config.py

'''
Model Configuration
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from shutil import copyfile
import os
import SharedArray as sa
import tensorflow as tf
import glob

print('[*] config...')

# class Dataset:
TRACK_NAMES = ['bass', 'drums', 'guitar', 'piano', 'strings']

def get_colormap():
    colormap = np.array([[1., 0., 0.],
                         [0., 1., 0.],
                         [0., 0., 1.],
                         [1., .5, 0.],
                         [0., .5, 1.]])
    return tf.constant(colormap, dtype=tf.float32, name='colormap')

###########################################################################
# Training
###########################################################################

class TrainingConfig:
    is_eval = True
    batch_size = 64
    epoch = 20
    iter_to_save = 100
    sample_size = 64
    print_batch = True
    drum_filter = np.tile([1,0.3,0,0,0,0.3], 16)
    scale_mask = [1., 0., 1., 0., 1., 1., 0., 1., 0., 1., 0., 1.]
    inter_pair = [(0,2), (0,3), (0,4), (2,3), (2,4), (3,4)]
    track_names = TRACK_NAMES
    track_dim = len(track_names)
    eval_map = np.array([
                    [1, 1, 1, 1, 1],  # metric_is_empty_bar
                    [1, 1, 1, 1, 1],  # metric_num_pitch_used
                    [1, 0, 1, 1, 1],  # metric_too_short_note_ratio
                    [1, 0, 1, 1, 1],  # metric_polyphonic_ratio
                    [1, 0, 1, 1, 1],  # metric_in_scale
                    [0, 1, 0, 0, 0],  # metric_drum_pattern
                    [1, 0, 1, 1, 1]   # metric_num_chroma_used
                ])

    exp_name = 'exp'
    gpu_num = '1'


###########################################################################
# Model Config
###########################################################################

class ModelConfig:
    output_w = 96
    output_h = 84
    lamda = 10
    batch_size = 64
    beta1 = 0.5
    beta2 = 0.9
    lr = 2e-4
    is_bn = True
    colormap = get_colormap()

# image
class MNISTConfig(ModelConfig):
    output_w = 28
    output_h = 28
    z_dim = 74
    output_dim = 1

# RNN
class RNNConfig(ModelConfig):
    track_names = ['All']
    track_dim = 1
    output_bar = 4
    z_inter_dim = 128
    output_dim = 5
    acc_idx = None
    state_size = 128

# onebar
class OneBarHybridConfig(ModelConfig):
    track_names = TRACK_NAMES
    track_dim = 5
    acc_idx = None
    z_inter_dim = 64
    z_intra_dim = 64
    output_dim = 1

class OneBarJammingConfig(ModelConfig):
    track_names = TRACK_NAMES
    track_dim = 5
    acc_idx = None
    z_intra_dim = 128
    output_dim = 1

class OneBarComposerConfig(ModelConfig):
    track_names = ['All']
    track_dim = 1
    acc_idx = None
    z_inter_dim = 128
    output_dim = 5

# nowbar

class NowBarHybridConfig(ModelConfig):
    track_names = TRACK_NAMES
    track_dim = 5
    acc_idx = 4
    z_inter_dim = 64
    z_intra_dim = 64
    output_dim = 1

class NowBarJammingConfig(ModelConfig):
    track_names = TRACK_NAMES
    track_dim = 5
    acc_idx = 4
    z_intra_dim = 128
    output_dim = 1

class NowBarComposerConfig(ModelConfig):
    track_names = ['All']
    track_dim = 1
    acc_idx = 4
    z_inter_dim = 128
    output_dim = 5

# Temporal
class TemporalHybridConfig(ModelConfig):
    track_names = TRACK_NAMES
    track_dim = 5
    output_bar = 4
    z_inter_dim = 32
    z_intra_dim = 32
    acc_idx = None
    output_dim = 1

class TemporalJammingConfig(ModelConfig):
    track_names = TRACK_NAMES
    track_dim = 5
    output_bar = 4
    z_intra_dim = 64
    output_dim = 1

class TemporalComposerConfig(ModelConfig):
    track_names = ['All']
    track_dim = 1
    output_bar = 4
    z_inter_dim = 64
    acc_idx = None
    output_dim = 5

class NowBarTemporalHybridConfig(ModelConfig):
    track_names = TRACK_NAMES
    acc_idx = 4
    track_dim = 5
    output_bar = 4
    z_inter_dim = 32
    z_intra_dim = 32
    acc_idx = 4
    output_dim = 1