1 files changed, 169 insertions, 0 deletions
diff --git a/tensorflow/python/layers/convolutional_test.py b/tensorflow/python/layers/convolutional_test.py
index da962b2f99..42a2d77534 100644
--- a/tensorflow/python/layers/convolutional_test.py
+++ b/tensorflow/python/layers/convolutional_test.py
@@ -651,5 +651,174 @@ class Conv2DTransposeTest(test.TestCase):
     self.assertEqual(len(variables.trainable_variables()), 4)
 
 
+class Conv3DTransposeTest(test.TestCase):
+
+  def testInvalidDataFormat(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32), seed=1)
+    with self.assertRaisesRegexp(ValueError, 'data_format'):
+      conv_layers.conv3d_transpose(volumes, 4, 3, data_format='invalid')
+
+  def testInvalidStrides(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32), seed=1)
+    with self.assertRaisesRegexp(ValueError, 'strides'):
+      conv_layers.conv3d_transpose(volumes, 4, 3, strides=(1, 2))
+
+    with self.assertRaisesRegexp(ValueError, 'strides'):
+      conv_layers.conv3d_transpose(volumes, 4, 3, strides=None)
+
+  def testInvalidKernelSize(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32), seed=1)
+    with self.assertRaisesRegexp(ValueError, 'kernel_size'):
+      conv_layers.conv3d_transpose(volumes, 4, (1, 2))
+
+    with self.assertRaisesRegexp(ValueError, 'kernel_size'):
+      conv_layers.conv3d_transpose(volumes, 4, None)
+
+  def testCreateConv3DTranspose(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32))
+    layer = conv_layers.Conv3DTranspose(4, [3, 3, 3], activation=nn_ops.relu)
+    output = layer.apply(volumes)
+    self.assertEqual(output.op.name, 'conv3d_transpose/Relu')
+    self.assertListEqual(output.get_shape().as_list(),
+                         [5, depth + 2, height + 2, width + 2, 4])
+    self.assertListEqual(layer.kernel.get_shape().as_list(), [3, 3, 3, 4, 32])
+    self.assertListEqual(layer.bias.get_shape().as_list(), [4])
+
+  def testCreateConv3DTransposeIntegerKernelSize(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32))
+    layer = conv_layers.Conv3DTranspose(4, 3)
+    output = layer.apply(volumes)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [5, depth + 2, height + 2, width + 2, 4])
+    self.assertListEqual(layer.kernel.get_shape().as_list(), [3, 3, 3, 4, 32])
+    self.assertListEqual(layer.bias.get_shape().as_list(), [4])
+
+  def testCreateConv3DTransposeChannelsFirst(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, 32, depth, height, width))
+    layer = conv_layers.Conv3DTranspose(
+        4, [3, 3, 3], data_format='channels_first')
+    output = layer.apply(volumes)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [5, 4, depth + 2, height + 2, width + 2])
+    self.assertListEqual(layer.kernel.get_shape().as_list(), [3, 3, 3, 4, 32])
+    self.assertListEqual(layer.bias.get_shape().as_list(), [4])
+
+  def testConv3DTransposePaddingSame(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 64), seed=1)
+    layer = conv_layers.Conv3DTranspose(
+        32, volumes.get_shape()[1:4], padding='same')
+    output = layer.apply(volumes)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [5, depth, height, width, 32])
+
+  def testCreateConv3DTransposeWithStrides(self):
+    depth, height, width = 4, 6, 8
+    # Test strides tuple.
+    volumes = random_ops.random_uniform((5, depth, height, width, 32), seed=1)
+    layer = conv_layers.Conv3DTranspose(
+        4, [3, 3, 3], strides=(2, 2, 2), padding='same')
+    output = layer.apply(volumes)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [5, depth * 2, height * 2, width * 2, 4])
+
+    # Test strides integer.
+    layer = conv_layers.Conv3DTranspose(4, [3, 3, 3], strides=2, padding='same')
+    output = layer.apply(volumes)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [5, depth * 2, height * 2, width * 2, 4])
+
+    # Test unequal strides.
