code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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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 get_predict_nodes(self, z=None, y=None, c=None, params=None,
config=None):
"""Return a dictionary of graph nodes for training."""
LOGGER.info("Building prediction nodes.")
with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE):
nodes = {'z': z}
... | Return a dictionary of graph nodes for training. | get_predict_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 concat_cond_lin(tensor_in, condition):
"""Concatenate conditioning vector on feature map axis."""
if condition is None:
return tensor_in
else:
return tf.concat([tensor_in, condition, 1]) | Concatenate conditioning vector on feature map axis. | concat_cond_lin | python | salu133445/musegan | v1/musegan/libs/ops.py | https://github.com/salu133445/musegan/blob/master/v1/musegan/libs/ops.py | MIT |
def concat_prev(tensor_in, condition):
"""Concatenate conditioning vector on feature map axis."""
if condition is None:
return tensor_in
else:
if tensor_in.get_shape()[1:3] == condition.get_shape()[1:3]:
pad_shape = tf.stack([tf.shape(tensor_in)[0], tensor_in.get_shape()[1], tens... | Concatenate conditioning vector on feature map axis. | concat_prev | 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 load_data():
"""Load and return the training data."""
print('[*] Loading data...')
# Load data from SharedArray
if CONFIG['data']['training_data_location'] == 'sa':
import SharedArray as sa
x_train = sa.attach(CONFIG['data']['training_data'])
# Load data from hard disk
elif... | Load and return the training data. | load_data | python | salu133445/musegan | v2/main.py | https://github.com/salu133445/musegan/blob/master/v2/main.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 get_qualified_note_rate(pianoroll, 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."""
padded = np.pad(pianoroll.astype(int), ((1, 1), (0, 0)), 'constant')
diff = np.diff(padded, axis=0... | 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. | get_qualified_note_rate | python | salu133445/musegan | v2/musegan/utils/metrics.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/metrics.py | MIT |
def to_chroma(pianoroll):
"""Return the chroma features (not normalized)."""
padded = np.pad(pianoroll, ((0, 0), (0, 12 - pianoroll.shape[1] % 12)),
'constant')
return np.sum(np.reshape(padded, (pianoroll.shape[0], 12, -1)), 2) | Return the chroma features (not normalized). | to_chroma | 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 load_data(filepath, location):
"""Load and return the training data."""
print('[*] Loading data...')
# Load data from SharedArray
if location == 'sa':
import SharedArray as sa
data = sa.attach(filepath)
# Load data from hard disk
elif locatio... | Load and return the training data. | load_data | 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 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 | v2/musegan/utils/ops.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/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 | v2/musegan/utils/ops.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/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 | v2/musegan/utils/ops.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/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 | v2/musegan/utils/ops.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/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 | v2/musegan/utils/ops.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/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 | v2/musegan/utils/ops.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/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 | v2/musegan/utils/ops.py | https://github.com/salu133445/musegan/blob/master/v2/musegan/utils/ops.py | MIT |
def parse_arguments():
"""Parse and return the command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument('filepath', help="Path to the data file.")
parser.add_argument('--name', help="File name to save in SharedArray. "
"Default to use the o... | Parse and return the command line arguments. | parse_arguments | python | salu133445/musegan | v2/training_data/store_to_sa.py | https://github.com/salu133445/musegan/blob/master/v2/training_data/store_to_sa.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 |
def connection_made(self, transport):
"""
The connection has been made. Here we need to save off our transport,
do basic HTTP/2 connection setup, and then start our data writing
coroutine.
"""
self.transport = transport
self.conn.initiate_connection()
self... |
The connection has been made. Here we need to save off our transport,
do basic HTTP/2 connection setup, and then start our data writing
coroutine.
| connection_made | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def window_opened(self, event):
"""
The flow control window got opened.
This is important because it's possible that we were unable to send
some WSGI data because the flow control window was too small. If that
happens, the sending_loop coroutine starts buffering data.
A... |
The flow control window got opened.
This is important because it's possible that we were unable to send
some WSGI data because the flow control window was too small. If that
happens, the sending_loop coroutine starts buffering data.
As the window gets opened, we need to unbuff... | window_opened | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
async def sending_loop(self):
"""
A call that loops forever, attempting to send data. This sending loop
contains most of the flow-control smarts of this class: it pulls data
off of the asyncio queue and then attempts to send it.
The difficulties here are all around flow control.... |
A call that loops forever, attempting to send data. This sending loop
contains most of the flow-control smarts of this class: it pulls data
off of the asyncio queue and then attempts to send it.
The difficulties here are all around flow control. Specifically, a
chunk of data ma... | sending_loop | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def request_received(self, event):
"""
A HTTP/2 request has been received. We need to invoke the WSGI
application in a background thread to handle it.
