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test_data.py
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test_data.py
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import unittest
import os
import shutil
import logging
from aria import tokenizer
from aria.config import load_config
from aria.data import datasets
from aria.data.datasets import _noise_midi_dict
from aria.data.midi import MidiDict
logger = logging.getLogger(__name__)
if not os.path.isdir("tests/test_results"):
os.makedirs("tests/test_results")
def setup_logger():
logger = logging.getLogger(__name__)
for h in logger.handlers[:]:
logger.removeHandler(h)
logger.propagate = False
logger.setLevel(logging.INFO)
formatter = logging.Formatter(
"[%(asctime)s] tests.test_data: [%(levelname)s] %(message)s"
)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
ch.setFormatter(formatter)
logger.addHandler(ch)
def get_short_seq():
return [
("prefix", "instrument", "piano"),
("prefix", "instrument", "drum"),
("prefix", "composer", "bach"),
"<S>",
("piano", 62, 50),
("dur", 50),
("wait", 100),
("drum", 50),
("piano", 64, 70),
("dur", 100),
("wait", 100),
"<E>",
]
class TestMidiDict(unittest.TestCase):
def test_resolve_pedal(self):
midi_dict = MidiDict.from_midi("tests/test_data/maestro.mid")
midi_dict.resolve_pedal()
self.assertListEqual(midi_dict.pedal_msgs, [])
mid = midi_dict.to_midi()
mid.save("tests/test_results/maestro_npedal.mid")
class TestMidiDataset(unittest.TestCase):
def test_build(self):
dataset = datasets.MidiDataset.build(
dir="tests/test_data",
recur=False,
)
self.assertEqual(len(dataset), 7)
self.assertEqual(type(dataset[0]), MidiDict)
def test_save_load(self):
dataset = datasets.MidiDataset.build(
dir="tests/test_data",
recur=False,
)
dataset.save("tests/test_results/mididict_dataset.jsonl")
dataset_reloaded = datasets.MidiDataset.load(
"tests/test_results/mididict_dataset.jsonl"
)
self.assertEqual(len(dataset_reloaded), 7)
self.assertEqual(type(dataset[0]), type(dataset_reloaded[0]))
def test_build_to_file(self):
datasets.MidiDataset.build_to_file(
dir="tests/test_data",
save_path="tests/test_results/mididict_dataset_direct.jsonl",
recur=False,
overwrite=True,
)
dataset_reloaded = datasets.MidiDataset.load(
load_path="tests/test_results/mididict_dataset_direct.jsonl",
)
self.assertEqual(len(dataset_reloaded), 7)
self.assertEqual(type(dataset_reloaded[0]), MidiDict)
def test_split_from_file(self):
datasets.MidiDataset.build_to_file(
dir="tests/test_data",
save_path="tests/test_results/mididict_dataset.jsonl",
recur=False,
overwrite=True,
)
datasets.MidiDataset.split_from_file(
load_path="tests/test_results/mididict_dataset.jsonl",
train_val_ratio=0.7,
repeatable=True,
overwrite=True,
)
self.assertTrue(
os.path.isfile("tests/test_results/mididict_dataset_train.jsonl")
)
self.assertTrue(
os.path.isfile("tests/test_results/mididict_dataset_val.jsonl")
)
def test_data_hash(self):
mid_1 = MidiDict.from_midi("tests/test_data/pop.mid")
mid_2 = MidiDict.from_midi("tests/test_data/pop_copy.mid")
self.assertEqual(mid_1.calculate_hash(), mid_2.calculate_hash())
def test_concat(self):
if (
os.path.exists("tests/test_results/mididict_dataset_train.jsonl")
and os.path.exists("tests/test_results/mididict_dataset_val.jsonl")
and os.path.exists("tests/test_results/mididict_dataset.jsonl")
):
datasets.MidiDataset.combine_datasets_from_file(
"tests/test_results/mididict_dataset_train.jsonl",
"tests/test_results/mididict_dataset_val.jsonl",
"tests/test_results/mididict_dataset.jsonl",
output_path="tests/test_results/mididict_dataset_concat.jsonl",
)
self.assertAlmostEqual(
len(
datasets.MidiDataset.load(
"tests/test_results/mididict_dataset_concat.jsonl"
