def test_save():
# Empty content
DictContainer({}).save(filename=os.path.join(tempfile.gettempdir(), 'saved.yaml'))
# Content
data2 = {
'section1': {
'field1': 1,
'field2': [1, 2, 3, 4]
},
'section2': {
'field1': {
'field1': [1, 2, 3, 4]
},
'field2': [1, 2, 3, 4]
}
}
DictContainer(data2).save(filename=os.path.join(tempfile.gettempdir(), 'saved.yaml'))
d = DictContainer().load(filename=os.path.join(tempfile.gettempdir(), 'saved.yaml'))
nose.tools.assert_dict_equal(d, data2)
def test_empty():
# Test #1
d = DictContainer({})
nose.tools.eq_(d.empty(), True)
# Test #2
d = DictContainer({'sec': 1})
nose.tools.eq_(d.empty(), False)
def test_load():
# YAML
tmp = tempfile.NamedTemporaryFile('r+', suffix='.yaml', dir='/tmp', delete=False)
try:
tmp.write('section:\n')
tmp.write(' field1: 1\n')
tmp.write(' field2: 2\n')
tmp.close()
m = DictContainer().load(filename=tmp.name)
nose.tools.assert_dict_equal(m, {'section': {'field1': 1, 'field2': 2}})
finally:
os.unlink(tmp.name)
# Json
tmp = tempfile.NamedTemporaryFile('r+', suffix='.json', dir='/tmp', delete=False)
try:
tmp.write('{"section":{"field1":1,"field2":2}}\n')
tmp.close()
m = DictContainer().load(filename=tmp.name)
nose.tools.assert_dict_equal(m, {'section': {'field1': 1, 'field2': 2}})
finally:
os.unlink(tmp.name)
# pickle
tmp = tempfile.NamedTemporaryFile('rb+', suffix='.pickle', dir='/tmp', delete=False)
try:
data2 = {
'section': {
'field1': 1,
'field2': 2
}
}
pickle.dump(data2, tmp, protocol=pickle.HIGHEST_PROTOCOL)
tmp.close()
m = DictContainer().load(filename=tmp.name)
nose.tools.assert_dict_equal(m, {'section': {'field1': 1, 'field2': 2}})
finally:
os.unlink(tmp.name)
# msgpack
tmp = tempfile.NamedTemporaryFile('rb+', suffix='.msgpack', dir='/tmp', delete=False)
try:
data2 = {
'section': {
'field1': 1,
'field2': 2
}
}
msgpack.dump(data2, tmp)
tmp.close()
m = DictContainer().load(filename=tmp.name)
nose.tools.assert_dict_equal(m, {'section': {'field1': 1, 'field2': 2}})
finally:
os.unlink(tmp.name)
# Txt
tmp = tempfile.NamedTemporaryFile('r+', suffix='.txt', dir='/tmp', delete=False)
try:
tmp.write('line1\n')
tmp.write('line2\n')
tmp.write('line3\n')
tmp.close()
m = DictContainer().load(filename=tmp.name)
nose.tools.assert_dict_equal(m, {0: 'line1\n', 1: 'line2\n', 2: 'line3\n'})
finally:
os.unlink(tmp.name)
def model_summary_string(keras_model, mode='keras'):
"""Model summary in a formatted string, similar to Keras model summary function.
Parameters
----------
keras_model : keras model
Keras model
mode : str
Summary mode ['extended', 'keras']. In case 'keras', standard Keras summary is returned.
