattenUnknownAxes(self):
with self.cached_session() as sess:
x = tf.compat.v1.placeholder(shape=(5, None, None), dtype='float32')
y = core_layers.Flatten()(x)
np_output = sess.run(y, feed_dict={x: np.zeros((5, 2, 3))})
self.assertEqual(list(np_output.shape), [5, 6])
self.assertEqual(y.get_shape().as_list(), [5, None])
x = tf.compat.v1.placeholder(shape=(5, None, 2), dtype='float32')
y = core_layers.Flatten()(x)
np_output = sess.run(y, feed_dict={x: np.zeros((5, 3, 2))})
self.assertEqual(list(np_output.shape), [5, 6])
self.assertEqual(y.get_shape().as_list(), [5, None])
def testCreateFlatten(self):
with self.cached_session() as sess:
x = tf.compat.v1.placeholder(shape=(None, 2, 3), dtype='float32')
y = core_layers.Flatten()(x)
np_output = sess.run(y, feed_dict={x: np.zeros((3, 2, 3))})
self.assertEqual(list(np_output.shape), [3, 6])
self.assertEqual(y.get_shape().as_list(), [None, 6])
x = tf.compat.v1.placeholder(shape=(1, 2, 3, 2), dtype='float32')
y = core_layers.Flatten()(x)
np_output = sess.run(y, feed_dict={x: np.zeros((1, 2, 3, 2))})
self.assertEqual(list(np_output.shape), [1, 12])
self.assertEqual(y.get_shape().as_list(), [1, 12])
def testFlattenLargeDim(self):
if any(platform.win32_ver()):
self.skipTest('values are truncated on windows causing test failures')
x = tf.compat.v1.placeholder(shape=(None, 21316, 21316, 80), dtype='float32')
y = core_layers.Flatten()(x)
self.assertEqual(y.shape.as_list(), [None, 21316 * 21316 * 80])
def testFlatten0D(self):
x = tf.compat.v1.placeholder(shape=(None,), dtype='float32')
y = core_layers.Flatten()(x)
with self.cached_session() as sess:
np_output = sess.run(y, feed_dict={x: np.zeros((5,))})
self.assertEqual(list(np_output.shape), [5, 1])
self.assertEqual(y.shape.as_list(), [None, 1])
def testDataFormat4d(self):
np_input_channels_last = np.arange(
24, dtype='float32').reshape([1, 4, 3, 2])
with self.test_session() as sess:
x = tf.compat.v1.placeholder(shape=(1, 4, 3, 2), dtype='float32')
y = core_layers.Flatten(data_format='channels_last')(x)
np_output_cl = sess.run(y, feed_dict={x: np_input_channels_last})
x = tf.compat.v1.placeholder(shape=(1, 2, 4, 3), dtype='float32')
y = core_layers.Flatten(data_format='channels_first')(x)
np_input_channels_first = np.transpose(np_input_channels_last,
[0, 3, 1, 2])
np_output_cf = sess.run(y, feed_dict={x: np_input_channels_first})
self.assertAllEqual(np_output_cl, np_output_cf)
def testFlattenLargeBatchDim(self):
batch_size = np.iinfo(np.int32).max + 10
x = tf.compat.v1.placeholder(
shape=(batch_size, None, None, 1), dtype='float32')
y = core_layers.Flatten()(x)
self.assertEqual(y.shape.as_list(), [batch_size, None])
