def f(notes, style):
time_steps = int(notes.get_shape()[1])
# TODO: Experiment with when to apply conv
note_octave = TimeDistributed(
Conv1D(OCTAVE_UNITS, 2 * OCTAVE, padding='same'))(notes)
#for
#note_octave =
note_octave = Activation('tanh')(note_octave)
note_octave = Dropout(dropout)(note_octave)
# Create features for every single note.
note_features = Concatenate()([
Lambda(pitch_pos_in_f(time_steps))(notes),
Lambda(pitch_class_in_f(time_steps))(notes),
Lambda(pitch_bins_f(time_steps))(notes), note_octave
#TimeDistributed(RepeatVector(NUM_NOTES))(beat)
])
x = note_features
# [batch, notes, time, features]
x = Permute((2, 1, 3))(x)
# Apply LSTMs
for l in range(TIME_AXIS_LAYERS):
# Integrate style
style_proj = Dense(int(x.get_shape()[3]))(style)
style_proj = TimeDistributed(RepeatVector(NUM_NOTES))(style_proj)
style_proj = Activation('tanh')(style_proj)
style_proj = Dropout(dropout)(style_proj)
style_proj = Permute((2, 1, 3))(style_proj)
x = Add()([x, style_proj])
x = TimeDistributed(LSTM(TIME_AXIS_UNITS,
return_sequences=True))(x)
x = Dropout(dropout)(x)
# [batch, time, notes, features]
return Permute((2, 1, 3))(x)
def f(notes, beat, style):
time_steps = int(notes.get_shape()[1])
# TODO: Experiment with when to apply conv
note_octave = TimeDistributed(Conv1D(OCTAVE_UNITS, 2 * OCTAVE, padding='same'))(notes)
note_octave = Activation('tanh')(note_octave)
note_octave = Dropout(dropout)(note_octave)
# Create features for every single note.
note_features = Concatenate()([
Lambda(pitch_pos_in_f(time_steps))(notes),
Lambda(pitch_class_in_f(time_steps))(notes),
Lambda(pitch_bins_f(time_steps))(notes),
note_octave,
TimeDistributed(RepeatVector(NUM_NOTES))(beat)
])
x = note_features
# [batch, notes, time, features]
x = Permute((2, 1, 3))(x)
# Apply LSTMs
for l in range(TIME_AXIS_LAYERS):
# Integrate style
style_proj = Dense(int(x.get_shape()[3]))(style)
style_proj = TimeDistributed(RepeatVector(NUM_NOTES))(style_proj)
style_proj = Activation('tanh')(style_proj)
style_proj = Dropout(dropout)(style_proj)
style_proj = Permute((2, 1, 3))(style_proj)
x = Add()([x, style_proj])
x = TimeDistributed(LSTM(TIME_AXIS_UNITS, return_sequences=True))(x)
x = Dropout(dropout)(x)
# [batch, time, notes, features]
return Permute((2, 1, 3))(x)
def block_warp(block_name,input_layer,filters,kernal_size=3, dilation_rate=1,depthfilter=4,stride=1):
def conv_block(input_layer,filters,k=3):
y = Conv3D(filters=filters, kernel_size=k, padding='same')(input_layer)
y = BatchNormalization()(y)
y = Activation('relu')(y)
return y
if block_name == 'conv':
y = conv_block(input_layer,filters)
y = conv_block(y,filters)
elif block_name == 'dialtion':
y = Conv3D(filters=filters, kernel_size=kernal_size, padding='same', dilation_rate=dilation_rate)(input_layer)
y = BatchNormalization()(y)
y = Activation('relu')(y)
elif block_name == 'deconv':
y = Conv3DTranspose(filters=filters,kernel_size=3,strides=2,padding='same')(input_layer)
y = BatchNormalization()(y)
y = Activation('relu')(y)
elif block_name == 'time_conv':
y = TimeDistributed(Conv2D(filters=filters,kernel_size=3,padding='same'))(input_layer)
y = TimeDistributed(BatchNormalization())(y)
y = TimeDistributed(Activation('relu'))(y)
elif block_name == 'time_deconv':
y = TimeDistributed(Conv2DTranspose(filters=filters,kernel_size=3,padding='same',strides=2))(input_layer)
y = TimeDistributed(BatchNormalization())(y)
y = TimeDistributed(Activation('relu'))(y)
elif block_name == 'inception':
filters = filters//4
c1 = conv_block(input_layer,filters,1)
c3 = conv_block(input_layer,filters,1)
c3 = conv_block(c3,filters,3)
c5 = MaxPool3D(pool_size=3,padding='same',strides=1)(input_layer)
c5 = conv_block(c5,filters,3)
c7 = conv_block(input_layer,filters,1)
c7 = conv_block(c7, filters, 3)
c7 = conv_block(c7, filters, 3)
y = concatenate([c1,c3,c5,c7])
# c_all = BatchNormalization()(c_all)
# y = Activation('relu')(c_all)
elif block_name == 'sep':
input_layer = Permute((4,1,2,3))(input_layer)
sz = input_layer.get_shape()
shape = tuple(int(sz[i]) for i in range(1, 5))
input_layer = Reshape(shape+(1,))(input_layer)
conv1 = TimeDistributed(Conv3D(filters=depthfilter,kernel_size=kernal_size,padding='same',strides=stride))(input_layer)
conv1 = Permute((2,3,4,1,5))(conv1)
sz = conv1.get_shape()
shape = tuple(int(sz[i]) for i in range(1, 4))
conv1 = Reshape(shape+(-1,))(conv1)
conv1 = Conv3D(filters=filters,kernel_size=1,padding='same')(conv1)
conv1 = BatchNormalization()(conv1)
y = Activation('relu')(conv1)
else:
raise ValueError("layer error")
return y
