/
simple_tcn_eval.py
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simple_tcn_eval.py
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from simple_tcn import TCNClassifier, NetworkInterface, N_MIDI_PITCH, CONTEXT_LENGTH
import numpy as np
from midi_structure import get_piano_roll, prepare_quantization, evaluate_result, evaluations_to_latex, get_split
import pretty_midi
import os
from settings import LMD_MATCHED_FOLDER, RWC_DATASET_PATH
import matplotlib.pyplot as plt
import torch
from crf import CRFDecoder
from metrical_crf import get_ternary_transition
from scipy.ndimage.filters import uniform_filter1d
def decode(log_observations):
log_observations = torch.tensor(log_observations)
# log_transitions = get_log_transitions(4)
log_transitions, indices = get_ternary_transition(np.array([-5.0, -4.0, -3.0, -2.0]), np.array([-8.0, -7.0, -6.0, -5.0]))
log_observations = log_observations[:, indices]
crf = CRFDecoder(torch.tensor(log_transitions))
result = crf.viterbi_decode(log_observations[None]).squeeze(0).numpy()
return indices[result]
def model_eval(model, midi_path, subbeat_count=4, drums=1, melody=1, others=1, visualize=True, tracks=None, crf=True):
# print('Evaluating:', midi_path)
try:
midi = pretty_midi.PrettyMIDI(midi_path)
except:
print('Midi load failed: %s' % midi_path)
return None
n_subbeat, downbeat_bins, boundaries, subbeat_time = prepare_quantization(midi, subbeat_count)
piano_rolls = [get_piano_roll(ins, boundaries, False, ignore_drums=True) for ins in midi.instruments]
onset_rolls = [get_piano_roll(ins, boundaries, True, ignore_drums=True) for ins in midi.instruments]
drum_rolls = [get_piano_roll(ins, boundaries, True, ignore_drums=False, ignore_non_drums=True) for ins in midi.instruments]
rolls = []
ins_names = []
# collect all drum tracks first
for j, ins in enumerate(midi.instruments):
if (ins.is_drum):
if (drums == 0 or (tracks is not None and j not in tracks)):
continue
roll = np.concatenate((onset_rolls[j], piano_rolls[j], drum_rolls[j]), axis=-1)
rolls.append(roll)
ins_names.append('drums:%d' % j)
if (len(rolls) > 1):
rolls = [np.max(rolls, axis=0)]
ins_names = ['drums:-1']
for j, ins in enumerate(midi.instruments):
if (ins.is_drum):
continue
if ('mel' in ins.name.lower() or 'vocal' in ins.name.lower()):
if (melody == 0 or (tracks is not None and j not in tracks)):
continue
ins_name = 'melody'
else:
ins_name = pretty_midi.program_to_instrument_name(ins.program) + '(%d)' % ins.program
if (others == 0 or (tracks is not None and j not in tracks)):
continue
roll = np.concatenate((onset_rolls[j], piano_rolls[j], drum_rolls[j]), axis=-1)
rolls.append(roll)
ins_names.append('%s:%d' % (ins_name, j))
# visualized_preds, _ = model.inference(roll.astype(np.float32))
# visualized_preds = np.cumsum(visualized_preds[:, ::-1], axis=1)
# plt.figure(figsize=(26, 6))
# plt.imshow(np.concatenate((piano_rolls[j][:, ::-1], np.repeat(visualized_preds, 16, axis=1)), axis=1).T, interpolation='nearest')
# plt.title(os.path.basename(midi_path))
# plt.show()
if (len(rolls) == 0):
print('No track!')