Default value keras
Returns
-------
str
Model summary
"""
ui = FancyStringifier()
output = ''
output += ui.line('Model summary') + '\n'
if mode == 'extended':
layer_name_map = {
'BatchNormalization': 'BatchNorm',
}
import keras
from distutils.version import LooseVersion
import keras.backend as keras_backend
output += ui.row('Layer type',
'Output',
'Param',
'Name',
'Connected to',
'Activ.',
'Init',
widths=[15, 25, 10, 20, 25, 10, 10],
indent=4) + '\n'
output += ui.row('-', '-', '-', '-', '-', '-', '-') + '\n'
for layer in keras_model.layers:
connections = []
if LooseVersion(keras.__version__) >= LooseVersion('2.1.3'):
for node_index, node in enumerate(layer._inbound_nodes):
for i in range(len(node.inbound_layers)):
inbound_layer = node.inbound_layers[i].name
inbound_node_index = node.node_indices[i]
inbound_tensor_index = node.tensor_indices[i]
connections.append(inbound_layer + '[' +
str(inbound_node_index) + '][' +
str(inbound_tensor_index) + ']')
else:
for node_index, node in enumerate(layer.inbound_nodes):
for i in range(len(node.inbound_layers)):
inbound_layer = node.inbound_layers[i].name
inbound_node_index = node.node_indices[i]
inbound_tensor_index = node.tensor_indices[i]
connections.append(inbound_layer + '[' +
str(inbound_node_index) + '][' +
str(inbound_tensor_index) + ']')
config = DictContainer(layer.get_config())
layer_name = layer.__class__.__name__
if layer_name in layer_name_map:
layer_name = layer_name_map[layer_name]
if config.get_path(
'kernel_initializer.class_name') == 'VarianceScaling':
init = str(
config.get_path('kernel_initializer.config.distribution',
'---'))
elif config.get_path(
'kernel_initializer.class_name') == 'RandomUniform':
init = 'uniform'
else:
init = '---'
output += ui.row(
layer_name, str(layer.output_shape), str(layer.count_params()),
str(layer.name),
str(connections[0]) if len(connections) > 0 else '---',
str(config.get('activation', '---')), init) + '\n'
trainable_count = int(
numpy.sum([
keras_backend.count_params(p)
for p in set(keras_model.trainable_weights)
]))
non_trainable_count = int(
numpy.sum([
keras_backend.count_params(p)
for p in set(keras_model.non_trainable_weights)
]))
output += ui.line('') + '\n'
output += ui.line(
'Parameters',
indent=4,
) + '\n'
output += ui.data(indent=6,
field='Total',
value=trainable_count + non_trainable_count) + '\n'
output += ui.data(indent=6, field='Trainable',
value=trainable_count) + '\n'
output += ui.data(
indent=6, field='Non-Trainable', value=non_trainable_count) + '\n'
else:
output_buffer = []
keras_model.summary(print_fn=output_buffer.append)
for line in output_buffer:
output += ui.line(line, indent=4) + '\n'
output += ui.line('') + '\n'
output += ui.data(
indent=4, field='Input shape', value=keras_model.input_shape) + '\n'
output += ui.data(
indent=4, field='Output shape', value=keras_model.output_shape) + '\n'
return output
def __init__(self, filename=None, content_type=None, local_md5=None,
remote_file=None, remote_md5=None, remote_bytes=None,
**kwargs):
"""Constructor
Parameters
----------
filename : str
Local filename.
content_type : str or list of str
Content type, valid labels ['code', 'documentation', 'meta', 'audio', 'features'].
local_md5 : str
Checksum of local file (MD5).
remote_file : str
URL to remote filename.
remote_md5 : str
Checksum of remote file (MD5).
remote_bytes : int
Remote file size in bytes
"""
self.socket_timeout = 120
# Local
self.filename = filename
self.content_type = content_type
self._local_md5 = local_md5
self._local_bytes = None
self._local_modified = None
# Remote
self._remote_file = None
self.remote_file = remote_file
self.remote_md5 = remote_md5
self._remote_bytes = remote_bytes
self._remote_status = None
self._remote_modified = None
# Run DictContainer init
DictContainer.__init__(self, **kwargs)
# Check remote url
if self.remote_file is not None and validators.url(self.remote_file) is not True:
message = '{name}: Remote file URL not valid [{url}]'.format(
name=self.__class__.__name__,
url=self.remote_file,
)
self.logger.exception(message)
raise ValueError(message)
# Check local filename
if self.filename is None:
message = '{name}: Local file not set.'.format(
name=self.__class__.__name__)
self.logger.exception(message)
raise ValueError(message)
# Check content types
if self.content_type is not None:
# Validate content type
if isinstance(self.content_type, str):
self.content_type = [self.content_type]
if isinstance(self.content_type, list):
for content_type in self.content_type:
if content_type not in self.valid_content_types:
message = '{name}: Invalid content type given for file [{filename}], type [{content_type}]'.format(
name=self.__class__.__name__,
content_type=content_type,
filename=self.remote_file
)
self.logger.exception(message)
raise ValueError(message)
def test_wrong_path2():
with dcase_util.utils.DisableLogger():
DictContainer(data).set_path(path=9, new_value=1)
def test_load_wrong_type2():
with dcase_util.utils.DisableLogger():
DictContainer().load(filename=os.path.join(tempfile.gettempdir(), 'wrong.abc'))
class DCASE2018_Task4_DevelopmentSet(AudioTaggingDataset):
"""DCASE 2018 Large-scale weakly labeled semi-supervised sound event detection in domestic environments
"""
def __init__(self,
storage_name='DCASE2018-task4-development',
data_path=None,
local_path=None,
included_content_types=None,
**kwargs):
"""
Constructor
Parameters
----------
storage_name : str
Name to be used when storing dataset on disk
data_path : str
Root path where the dataset is stored. If None, os.path.join(tempfile.gettempdir(), 'dcase_util_datasets')
is used.
local_path : str
Direct storage path setup for the dataset. If None, data_path and storage_name are used to create one.
