def get_featuremaps(self, sequences=None):
"""Turn sequences into features for training/evaluation.
Encodes sequences into randomly cropped and masked pianorolls, and returns
a padded Batch containing three channels: the pianorolls, the corresponding
masks and their lengths before padding (but after cropping).
Args:
sequences: the collection of sequences to convert. If not given, the
entire dataset is converted.
Returns:
A Batch containing pianorolls, masks and piece lengths.
"""
if sequences is None:
sequences = self.get_sequences()
pianorolls = []
masks = []
for sequence in sequences:
pianoroll = self.encoder.encode(sequence)
pianoroll = lib_util.random_crop(pianoroll, self.hparams.crop_piece_len)
mask = lib_mask.get_mask(
self.hparams.maskout_method,
pianoroll.shape,
separate_instruments=self.hparams.separate_instruments,
blankout_ratio=self.hparams.corrupt_ratio)
pianorolls.append(pianoroll)
masks.append(mask)
(pianorolls, masks), lengths = lib_util.pad_and_stack(pianorolls, masks)
assert pianorolls.ndim == 4 and masks.ndim == 4
assert pianorolls.shape == masks.shape
return Batch(pianorolls=pianorolls, masks=masks, lengths=lengths)
def get_featuremaps(self, sequences=None):
"""Turn sequences into features for training/evaluation.
"""
#TODO
if sequences is None:
sequences = self.get_sequences()
pianorolls = []
masks = []
for pianoroll in sequences:
pianoroll = lib_util.random_crop(pianoroll,
self.hparams.crop_piece_len)
mask = lib_mask.get_mask(
self.hparams.maskout_method,
pianoroll.shape,
separate_instruments=self.hparams.separate_instruments,
blankout_ratio=self.hparams.corrupt_ratio)
pianorolls.append(pianoroll)
masks.append(mask)
(pianorolls,
masks), lengths = lib_util.pad_and_stack(pianorolls, masks)
assert pianorolls.ndim == 4 and masks.ndim == 4
assert pianorolls.shape == masks.shape
return Batch(pianorolls=pianorolls, masks=masks, lengths=lengths)
def get_featuremaps(self, sequences=None):
"""Turn sequences into features for training/evaluation.
Encodes sequences into randomly cropped and masked pianorolls, and returns
a padded Batch containing three channels: the pianorolls, the corresponding
masks and their lengths before padding (but after cropping).
Args:
sequences: the collection of sequences to convert. If not given, the
entire dataset is converted.
Returns:
A Batch containing pianorolls, masks and piece lengths.
"""
if sequences is None:
sequences = self.get_sequences()
pianorolls = []
masks = []
for sequence in sequences:
pianoroll = self.encoder.encode(sequence)
pianoroll = lib_util.random_crop(pianoroll, self.hparams.crop_piece_len)
mask = lib_mask.get_mask(
self.hparams.maskout_method,
pianoroll.shape,
separate_instruments=self.hparams.separate_instruments,
blankout_ratio=self.hparams.corrupt_ratio)
pianorolls.append(pianoroll)
masks.append(mask)
(pianorolls, masks), lengths = lib_util.pad_and_stack(pianorolls, masks)
assert pianorolls.ndim == 4 and masks.ndim == 4
assert pianorolls.shape == masks.shape
return Batch(pianorolls=pianorolls, masks=masks, lengths=lengths)