def main(stash_file, input_key, transform_file,
param_file, output_file, verbose=True):
transform = optimus.load(transform_file, param_file)
stash = biggie.Stash(stash_file)
futil.create_directory(os.path.split(output_file)[0])
output = biggie.Stash(output_file)
util.process_stash(stash, transform, output, input_key, verbose=verbose)
def main(args):
stash = biggie.Stash(args.input_file)
futils.create_directory(path.split(args.output_file)[0])
stash_out = biggie.Stash(args.output_file)
total_count = len(stash)
args = ['cqt', [2, 4, 8, 16, 32, 64, 128], 0, 'mean']
for idx, key in enumerate(stash.keys()):
stash_out.add(key, pool_entity(stash.get(key), *args))
print "[%s] %12d / %12d: %s" % (time.asctime(), idx, total_count, key)
stash_out.close()
def main(args):
dset = biggie.Stash(args.input_file)
futils.create_directory(path.split(args.output_file)[0])
dout = biggie.Stash(args.output_file)
beat_times = json.load(open(args.beat_times))
total_count = len(dset)
for idx, key in enumerate(dset.keys()):
boundaries = subdivide_boundaries(beat_times[key], args.subdivide)
dout.add(
key, beat_sync(dset.get(key), boundaries,
pool_func=args.pool_func))
print "[%s] %12d / %12d: %s" % (time.asctime(), idx, total_count, key)
dout.close()
def main(args):
in_stash = biggie.Stash(args.data_file)
utils.create_directory(os.path.dirname(args.output_file))
if os.path.exists(args.output_file):
os.remove(args.output_file)
out_stash = biggie.Stash(args.output_file)
STATUS['total'] = len(in_stash)
for idx, key in enumerate(in_stash.keys()):
new_entity = wrap_cqt_for_key(in_stash.get(key), args.length,
args.stride)
out_stash.add(key, new_entity)
out_stash.close()
def main(args):
in_stash = biggie.Stash(args.data_file)
futils.create_directory(os.path.split(args.output_file)[0])
if os.path.exists(args.output_file):
os.remove(args.output_file)
out_stash = biggie.Stash(args.output_file)
total_count = len(in_stash.keys())
for idx, key in enumerate(in_stash.keys()):
out_stash.add(key,
wrap_entity(in_stash.get(key), args.length, args.stride))
print "[%s] %12d / %12d: %s" % (time.asctime(), idx, total_count, key)
out_stash.close()
def main(args):
trainer, predictor = models.MODELS[args.model_name]()
time_dim = trainer.inputs['data'].shape[2]
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
print "Opening %s" % args.training_file
stash = biggie.Stash(args.training_file)
stream = D.create_chord_index_stream(stash,
time_dim,
VOCAB,
sample_func=D.slice_chroma_entity,
working_size=25)
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.output_directory)
hyperparams = dict(learning_rate=LEARNING_RATE, dropout=DROPOUT)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
trainer, predictor = models.MODELS[args.model_name]()
time_dim = trainer.inputs['data'].shape[2]
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
print "Opening %s" % args.training_file
stash = biggie.Stash(args.training_file, cache=True)
stream = S.minibatch(D.create_target_stream(
stash,
time_dim,
max_pitch_shift=0,
bins_per_pitch=1,
mapper=D.FX.note_numbers_to_pitch,
sample_func=D.slice_note_entity),
batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.output_directory)
hyperparams = dict(learning_rate=LEARNING_RATE, dropout=DROPOUT)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
sim_margin = -RADIUS * args.margin
trainer, predictor, zerofilter = models.iX_c3f2_oY(20, 3, 'xlarge')
time_dim = trainer.inputs['cqt'].shape[2]
if args.init_param_file:
print("Loading parameters: {0}".format(args.init_param_file))
trainer.load_param_values(args.init_param_file)
print("Opening {0}".format(args.training_file))
stash = biggie.Stash(args.training_file, cache=True)
stream = S.minibatch(
D.create_pairwise_stream(stash, time_dim,
working_size=100, threshold=0.05),
