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import sys
from functools import reduce
import librosa
import numpy as np
import torch
from torch.nn.modules.module import _addindent
from .constants import * # noqa: F403
def cycle(iterable):
while True:
for item in iterable:
yield item
def summary(model, file=sys.stdout):
def repr(model):
# We treat the extra repr like the sub-module, one item per line
extra_lines = []
extra_repr = model.extra_repr()
# empty string will be split into list ['']
if extra_repr:
extra_lines = extra_repr.split('\n')
child_lines = []
total_params = 0
for key, module in model._modules.items():
mod_str, num_params = repr(module)
mod_str = _addindent(mod_str, 2)
child_lines.append('(' + key + '): ' + mod_str)
total_params += num_params
lines = extra_lines + child_lines
for name, p in model._parameters.items():
if hasattr(p, 'shape'):
total_params += reduce(lambda x, y: x * y, p.shape)
main_str = model._get_name() + '('
if lines:
# simple one-liner info, which most builtin Modules will use
if len(extra_lines) == 1 and not child_lines:
main_str += extra_lines[0]
else:
main_str += '\n ' + '\n '.join(lines) + '\n'
main_str += ')'
if file is sys.stdout:
main_str += ', \033[92m{:,}\033[0m params'.format(total_params)
else:
main_str += ', {:,} params'.format(total_params)
return main_str, total_params
string, count = repr(model)
if file is not None:
if isinstance(file, str):
file = open(file, 'w')
print(string, file=file)
file.flush()
return count
def to_local_average_cents(salience, center=None, thred=0.05):
"""
find the weighted average cents near the argmax bin
"""
if not hasattr(to_local_average_cents, 'cents_mapping'):
# the bin number-to-cents mapping
to_local_average_cents.cents_mapping = (
20 * torch.arange(N_CLASS) + CONST).to(salience.device) # noqa: F405
if salience.ndim == 1:
if center is None:
center = int(torch.argmax(salience))
start = max(0, center - 4)
end = min(len(salience), center + 5)
salience = salience[start:end]
product_sum = torch.sum(
salience * to_local_average_cents.cents_mapping[start:end])
weight_sum = torch.sum(salience)
return product_sum / weight_sum if torch.max(salience) > thred else 0
if salience.ndim == 2:
return torch.Tensor([to_local_average_cents(salience[i, :], None, thred) for i in
range(salience.shape[0])]).to(salience.device)
raise Exception("label should be either 1d or 2d ndarray")
def to_viterbi_cents(salience, thred=0.05):
# Create viterbi transition matrix
if not hasattr(to_viterbi_cents, 'transition'):
xx, yy = torch.meshgrid(range(N_CLASS), range(N_CLASS)) # noqa: F405
transition = torch.maximum(30 - abs(xx - yy), 0)
transition = transition / transition.sum(axis=1, keepdims=True)
to_viterbi_cents.transition = transition
# Convert to probability
prob = salience.T
prob = prob / prob.sum(axis=0)
# Perform viterbi decoding
path = librosa.sequence.viterbi(prob.detach().cpu().numpy(), to_viterbi_cents.transition).astype(np.int64)
return torch.Tensor([to_local_average_cents(salience[i, :], path[i], thred) for i in
range(len(path))]).to(salience.device)
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