def build(self, config):
"""Build the generator."""
nets = OrderedDict()
nets['shared'] = NeuralNet(self.tensor_in,
config['net_g']['shared'],
name='shared')
nets['pitch_time_private'] = [
NeuralNet(nets['shared'].tensor_out,
config['net_g']['pitch_time_private'],
name='pt_' + str(idx))
for idx in range(config['num_track'])
]
nets['time_pitch_private'] = [
NeuralNet(nets['shared'].tensor_out,
config['net_g']['time_pitch_private'],
name='tp_' + str(idx))
for idx in range(config['num_track'])
]
nets['merged_private'] = [
NeuralNet(tf.concat([
nets['pitch_time_private'][idx].tensor_out,
nets['time_pitch_private'][idx].tensor_out
], -1),
config['net_g']['merged_private'],
name='merged_' + str(idx))
for idx in range(config['num_track'])
]
tensor_out = tf.concat(
[nn.tensor_out for nn in nets['merged_private']], -1)
return tensor_out, nets
def build(self, config):
"""Build the end-to-end generator."""
nets = OrderedDict()
nets['shared'] = NeuralNet(self.tensor_in,
config['net_g']['shared'],
name='shared')
nets['pitch_time_private'] = [
NeuralNet(nets['shared'].tensor_out,
config['net_g']['pitch_time_private'],
name='pt_' + str(idx))
for idx in range(config['num_track'])
]
nets['time_pitch_private'] = [
NeuralNet(nets['shared'].tensor_out,
config['net_g']['time_pitch_private'],
name='tp_' + str(idx))
for idx in range(config['num_track'])
]
nets['merged_private'] = [
NeuralNet(tf.concat([
nets['pitch_time_private'][idx].tensor_out,
nets['time_pitch_private'][idx].tensor_out
], -1),
config['net_g']['merged_private'],
name='merged_' + str(idx))
for idx in range(config['num_track'])
]
nets['refiner_private'] = [
NeuralNet(nets['merged_private'][idx].tensor_out,
config['net_r']['private'],
slope_tensor=self.slope_tensor,
name='refiner_private' + str(idx))
for idx in range(config['num_track'])
]
# 'private' chamged to 'refiner_private'
return (tf.concat([nn.tensor_out for nn in nets['refiner_private']],
-1), nets,
tf.concat([
nn.layers[-1].preactivated
for nn in nets['refiner_private']
], -1))
def build(self, config):
"""Build the discriminator."""
nets = OrderedDict()
nets['shared'] = NeuralNet(self.tensor_in,
self.resolution,
config['net_d']['shared'],
name='shared')
return nets['shared'].tensor_out, nets
def build(self, config):
"""Build the generator."""
nets = OrderedDict()
nets['shared'] = NeuralNet(self.tensor_in,
self.resolution,
config['net_g']['shared'],
name='shared')
nets['refiner'] = [
NeuralNet(tf.expand_dims(self.tensor_in[..., idx], -1),
config['net_g']['refined'],
slope_tensor=self.slope_tensor,
name='refiner' + str(idx))
for idx in range(config['num_track'])
]
return (tf.concat([nn.tensor_out for nn in nets['refiner']], -1), nets,
tf.concat(
[nn.layers[-1].preactivated for nn in nets['refiner']],
-1))
def build(self, config):
"""Build the discriminator."""
nets = OrderedDict()
# main stream
nets['pitch_time_private'] = [
NeuralNet(tf.expand_dims(self.tensor_in[..., idx], -1),
config['net_d']['pitch_time_private'],
name='pt_' + str(idx))
for idx in range(config['num_track'])
]
nets['time_pitch_private'] = [
NeuralNet(tf.expand_dims(self.tensor_in[..., idx], -1),
config['net_d']['time_pitch_private'],
name='tp_' + str(idx))
for idx in range(config['num_track'])
]
nets['merged_private'] = [
NeuralNet(tf.concat(
[x.tensor_out, nets['time_pitch_private'][idx].tensor_out],
-1),
config['net_d']['merged_private'],
name='merged_' + str(idx))
for idx, x in enumerate(nets['pitch_time_private'])
]
nets['shared'] = NeuralNet(tf.concat(
[nn.tensor_out for nn in nets['merged_private']], -1),
config['net_d']['shared'],
name='shared')
# chroma stream
reshaped = tf.reshape(
self.tensor_in,
(-1, config['num_bar'], config['num_beat'],
config['beat_resolution'], config['num_pitch'] // 12, 12,
config['num_track']))
self.chroma = tf.reduce_sum(reshaped, axis=(3, 4))
nets['chroma'] = NeuralNet(self.chroma,
config['net_d']['chroma'],
name='chroma')
# onset stream
padded = tf.pad(self.tensor_in[:, :, :-1, :, 1:],
[[0, 0], [0, 0], [1, 0], [0, 0], [0, 0]])
self.onset = tf.concat([
tf.expand_dims(self.tensor_in[..., 0], -1),
self.tensor_in[..., 1:] - padded
], -1)
nets['onset'] = NeuralNet(self.onset,
config['net_d']['onset'],
name='onset')
if (config['net_d']['chroma'] is not None
or config['net_d']['onset'] is not None):
to_concat = [nets['shared'].tensor_out]
if config['net_d']['chroma'] is not None:
to_concat.append(nets['chroma'].tensor_out)
if config['net_d']['onset'] is not None:
to_concat.append(nets['onset'].tensor_out)
concated = tf.concat(to_concat, -1)
else:
concated = nets['shared'].tensor_out
# merge streams
nets['merged'] = NeuralNet(concated,
config['net_d']['merged'],
name='merged')
return nets['merged'].tensor_out, nets
def build(self, config):
"""Build the generator."""
