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)
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)
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 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 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 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 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 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 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 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')