batch_size=BATCH_SIZE)
stream = D.batch_filter(
stream, zerofilter, threshold=2.0**-16, min_batch=1,
max_consecutive_skips=100, sim_margin=sim_margin, diff_margin=RADIUS)
print("Starting '{0}'".format(args.trial_name))
driver = optimus.Driver(
graph=trainer,
name=args.trial_name,
output_directory=futil.create_directory(args.output_directory))
hyperparams = dict(
learning_rate=LEARNING_RATE,
sim_margin=sim_margin, diff_margin=RADIUS)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
trainer, predictor = models.MODELS[args.model_name]()
time_dim = trainer.inputs['data'].shape[2]
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
print "Opening %s" % args.training_file
stash = biggie.Stash(args.training_file, cache=True)
stream = D.create_chord_index_stream(stash,
time_dim,
max_pitch_shift=0,
lexicon=VOCAB)
# Load prior
stat_file = "%s.json" % path.splitext(args.training_file)[0]
prior = np.array(json.load(open(stat_file))['prior'], dtype=float)
trainer.nodes['prior'].weight.value = 1.0 / prior.reshape(1, -1)
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.output_directory)
hyperparams = dict(learning_rate=LEARNING_RATE, dropout=DROPOUT)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
trainer, predictor = models.MODELS[args.model_name]()
time_dim = trainer.inputs['cqt'].shape[2]
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
print "Opening %s" % args.training_file
stash = biggie.Stash(args.training_file, cache=True)
stream = D.create_uniform_chord_stream(
stash,
time_dim,
pitch_shift=0,
vocab_dim=VOCAB,
working_size=5,
)
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.output_directory)
hyperparams = dict(learning_rate=LEARNING_RATE)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
config = json.load(open(args.config))
penalty_values = [float(_) for _ in config['penalty_values']]
label_map = ENCODERS[args.label_type]
for f in futils.load_textlist(args.posterior_filelist):
print "[{0}] Decoding {1}".format(time.asctime(), f)
# Read the whole stash to memory because the hdf5 reference doesn't
# survive parallelization.
stash = biggie.Stash(f)
keys = stash.keys()
stash = {k: biggie.Entity(**stash.get(k).values()) for k in keys}
# Parse the posterior stash filepath for its model's params
parts = list(os.path.splitext(f)[0].split('outputs/')[-1].split('/'))
if len(parts) == 4:
parts.append("best")
model, dropout, fold_idx, split, checkpoint = parts
model_params = dict(model=model,
dropout=dropout,
fold_idx=fold_idx,
split=split,
checkpoint=checkpoint)
output_dir = os.path.join(args.output_directory, checkpoint)
posterior_stash_to_jams(stash, penalty_values, output_dir, label_map,
model_params)
def main(args):
predictor = pca_lda_graph(20, args.n_components, 3)
input_shape = list(predictor.inputs['cqt'].shape)
time_dim = input_shape[2]
input_shape[0] = args.num_points
print("Opening {0}".format(args.training_file))
stash = biggie.Stash(args.training_file, cache=True)
stream = D.create_labeled_stream(stash,
time_dim,
working_size=1000,
threshold=0.05)
print("Starting '{0}'".format(args.trial_name))
data, labels = np.zeros(input_shape), []
for idx, x in enumerate(stream):
data[idx, ...] = x.cqt
labels.append(x.label)
if len(labels) == args.num_points:
break
elif (len(labels) % PRINT_FREQ) == 0:
print("[{0}] {1:5} / {2:5}"
"".format(time.asctime(), len(labels), args.num_points))
predictor.param_values = fit_params(data, labels, args.n_components, 3)
output_directory = futil.create_directory(args.output_directory)
predictor_file = path.join(output_directory, args.predictor_file)
param_file = predictor_file.replace(".json", ".npz")
optimus.save(predictor, def_file=predictor_file, param_file=param_file)
def main(args):
"""Main routine for importing data."""