"""
kwargs['included_content_types'] = included_content_types
kwargs['data_path'] = data_path
kwargs['storage_name'] = storage_name
kwargs['local_path'] = local_path
kwargs['dataset_group'] = 'event'
kwargs['dataset_meta'] = {
'authors':
'Nicolas Turpault, Romain Serizel, Hamid Eghbal-zadeh, Ankit Parag Shah',
'title':
'Task 4 Large-scale weakly labeled semi-supervised sound event detection in domestic environments',
'url':
'https://github.com/DCASE-REPO/dcase2018_baseline/tree/master/task4/',
'audio_source': 'Field recording',
'audio_type': 'Natural',
'recording_device_model': None,
'microphone_model': None,
'licence': 'MIT'
}
kwargs['crossvalidation_folds'] = 2
kwargs['default_audio_extension'] = 'wav'
kwargs['package_list'] = [
{
'content_type': 'meta',
'filename': 'dataset/metadata/train/weak.csv'
},
{
'content_type': 'meta',
'filename': 'dataset/metadata/train/unlabel_in_domain.csv'
},
{
'content_type': 'meta',
'filename': 'dataset/metadata/test/test.csv'
},
{
'content_type': 'meta',
'filename': 'dataset/metadata/train/unlabel_out_of_domain.csv',
},
]
super(DCASE2018_Task4_DevelopmentSet, self).__init__(**kwargs)
def extract_packages(self):
"""Extract the dataset packages
Raises
------
IOError
Local package was not found.
Returns
-------
self
"""
# Make sure evaluation_setup directory exists
Path().makedirs(
path=os.path.join(self.local_path, self.evaluation_setup_folder))
log = FancyLogger()
item_access_log_filename = os.path.join(self.local_path,
'item_access_error.log.csv')
if 'audio' in self.included_content_types or self.included_content_types == [
'all'
]: # mean process audio
log.title("Download_data")
log.info(
"Once database is downloaded, do not forget to check your missing_files"
)
non_existing_videos = pandas.DataFrame(
columns=["filename", "error"])
log.line("check files exist or download data")
# Collect file ids
for package in self.package_list:
if package.get('content_type') == "meta":
base_filepath = os.path.splitext(
package.get('filename').split('/')[-1])[0]
if 'train' in package.get('filename'):
result_audio_directory = os.path.join(
self.local_path, 'dataset/audio/train',
base_filepath)
else:
result_audio_directory = os.path.join(
self.local_path, 'dataset/audio/test')
missing_files = download(package.get('filename'),
result_audio_directory,
n_jobs=3)
if not missing_files.empty:
non_existing_videos = non_existing_videos.append(
missing_files, ignore_index=True)
# Save list of non-accessible videos
ListDictContainer(non_existing_videos.to_dict(orient="records"),
filename=item_access_log_filename).save(
fields=['filename', 'error'])
# Evaluation setup filenames
train_filename_fold1 = self.evaluation_setup_filename(
setup_part='train', fold=1, file_extension='csv')
test_filename_fold1 = self.evaluation_setup_filename(
setup_part='test', fold=1, file_extension='csv')
train_filename_fold2 = self.evaluation_setup_filename(
setup_part='train', fold=2, file_extension='csv')
test_filename_fold2 = self.evaluation_setup_filename(
setup_part='test', fold=2, file_extension='csv')
evaluate_filename = self.evaluation_setup_filename(
setup_part='evaluate', fold=2, file_extension='csv')
# Check that evaluation setup exists
evaluation_setup_exists = True
if not os.path.isfile(train_filename_fold1) or not os.path.isfile(test_filename_fold1) \
or not os.path.isfile(train_filename_fold2) or not os.path.isfile(test_filename_fold2) \
or not os.path.isfile(evaluate_filename) or not self.meta_container.exists():
evaluation_setup_exists = False
if not evaluation_setup_exists:
# Evaluation setup was not found, generate one
item_access_log_filename = os.path.join(
self.local_path, 'item_access_error.log.csv')
non_existing_videos = ListDictContainer().load(
filename=item_access_log_filename,
delimiter=',').get_field_unique('filename')
train_meta_weak_fold1 = MetaDataContainer()
audio_path = 'dataset/audio/train/weak'
for item in MetaDataContainer().load(os.path.join(
