code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def call(
self,
input_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (:obj:`tf.Tensor... |
Applies embedding based on inputs tensor.
Returns:
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
| call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` ... | call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
config.vocab_size - 1]``.
| call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-S... | call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
... | call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
1]``.
| call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
start_positions (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Pos... | call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoFormer sequence h... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoFormer sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
... | build_inputs_with_special_tokens | python | JunnYu/RoFormer_pytorch | src/roformer/tokenization_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/tokenization_roformer.py | Apache-2.0 |
def get_special_tokens_mask(
self,
token_ids_0: List[int],
token_ids_1: Optional[List[int]] = None,
already_has_special_tokens: bool = False,
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when addin... |
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` method.
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, ... | get_special_tokens_mask | python | JunnYu/RoFormer_pytorch | src/roformer/tokenization_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/tokenization_roformer.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
sequence pair mask has the following format:
... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If :obj:`token_ids_1` is :obj... | create_token_type_ids_from_sequences | python | JunnYu/RoFormer_pytorch | src/roformer/tokenization_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/tokenization_roformer.py | Apache-2.0 |
def parse_arguments():
"""Parse and return the command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument('--result_dir',
help="Directory where the results are saved.")
parser.add_argument('--checkpoint_dir',
help="Directory that con... | Parse and return the command line arguments. | parse_arguments | python | salu133445/musegan | src/inference.py | https://github.com/salu133445/musegan/blob/master/src/inference.py | MIT |
def setup():
"""Parse command line arguments, load model parameters, load configurations
and setup environment."""
# Parse the command line arguments
args = parse_arguments()
# Load parameters
params = load_yaml(args.params)
# Load training configurations
config = load_yaml(args.config... | Parse command line arguments, load model parameters, load configurations
and setup environment. | setup | python | salu133445/musegan | src/inference.py | https://github.com/salu133445/musegan/blob/master/src/inference.py | MIT |
def slerp(a, b, t):
"""Return the spherical linear interpolation of point `a` and `b` at
specific ratio `t`."""
omega = np.arccos(np.dot(a / np.linalg.norm(a), b / np.linalg.norm(b)))
so = np.sin(omega)
return np.sin((1 - t) * omega) / so * a + np.sin(t * omega) / so * b | Return the spherical linear interpolation of point `a` and `b` at
specific ratio `t`. | slerp | python | salu133445/musegan | src/interpolation.py | https://github.com/salu133445/musegan/blob/master/src/interpolation.py | MIT |
def lerp(a, b1, b2, t1, t2):
"""Return the 2D linear interpolation of point `a`, `b1` and `b2` at
specific ratio `t1` and `t2`."""
inter1 = a * (1 - t1) + b1 * t1
inter2 = b2 * (1 - t1) + (b2 + (b1 - a)) * t1
return inter1 * (1 - t2) + t2 * inter2 | Return the 2D linear interpolation of point `a`, `b1` and `b2` at
specific ratio `t1` and `t2`. | lerp | python | salu133445/musegan | src/interpolation.py | https://github.com/salu133445/musegan/blob/master/src/interpolation.py | MIT |
def get_input_z(config, params):
"""Return input latent code to the generator."""
if ((config['mode'] == 'slerp') and (config['rows'] > 1)
and (config['columns'] > 1)):
raise ValueError("Only supports 1D interpolation in 'slerp' mode.")
a = scipy.stats.truncnorm.rvs(
config['low... | Return input latent code to the generator. | get_input_z | python | salu133445/musegan | src/interpolation.py | https://github.com/salu133445/musegan/blob/master/src/interpolation.py | MIT |
def create_shared_array(name, shape, dtype):
"""Create shared array. Prompt if a file with the same name existed."""
try:
return sa.create(name, shape, dtype)
except FileExistsError:
response = ""
while response.lower() not in ["y", "n", "yes", "no"]:
response = input(
... | Create shared array. Prompt if a file with the same name existed. | create_shared_array | python | salu133445/musegan | src/process_data.py | https://github.com/salu133445/musegan/blob/master/src/process_data.py | MIT |
def main():
"""Load and save an array to shared memory."""
filepath, name, prefix, dtype = parse_arguments()
if name is None:
name = os.path.splitext(os.path.basename(filepath))[0]
if prefix is not None:
name = prefix + "_" + name
print("Loading data from '{}'.".format(file... | Load and save an array to shared memory. | main | python | salu133445/musegan | src/process_data.py | https://github.com/salu133445/musegan/blob/master/src/process_data.py | MIT |
def setup_dirs(config):
"""Setup an experiment directory structure and update the `params`
dictionary with the directory paths."""
# Get experiment directory structure
config['exp_dir'] = os.path.realpath(config['exp_dir'])
config['src_dir'] = os.path.join(config['exp_dir'], 'src')
config['eval_... | Setup an experiment directory structure and update the `params`
dictionary with the directory paths. | setup_dirs | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def setup():
"""Parse command line arguments, load model parameters, load configurations,
setup environment and setup loggers."""
# Parse the command line arguments
args = parse_arguments()
# Load parameters
params = load_yaml(args.params)
if params.get('is_accompaniment') and params.get('c... | Parse command line arguments, load model parameters, load configurations,
setup environment and setup loggers. | setup | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def load_training_data(params, config):
"""Load and return the training data."""
# Load data
if params['is_conditional']:
raise ValueError("Not supported yet.")
else:
labels = None
LOGGER.info("Loading training data.")
data = load_data(config['data_source'], config['data_filename... | Load and return the training data. | load_training_data | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def load_or_create_samples(params, config):
"""Load or create the samples used as the sampler inputs."""