)
),
len(
datasets.MidiDataset.load(
"tests/test_results/mididict_dataset.jsonl"
)
),
)
class TestPretrainingDataset(unittest.TestCase):
def test_build(self):
MAX_SEQ_LEN = 4096
tknzr = tokenizer.AbsTokenizer(return_tensors=False)
mididict_dataset = datasets.MidiDataset.build(
dir="tests/test_data",
recur=False,
)
mididict_dataset.save("tests/test_results/mididict_dataset.jsonl")
if os.path.exists("tests/test_results/pretrain_dataset_buff_1"):
shutil.rmtree("tests/test_results/pretrain_dataset_buff_1")
if os.path.exists("tests/test_results/pretrain_dataset_buff_2"):
shutil.rmtree("tests/test_results/pretrain_dataset_buff_2")
dataset_from_file = datasets.PretrainingDataset.build(
tokenizer=tknzr,
save_dir="tests/test_results/pretrain_dataset_buff_1",
max_seq_len=MAX_SEQ_LEN,
num_epochs=3,
midi_dataset_path="tests/test_results/mididict_dataset.jsonl",
)
dataset_from_mdset = datasets.PretrainingDataset.build(
tokenizer=tknzr,
save_dir="tests/test_results/pretrain_dataset_buff_2",
max_seq_len=MAX_SEQ_LEN,
num_epochs=3,
midi_dataset=mididict_dataset,
)
def test_aug(self):
MAX_SEQ_LEN = 512
tknzr = tokenizer.AbsTokenizer(return_tensors=False)
mididict_dataset = datasets.MidiDataset.build(
dir="tests/test_data",
recur=False,
)
if os.path.exists("tests/test_results/pretrain_dataset_buff"):
shutil.rmtree("tests/test_results/pretrain_dataset_buff")
pretrain_dataset = datasets.PretrainingDataset.build(
tokenizer=tknzr,
save_dir="tests/test_results/pretrain_dataset_buff",
max_seq_len=MAX_SEQ_LEN,
num_epochs=1,
midi_dataset=mididict_dataset,
)
pretrain_dataset.set_transform(tknzr.export_data_aug())
for idx, seq in enumerate(tknzr.decode(pretrain_dataset[0][0])):
for _idx, tok in enumerate(seq):
if tok == tknzr.unk_tok:
logger.warning(f"unk_tok seen at seq={idx}, idx={_idx}")
logger.info(f"data_aug_1: {tknzr.decode(pretrain_dataset[0][0][:50])}")
logger.info(f"data_aug_2: {tknzr.decode(pretrain_dataset[0][0][:50])}")
class TestFinetuningDataset(unittest.TestCase):
def test_noise(self):
config = load_config()["data"]["finetuning"]["noising"]
midi_dict = MidiDict.from_midi("tests/test_data/clean/1.mid")
noisy_midi_dict = _noise_midi_dict(midi_dict, config)
noisy_midi = noisy_midi_dict.to_midi()
noisy_midi.save("tests/test_results/noisy.mid")
def test_build(self):
MAX_SEQ_LEN = 4096
tknzr = tokenizer.SeparatedAbsTokenizer(return_tensors=False)
clean_mididict_dataset = datasets.MidiDataset.build(
dir="tests/test_data/clean",
recur=True,
shuffle=False,
)
noisy_mididict_dataset = datasets.MidiDataset.build(
dir="tests/test_data/noisy",
recur=True,
shuffle=False,
)
if os.path.exists("tests/test_results/clean.jsonl"):
os.remove("tests/test_results/clean.jsonl")
if os.path.exists("tests/test_results/noisy.jsonl"):
os.remove("tests/test_results/noisy.jsonl")
clean_mididict_dataset.save("tests/test_results/clean.jsonl")
noisy_mididict_dataset.save("tests/test_results/noisy.jsonl")
if os.path.exists("tests/test_results/comb"):
shutil.rmtree("tests/test_results/comb")
finetuning_dataset = datasets.FinetuningDataset.build(
tokenizer=tknzr,
save_dir="tests/test_results/comb",
max_seq_len=MAX_SEQ_LEN,
num_epochs=2,
clean_dataset_path="tests/test_results/clean.jsonl",
noisy_dataset_paths=["tests/test_results/noisy.jsonl"],
)
finetuning_dataset.init_epoch(0)
for seq, tgt, mask in finetuning_dataset:
tokenized_seq = tknzr.decode(seq)
if (
tknzr.inst_start_tok in tokenized_seq
and tknzr.bos_tok not in tokenized_seq
):
detokenized_midi_dict = tknzr.detokenize(tokenized_seq)
res = detokenized_midi_dict.to_midi()
res.save(f"tests/test_results/comb.mid")
break
setup_logger()
if __name__ == "__main__":
unittest.main()