self.local_path, 'dataset/metadata/train/'
'weak.csv'),
fields=["filename", "tags"],
csv_header=True):
if item.filename not in non_existing_videos:
if not item.filename.endswith(
self.default_audio_extension):
item.filename = os.path.join(
audio_path,
os.path.splitext(item.filename)[0] + '.' +
self.default_audio_extension)
else:
item.filename = Path(path=item.filename).modify(
path_base=audio_path)
# Only collect items which exists if audio present
if 'audio' in self.included_content_types or 'all' in self.included_content_types:
if os.path.isfile(
os.path.join(self.local_path, item.filename)):
train_meta_weak_fold1.append(item)
else:
train_meta_weak_fold1.append(item)
train_meta_weak_fold1.save(filename=train_filename_fold1,
csv_header=True,
file_format="CSV")
test_meta_unlabel_fold1 = MetaDataContainer()
audio_path = 'dataset/audio/train/unlabel_in_domain'
for item in MetaDataContainer().load(os.path.join(
self.local_path, 'dataset/metadata/train/'
'unlabel_in_domain.csv'),
csv_header=True):
if item.filename not in non_existing_videos:
# If not the right extension, change it
if not item.filename.endswith(
self.default_audio_extension):
item.filename = os.path.join(
audio_path,
os.path.splitext(item.filename)[0] + '.' +
self.default_audio_extension)
else:
item.filename = Path(path=item.filename).modify(
path_base=audio_path)
# Only collect items which exists if audio present
if 'audio' in self.included_content_types or 'all' in self.included_content_types:
if os.path.isfile(
os.path.join(self.local_path, item.filename)):
test_meta_unlabel_fold1.append(item)
else:
test_meta_unlabel_fold1.append(item)
test_meta_unlabel_fold1.save(filename=test_filename_fold1,
csv_header=True,
file_format="CSV")
# Fold 2 train is all the data used in fold 1
train_meta_weak_fold2 = MetaDataContainer()
train_meta_weak_fold2 += MetaDataContainer().load(
train_filename_fold1, csv_header=True, file_format="CSV")
for item in MetaDataContainer().load(test_filename_fold1,
csv_header=True,
file_format="CSV"):
item.tags = []
train_meta_weak_fold2.append(item)
train_meta_weak_fold2.save(filename=train_filename_fold2,
csv_header=True)
# Evaluate meta is the groundtruth file with test annotations test.csv
evaluate_meta = MetaDataContainer()
audio_path = 'dataset/audio/test'
for item in MetaDataContainer().load(os.path.join(
self.local_path, 'dataset/metadata/test/test.csv'),
csv_header=True):
if item.filename not in non_existing_videos:
if not item.filename.endswith(
self.default_audio_extension):
item.filename = os.path.join(
audio_path,
os.path.splitext(item.filename)[0] + '.' +
self.default_audio_extension)
else:
item.filename = Path(path=item.filename).modify(
path_base=audio_path)
# Only collect items which exists
if 'audio' in self.included_content_types or 'all' in self.included_content_types:
if os.path.isfile(
os.path.join(self.local_path, item.filename)):
evaluate_meta.append(item)
else:
evaluate_meta.append(item)
evaluate_meta.save(filename=evaluate_filename,
csv_header=True,
file_format="CSV")
# Test meta is filenames of evaluation, labels will be predicted
test_meta_strong_fold2 = MetaDataContainer()
for filename in evaluate_meta.unique_files:
test_meta_strong_fold2.append(
MetaDataItem({'filename': filename}))
test_meta_strong_fold2.save(filename=test_filename_fold2,
csv_header=True,
file_format="CSV")
# meta_data is the default meta container containing all files of the dataset
meta_data = MetaDataContainer()
meta_data += MetaDataContainer().load(train_filename_fold1,
csv_header=True,
file_format="CSV")
meta_data += MetaDataContainer().load(test_filename_fold1,
csv_header=True,
file_format="CSV")
meta_data += MetaDataContainer().load(test_filename_fold2,
csv_header=True,
file_format="CSV")
# Save meta
meta_data.save(filename=self.meta_file)
log.foot()
return self
def load_crossvalidation_data(self):
"""Load cross-validation into the container.