# Load sample_z
LOGGER.info("Loading sample_z.")
sample_z_path = os.path.join(config['model_dir'], 'sample_z.npy')
if os.path.exists(sample_z_path):
sample_z = np.load(sample_z_path)
... | Load or create the samples used as the sampler inputs. | load_or_create_samples | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def get_n_params(var_list):
"""Return the number of variables in a variable list."""
return int(np.sum([np.product(
[x.value for x in var.get_shape()]) for var in var_list])) | Return the number of variables in a variable list. | get_n_params | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def load_data_from_npz(filename):
"""Load and return the training data from a npz file (sparse format)."""
with np.load(filename) as f:
data = np.zeros(f['shape'], np.bool_)
data[[x for x in f['nonzero']]] = True
return data | Load and return the training data from a npz file (sparse format). | load_data_from_npz | python | salu133445/musegan | src/musegan/data.py | https://github.com/salu133445/musegan/blob/master/src/musegan/data.py | MIT |
def get_samples(n_samples, data, labels=None, use_random_transpose=False):
"""Return some random samples of the training data."""
indices = np.random.choice(len(data), n_samples, False)
if np.issubdtype(data.dtype, np.bool_):
sample_data = data[indices] * 2. - 1.
else:
sample_data = data... | Return some random samples of the training data. | get_samples | python | salu133445/musegan | src/musegan/data.py | https://github.com/salu133445/musegan/blob/master/src/musegan/data.py | MIT |
def get_dataset(data, labels=None, batch_size=None, data_shape=None,
use_random_transpose=False, num_threads=1):
"""Create and return a tensorflow dataset from an array."""
if labels is None:
dataset = tf.data.Dataset.from_generator(
lambda: _gen_data(data), tf.float32)
... | Create and return a tensorflow dataset from an array. | get_dataset | python | salu133445/musegan | src/musegan/data.py | https://github.com/salu133445/musegan/blob/master/src/musegan/data.py | MIT |
def vector_to_image(array, inverted=True):
"""
Convert a batched vector array to an image array.
Arguments
---------
array : `np.array`, ndim=2
The vector array.
Returns
-------
image : `np.array`, ndim=4
The image array.
"""
if array.ndim != 2:
raise Va... |
Convert a batched vector array to an image array.
Arguments
---------
array : `np.array`, ndim=2
The vector array.
Returns
-------
image : `np.array`, ndim=4
The image array.
| vector_to_image | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
def pianoroll_to_image(pianoroll, colormap=None, inverted=True,
boundary_width=1, boundary_color=0, frame=False,
gamma=1.):
"""
Convert a batched pianoroll array to an image array.
Arguments
---------
pianoroll : `np.array`, ndim=5
The pianoroll... |
Convert a batched pianoroll array to an image array.
Arguments
---------
pianoroll : `np.array`, ndim=5
The pianoroll array. The shape is (n_pianorolls, n_bars, n_timestep,
n_pitches, n_tracks).
boundary_width : int
Linewidth of the boundary lines. Default to 0.
boundar... | pianoroll_to_image | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
def image_pair(image1, image2, mode='side-by-side', boundary_width=1,
boundary_color=0, frame=False):
"""
Pair two image arrays to one single image array.
Arguments
---------
image1 : `np.array`, ndim=4
The image array at the left in 'side-by-side' mode or at the top in
... |
Pair two image arrays to one single image array.
Arguments
---------
image1 : `np.array`, ndim=4
The image array at the left in 'side-by-side' mode or at the top in
'top-bottom' mode.
image2 : `np.array`, ndim=4
The image array at the right in 'side-by-side' mode or at the ... | image_pair | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
def image_grid(image, grid_shape, grid_width=3, grid_color=0, frame=True):
"""
Convert a batched image array to one merged grid image array.
Arguments
---------
pianoroll : `np.array`, ndim=4
The pianoroll array. The first axis is the batch axis. The second and
third axes are the ti... |
Convert a batched image array to one merged grid image array.
Arguments
---------
pianoroll : `np.array`, ndim=4
The pianoroll array. The first axis is the batch axis. The second and
third axes are the time and pitch axes, respectively, of the pianorolls.
The last axis is the t... | image_grid | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
def save_pianoroll(filename, pianoroll, programs, is_drums, tempo,
beat_resolution, lowest_pitch):
"""Saves a batched pianoroll array to a npz file."""
if not np.issubdtype(pianoroll.dtype, np.bool_):
raise TypeError("Input pianoroll array must have a boolean dtype.")
if pianoroll... | Saves a batched pianoroll array to a npz file. | save_pianoroll | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
def get_adv_losses(discriminator_real_outputs, discriminator_fake_outputs,
kind):
"""Return the corresponding GAN losses for the generator and the
discriminator."""
if kind == 'classic':
loss_fn = classic_gan_losses
elif kind == 'nonsaturating':
loss_fn = nonsaturating... | Return the corresponding GAN losses for the generator and the
discriminator. | get_adv_losses | python | salu133445/musegan | src/musegan/losses.py | https://github.com/salu133445/musegan/blob/master/src/musegan/losses.py | MIT |
def classic_gan_losses(discriminator_real_outputs, discriminator_fake_outputs):
"""Return the classic GAN losses for the generator and the discriminator.
(Generator) log(1 - sigmoid(D(G(z))))
(Discriminator) - log(sigmoid(D(x))) - log(1 - sigmoid(D(G(z))))
"""
discriminator_loss_real = tf.los... | Return the classic GAN losses for the generator and the discriminator.