futils.create_directory(path.split(args.output_file)[0])
if args.verbose:
print "[%s] Creating: %s" % (time.asctime(), args.output_file)
stash = biggie.Stash(args.output_file)
populate_stash(futils.load_textlist(args.key_list), args.cqt_directory,
args.lab_directory, stash, np.float32)
def main(args):
stash = biggie.Stash(args.posterior_file)
thresh = [0.02] * 5 + [0.0] * 9
partition_labels = util.partition(stash, likelihood_threshold, thresh)
partition_labels = dict([(k, v.tolist())
for k, v in partition_labels.items()])
with open(args.partition_file, 'w') as fp:
json.dump(partition_labels, fp)
def main(args):
fpath = os.path.join(args.data_directory, "{0}.hdf5")
train = biggie.Stash(fpath.format('train'), cache=True)
valid = biggie.Stash(fpath.format('valid'), cache=True)
test = biggie.Stash(fpath.format('test'), cache=True)
results = classify(train,
valid,
test,
num_train=50000,
num_valid=10000,
num_test=25000)
for k in 'train', 'valid', 'test':
print("{0}: {1:.4}".format(k, results['{0}_score'.format(k)]))
output_dir = os.path.split(args.stats_file)[0]
futil.create_directory(output_dir)
with open(args.stats_file, 'w') as fp:
json.dump(results, fp, indent=2)
def main(args):
dset = biggie.Stash(args.input_file)
labseg = json.load(open(args.labseg))
out_dir = futils.create_directory(args.output_directory)
total_count = len(dset)
for idx, key in enumerate(dset.keys()):
out_file = path.join(out_dir, "%s.%s" % (key, FILE_EXT))
mdict = entity_to_mdict(dset.get(key), labseg[key])
MLAB.savemat(out_file, mdict=mdict)
print "[%s] %12d / %12d: %s" % (time.asctime(), idx, total_count, key)
def main(args):
param_files = futils.load_textlist(args.param_textlist)
param_files.sort()
param_files = param_files[args.start_index::args.stride]
transform = optimus.load(args.transform_file)
stash = biggie.Stash(args.validation_file, cache=True)
output_dir = futils.create_directory(args.output_dir)
for fidx, param_file in enumerate(param_files):
transform.load_param_values(param_file)
output_file = params_to_output_file(param_file, output_dir)
futils.create_directory(os.path.split(output_file)[0])
if os.path.exists(output_file):
os.remove(output_file)
output = biggie.Stash(output_file)
util.process_stash(stash, transform, output,
args.field, verbose=args.verbose)
def main(args):
stash = biggie.Stash(args.input_file)
futil.create_directory(path.split(args.output_file)[0])
stats = dict()
vocab = lex.Strict(157)
stats['prior'] = D.class_prior_v157(stash, vocab).tolist()
with open(args.output_file, 'w') as fp:
json.dump(stats, fp)
def main(args):
class_data = np.load(args.centers_file)
pca, ann = build_model(centers=class_data['centers'],
class_idxs=class_data['chord_idx'],
n_neighbors=args.n_neighbors,
n_components=args.n_components)
pool = joblib.Parallel(n_jobs=NUM_CPUS)
stash = biggie.Stash(args.stash_file)
predictions = predict_all(stash, pca, ann)
with open(args.output_file, 'w') as fp:
json.dump(predictions, fp, indent=2)
def main(args):
trainer, predictor = build_model()
if args.init_param_file:
print "Loading parameters: %s" % args.init_param_file
trainer.load_param_values(args.init_param_file)
optimus.random_init(trainer.params['layer3'].weights)
optimus.random_init(trainer.params['layer3'].bias)
# 3. Create Data
print "Loading %s" % args.training_file
stash = biggie.Stash(args.training_file)
stream = D.create_stash_stream(stash,
TIME_DIM,
pitch_shift=0,
vocab_dim=VOCAB,
pool_size=25)
if args.secondary_source:
stash2 = biggie.Stash(args.secondary_source)
stream2 = D.create_uniform_chord_stream(stash2,
TIME_DIM,
pitch_shift=0,
vocab_dim=VOCAB,
working_size=5)
stream = S.mux([stream, stream2], [0.5, 0.5])
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
hyperparams = dict(learning_rate=LEARNING_RATE)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
"""Main routine for importing data."""