Returns
-------
self
"""
# Reset cross validation data and insert 'all_data'
self.crossvalidation_data = DictContainer({
'train': {
'all_data': self.meta_container
},
'test': {
'all_data': self.meta_container
},
'evaluate': {
'all_data': self.meta_container
},
})
for crossvalidation_set in list(self.crossvalidation_data.keys()):
for item in self.crossvalidation_data[crossvalidation_set][
'all_data']:
self.process_meta_item(item=item)
# Load cross validation folds
for fold in self.folds():
# Initialize data
self.crossvalidation_data['train'][fold] = MetaDataContainer()
self.crossvalidation_data['test'][fold] = MetaDataContainer()
self.crossvalidation_data['evaluate'][fold] = MetaDataContainer()
# Get filenames
train_filename = self.evaluation_setup_filename(
setup_part='train', fold=fold, file_extension="csv")
test_filename = self.evaluation_setup_filename(
setup_part='test', fold=fold, file_extension="csv")
evaluate_filename = self.evaluation_setup_filename(
setup_part='evaluate', fold=fold, file_extension="csv")
if os.path.isfile(train_filename):
# Training data for fold exists, load and process it
self.crossvalidation_data['train'][fold] += MetaDataContainer(
filename=train_filename).load()
if os.path.isfile(test_filename):
# Testing data for fold exists, load and process it
self.crossvalidation_data['test'][fold] += MetaDataContainer(
filename=test_filename).load()
if os.path.isfile(evaluate_filename):
# Evaluation data for fold exists, load and process it
self.crossvalidation_data['evaluate'][
fold] += MetaDataContainer(
filename=evaluate_filename).load()
# Process items
for item in self.crossvalidation_data['train'][fold]:
self.process_meta_item(item=item)
for item in self.crossvalidation_data['test'][fold]:
self.process_meta_item(item=item)
for item in self.crossvalidation_data['evaluate'][fold]:
self.process_meta_item(item=item)
return self
def load_crossvalidation_data(self):
"""Load cross-validation into the container.
Returns
-------
self
"""
# Reset cross validation data and insert 'all_data'
self.crossvalidation_data = DictContainer({
'train': {
'all_data': self.meta_container
},
'test': {
'all_data': self.meta_container
},
'evaluate': {
'all_data': self.meta_container
},
})
for crossvalidation_set in list(self.crossvalidation_data.keys()):
for item in self.crossvalidation_data[crossvalidation_set][
'all_data']:
self.process_meta_item(item=item)
# Load cross validation folds
for fold in self.folds():
# Initialize data
self.crossvalidation_data['train'][fold] = MetaDataContainer()
self.crossvalidation_data['test'][fold] = MetaDataContainer()
self.crossvalidation_data['evaluate'][fold] = MetaDataContainer()
# Get filenames
train_filename = self.evaluation_setup_filename(
setup_part='train', fold=fold, file_extension="csv")
test_filename = self.evaluation_setup_filename(
setup_part='test', fold=fold, file_extension="csv")
evaluate_filename = self.evaluation_setup_filename(
setup_part='evaluate', fold=fold, file_extension="csv")
if os.path.isfile(train_filename):
# Training data for fold exists, load and process it
self.crossvalidation_data['train'][fold] += MetaDataContainer(
filename=train_filename).load()
if os.path.isfile(test_filename):
# Testing data for fold exists, load and process it
self.crossvalidation_data['test'][fold] += MetaDataContainer(
filename=test_filename).load()
if os.path.isfile(evaluate_filename):
# Evaluation data for fold exists, load and process it
self.crossvalidation_data['evaluate'][
fold] += MetaDataContainer(
filename=evaluate_filename).load()
# Process items
for item in self.crossvalidation_data['train'][fold]:
self.process_meta_item(item=item)
for item in self.crossvalidation_data['test'][fold]:
self.process_meta_item(item=item)
for item in self.crossvalidation_data['evaluate'][fold]:
self.process_meta_item(item=item)
return self
def system_meta(self,
results,
task=None,
check_development_dataset=True,
check_evaluation_dataset=True):
"""Check system result scores given in the meta data
Parameters
----------
results : dict
Result meta data
task : str, optional
Temporal override for the task parameter given to class constructor.