(Generator) log(1 - sigmoid(D(G(z))))
(Discriminator) - log(sigmoid(D(x))) - log(1 - sigmoid(D(G(z))))
| classic_gan_losses | python | salu133445/musegan | src/musegan/losses.py | https://github.com/salu133445/musegan/blob/master/src/musegan/losses.py | MIT |
def nonsaturating_gan_losses(discriminator_real_outputs,
discriminator_fake_outputs):
"""Return the non-saturating GAN losses for the generator and the
discriminator.
(Generator) -log(sigmoid(D(G(z))))
(Discriminator) -log(sigmoid(D(x))) - log(1 - sigmoid(D(G(z))))
... | Return the non-saturating GAN losses for the generator and the
discriminator.
(Generator) -log(sigmoid(D(G(z))))
(Discriminator) -log(sigmoid(D(x))) - log(1 - sigmoid(D(G(z))))
| nonsaturating_gan_losses | python | salu133445/musegan | src/musegan/losses.py | https://github.com/salu133445/musegan/blob/master/src/musegan/losses.py | MIT |
def wasserstein_gan_losses(discriminator_real_outputs,
discriminator_fake_outputs):
"""Return the Wasserstein GAN losses for the generator and the
discriminator.
(Generator) -D(G(z))
(Discriminator) D(G(z)) - D(x)
"""
generator_loss = -tf.reduce_mean(discriminat... | Return the Wasserstein GAN losses for the generator and the
discriminator.
(Generator) -D(G(z))
(Discriminator) D(G(z)) - D(x)
| wasserstein_gan_losses | python | salu133445/musegan | src/musegan/losses.py | https://github.com/salu133445/musegan/blob/master/src/musegan/losses.py | MIT |
def hinge_gan_losses(discriminator_real_outputs, discriminator_fake_outputs):
"""Return the Hinge GAN losses for the generator and the discriminator.
(Generator) -D(G(z))
(Discriminator) max(0, 1 - D(x)) + max(0, 1 + D(G(z)))
"""
generator_loss = -tf.reduce_mean(discriminator_fake_outputs)
... | Return the Hinge GAN losses for the generator and the discriminator.
(Generator) -D(G(z))
(Discriminator) max(0, 1 - D(x)) + max(0, 1 + D(G(z)))
| hinge_gan_losses | python | salu133445/musegan | src/musegan/losses.py | https://github.com/salu133445/musegan/blob/master/src/musegan/losses.py | MIT |
def to_chroma(pianoroll):
"""Return the chroma features (not normalized)."""
if pianoroll.get_shape().ndims != 5:
raise ValueError("Input tensor must have 5 dimensions.")
remainder = pianoroll.get_shape()[3] % 12
if remainder:
pianoroll = tf.pad(
pianoroll, ((0, 0), (0, 0), (... | Return the chroma features (not normalized). | to_chroma | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def empty_bar_rate(tensor):
"""Return the ratio of empty bars to the total number of bars."""
if tensor.get_shape().ndims != 5:
raise ValueError("Input tensor must have 5 dimensions.")
return tf.reduce_mean(
tf.cast(tf.reduce_any(tensor > 0.5, (2, 3)), tf.float32), (0, 1)) | Return the ratio of empty bars to the total number of bars. | empty_bar_rate | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def n_pitches_used(tensor):
"""Return the number of unique pitches used per bar."""
if tensor.get_shape().ndims != 5:
raise ValueError("Input tensor must have 5 dimensions.")
return tf.reduce_mean(tf.reduce_sum(tf.count_nonzero(tensor, 3), 2), [0, 1]) | Return the number of unique pitches used per bar. | n_pitches_used | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def qualified_note_rate(tensor, threshold=2):
"""Return the ratio of the number of the qualified notes (notes longer than
`threshold` (in time step)) to the total number of notes in a piano-roll."""
if tensor.get_shape().ndims != 5:
raise ValueError("Input tensor must have 5 dimensions.")
def _q... | Return the ratio of the number of the qualified notes (notes longer than
`threshold` (in time step)) to the total number of notes in a piano-roll. | qualified_note_rate | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def _qualified_note_rate(array, threshold):
"""Return the ratio of the number of the qualified notes (notes longer
than `threshold` (in time step)) to the total number of notes in a
piano-roll."""
n_tracks = array.shape[-1]
reshaped = array.reshape(-1, array.shape[1] * array.shap... | Return the ratio of the number of the qualified notes (notes longer
than `threshold` (in time step)) to the total number of notes in a
piano-roll. | _qualified_note_rate | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def polyphonic_rate(tensor, threshold=2):
"""Return the ratio of the number of time steps where the number of pitches
being played is larger than `threshold` to the total number of time steps"""
if tensor.get_shape().ndims != 5:
raise ValueError("Input tensor must have 5 dimensions.")
n_poly = t... | Return the ratio of the number of time steps where the number of pitches
being played is larger than `threshold` to the total number of time steps | polyphonic_rate | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def _drum_pattern_mask(n_timesteps, tolerance=0.1):
"""Return a drum pattern mask with the given tolerance."""
if n_timesteps not in (96, 48, 24, 72, 36, 64, 32, 16):
raise ValueError("Unsupported number of timesteps for the drum in "
"pattern metric.")
i... | Return a drum pattern mask with the given tolerance. | _drum_pattern_mask | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def _scale_mask(key=3):
"""Return a scale mask for the given key. Default to C major scale."""
a_scale_mask = np.array([[[1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0]]], bool)
return np.expand_dims(np.roll(a_scale_mask, -key, 2), -1) | Return a scale mask for the given key. Default to C major scale. | _scale_mask | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def _tonal_matrix(r1=1.0, r2=1.0, r3=0.5):
"""Compute and return a tonal matrix for computing the tonal distance
[1]. Default argument values are set as suggested by the paper.
