def call(self, z_pitch, z_vel, z=None):
"""Forward pass for the MIDI decoder.
Args:
z_pitch: Tensor containing encoded pitch in MIDI scale. [batch, time, 1].
z_vel: Tensor containing encoded velocity in MIDI scale. [batch, time, 1].
z: Additional non-MIDI latent tensor. [batch, time, n_z]
Returns:
A dictionary to feed into a processor group.
"""
# pylint: disable=unused-argument
# x = tf.concat([z_pitch, z_vel], axis=-1) # TODO(jesse): Allow velocity.
x = z_pitch
x = self.net(x) if z is None else self.net([x, z])
if self.norm is not None:
x = self.norm(x)
x = self.dense_out(x)
outputs = nn.split_to_dict(x, self.output_splits)
if self.f0_residual:
outputs['f0_midi'] += z_pitch
outputs['f0_hz'] = core.midi_to_hz(outputs['f0_midi'])
return outputs
def call(self, conditioning):
"""Updates conditioning with dictionary of decoder outputs."""
x = self.decode(conditioning)
outputs = nn.split_to_dict(x, self.output_splits)
if isinstance(outputs, dict):
conditioning.update(outputs)
else:
raise ValueError('Decoder must output a dictionary of signals.')
return conditioning
def test_output_is_correct(self):
tensor_splits = (('x1', 1), ('x2', 2), ('x3', 3))
x1 = np.zeros((2, 3, 1), dtype=np.float32) + 1.0
x2 = np.zeros((2, 3, 2), dtype=np.float32) + 2.0
x3 = np.zeros((2, 3, 3), dtype=np.float32) + 3.0
x = tf.constant(np.concatenate([x1, x2, x3], axis=2))
output = nn.split_to_dict(x, tensor_splits)
self.assertSetEqual(set(['x1', 'x2', 'x3']), set(output.keys()))
self.assertAllEqual(x1, output.get('x1'))
self.assertAllEqual(x2, output.get('x2'))
self.assertAllEqual(x3, output.get('x3'))