data_splits = json.load(open(args.split_file))
output_file_fmt = path.join(args.output_directory, FILE_FMT)
for fold in data_splits:
for split in data_splits[fold]:
output_file = output_file_fmt % (fold, split)
futils.create_directory(path.split(output_file)[0])
if args.verbose:
print "[%s] Creating: %s" % (time.asctime(), output_file)
stash = biggie.Stash(output_file)
populate_stash(data_splits[fold][split], args.cqt_directory,
args.jams_directory, stash, np.float32)
def main(args):
stash = biggie.Stash(args.training_file)
valid_idx = range(157)
streams, chord_idx = chord_streamer(stash,
win_length=20,
working_size=4,
valid_idx=valid_idx,
n_samples=None,
batch_size=50)
quants = multi_fit(streams, args.n_clusters, args.n_iter)
centers = np.array([q.cluster_centers_ for q in quants])
counts = np.array([q.cluster_sizes_ for q in quants], dtype=int)
np.savez(args.output_file,
centers=centers,
counts=counts,
chord_idx=chord_idx)
def main(args):
"""Main routine for importing data."""
partitions = json.load(open(args.split_file))
output_file_fmt = path.join(args.output_directory, FILE_FMT)
for set_name, subset in partitions.items():
for fold_idx, splits in subset.items():
for split, keys in splits.items():
output_file = output_file_fmt.format(subset=set_name,
fold_idx=fold_idx,
split=split)
futil.create_directory(path.split(output_file)[0])
if args.verbose:
print("[{0}] Creating: {1}"
"".format(time.asctime(), output_file))
stash = biggie.Stash(output_file)
populate_stash(keys, args.cqt_directory, stash, np.float32)
stash.close()
def main(args):
if not os.path.exists(args.posterior_file):
print "File does not exist: %s" % args.posterior_file
return
dset = biggie.Stash(args.posterior_file)
stats = json.load(open(args.validation_file))
penalty = float(stats['best_config']['penalty'])
estimations = dict()
for idx, key in enumerate(dset.keys()):
estimations[key] = estimate_classes(
dset.get(key), util.viterbi, penalty=penalty)
print "[%s] %12d / %12d: %s" % (time.asctime(), idx, len(dset), key)
futil.create_directory(os.path.split(args.estimation_file)[0])
with open(args.estimation_file, 'w') as fp:
json.dump(estimations, fp, indent=2)
futil.create_directory(os.path.split(args.estimation_file)[0])
with open(args.estimation_file, 'w') as fp:
json.dump(estimations, fp, indent=2)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt', shape=(None, 1, TIME_DIM, 252))
chord_idx = optimus.Input(name='chord_idx', shape=(None, ), dtype='int32')
learning_rate = optimus.Input(name='learning_rate', shape=None)
margin = optimus.Input(name='margin', shape=None)
margin_weight = optimus.Input(name='margin_weight', shape=None)
nll_weight = optimus.Input(name='nll_weight', shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(name='layer0',
input_shape=input_data.shape,
weight_shape=(12, 1, 9, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(16, None, 7, 15),
act_type='relu')
layer2 = optimus.Conv3D(name='layer2',
input_shape=layer1.output.shape,
weight_shape=(20, None, 6, 15),
act_type='relu')
layer3 = optimus.Affine(name='layer3',
input_shape=layer2.output.shape,
output_shape=(None, 512),
act_type='relu')
chord_classifier = optimus.Affine(name='chord_classifier',
input_shape=layer3.output.shape,
output_shape=(None, VOCAB),
act_type='sigmoid')
all_nodes = [layer0, layer1, layer2, layer3, chord_classifier]
# 1.1 Create Losses
nll = optimus.NegativeLogLikelihood(name="nll", weighted=True)
likelihood_margin = optimus.LikelihoodMargin(name="likelihood_margin",
mode='l1',
weighted=True)
# likelihood_margin = optimus.NLLMargin(
# name="likelihood_margin",
# mode='l2',
# weighted=True)
# 2. Define Edges
trainer_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, likelihood_margin.likelihood),
(chord_idx, likelihood_margin.target_idx),
(margin, likelihood_margin.margin),
(margin_weight, likelihood_margin.weight),
(chord_classifier.output, nll.likelihood), (chord_idx, nll.target_idx),
(nll_weight, nll.weight)
])
update_manager = optimus.ConnectionManager([
(learning_rate, layer0.weights), (learning_rate, layer0.bias),