check_development_dataset : bool
Check development dataset results
check_evaluation_dataset : bool
Check evaluation dataset results
Returns
-------
self
"""
if task is None:
task = self.task
if results is None:
self.error_log.append(u'No results section')
else:
results = DictContainer(results)
if check_development_dataset:
# Check development dataset results
if results.get('development_dataset') is None:
self.error_log.append(u'No development results given')
else:
if task == 'ASC':
if results.get_path(
'development_dataset.overall.accuracy'
) is None:
self.error_log.append(
u'No development overall result given ')
elif task == 'SED_event':
if results.get_path(
'development_dataset.event_based.overall.er'
) is None:
self.error_log.append(
u'No development overall result given [event_based.overall.er]'
)
if results.get_path(
'development_dataset.event_based.overall.f1'
) is None:
self.error_log.append(
u'No development overall result given [event_based.overall.f1]'
)
elif task == 'SED_segment':
if results.get_path(
'development_dataset.segment_based.overall.er'
) is None:
self.error_log.append(
u'No development overall result given [segment_based.overall.er]'
)
if results.get_path(
'development_dataset.segment_based.overall.f1'
) is None:
self.error_log.append(
u'No development overall result given [segment_based.overall.f1]'
)
elif task == 'task4':
pass
# Check development dataset / class wise results
if task == 'ASC':
if results.get_path(
'development_dataset.class_wise') is None:
self.error_log.append(
u'No class_wise development results given')
else:
if len(
results.get_path(
'development_dataset.class_wise')
) != len(self.class_labels):
self.error_log.append(
u'Incorrect number class-wise development results given [{class_wise:d}/{target:d}]'
.format(class_wise=len(
results.get_path(
'development_dataset.class_wise')),
target=len(self.class_labels)))
for class_label, class_data in iteritems(
results.get_path(
'development_dataset.class_wise')):
if 'accuracy' not in class_data or class_data[
'accuracy'] is None:
self.error_log.append(
u'Incorrect class-wise development results given for [{class_label:s}]'
.format(class_label=class_label))
elif task == 'SED_event':
if results.get_path(
'development_dataset.event_based.class_wise'
) is not None:
if len(
results.get_path(
'development_dataset.event_based.class_wise'
)) != len(self.class_labels):
self.error_log.append(
u'Incorrect number class-wise development results given [{class_wise:d}/{target:d}]'
.format(class_wise=len(
results.get_path(
'development_dataset.event_based.class_wise'
)),
target=len(self.class_labels)))
for class_label, class_data in iteritems(
results.get_path(
'development_dataset.event_based.class_wise'
)):
if class_data.get('er') is None:
self.error_log.append(
u'Incorrect class-wise development results given for [{class_label:s} / er]'
.format(class_label=class_label))
if class_data.get('f1') is None:
self.error_log.append(
u'Incorrect class-wise development results given for [{class_label:s} / f1]'
.format(class_label=class_label))
else:
self.error_log.append(
u'No class_wise development results given')
elif task == 'SED_segment':
if results.get_path(
'development_dataset.segment_based.class_wise'
) is not None:
if len(
results.get_path(
'development_dataset.segment_based.class_wise'
)) != len(self.class_labels):
self.error_log.append(
u'Incorrect number class-wise development results given [{class_wise:d}/{target:d}]'
.format(class_wise=len(
results.get_path(
'development_dataset.segment_based.class_wise'
)),
target=len(self.class_labels)))
for class_label, class_data in iteritems(
results.get_path(
'development_dataset.segment_based.class_wise'
)):
if class_data.get('er') is None:
self.error_log.append(
u'Incorrect class-wise development results given for [{class_label:s} / er]'
.format(class_label=class_label))
if class_data.get('f1') is None:
self.error_log.append(
u'Incorrect class-wise development results given for [{class_label:s} / f1]'
.format(class_label=class_label))
else:
self.error_log.append(
