"""
To create summary and images from a search_result of bayesian optimization
"""
from termcolor import cprint
from src import BayesianOpt as BO
from src import Args
from src.Args import Parser, ArgType
def main(args):
search_result, dimensions = BO.load.from_checkpoint(args.folder /
'checkpoint')
BO.save.save_search_result(search_result=search_result,
dimensions=dimensions,
folder_path=args.folder)
cprint('---------- Done ----------', 'grey', 'on_green')
if __name__ == '__main__':
parser = Parser(argtype=ArgType.HPSummary)
args = parser.parse_args()
args = Args.preprocess.hp_summary(args)
main(args)
n_random_starts = 1
if from_checkpoint:
n_random_starts = 0
search_result = gp_minimize(func=fitness,
dimensions=dimensions.dimensions,
acq_func='EI',
n_calls=args.n_calls,
x0=dimensions.default_dim,
callback=[checkpoint_callback],
n_random_starts=n_random_starts)
BO.save.save_search_result(search_result=search_result,
dimensions=dimensions,
folder_path=folder_path)
cprint('---------- Done ----------', 'grey', 'on_green')
# ----------------------------------------------------------------------------------------------------
# Script
# ----------------------------------------------------------------------------------------------------
if __name__ == '__main__':
parser = Parser(argtype=ArgType.HPSearch)
args = parser.parse_args()
args = Args.preprocess.bayesian_opt(args)
main(args)
if not args.no_midi:
roots.append('generated_midis')
if not args.no_hp:
roots.append('search_hp')
if not args.no_model:
roots.append('saved_models')
else:
if args.midi:
roots.append('generated_midis')
if args.hp:
roots.append('hp_search')
if args.model:
roots.append('saved_models')
to_zip = []
for root in roots:
to_zip.extend(get_all_file_paths(root))
with ZipFile('my_zip.zip', 'w') as zip:
for file in to_zip:
zip.write(file)
print('Done zipping in my_zip.zip')
if __name__ == '__main__':
parser = Parser(argtype=ArgType.Zip)
args = parser.parse_args()
args = Args.preprocess.zip(args)
main(args)
midi_generator.generate_fill(no_duration=args.no_duration, verbose=1)
if args.replicate_fill:
midi_generator.replicate_fill(save_images=True, no_duration=args.no_duration, verbose=1, noise=args.noise)
# -------------------- Redo song generate --------------------
if args.redo_generate:
midi_generator.redo_song_generate(song_number=args.song_number, save_images=True, no_duration=args.no_duration,
noise=args.noise)
# -------------------- Redo song replicate --------------------
if args.redo_replicate:
midi_generator.redo_song_replicate(song_number=args.song_number, save_images=True, no_duration=args.no_duration,
noise=args.noise)
# -------------------- Debug batch generation --------------------
if args.check_batch > -1:
for i in range(len(midi_generator.sequence)):
midi_generator.compare_test_predict_on_batch(i)
cprint('---------- Done ----------', 'grey', 'on_green')
if __name__ == '__main__':
# create a separate main function because original main function is too mainstream
parser = Parser(argtype=ArgType.Train)
args = parser.parse_args()
args = Args.preprocess.train(args)
main(args)
def main(args):
"""
Entry point
"""
if args.pc:
data_path = os.path.join('../Dataset', args.data)
else:
data_path = os.path.join('../../../../../../storage1/valentin',
args.data)
data_transformed_path = data_path + '_transformed'
if not args.pc:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
my_model = MidiGenerator.with_model(id=args.load) # Load the model
my_model.generate_from_data(length=args.length,
nb_seeds=args.nb_seeds,
save_images=args.images,
no_duration=args.no_duration)
cprint('---------- Done ----------', 'grey', 'on_green')
if __name__ == '__main__':