(learning_rate, layer1.weights), (learning_rate, layer1.bias),
(learning_rate, layer2.weights), (learning_rate, layer2.bias),
(learning_rate, layer3.weights), (learning_rate, layer3.bias),
(learning_rate, chord_classifier.weights),
(learning_rate, chord_classifier.bias)
])
trainer = optimus.Graph(name=GRAPH_NAME,
inputs=[
input_data, chord_idx, margin, learning_rate,
margin_weight, nll_weight
],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[nll, likelihood_margin],
updates=update_manager.connections)
optimus.random_init(chord_classifier.weights)
optimus.random_init(chord_classifier.bias)
validator = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx, margin, margin_weight, nll_weight],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[nll, likelihood_margin])
posterior = optimus.Output(name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, posterior)
])
predictor = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
stash = biggie.Stash(args.training_file)
stream = S.minibatch(D.create_uniform_quality_stream(stash,
TIME_DIM,
vocab_dim=VOCAB),
batch_size=50)
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {
learning_rate.name: LEARNING_RATE,
margin_weight.name: MARGIN_WEIGHT,
nll_weight.name: NLL_WEIGHT,
margin.name: MARGIN
}
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt',
shape=(None, 1, TIME_DIM, PITCH_DIM))
chord_idx = optimus.Input(name='chord_idx', shape=(None, ), dtype='int32')
learning_rate = optimus.Input(name='learning_rate', shape=None)
# 1.2 Create Nodes
input_scalar = optimus.Normalize(name='input_scalar',
mode='l2',
scale_factor=50.0)
layer0 = optimus.Conv3D(name='layer0',
input_shape=input_data.shape,
weight_shape=(32, 1, 5, 19),
pool_shape=(2, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(64, None, 5, 15),
act_type='relu')
layer2 = optimus.Conv3D(name='layer2',
input_shape=layer1.output.shape,
weight_shape=(128, None, 3, 15),
act_type='relu')
layer3 = optimus.Affine(name='layer3',
input_shape=layer2.output.shape,
output_shape=(
None,
1024,
),
act_type='relu')
chord_classifier = optimus.Softmax(name='chord_classifier',
input_shape=layer3.output.shape,
n_out=VOCAB,
act_type='linear')
all_nodes = [
input_scalar, layer0, layer1, layer2, layer3, chord_classifier
]
# 1.1 Create Losses
chord_nll = optimus.NegativeLogLikelihood(name="chord_nll")
# 2. Define Edges
trainer_edges = optimus.ConnectionManager([
(input_data, input_scalar.input), (input_scalar.output, layer0.input),
(layer0.output, layer1.input), (layer1.output, layer2.input),
(layer2.output, layer3.input), (layer3.output, chord_classifier.input),
(chord_classifier.output, chord_nll.likelihood),
(chord_idx, chord_nll.target_idx)
])
update_manager = optimus.ConnectionManager([
(learning_rate, layer0.weights), (learning_rate, layer0.bias),
(learning_rate, layer1.weights), (learning_rate, layer1.bias),
(learning_rate, layer2.weights), (learning_rate, layer2.bias),
(learning_rate, layer3.weights), (learning_rate, layer3.bias),
(learning_rate, chord_classifier.weights),
(learning_rate, chord_classifier.bias)
])
trainer = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, chord_idx, learning_rate],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll],
updates=update_manager.connections)
for n in all_nodes[1:]:
optimus.random_init(n.weights)
optimus.random_init(n.bias)
if args.init_param_file:
param_values = dict(np.load(args.init_param_file))
keys = param_values.keys()
for key in keys:
if chord_classifier.name in key or layer3.name in key:
print "skipping %s" % key
del param_values[key]
trainer.param_values = param_values
posterior = optimus.Output(name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, input_scalar.input), (input_scalar.output, layer0.input),
(layer0.output, layer1.input), (layer1.output, layer2.input),
(layer2.output, layer3.input), (layer3.output, chord_classifier.input),
(chord_classifier.output, posterior)
])
predictor = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
print "Loading %s" % args.training_file
stash = biggie.Stash(args.training_file)
