def test_Conv3DTranspose_pickle(): tg = TensorGraph() feature = Feature(shape=(tg.batch_size, 10, 10, 10, 1)) layer = Conv3DTranspose(num_outputs=3, in_layers=feature) tg.add_output(layer) tg.set_loss(layer) tg.build() tg.save()
def test_conv_3D_transpose(self):
"""Test that Conv3DTranspose can be invoked."""
length = 4
width = 5
depth = 6
in_channels = 2
out_channels = 3
batch_size = 20
in_tensor = np.random.rand(batch_size, length, width, depth, in_channels)
with self.session() as sess:
in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
out_tensor = Conv3DTranspose(
out_channels, kernel_size=1, stride=(2, 3, 1))(in_tensor)
sess.run(tf.global_variables_initializer())
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, 2 * length, 3 * width, depth,
out_channels)