u'No class_wise development results given')
elif task == 'task4':
pass
if check_evaluation_dataset:
# Check evaluation dataset results
if 'evaluation_dataset' not in results:
self.error_log.append(u'No evaluation results given')
else:
if task == 'ASC':
if results.get_path(
'evaluation_dataset.overall') is None:
self.error_log.append(
u'No evaluation results given')
if results.get_path(
'evaluation_dataset.class_wise') is not None:
if len(
results.get_path(
'evaluation_dataset.class_wise')
) != len(self.class_labels):
self.error_log.append(
u'Incorrect number class-wise evaluation results given [{class_wise:d}/{target:d}]'
.format(class_wise=len(
results.get_path(
'evaluation_dataset.class_wise')),
target=len(self.class_labels)))
for class_label, class_data in iteritems(
results.get_path(
'evaluation_dataset.class_wise')):
if class_data.get('accuracy') is None:
self.error_log.append(
u'Incorrect class-wise evaluation results given for [{class_label:s}]'
.format(class_label=class_label))
else:
self.error_log.append(
u'No class_wise development results given')
elif task == 'SED_event':
if results.get_path(
'evaluation_dataset.event_based.overall.er'
) is None:
self.error_log.append(
u'No evaluation results given [event_based.overall.er]'
)
if results.get_path(
'evaluation_dataset.event_based.overall.f1'
) is None:
self.error_log.append(
u'No evaluation results given [event_based.overall.f1]'
)
if results.get_path(
'evaluation_dataset.event_based.class_wise'
) is not None:
if len(
results.get_path(
'evaluation_dataset.event_based.class_wise'
)) != len(self.class_labels):
self.error_log.append(
u'Incorrect number class-wise evaluation results given [{class_wise:d}/{target:d}]'
.format(class_wise=len(
results.get_path(
'evaluation_dataset.event_based.class_wise'
)),
target=len(self.class_labels)))
for class_label, class_data in iteritems(
results.get_path(
'evaluation_dataset.event_based.class_wise'
)):
if class_data.get('er') is None:
self.error_log.append(
u'Incorrect class-wise evaluation results given for [{class_label:s} / er]'
.format(class_label=class_label))
if class_data.get('f1') is None:
self.error_log.append(
u'Incorrect class-wise evaluation results given for [{class_label:s} / f1]'
.format(class_label=class_label))
else:
self.error_log.append(
u'No class_wise evaluation results given')
elif task == 'SED_segment':
if results.get_path(
'evaluation_dataset.segment_based.overall.er'
) is None:
self.error_log.append(
u'No evaluation results given [segment_based.overall.er]'
)
if results.get_path(
'evaluation_dataset.segment_based.overall.f1'
) is None:
self.error_log.append(
u'No evaluation results given [segment_based.overall.f1]'
)
if results.get_path(
'evaluation_dataset.segment_based.class_wise'
) is not None:
if len(
results.get_path(
'evaluation_dataset.segment_based.class_wise'
)) != len(self.class_labels):
self.error_log.append(
u'Incorrect number class-wise evaluation results given [{class_wise:d}/{target:d}]'
.format(class_wise=len(
results.get_path(
'evaluation_dataset.segment_based.class_wise'
)),
target=len(self.class_labels)))
for class_label, class_data in iteritems(
results.get_path(
'evaluation_dataset.segment_based.class_wise'
)):
if class_data.get('er') is None:
self.error_log.append(
u'Incorrect class-wise evaluation results given for [{class_label:s} / er]'
.format(class_label=class_label))
if class_data.get('f1') is None:
self.error_log.append(
u'Incorrect class-wise evaluation results given for [{class_label:s} / f1]'
.format(class_label=class_label))
else:
self.error_log.append(
u'No class_wise evaluation results given')
elif task == 'task4':
pass
return self
def setup_logging(parameters=None,
coloredlogs=False,
logging_file=None,
default_setup_file='logging.yaml',
default_level=logging.INFO,
environmental_variable='LOG_CFG'):
"""Setup logging configuration
Parameters
----------
parameters : dict
Parameters in dict
Default value None
coloredlogs : bool
Use coloredlogs
Default value False
logging_file : str
Log filename for file based logging, if none given no file logging is used.