stream = S.minibatch(D.create_uniform_chord_stream(stash,
TIME_DIM,
pitch_shift=0,
vocab_dim=VOCAB,
working_size=10),
batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE}
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt', shape=(None, 6, TIME_DIM, 40))
target = optimus.Input(name='target', shape=(None, VOCAB))
learning_rate = optimus.Input(name='learning_rate', shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(name='layer0',
input_shape=input_data.shape,
weight_shape=(32, None, 5, 5),
pool_shape=(2, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(64, None, 5, 7),
act_type='relu')
layer2 = optimus.Conv3D(name='layer2',
input_shape=layer1.output.shape,
weight_shape=(128, None, 3, 6),
act_type='relu')
layer3 = optimus.Affine(name='layer3',
input_shape=layer2.output.shape,
output_shape=(
None,
1024,
),
act_type='relu')
chord_estimator = optimus.Affine(name='chord_estimator',
input_shape=layer3.output.shape,
output_shape=(
None,
VOCAB,
),
act_type='sigmoid')
all_nodes = [layer0, layer1, layer2, layer3, chord_estimator]
# 1.1 Create Losses
chord_xentropy = optimus.CrossEntropy(name="chord_xentropy")
# 2. Define Edges
trainer_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_estimator.input),
(chord_estimator.output, chord_xentropy.prediction),
(target, chord_xentropy.target)
])
update_manager = optimus.ConnectionManager([
(learning_rate, layer0.weights), (learning_rate, layer0.bias),
(learning_rate, layer1.weights), (learning_rate, layer1.bias),
(learning_rate, layer2.weights), (learning_rate, layer2.bias),
(learning_rate, layer3.weights), (learning_rate, layer3.bias),
(learning_rate, chord_estimator.weights),
(learning_rate, chord_estimator.bias)
])
trainer = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, target, learning_rate],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_xentropy],
updates=update_manager.connections,
momentum=None)
for n in all_nodes:
optimus.random_init(n.weights, 0, 0.01)
optimus.random_init(n.bias, 0, 0.01)
validator = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, target],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_xentropy])
posterior = optimus.Output(name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_estimator.input),
(chord_estimator.output, posterior)
])
predictor = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
print "Loading %s" % args.training_file
stash = biggie.Stash(args.training_file)
# partition_labels = json.load(
# open("/home/ejhumphrey/Dropbox/tmp/train0_v2_merged_partition.json"))
stream = D.create_uniform_chord_stream(stash,
TIME_DIM,
pitch_shift=False,
vocab_dim=VOCAB,
working_size=5)
stream = S.minibatch(FX.chord_index_to_affinity_vectors(
FX.wrap_cqt(stream, length=40, stride=36), VOCAB),
batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE}
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt',
shape=(None, 1, TIME_DIM, PITCH_DIM))
chord_idx = optimus.Input(name='chord_idx', shape=(None, ), dtype='int32')
learning_rate = optimus.Input(name='learning_rate', shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(name='layer0',
input_shape=input_data.shape,
weight_shape=(32, 1, 5, 19),
pool_shape=(2, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(64, None, 5, 15),
act_type='relu')
layer2 = optimus.Conv3D(name='layer2',
input_shape=layer1.output.shape,
weight_shape=(128, None, 3, 15),
act_type='relu')
layer3 = optimus.Affine(name='layer3',
input_shape=layer2.output.shape,
output_shape=(
None,
1024,
),
act_type='relu')
chord_classifier = optimus.Softmax(name='chord_classifier',
input_shape=layer3.output.shape,
n_out=VOCAB,
act_type='linear')
all_nodes = [layer0, layer1, layer2, layer3, chord_classifier]
# 1.1 Create Losses
chord_mce = optimus.ClassificationError(name="chord_mce")
# 2. Define Edges
trainer_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, chord_mce.prediction),
(chord_idx, chord_mce.target_idx)
])
update_manager = optimus.ConnectionManager([
(learning_rate, layer0.weights), (learning_rate, layer0.bias),
(learning_rate, layer1.weights), (learning_rate, layer1.bias),