[1] Christopher Harte, Mark Sandler, and Martin Gasser. Detecting
harmonic change in musical audio. In Proc. A... | Compute and return a tonal matrix for computing the tonal distance
[1]. Default argument values are set as suggested by the paper.
[1] Christopher Harte, Mark Sandler, and Martin Gasser. Detecting
harmonic change in musical audio. In Proc. ACM MM Workshop on Audio and
Music Computing Mu... | _tonal_matrix | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def _to_tonal_space(tensor):
"""Return the tensor in tonal space where chroma features are normalized
per beat."""
tonal_matrix = tf.constant(_tonal_matrix(), tf.float32)
beat_chroma = tf.reduce_sum(tf.reshape(
tensor, (-1, beat_resolution, 12, tensor.get_shape()[4])), 1)
... | Return the tensor in tonal space where chroma features are normalized
per beat. | _to_tonal_space | python | salu133445/musegan | src/musegan/metrics.py | https://github.com/salu133445/musegan/blob/master/src/musegan/metrics.py | MIT |
def get_train_nodes(self, x, z=None, y=None, c=None, params=None,
config=None):
"""Return a dictionary of graph nodes for training."""
LOGGER.info("Building training nodes.")
with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE) as scope:
nodes = {}
... | Return a dictionary of graph nodes for training. | get_train_nodes | python | salu133445/musegan | src/musegan/model.py | https://github.com/salu133445/musegan/blob/master/src/musegan/model.py | MIT |
def _array_to_image(array, colormap=None):
"""Convert an array to an image array and return it."""
if array.ndim == 2:
return vector_to_image(array)
return pianoroll_to_image(array, colormap) | Convert an array to an image array and return it. | _array_to_image | python | salu133445/musegan | src/musegan/model.py | https://github.com/salu133445/musegan/blob/master/src/musegan/model.py | MIT |
def make_sure_path_exists(path):
"""Create intermidate directories if the path does not exist."""
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise | Create intermidate directories if the path does not exist. | make_sure_path_exists | python | salu133445/musegan | src/musegan/utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/utils.py | MIT |
def update_not_none(dict1, dict2):
"""Update the values of keys in `dict1` with the values of the same key from
`dict2` if the values in `dict2` is not None."""
for key, value in dict2.items():
if value is not None:
dict1[key] = value | Update the values of keys in `dict1` with the values of the same key from
`dict2` if the values in `dict2` is not None. | update_not_none | python | salu133445/musegan | src/musegan/utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/utils.py | MIT |
def update_existing(dict1, dict2):
"""Update the values of keys in `dict1` with the values of the same key from
`dict2` if the values in `dict2` is not None and the same key is in `dict1`.
"""
for key, value in dict2.items():
if value is not None and key in dict1:
dict1[key] = value | Update the values of keys in `dict1` with the values of the same key from
`dict2` if the values in `dict2` is not None and the same key is in `dict1`.
| update_existing | python | salu133445/musegan | src/musegan/utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/utils.py | MIT |
def load_component(component, name, class_name):
"""Load and return component network from file."""
imported = importlib.import_module(
'.'.join(('musegan.presets', component, name)))
return getattr(imported, class_name) | Load and return component network from file. | load_component | python | salu133445/musegan | src/musegan/utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/utils.py | MIT |
def add_file_handler(logger, log_filepath, loglevel=FILE_LOGLEVEL,
log_format=FILE_LOG_FORMAT):
"""Add a file handler to the logger."""
file_handler = logging.FileHandler(log_filepath)
file_handler.setLevel(loglevel)
file_handler.setFormatter(logging.Formatter(log_format))
logge... | Add a file handler to the logger. | add_file_handler | python | salu133445/musegan | src/musegan/utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/utils.py | MIT |
def setup_loggers(log_dir, loglevel=FILE_LOGLEVEL, log_format=FILE_LOG_FORMAT):
"""Setup the loggers with file handlers."""
for name in logging.Logger.manager.loggerDict.keys():
if name.startswith('musegan'):
add_file_handler(
logging.getLogger(name), os.path.join(log_dir, na... | Setup the loggers with file handlers. | setup_loggers | python | salu133445/musegan | src/musegan/utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/utils.py | MIT |
def binary_round(x):
"""
Rounds a tensor whose values are in [0,1] to a tensor with values in
{0, 1}, using the straight through estimator for the gradient.
"""
g = tf.get_default_graph()
with ops.name_scope("BinaryRound") as name:
with g.gradient_override_map({"Round": "Identity"}):
... |
Rounds a tensor whose values are in [0,1] to a tensor with values in
{0, 1}, using the straight through estimator for the gradient.
| binary_round | python | salu133445/musegan | src/musegan/presets/binary_ops.py | https://github.com/salu133445/musegan/blob/master/src/musegan/presets/binary_ops.py | MIT |
def bernoulli_sample(x):
"""
Uses a tensor whose values are in [0,1] to sample a tensor with values
in {0, 1}, using the straight through estimator for the gradient.
E.g., if x is 0.6, bernoulliSample(x) will be 1 with probability 0.6,
and 0 otherwise, and the gradient will be pass-through (identit... |
Uses a tensor whose values are in [0,1] to sample a tensor with values
in {0, 1}, using the straight through estimator for the gradient.