# create a separate main function because original main function is too mainstream
parser = Parser(argtype=ArgType.Generate)
args = parser.parse_args()
args = Args.preprocess.generate(args)
main(args)
'all_shapes': all_shapes,
'input_size': all_shapes[0][0][2], # The number of notes
'notes_range': args.notes_range,
'mono': args.mono,
'nb_files_per_npy': g.midi.nb_files_per_npy,
'transposed': not args.no_transpose
},
dump_file)
summary.summarize(
# Function params
path=data_transformed_path,
title=args.data,
# Summary params
nb_files=nb_valid_files,
nb_instruments=len(args.instruments),
instruments=args.instruments,
input_size=all_shapes[0][0][2],
notes_range=args.notes_range)
print('Number of songs :', colored('{0}'.format(nb_valid_files), 'blue'))
print(colored('---------- Done ----------', 'grey', 'on_green'))
if __name__ == '__main__':
# create a separate main function because original main function is too mainstream
parser = Parser(argtype=ArgType.ComputeData)
args = parser.parse_args()
args = Args.preprocess.compute_data(args)
main(args)
id_saved_folder_base = '' if len(cp_id) == 1 else cp_id[0]
elif args.bo_name:
id_saved_folder_base = args.bo_name + '-'
else:
id_saved_folder_base = ''
i_cp = int(args.from_checkpoint.split('-')
[-1]) if args.from_checkpoint is not None else 0
if not args.in_place and args.from_checkpoint is not None:
i_cp += 1
for script in range(1, args.nscripts):
print(f'Script {script + 1}/{args.nscripts}')
s = 'python bayesian-opt.py'
# We only need to say the number of call and the check point
# And the if in_place, we keep it
s += f' --n-calls {args.n_calls}'
s += f' --from-checkpoint {id_saved_folder_base}{i_cp}'
if args.in_place:
s += ' --in-place'
else:
i_cp += 1
os.system(s)
if __name__ == '__main__':
parser = Parser(argtype=ArgType.NScriptsBO)
args = parser.parse_args()
args = Args.preprocess.n_scripts_bo(args)
main(args)
:param args:
:return:
"""
if not args.midi:
shutil.rmtree(path='generated_midis', ignore_errors=True)
if not args.hp:
shutil.rmtree(path='hp_search', ignore_errors=True)
if not args.model:
shutil.rmtree(path='saved_models', ignore_errors=True)
if not args.tensorboard:
shutil.rmtree(path='tensorboard', ignore_errors=True)
if not args.temp:
shutil.rmtree(path='temp', ignore_errors=True)
if not args.data_temp:
data_temp = EPath(g.path.get_data_folder_path(args)) / 'temp'
shutil.rmtree(path=data_temp, ignore_errors=True)
if not args.zip:
zip_path = EPath('my_zip.zip')
if zip_path.exists():
zip_path.unlink()
print('Done cleaning')
if __name__ == '__main__':
parser = Parser(argtype=ArgType.Clean)
args = parser.parse_args()
args = Args.preprocess.clean(args)
main(args)
# matrix_midi = np.transpose(matrix_midi, , 3))
output_notes = Midi.create.matrix_to_midi(matrix_midi,
instruments=args.instruments,
notes_range=args.notes_range,
mono=args.mono)
Midi.create.save_midi(output_notes, args.instruments,
checked_file_name.as_posix())
Images.pianoroll.save_array_as_pianoroll(array=matrix_midi,
folder_path=midi_path.parent,
name=midi_path.stem +
'_checked',
seed_length=0,
mono=args.mono or args.bach)
min_notes_range += args.notes_range[0]
max_notes_range += args.notes_range[0]
print('----------', colored('Checking is done', 'white', 'on_green'),
'----------')
print('Number of correct files :', colored(nb_correct_files, 'magenta'),
'-- nb measures :', colored(nb_measures, 'magenta'))
print('Range of notes :', colored(min_notes_range, 'magenta'), ':',
colored(max_notes_range, 'magenta'))
if __name__ == '__main__':
# create a separate main function because original main function is too mainstream
parser = Parser(argtype=ArgType.CheckData)
args = parser.parse_args()
args = Args.preprocess.check_dataset(args)
main(args)