(learning_rate, layer2.weights), (learning_rate, layer2.bias),
(learning_rate, layer3.weights), (learning_rate, layer3.bias),
(learning_rate, chord_classifier.weights),
(learning_rate, chord_classifier.bias)
])
trainer = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, chord_idx, learning_rate],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_mce],
updates=update_manager.connections)
for n in all_nodes:
optimus.random_init(n.weights)
optimus.random_init(n.bias)
if args.init_param_file:
trainer.load_param_values(args.init_param_file)
posterior = optimus.Output(name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, posterior)
])
predictor = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
print "Loading %s" % args.training_file
stash = biggie.Stash(args.training_file)
synth_stash = biggie.Stash(args.secondary_source)
stream = D.muxed_uniform_chord_stream(stash,
synth_stash,
TIME_DIM,
pitch_shift=0,
vocab_dim=VOCAB,
working_size=10)
# stream = D.create_uniform_chord_stream(
# stash, TIME_DIM, pitch_shift=0, vocab_dim=VOCAB, working_size=10)
# if args.secondary_source:
# print "Loading %s" % args.secondary_source
# stash2 = biggie.Stash(args.secondary_source)
# stream2 = D.create_uniform_chord_stream(
# stash2, TIME_DIM, pitch_shift=0, vocab_dim=VOCAB, working_size=5)
# stream = S.mux([stream, stream2], [0.5, 0.5])
stream = S.minibatch(stream, batch_size=BATCH_SIZE)
print "Starting '%s'" % args.trial_name
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE}
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(
name='cqt',
shape=(None, 1, TIME_DIM, 252))
chord_idx = optimus.Input(
name='chord_idx',
shape=(None,),
dtype='int32')
learning_rate = optimus.Input(
name='learning_rate',
shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(
name='layer0',
input_shape=input_data.shape,
weight_shape=(12, 1, 9, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(
name='layer1',
input_shape=layer0.output.shape,
weight_shape=(16, None, 7, 15),
act_type='relu')
layer2 = optimus.Conv3D(
name='layer2',
input_shape=layer1.output.shape,
weight_shape=(20, None, 6, 15),
act_type='relu')
layer3 = optimus.Affine(
name='layer3',
input_shape=layer2.output.shape,
output_shape=(None, 512,),
act_type='relu')
chord_classifier = optimus.Softmax(
name='chord_classifier',
input_shape=layer3.output.shape,
n_out=VOCAB,
act_type='linear')
all_nodes = [layer0, layer1, layer2, layer3, chord_classifier]
# 1.1 Create Losses
chord_nll = optimus.NegativeLogLikelihood(
name="chord_nll")
# 2. Define Edges
trainer_edges = optimus.ConnectionManager([
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, chord_nll.likelihood),
(chord_idx, chord_nll.target_idx)])
update_manager = optimus.ConnectionManager([
(learning_rate, layer0.weights),
(learning_rate, layer0.bias),
(learning_rate, layer1.weights),
(learning_rate, layer1.bias),
(learning_rate, layer2.weights),
(learning_rate, layer2.bias),
(learning_rate, layer3.weights),
(learning_rate, layer3.bias),
(learning_rate, chord_classifier.weights),
(learning_rate, chord_classifier.bias)])
print "Building trainer"
trainer = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx, learning_rate],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll],
updates=update_manager.connections)
optimus.random_init(chord_classifier.weights)
print "Building validator"
validator = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_nll])
posterior = optimus.Output(
name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, layer0.input),
(layer0.output, layer1.input),
(layer1.output, layer2.input),
(layer2.output, layer3.input),
(layer3.output, chord_classifier.input),
(chord_classifier.output, posterior)])
print "Building predictor"
predictor = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
print "Opening Data"
stash = biggie.Stash(args.training_file)
stream = S.minibatch(
D.create_uniform_quality_stream(stash, TIME_DIM),
batch_size=50,
functions=[FX.pitch_shift(), FX.map_to_chord_index(VOCAB)])
driver = optimus.Driver(
graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE}
print "...aaand we're off!"