E.g., if x is 0.6, bernoulliSample(x) will be 1 with probability 0.6,
and 0 otherwise, and the gradient will be pass-through (identity).
| bernoulli_sample | python | salu133445/musegan | src/musegan/presets/binary_ops.py | https://github.com/salu133445/musegan/blob/master/src/musegan/presets/binary_ops.py | MIT |
def pass_through_sigmoid(x, slope=1):
"""Sigmoid that uses identity function as its gradient"""
g = tf.get_default_graph()
with ops.name_scope("PassThroughSigmoid") as name:
with g.gradient_override_map({"Sigmoid": "Identity"}):
return tf.sigmoid(x, name=name) | Sigmoid that uses identity function as its gradient | pass_through_sigmoid | python | salu133445/musegan | src/musegan/presets/binary_ops.py | https://github.com/salu133445/musegan/blob/master/src/musegan/presets/binary_ops.py | MIT |
def binary_stochastic_ST(x, slope_tensor=None, pass_through=True,
stochastic=True):
"""
Sigmoid followed by either a random sample from a bernoulli distribution
according to the result (binary stochastic neuron) (default), or a
sigmoid followed by a binary step function (if stoch... |
Sigmoid followed by either a random sample from a bernoulli distribution
according to the result (binary stochastic neuron) (default), or a
sigmoid followed by a binary step function (if stochastic == False).
Uses the straight through estimator. See
https://arxiv.org/abs/1308.3432.
Arguments:
... | binary_stochastic_ST | python | salu133445/musegan | src/musegan/presets/binary_ops.py | https://github.com/salu133445/musegan/blob/master/src/musegan/presets/binary_ops.py | MIT |
def binary_stochastic_REINFORCE(x, loss_op_name="loss_by_example"):
"""
Sigmoid followed by a random sample from a bernoulli distribution
according to the result (binary stochastic neuron). Uses the REINFORCE
estimator. See https://arxiv.org/abs/1308.3432.
NOTE: Requires a loss operation with name ... |
Sigmoid followed by a random sample from a bernoulli distribution
according to the result (binary stochastic neuron). Uses the REINFORCE
estimator. See https://arxiv.org/abs/1308.3432.
NOTE: Requires a loss operation with name matching the argument for
loss_op_name in the graph. This loss operatio... | binary_stochastic_REINFORCE | python | salu133445/musegan | src/musegan/presets/binary_ops.py | https://github.com/salu133445/musegan/blob/master/src/musegan/presets/binary_ops.py | MIT |
def _binaryStochastic_REINFORCE(op, _):
"""Unbiased estimator for binary stochastic function based on REINFORCE."""
loss_op_name = op.graph.get_collection("REINFORCE")[0][op.name]
loss_tensor = op.graph.get_operation_by_name(loss_op_name).outputs[0]
sub_tensor = op.outputs[0].consumers()[0].outputs[0] ... | Unbiased estimator for binary stochastic function based on REINFORCE. | _binaryStochastic_REINFORCE | python | salu133445/musegan | src/musegan/presets/binary_ops.py | https://github.com/salu133445/musegan/blob/master/src/musegan/presets/binary_ops.py | MIT |
def binary_wrapper(pre_activations_tensor, estimator,
stochastic_tensor=tf.constant(True), pass_through=True,
slope_tensor=tf.constant(1.0)):
"""
Turns a layer of pre-activations (logits) into a layer of binary
stochastic neurons
Keyword arguments:
*estimator: ... |
Turns a layer of pre-activations (logits) into a layer of binary
stochastic neurons
Keyword arguments:
*estimator: either ST or REINFORCE
*stochastic_tensor: a boolean tensor indicating whether to sample from a
bernoulli distribution (True, default) or use a step_function (e.g.,
fo... | binary_wrapper | python | salu133445/musegan | src/musegan/presets/binary_ops.py | https://github.com/salu133445/musegan/blob/master/src/musegan/presets/binary_ops.py | MIT |
def eval(self, batch, output_type=0, quiet=False, save_fig=False, fig_dir='./'):
"""
Evaluate one batch of bars according to eval_map and eval_pair
Args:
batch (tensor): The input tensor.
output_type (int): 0 for scalar (mean of list), 1 for list
quiet (bool):... |
Evaluate one batch of bars according to eval_map and eval_pair
Args:
batch (tensor): The input tensor.
output_type (int): 0 for scalar (mean of list), 1 for list
quiet (bool): if true, print the values
save_fig (bool): if true, plot figures and save them ... | eval | python | salu133445/musegan | v1/musegan/eval/metrics.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/eval/metrics.py | MIT |
def get_placeholder(default_tensor=None, shape=None, name=None):
"""Return a placeholder_wirh_default if default_tensor given, otherwise a new placeholder is created and return"""
if default_tensor is not None:
return default_tensor
else:
if shape is None:
raise ValueError('One o... | Return a placeholder_wirh_default if default_tensor given, otherwise a new placeholder is created and return | get_placeholder | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def batch_norm(tensor_in, apply=True):
"""
Apply a batch normalization layer on the input tensor and return the resulting tensor.
Args:
tensor_in (tensor): The input tensor.
apply (bool): True to apply. False to bypass batch normalization. Defaults to True.
Returns:
tensor: The... |
Apply a batch normalization layer on the input tensor and return the resulting tensor.
Args:
tensor_in (tensor): The input tensor.
apply (bool): True to apply. False to bypass batch normalization. Defaults to True.