nets = OrderedDict()
# Tile shared latent vector along time axis
if 'shared' in self.tensor_in:
tiled_shared = tf.reshape(
tf.tile(self.tensor_in['shared'], (1, 4)),
(-1, 4, self.tensor_in['shared'].get_shape()[1])
)
# Define shared temporal generator
if 'temporal_shared' in self.tensor_in:
nets['temporal_shared'] = NeuralNet(
self.tensor_in['temporal_shared'],
config['net_g']['temporal_shared'], name='temporal_shared'
)
# Shared bar generator mode
if config['net_g']['bar_generator_type'] == 'shared':
if ('private' in self.tensor_in
or 'temporal_private' in self.tensor_in):
raise ValueError("Private latent vectors received for a shared"
"bar generator")
# Get the final input for the bar generator
z_input = tf.concat([tiled_shared,
nets['temporal_shared'].tensor_out], -1)
nets['bar_main'] = NeuralNet(z_input, config['net_g']['bar_main'],
name='bar_main')
nets['bar_pitch_time'] = NeuralNet(
nets['bar_main'].tensor_out, config['net_g']['bar_pitch_time'],
name='bar_pitch_time'
)
nets['bar_time_pitch'] = NeuralNet(
nets['bar_main'].tensor_out, config['net_g']['bar_time_pitch'],
name='bar_time_pitch'
)
if config['net_g']['bar_merged'][-1][1][0] is None:
config['net_g']['bar_merged'][-1][1][0] = config['num_track']
nets['bar_merged'] = NeuralNet(
tf.concat([nets['bar_pitch_time'].tensor_out,
nets['bar_time_pitch'].tensor_out], -1),
config['net_g']['bar_merged'], name='bar_merged'
)
tensor_out = nets['bar_merged'].tensor_out
# Private bar generator mode
elif config['net_g']['bar_generator_type'] == 'private':
# Tile private latent vector along time axis
if 'private' in self.tensor_in:
tiled_private = [
tf.reshape(
tf.tile(self.tensor_in['private'][..., idx], (1, 4)),
(-1, 4, self.tensor_in['private'].get_shape()[1])
)
for idx in range(config['num_track'])
]
# Define private temporal generator
if 'temporal_private' in self.tensor_in:
nets['temporal_private'] = [
NeuralNet(self.tensor_in['temporal_private'][..., idx],
config['net_g']['temporal_private'],
name='temporal_private_'+str(idx))
for idx in range(config['num_track'])
]
# Get the final input for each bar generator
z_input = []
for idx in range(config['num_track']):
to_concat = []
if config['net_g']['z_dim_shared'] > 0:
to_concat.append(tiled_shared)
if config['net_g']['z_dim_private'] > 0:
to_concat.append(tiled_private[idx])
if config['net_g']['z_dim_temporal_shared'] > 0:
to_concat.append(nets['temporal_shared'].tensor_out)
if config['net_g']['z_dim_temporal_private'] > 0:
to_concat.append(nets['temporal_private'][idx].tensor_out)
z_input.append(tf.concat(to_concat, -1))
# Bar generators
# print(nets['temporal_private'][idx].tensor_out.get_shape())
nets['bar_main'] = [
NeuralNet(z_input[idx], config['net_g']['bar_main'],
name='bar_main_'+str(idx))
for idx in range(config['num_track'])
]
nets['bar_pitch_time'] = [
NeuralNet(nets['bar_main'][idx].tensor_out,
config['net_g']['bar_pitch_time'],
name='bar_pitch_time_'+str(idx))
for idx in range(config['num_track'])
]
nets['bar_time_pitch'] = [
NeuralNet(nets['bar_main'][idx].tensor_out,
config['net_g']['bar_time_pitch'],
name='bar_time_pitch_'+str(idx))
for idx in range(config['num_track'])
]
nets['bar_merged'] = [
NeuralNet(
tf.concat([nets['bar_pitch_time'][idx].tensor_out,
nets['bar_time_pitch'][idx].tensor_out], -1),
config['net_g']['bar_merged'], name='bar_merged_'+str(idx)
)
for idx in range(config['num_track'])
]
tensor_out = tf.concat(
[l.tensor_out for l in nets['bar_merged']], -1)
return tensor_out, nets