"""
# First, we are going to want an object to hold all the relevant state
# for this request/response. For that, we have... |
A HTTP/2 request has been received. We need to invoke the WSGI
application in a background thread to handle it.
| request_received | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def data_frame_received(self, event):
"""
Data has been received by WSGI server and needs to be dispatched to a
running application.
Note that the flow control window is not modified here. That's
deliberate: see Stream.__next__ for a longer discussion of why.
"""
... |
Data has been received by WSGI server and needs to be dispatched to a
running application.
Note that the flow control window is not modified here. That's
deliberate: see Stream.__next__ for a longer discussion of why.
| data_frame_received | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def reset_stream(self, event):
"""
A stream got forcefully reset.
This is a tricky thing to deal with because WSGI doesn't really have a
good notion for it. Essentially, you have to let the application run
until completion, but not actually let it send any data.
We do t... |
A stream got forcefully reset.
This is a tricky thing to deal with because WSGI doesn't really have a
good notion for it. Essentially, you have to let the application run
until completion, but not actually let it send any data.
We do that by discarding any data we currently ha... | reset_stream | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def data_for_stream(self, stream_id, data):
"""
Thread-safe method called from outside the main asyncio thread in order
to send data on behalf of a WSGI application.
Places data being written by a stream on an asyncio queue. Returns a
threading event that will fire when that dat... |
Thread-safe method called from outside the main asyncio thread in order
to send data on behalf of a WSGI application.
Places data being written by a stream on an asyncio queue. Returns a
threading event that will fire when that data is sent.
| data_for_stream | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def open_flow_control_window(self, stream_id, increment):
"""
Opens a flow control window for the given stream by the given amount.
Called from a WSGI thread. Does not return an event because there's no
need to block on this action, it may take place at any time.
"""
def ... |
Opens a flow control window for the given stream by the given amount.
Called from a WSGI thread. Does not return an event because there's no
need to block on this action, it may take place at any time.
| open_flow_control_window | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def run_in_threadpool(self, wsgi_application, environ):
"""
This method should be invoked in a threadpool. At the point this method
is invoked, the only safe methods to call from the original thread are
``receive_data`` and ``request_complete``: any other method is unsafe.
This ... |
This method should be invoked in a threadpool. At the point this method
is invoked, the only safe methods to call from the original thread are
``receive_data`` and ``request_complete``: any other method is unsafe.
This method handles the WSGI logic. It invokes the application callable
... | run_in_threadpool | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def read(self, size=None):
"""
Called by the WSGI application to read data.
This method is the one of two that explicitly pumps the input data
queue, which means it deals with the ``_complete`` flag and the
``END_DATA_SENTINEL``.
"""
# If we've already seen the E... |
Called by the WSGI application to read data.
This method is the one of two that explicitly pumps the input data
queue, which means it deals with the ``_complete`` flag and the
``END_DATA_SENTINEL``.
| read | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def readlines(self, hint=None):
"""
Called by the WSGI application to read several lines of data.
"""
data = self.read(hint)
lines = data.splitlines(keepends=True)
return lines |
Called by the WSGI application to read several lines of data.
| readlines | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def start_response(self, status, response_headers, exc_info=None):
"""
This is the PEP-3333 mandated start_response callable.
All it does is store the headers for later sending, and return our
```write`` callable.
"""
if self._headers_emitted and exc_info is not None:
... |
This is the PEP-3333 mandated start_response callable.
All it does is store the headers for later sending, and return our
```write`` callable.
| start_response | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def write(self, data):
"""
Provides some data to write.
This function *blocks* until such time as the data is allowed by
HTTP/2 flow control. This allows a client to slow or pause the response
as needed.
This function is not supposed to be used, according to PEP-3333, b... |
Provides some data to write.
This function *blocks* until such time as the data is allowed by
HTTP/2 flow control. This allows a client to slow or pause the response
as needed.
This function is not supposed to be used, according to PEP-3333, but
once we have it it beco... | write | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def _emit_headers(self):
"""
Sends the response headers.
This is only called from the write callable and should only ever be
called once. It does some minor processing (converts the status line
into a status code because reason phrases are evil) and then passes
the heade... |
Sends the response headers.
This is only called from the write callable and should only ever be
called once. It does some minor processing (converts the status line
into a status code because reason phrases are evil) and then passes
the headers on to the server. This call expli... | _emit_headers | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
def _build_environ_dict(headers, stream):
"""
Build the WSGI environ dictionary for a given request. To do that, we'll
temporarily create a dictionary for the headers. While this isn't actually
a valid way to represent headers, we know that the special headers we need
can only have one appearance in... |
Build the WSGI environ dictionary for a given request. To do that, we'll
temporarily create a dictionary for the headers. While this isn't actually
a valid way to represent headers, we know that the special headers we need
can only have one appearance in the block.
This code is arguably somewhat i... | _build_environ_dict | python | python-hyper/h2 | examples/asyncio/wsgi-server.py | https://github.com/python-hyper/h2/blob/master/examples/asyncio/wsgi-server.py | MIT |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.