Returns:
tensor: The resulting tensor.
| batch_norm | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def lrelu(tensor_in, alpha=0.2):
"""Apply a leaky ReLU layer on the input tensor and return the resulting tensor. (alpha defaults to 0.2)"""
if tensor_in is not None:
return tf.maximum(tensor_in, alpha*tensor_in)
else:
return tensor_in | Apply a leaky ReLU layer on the input tensor and return the resulting tensor. (alpha defaults to 0.2) | lrelu | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def relu(tensor_in):
"""Apply a ReLU layer on the input tensor and return the resulting tensor."""
if tensor_in is not None:
return tf.nn.relu(tensor_in)
else:
return tensor_in | Apply a ReLU layer on the input tensor and return the resulting tensor. | relu | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def concat_cond_conv(x, condition=None):
"""Concatenate conditioning vector on feature map axis."""
if condition is None:
return x
else:
reshape_shape = tf.stack([tf.shape(x)[0], 1, 1, condition.get_shape()[1]])
out_shape = tf.stack([tf.shape(x)[0], x.get_shape()[1], x.get_shape()[2]... | Concatenate conditioning vector on feature map axis. | concat_cond_conv | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def conv2d(tensor_in, out_channels, kernels, strides, stddev=0.02, name='conv2d', reuse=None, padding='VALID'):
"""
Apply a 2D convolution layer on the input tensor and return the resulting tensor.
Args:
tensor_in (tensor): The input tensor.
out_channels (int): The number of output channels... |
Apply a 2D convolution layer on the input tensor and return the resulting tensor.
Args:
tensor_in (tensor): The input tensor.
out_channels (int): The number of output channels.
kernels (list of int): The size of the kernel. [kernel_height, kernel_width]
strides (list of int): T... | conv2d | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def transconv2d(tensor_in, out_shape, out_channels, kernels, strides, stddev=0.02, name='transconv2d', reuse=None,
padding='VALID'):
"""
Apply a 2D transposed convolution layer on the input tensor and return the resulting tensor.
Args:
tensor_in (tensor): The input tensor.
o... |
Apply a 2D transposed convolution layer on the input tensor and return the resulting tensor.
Args:
tensor_in (tensor): The input tensor.
out_shape (list of int): The output shape. [height, width]
out_channels (int): The number of output channels.
kernels (list of int): The size... | transconv2d | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def linear(tensor_in, output_size, stddev=0.02, bias_init=0.0, name='linear', reuse=None):
"""
Apply a linear layer on the input tensor and return the resulting tensor.
Args:
tensor_in (tensor): The input tensor.
output_size (int): The output size.
stddev (float): The value passed t... |
Apply a linear layer on the input tensor and return the resulting tensor.
Args:
tensor_in (tensor): The input tensor.
output_size (int): The output size.
stddev (float): The value passed to the truncated normal initializer for weights. Defaults to 0.02.
bias_init (float): The v... | linear | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def to_chroma_tf(bar_or_track_bar, is_normalize=True):
"""Return the chroma tensor of the input tensor"""
out_shape = tf.stack([tf.shape(bar_or_track_bar)[0], bar_or_track_bar.get_shape()[1], 12, 7,
bar_or_track_bar.get_shape()[3]])
chroma = tf.reduce_sum(tf.reshape(tf.cast(bar_or_t... | Return the chroma tensor of the input tensor | to_chroma_tf | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def to_binary_tf(bar_or_track_bar, threshold=0.0, track_mode=False, melody=False):
"""Return the binarize tensor of the input tensor (be careful of the channel order!)"""
if track_mode:
# melody track
if melody:
melody_is_max = tf.equal(bar_or_track_bar, tf.reduce_max(bar_or_track_ba... | Return the binarize tensor of the input tensor (be careful of the channel order!) | to_binary_tf | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def to_image_tf(tensor_in, colormap=None):
"""Reverse the second dimension and swap the second dimension and the third dimension"""
if colormap is None:
colormap = get_colormap()
shape = tf.stack([-1, tensor_in.get_shape()[1], tensor_in.get_shape()[2], 3])
recolored = tf.reshape(tf.matmul(tf.res... | Reverse the second dimension and swap the second dimension and the third dimension | to_image_tf | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def get_adversarial_loss(self, discriminator, scope_to_reuse=None):
"""Return the adversarial losses for the generator and the
discriminator."""
if self.config['gan']['type'] == 'gan':
d_loss_real = tf.losses.sigmoid_cross_entropy(
tf.ones_like(self.D_real.tensor_out)... | Return the adversarial losses for the generator and the
discriminator. | get_adversarial_loss | python | salu133445/musegan | v2/musegan/model.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/model.py | MIT |
def get_statistics(self):
"""Return model statistics (number of paramaters for each component)."""
def get_num_parameter(var_list):
"""Given the variable list, return the total number of parameters.
"""
return int(np.sum([np.product([x.value for x in var.get_shape()])... | Return model statistics (number of paramaters for each component). | get_statistics | python | salu133445/musegan | v2/musegan/model.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/model.py | MIT |
def get_num_parameter(var_list):
"""Given the variable list, return the total number of parameters.
"""
return int(np.sum([np.product([x.value for x in var.get_shape()])
for var in var_list])) | Given the variable list, return the total number of parameters.
| get_num_parameter | python | salu133445/musegan | v2/musegan/model.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/model.py | MIT |
def save_statistics(self, filepath=None):
"""Save model statistics to file. Default to save to the log directory
given as a global variable."""
if filepath is None:
filepath = os.path.join(self.config['log_dir'],
'model_statistics.txt')
wit... | Save model statistics to file. Default to save to the log directory
given as a global variable. | save_statistics | python | salu133445/musegan | v2/musegan/model.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/model.py | MIT |
def save_summary(self, filepath=None):
"""Save model summary to file. Default to save to the log directory
given as a global variable."""
if filepath is None:
filepath = os.path.join(self.config['log_dir'], 'model_summary.txt')
with open(filepath, 'w') as f:
f.wri... | Save model summary to file. Default to save to the log directory
given as a global variable. | save_summary | python | salu133445/musegan | v2/musegan/model.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/model.py | MIT |
def save(self, filepath=None):
"""Save the model to a checkpoint file. Default to save to the log
directory given as a global variable."""