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)
def main(args):
# 1.1 Create Inputs
input_data = optimus.Input(name='cqt', shape=(None, 1, TIME_DIM, 252))
chord_idx = optimus.Input(name='chord_idx', shape=(None, ), dtype='int32')
is_chord = optimus.Input(name='is_chord', shape=(None, ))
learning_rate = optimus.Input(name='learning_rate', shape=None)
# 1.2 Create Nodes
layer0 = optimus.Conv3D(name='layer0',
input_shape=input_data.shape,
weight_shape=(12, 1, 9, 19),
pool_shape=(1, 3),
act_type='relu')
layer1 = optimus.Conv3D(name='layer1',
input_shape=layer0.output.shape,
weight_shape=(16, None, 7, 15),
act_type='relu')
layer2 = optimus.Conv3D(name='layer2',
input_shape=layer1.output.shape,
weight_shape=(20, None, 6, 15),
act_type='relu')
layer3 = optimus.Affine(name='layer3',
input_shape=layer2.output.shape,
output_shape=(
None,
512,
),
act_type='relu')
chord_estimator = optimus.Affine(name='chord_estimator',
input_shape=layer3.output.shape,
output_shape=(None, VOCAB),
act_type='sigmoid')
all_nodes = [layer0, layer1, layer2, layer3, chord_estimator]
# 1.1 Create Losses
chord_mse = optimus.SparseMeanSquaredError(
# chord_mse = optimus.SparseCrossEntropy(
name="chord_mse")
# 2. Define Edges
trainer_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_estimator.input),
(chord_estimator.output, chord_mse.prediction),
(chord_idx, chord_mse.index), (is_chord, chord_mse.target)
])
update_manager = optimus.ConnectionManager([
(learning_rate, layer0.weights), (learning_rate, layer0.bias),
(learning_rate, layer1.weights), (learning_rate, layer1.bias),
(learning_rate, layer2.weights), (learning_rate, layer2.bias),
(learning_rate, layer3.weights), (learning_rate, layer3.bias),
(learning_rate, chord_estimator.weights),
(learning_rate, chord_estimator.bias)
])
print "Building trainer"
trainer = optimus.Graph(
name=GRAPH_NAME,
inputs=[input_data, chord_idx, is_chord, learning_rate],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_mse],
updates=update_manager.connections)
optimus.random_init(chord_estimator.weights)
print "Building validator"
validator = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data, chord_idx, is_chord],
nodes=all_nodes,
connections=trainer_edges.connections,
outputs=[optimus.Graph.TOTAL_LOSS],
losses=[chord_mse])
posterior = optimus.Output(name='posterior')
predictor_edges = optimus.ConnectionManager([
(input_data, layer0.input), (layer0.output, layer1.input),
(layer1.output, layer2.input), (layer2.output, layer3.input),
(layer3.output, chord_estimator.input),
(chord_estimator.output, posterior)
])
print "Building predictor"
predictor = optimus.Graph(name=GRAPH_NAME,
inputs=[input_data],
nodes=all_nodes,
connections=predictor_edges.connections,
outputs=[posterior])
# 3. Create Data
print "Opening Data"
stash = biggie.Stash(args.training_file)
stream = S.minibatch(D.create_contrastive_quality_stream(stash,
TIME_DIM,
vocab_dim=VOCAB),
batch_size=50)
driver = optimus.Driver(graph=trainer,
name=args.trial_name,
output_directory=args.model_directory)
hyperparams = {learning_rate.name: LEARNING_RATE}
print "...aaand we're off!"
driver.fit(stream, hyperparams=hyperparams, **DRIVER_ARGS)
validator_file = path.join(driver.output_directory, args.validator_file)
optimus.save(validator, def_file=validator_file)
predictor_file = path.join(driver.output_directory, args.predictor_file)
optimus.save(predictor, def_file=predictor_file)