return None
# print('Tracks: %d' % (len(rolls)))
rolls = np.stack(rolls, axis=0)
log_final_pred, log_conf = model.inference_function('inference_song', rolls.astype(np.float32), return_log_prob=True)
log_final_pred = np.log(uniform_filter1d(np.exp(log_final_pred), size=5, axis=0))
used_downbeats = downbeat_bins[downbeat_bins < len(log_final_pred)]
log_downbeat_pred = log_final_pred[used_downbeats]
if (crf == True):
result = decode(log_downbeat_pred)
else:
result = np.argmax(log_downbeat_pred, axis=-1)
if (visualize):
onehot_result = np.eye(5)[result]
final_pred = np.exp(log_final_pred)
visualized_preds = np.cumsum(final_pred[:, ::-1], axis=1)
visualized_result = np.zeros((final_pred.shape[0], 5))
visualized_result[used_downbeats] = onehot_result
visualized_result = np.cumsum(visualized_result[:, ::-1], axis=1)
plt.figure(figsize=(26, 6))
plt.imshow(np.concatenate((rolls.max(axis=0)[:, ::-1], np.repeat(visualized_preds, 16, axis=1), np.repeat(visualized_result, 16, axis=1)), axis=1).T)
plt.title(os.path.basename(midi_path) + ' final')
plt.show()
gt_midi_path = 'annotation/%s_gt.mid' % os.path.basename(midi_path)
if (os.path.exists(gt_midi_path)):
evaluation = evaluate_result(result, gt_midi_path, downbeat_bins, subbeat_count, 4)
# print(('%s:\t' % gt_midi_path) + '\t'.join(str(x) for x in evaluation))
else:
evaluation = None
# print(conf)
output = pretty_midi.Instrument(program=0, is_drum=True, name='Layers')
for i, pred in enumerate(result):
for k in range(pred):
onset_time = subbeat_time[downbeat_bins[i]]
output.notes.append(pretty_midi.Note(velocity=100, pitch=40 + k, start=onset_time, end=onset_time + 0.5))
midi.instruments.append(output)
if not (os.path.exists('output/%s' % model.save_name)):
os.mkdir('output/%s' % model.save_name)
midi.write('output/%s/%s_crf.mid' % (model.save_name, os.path.basename(midi_path)))
np.savetxt('output/%s/%s_conf.txt' % (model.save_name, os.path.basename(midi_path)), log_conf)
f = open('output/%s/%s_conf_ins.txt' % (model.save_name, os.path.basename(midi_path)), 'w')
f.write(','.join(ins_names))
f.close()
return evaluation
def evaluate_lmd(model, count):
f = open('data/lmd_matched_usable_midi.txt', 'r')
lines = [line.strip() for line in f.readlines() if line.strip() != '']
f.close()
np.random.seed(6172)
np.random.shuffle(lines)
lines = lines[:count]
for line in lines:
model_eval(model, os.path.join(LMD_MATCHED_FOLDER, line), visualize=False)
def main():
model = NetworkInterface(TCNClassifier(384, 256, 6, 5, 0.1),
'simple_tcn_v2.2_fixed_shift', load_checkpoint=False)
# evaluate_lmd(model, 9999)
# exit(0)
# model_eval(model, R'E:\Dataset\lmd_matched\L\C\N\TRLCNWM128F423BB63\7596e59dea60afab6bbc7207aca8bd8c.mid')
print(evaluations_to_latex('Proposed\n(mel. only)',
[model_eval(model, R'input/POP909-%d.mid' % (i + 1), tracks=[0]) for i in range(5)]))
print(evaluations_to_latex('Proposed',
[model_eval(model, R'input/POP909-%d.mid' % (i + 1)) for i in range(5)]))
split_files = get_split('rwc_multitrack_hierarchy_v6_supervised', 'test')
print(evaluations_to_latex('Proposed\nw/o CRF',
[model_eval(model, os.path.join(RWC_DATASET_PATH, 'AIST.RWC-MDB-P-2001.SMF_SYNC', file),
drums=1, melody=1, others=1, visualize=False, crf=False) for file in split_files]))
print(evaluations_to_latex('Proposed\n(mel. only)',
[model_eval(model, os.path.join(RWC_DATASET_PATH, 'AIST.RWC-MDB-P-2001.SMF_SYNC', file),
drums=0, melody=1, others=0, visualize=False) for file in split_files]))
print(evaluations_to_latex('Proposed\n(no drums)',
[model_eval(model, os.path.join(RWC_DATASET_PATH, 'AIST.RWC-MDB-P-2001.SMF_SYNC', file),
drums=0, melody=1, others=1, visualize=False) for file in split_files]))
print(evaluations_to_latex('Proposed',
[model_eval(model, os.path.join(RWC_DATASET_PATH, 'AIST.RWC-MDB-P-2001.SMF_SYNC', file),
drums=1, melody=1, others=1, visualize=False) for file in split_files]))
exit(0)
evaluations = []
print(model.save_name)
for file in split_files:
evaluation = model_eval(model, os.path.join(RWC_DATASET_PATH, 'AIST.RWC-MDB-P-2001.SMF_SYNC', file),
drums=1, melody=1, others=1)
if (evaluation is not None):
evaluations.append(evaluation)
if (len(evaluations) > 0):
mean_evaluation = np.mean(evaluations, axis=0)
print('Mean evaluation:\t' + '\t'.join('%.4f' % x for x in mean_evaluation))
if __name__ == '__main__':
main()