if filepath is None:
filepath = os.path.join(self.config['checkpoint_dir'],
self.name + '.model')
pri... | Save the model to a checkpoint file. Default to save to the log
directory given as a global variable. | save | python | salu133445/musegan | v2/musegan/model.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/model.py | MIT |
def load_latest(self, checkpoint_dir=None):
"""Load the model from the latest checkpoint in a directory."""
if checkpoint_dir is None:
checkpoint_dir = self.config['checkpoint_dir']
print('[*] Loading checkpoint...')
with open(os.path.join(checkpoint_dir, 'checkpoint')) as f:... | Load the model from the latest checkpoint in a directory. | load_latest | python | salu133445/musegan | v2/musegan/model.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/model.py | MIT |
def save_samples(self, filename, samples, save_midi=False, shape=None,
postfix=None):
"""Save samples to an image file (and a MIDI file)."""
if shape is None:
shape = self.config['sample_grid']
if len(samples) > self.config['num_sample']:
samples = sa... | Save samples to an image file (and a MIDI file). | save_samples | python | salu133445/musegan | v2/musegan/model.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/model.py | MIT |
def run_sampler(self, targets, feed_dict, save_midi=False, postfix=None):
"""Run the target operation with feed_dict and save the samples."""
if not isinstance(targets, list):
targets = [targets]
results = self.sess.run(targets, feed_dict)
results = [result[:self.config['num_... | Run the target operation with feed_dict and save the samples. | run_sampler | python | salu133445/musegan | v2/musegan/model.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/model.py | MIT |
def build(self, config):
"""Build the end-to-end generator."""
nets = OrderedDict()
nets['shared'] = NeuralNet(self.tensor_in, config['net_g']['shared'],
name='shared')
nets['pitch_time_private'] = [
NeuralNet(nets['shared'].tensor_out,
... | Build the end-to-end generator. | build | python | salu133445/musegan | v2/musegan/bmusegan/components.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/bmusegan/components.py | MIT |
def get_image_grid(images, shape, grid_width=0, grid_color=0,
frame=False):
"""
Merge the input images and return a merged grid image.
Arguments
---------
images : np.array, ndim=3
The image array. Shape is (num_image, height, width).
shape : list or tuple of int
... |
Merge the input images and return a merged grid image.
Arguments
---------
images : np.array, ndim=3
The image array. Shape is (num_image, height, width).
shape : list or tuple of int
Shape of the image grid. (height, width)
grid_width : int
Width of the grid lines. Def... | get_image_grid | python | salu133445/musegan | v2/musegan/utils/image_io.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/image_io.py | MIT |
def save_image(filepath, phrases, shape, inverted=True, grid_width=3,
grid_color=0, frame=True):
"""
Save a batch of phrases to a single image grid.
Arguments
---------
filepath : str
Path to save the image grid.
phrases : np.array, ndim=5
The phrase array. Shape ... |
Save a batch of phrases to a single image grid.
Arguments
---------
filepath : str
Path to save the image grid.
phrases : np.array, ndim=5
The phrase array. Shape is (num_phrase, num_bar, num_time_step,
num_pitch, num_track).
shape : list or tuple of int
Shape o... | save_image | python | salu133445/musegan | v2/musegan/utils/image_io.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/image_io.py | MIT |
def get_tonal_matrix(r1=1.0, r2=1.0, r3=0.5):
"""Compute and return a tonal matrix for computing the tonal distance [1].
Default argument values are set as suggested by the paper.
[1] Christopher Harte, Mark Sandler, and Martin Gasser. Detecting harmonic
change in musical audio. In Proc. ACM MM Worksho... | Compute and return a tonal matrix for computing the tonal distance [1].
Default argument values are set as suggested by the paper.
[1] Christopher Harte, Mark Sandler, and Martin Gasser. Detecting harmonic
change in musical audio. In Proc. ACM MM Workshop on Audio and Music
Computing Multimedia, 2006.
... | get_tonal_matrix | python | salu133445/musegan | v2/musegan/utils/metrics.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/metrics.py | MIT |
def tonal_dist(chroma1, chroma2, tonal_matrix=None):
"""Return the tonal distance between two chroma features."""
if tonal_matrix is None:
tonal_matrix = get_tonal_matrix()
warnings.warn("`tonal matrix` not specified. Use default tonal matrix",
RuntimeWarning)
with warn... | Return the tonal distance between two chroma features. | tonal_dist | python | salu133445/musegan | v2/musegan/utils/metrics.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/metrics.py | MIT |
def plot_histogram(hist, fig_dir=None, title=None, max_hist_num=None):
"""Plot the histograms of the statistics"""
import matplotlib.pyplot as plt
hist = hist[~np.isnan(hist)]
u_value = np.unique(hist)
hist_num = len(u_value)
if max_hist_num is not None:
if len(u_value) > max_hist_num:
... | Plot the histograms of the statistics | plot_histogram | python | salu133445/musegan | v2/musegan/utils/metrics.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/metrics.py | MIT |
def print_metrics_mat(self, metrics_mat):
"""Print the intratrack metrics as a nice formatting table"""
print(' ' * 12, ' '.join(['{:^14}'.format(metric_name)
for metric_name in self.metric_names]))
for t, track_name in enumerate(self.track_names):
... | Print the intratrack metrics as a nice formatting table | print_metrics_mat | python | salu133445/musegan | v2/musegan/utils/metrics.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/metrics.py | MIT |
def print_metrics_pair(self, pair_matrix):
"""Print the intertrack metrics as a nice formatting table"""
for idx, pair in enumerate(self.tonal_distance_pairs):
print("{:12} {:12} {:12.5f}".format(
self.track_names[pair[0]], self.track_names[pair[1]],
pair_matr... | Print the intertrack metrics as a nice formatting table | print_metrics_pair | python | salu133445/musegan | v2/musegan/utils/metrics.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/metrics.py | MIT |
def eval(self, bars, verbose=False, mat_path=None, fig_dir=None):
"""Evaluate the input bars with the metrics"""
score_matrix = np.empty((len(self.metric_names), len(self.track_names),
bars.shape[0]))
score_matrix.fill(np.nan)
score_pair_matrix = np.zeros... | Evaluate the input bars with the metrics | eval | python | salu133445/musegan | v2/musegan/utils/metrics.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/metrics.py | MIT |
def eval_dataset(filepath, result_dir, location, config):
"""Run evaluation on a dataset stored in either shared array (if `location`
is 'sa') or in hard disk (if `location` is 'hd') and save the results to the
given directory.