+    layer = conv_layers.Conv3DTranspose(
+        4, [3, 3, 3], strides=(2, 1, 1), padding='same')
+    output = layer.apply(volumes)
+    self.assertListEqual(output.get_shape().as_list(),
+                         [5, depth * 2, height, width, 4])
+
+  def testConv3DTransposeKernelRegularizer(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32))
+    reg = lambda x: 0.1 * math_ops.reduce_sum(x)
+    layer = conv_layers.Conv3DTranspose(4, [3, 3, 3], kernel_regularizer=reg)
+    layer.apply(volumes)
+    loss_keys = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)
+    self.assertEqual(len(loss_keys), 1)
+    self.assertListEqual(layer.losses, loss_keys)
+
+  def testConv3DTransposeBiasRegularizer(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32))
+    reg = lambda x: 0.1 * math_ops.reduce_sum(x)
+    layer = conv_layers.Conv3DTranspose(4, [3, 3, 3], bias_regularizer=reg)
+    layer.apply(volumes)
+    loss_keys = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)
+    self.assertEqual(len(loss_keys), 1)
+    self.assertListEqual(layer.losses, loss_keys)
+
+  def testConv3DTransposeNoBias(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32))
+    layer = conv_layers.Conv3DTranspose(
+        4, [3, 3, 3], activation=nn_ops.relu, use_bias=False)
+    output = layer.apply(volumes)
+    self.assertEqual(output.op.name, 'conv3d_transpose/Relu')
+    self.assertListEqual(output.get_shape().as_list(),
+                         [5, depth + 2, height + 2, width + 2, 4])
+    self.assertListEqual(layer.kernel.get_shape().as_list(), [3, 3, 3, 4, 32])
+    self.assertEqual(layer.bias, None)
+
+  def testFunctionalConv3DTransposeReuse(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32), seed=1)
+    conv_layers.conv3d_transpose(volumes, 4, [3, 3, 3], name='deconv1')
+    self.assertEqual(len(variables.trainable_variables()), 2)
+    conv_layers.conv3d_transpose(
+        volumes, 4, [3, 3, 3], name='deconv1', reuse=True)
+    self.assertEqual(len(variables.trainable_variables()), 2)
+
+  def testFunctionalConv3DTransposeReuseFromScope(self):
+    with variable_scope.variable_scope('scope'):
+      depth, height, width = 5, 7, 9
+      volumes = random_ops.random_uniform((5, depth, height, width, 32), seed=1)
+      conv_layers.conv3d_transpose(volumes, 4, [3, 3, 3], name='deconv1')
+      self.assertEqual(len(variables.trainable_variables()), 2)
+    with variable_scope.variable_scope('scope', reuse=True):
+      conv_layers.conv3d_transpose(volumes, 4, [3, 3, 3], name='deconv1')
+      self.assertEqual(len(variables.trainable_variables()), 2)
+
+  def testFunctionalConv3DTransposeInitializerFromScope(self):
+    with self.test_session() as sess:
+      with variable_scope.variable_scope(
+          'scope', initializer=init_ops.ones_initializer()):
+        depth, height, width = 5, 7, 9
+        volumes = random_ops.random_uniform(
+            (5, depth, height, width, 32), seed=1)
+        conv_layers.conv3d_transpose(volumes, 4, [3, 3, 3], name='deconv1')
+        weights = variables.trainable_variables()
+        # Check the names of weights in order.
+        self.assertTrue('kernel' in weights[0].name)
+        self.assertTrue('bias' in weights[1].name)
+        sess.run(variables.global_variables_initializer())
+        weights = sess.run(weights)
+        # Check that the kernel weights got initialized to ones (from scope)
+        self.assertAllClose(weights[0], np.ones((3, 3, 3, 4, 32)))
+        # Check that the bias still got initialized to zeros.
+        self.assertAllClose(weights[1], np.zeros((4)))
+
+  def testFunctionalConv3DTransposeNoReuse(self):
+    depth, height, width = 5, 7, 9
+    volumes = random_ops.random_uniform((5, depth, height, width, 32), seed=1)
+    conv_layers.conv3d_transpose(volumes, 4, [3, 3, 3])
+    self.assertEqual(len(variables.trainable_variables()), 2)
+    conv_layers.conv3d_transpose(volumes, 4, [3, 3, 3])
+    self.assertEqual(len(variables.trainable_variables()), 4)
+
+
 if __name__ == '__main__':
   test.main()