# coding: utf-8
# author: luuil@outlook.com
r"""Util functions."""
from __future__ import print_function
import os
def is_exists(path):
if not os.path.exists(path):
print('Not exists: {}'.format(path))
return False
return True
def maybe_create_directory(dirname):
"""Check directory exists or create it."""
if not os.path.exists(dirname):
os.makedirs(dirname)
def maybe_download(url, dst_dir):
"""Download file.
If the file not exist then download it.
Args:
url: Web location of the file.
Returns:
path to downloaded file.
"""
import urllib.request
maybe_create_directory(dst_dir)
filename = url.split('/')[-1]
filepath = os.path.join(dst_dir, filename)
if not os.path.exists(filepath):
def _progress(count, block_size, total_size):
sys.stdout.write('\r>> Downloading %s %.1f%%' %
(filename,
float(count * block_size) / float(total_size) * 100.0))
sys.stdout.flush()
filepath, _ = urllib.request.urlretrieve(url, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Successfully downloaded:', filename, statinfo.st_size, 'bytes.')
return filepath
def maybe_download_and_extract(url, dst_dir):
"""Download and extract model tar file.
If the pretrained model we're using doesn't already exist, this function
downloads it from the TensorFlow.org website and unpacks it into a directory.
Args:
url: Web location of the tar file containing the pretrained model.
dst_dir: Destination directory to save downloaded and extracted file.
Returns:
None.
"""
import tarfile
filepath =maybe_download(url, dst_dir)
tarfile.open(filepath, 'r:gz').extractall(dst_dir)
def urban_labels(fpaths):
"""urban sound dataset labels."""
urban_label = lambda path: int(os.path.split(path)[-1].split('-')[1])
return [urban_label(p) for p in fpaths]
def train_test_val_split(X, Y, split=(0.2, 0.1), shuffle=True):
"""Split dataset into train/val/test subsets by 70:20:10(default).
Args:
X: List of data.
Y: List of labels corresponding to data.
split: Tuple of split ratio in `test:val` order.
shuffle: Bool of shuffle or not.
Returns:
Three dataset in `train:test:val` order.
"""
from sklearn.model_selection import train_test_split
assert len(X) == len(Y), 'The length of X and Y must be consistent.'
X_train, X_test_val, Y_train, Y_test_val = train_test_split(X, Y,
test_size=(split[0]+split[1]), shuffle=shuffle)
X_test, X_val, Y_test, Y_val = train_test_split(X_test_val, Y_test_val,
test_size=split[1], shuffle=False)
return (X_train, Y_train), (X_test, Y_test), (X_val, Y_val)
def calculate_flops(graph):
"""Calculate floating point operations with specified `graph`.
Print to stdout an analysis of the number of floating point operations in the
model broken down by individual operations.
"""
tf.profiler.profile(graph=graph,
options=tf.profiler.ProfileOptionBuilder.float_operation(), cmd='scope')
if __name__ == '__main__':
X, y = np.arange(20).reshape((10, 2)), np.arange(10)
print(X)
print(y)
tr, te, vl = train_test_val_split(X, y, shuffle=True)
print(tr)
print(te)
print(vl)
import sys
sys.path.append('..')
sys.path.append('../vggish')
from audio_model import define_audio_slim
from vggish_slim import define_vggish_slim
import tensorflow as tf
with tf.Graph().as_default() as graph:
# define_vggish_slim(training=False)
define_audio_slim(training=False)
calculate_flops(graph)
pass