"""
def load_data(filepath, location):
"""Load and return the t... | Run evaluation on a dataset stored in either shared array (if `location`
is 'sa') or in hard disk (if `location` is 'hd') and save the results to the
given directory.
| eval_dataset | python | salu133445/musegan | v2/musegan/utils/metrics.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/metrics.py | MIT |
def print_mat_file(mat_path, config):
"""Print the score matrices stored in a file."""
metrics = Metrics(config)
with np.load(mat_path) as loaded:
metrics.print_metrics_mat(loaded['score_matrix_mean'])
metrics.print_metrics_pair(loaded['score_pair_matrix_mean']) | Print the score matrices stored in a file. | print_mat_file | python | salu133445/musegan | v2/musegan/utils/metrics.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/metrics.py | MIT |
def write_midi(filepath, pianorolls, program_nums=None, is_drums=None,
track_names=None, velocity=100, tempo=120.0, beat_resolution=24):
"""
Write the given piano-roll(s) to a single MIDI file.
Arguments
---------
filepath : str
Path to save the MIDI file.
pianorolls : np... |
Write the given piano-roll(s) to a single MIDI file.
Arguments
---------
filepath : str
Path to save the MIDI file.
pianorolls : np.array, ndim=3
The piano-roll array to be written to the MIDI file. Shape is
(num_timestep, num_pitch, num_track).
program_nums : int or li... | write_midi | python | salu133445/musegan | v2/musegan/utils/midi_io.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/midi_io.py | MIT |
def save_midi(filepath, phrases, config):
"""
Save a batch of phrases to a single MIDI file.
Arguments
---------
filepath : str
Path to save the image grid.
phrases : list of np.array
Phrase arrays to be saved. All arrays must have the same shape.
pause : int
Length ... |
Save a batch of phrases to a single MIDI file.
Arguments
---------
filepath : str
Path to save the image grid.
phrases : list of np.array
Phrase arrays to be saved. All arrays must have the same shape.
pause : int
Length of pauses (in timestep) to be inserted between ph... | save_midi | python | salu133445/musegan | v2/musegan/utils/midi_io.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/midi_io.py | MIT |
def stream_complete(self, stream_id: int):
"""
When a stream is complete, we can send our response.
"""
try:
request_data = self.stream_data[stream_id]
except KeyError:
# Just return, we probably 405'd this already
return
headers = req... |
When a stream is complete, we can send our response.
| stream_complete | python | python-hyper/h2 | examples/asyncio/asyncio-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/asyncio-server.py | MIT |
def receive_data(self, data: bytes, flow_controlled_length: int, stream_id: int):
"""
We've received some data on a stream. If that stream is one we're
expecting data on, save it off (and account for the received amount of
data in flow control so that the client can send more data).
... |
We've received some data on a stream. If that stream is one we're
expecting data on, save it off (and account for the received amount of
data in flow control so that the client can send more data).
Otherwise, reset the stream.
| receive_data | python | python-hyper/h2 | examples/asyncio/asyncio-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/asyncio-server.py | MIT |
def stream_reset(self, stream_id):
"""
A stream reset was sent. Stop sending data.
"""
if stream_id in self.flow_control_futures:
future = self.flow_control_futures.pop(stream_id)
future.cancel() |
A stream reset was sent. Stop sending data.
| stream_reset | python | python-hyper/h2 | examples/asyncio/asyncio-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/asyncio-server.py | MIT |
async def send_data(self, data, stream_id):
"""
Send data according to the flow control rules.
"""
while data:
while self.conn.local_flow_control_window(stream_id) < 1:
try:
await self.wait_for_flow_control(stream_id)
except... |
Send data according to the flow control rules.
| send_data | python | python-hyper/h2 | examples/asyncio/asyncio-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/asyncio-server.py | MIT |
async def wait_for_flow_control(self, stream_id):
"""
Waits for a Future that fires when the flow control window is opened.
"""
f = asyncio.Future()
self.flow_control_futures[stream_id] = f
await f |
Waits for a Future that fires when the flow control window is opened.
| wait_for_flow_control | python | python-hyper/h2 | examples/asyncio/asyncio-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/asyncio-server.py | MIT |
def window_updated(self, stream_id, delta):
"""
A window update frame was received. Unblock some number of flow control
Futures.
"""
if stream_id and stream_id in self.flow_control_futures:
f = self.flow_control_futures.pop(stream_id)
f.set_result(delta)
... |
A window update frame was received. Unblock some number of flow control
Futures.
| window_updated | python | python-hyper/h2 | examples/asyncio/asyncio-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/asyncio-server.py | MIT |
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