Default value None
environmental_variable : str
Environmental variable to get the logging setup filename, if set will override default_setup_file
Default value 'LOG_CFG'
default_setup_file : str
Default logging parameter file, used if one is not set in given ParameterContainer
Default value 'logging.yaml'
default_level : logging.level
Default logging level, used if one is not set in given ParameterContainer
Default value 'logging.INFO'
Returns
-------
nothing
"""
class LoggerFilter(object):
def __init__(self, level):
self.__level = level
def filter(self, log_record):
return log_record.levelno <= self.__level
formatters = {
'simple': "[%(levelname).1s] %(message)s",
'normal': "%(asctime)s\t[%(name)-20s]\t[%(levelname)-8s]\t%(message)s",
'extended': "[%(asctime)s] [%(name)s]\t [%(levelname)-8s]\t %(message)s \t(%(filename)s:%(lineno)s)",
'extended2': "[%(levelname).1s] %(message)s \t(%(filename)s:%(lineno)s)",
'file_extended': "[%(levelname).1s] [%(asctime)s] %(message)s",
}
if not parameters:
logging_parameter_file = default_setup_file
value = os.getenv(environmental_variable, None)
if value:
# If environmental variable set
logging_parameter_file = value
if os.path.exists(logging_parameter_file):
with open(logging_parameter_file, 'rt') as f:
config = yaml.safe_load(f.read())
logging.config.dictConfig(config)
try:
# Check if coloredlogs is available
import coloredlogs
coloredlogs.install(
level=config['handlers']['console']['level'],
fmt=config['formatters'][config['handlers']['console']['formatter']]['format']
)
except ImportError:
pass
else:
if coloredlogs:
try:
# Check if coloredlogs is available
import coloredlogs
coloredlogs.install(
level=logging.INFO,
fmt=formatters['simple'],
reconfigure=True
)
except ImportError:
logger = logging.getLogger()
logger.setLevel(default_level)
console_info = logging.StreamHandler()
console_info.setLevel(logging.INFO)
console_info.setFormatter(logging.Formatter(formatters['simple']))
console_info.addFilter(LoggerFilter(logging.INFO))
logger.addHandler(console_info)
console_debug = logging.StreamHandler()
console_debug.setLevel(logging.DEBUG)
console_debug.setFormatter(logging.Formatter(formatters['simple']))
console_debug.addFilter(LoggerFilter(logging.DEBUG))
logger.addHandler(console_debug)
console_warning = logging.StreamHandler()
console_warning.setLevel(logging.WARNING)
console_warning.setFormatter(logging.Formatter(formatters['simple']))
console_warning.addFilter(LoggerFilter(logging.WARNING))
logger.addHandler(console_warning)
console_critical = logging.StreamHandler()
console_critical.setLevel(logging.CRITICAL)
console_critical.setFormatter(logging.Formatter(formatters['extended2']))
console_critical.addFilter(LoggerFilter(logging.CRITICAL))
logger.addHandler(console_critical)
console_error = logging.StreamHandler()
console_error.setLevel(logging.ERROR)
console_error.setFormatter(logging.Formatter(formatters['extended2']))
console_error.addFilter(LoggerFilter(logging.ERROR))
logger.addHandler(console_error)
if logging_file:
file_info = logging.handlers.RotatingFileHandler(
filename=logging_file,
maxBytes=10485760,
backupCount=20,
encoding='utf8'
)
file_info.setLevel(logging.INFO)
file_info.setFormatter(logging.Formatter(formatters['file_extended']))
logger.addHandler(file_info)
else:
logger = logging.getLogger()
logger.setLevel(default_level)
console_info = logging.StreamHandler()
console_info.setLevel(logging.INFO)
console_info.setFormatter(logging.Formatter(formatters['simple']))
console_info.addFilter(LoggerFilter(logging.INFO))
logger.addHandler(console_info)
console_debug = logging.StreamHandler()
console_debug.setLevel(logging.DEBUG)
console_debug.setFormatter(logging.Formatter(formatters['simple']))
console_debug.addFilter(LoggerFilter(logging.DEBUG))
logger.addHandler(console_debug)
console_warning = logging.StreamHandler()
console_warning.setLevel(logging.WARNING)
console_warning.setFormatter(logging.Formatter(formatters['simple']))
console_warning.addFilter(LoggerFilter(logging.WARNING))
logger.addHandler(console_warning)
console_critical = logging.StreamHandler()
console_critical.setLevel(logging.CRITICAL)
console_critical.setFormatter(logging.Formatter(formatters['extended2']))
console_critical.addFilter(LoggerFilter(logging.CRITICAL))
logger.addHandler(console_critical)
console_error = logging.StreamHandler()
console_error.setLevel(logging.ERROR)
console_error.setFormatter(logging.Formatter(formatters['extended2']))
console_error.addFilter(LoggerFilter(logging.ERROR))
logger.addHandler(console_error)
if logging_file:
file_info = logging.handlers.RotatingFileHandler(
filename=logging_file,
maxBytes=10485760,
backupCount=20,
encoding='utf8'
)
file_info.setLevel(logging.INFO)
file_info.setFormatter(logging.Formatter(formatters['file_extended']))
logger.addHandler(file_info)
else:
from dcase_util.containers import DictContainer
parameters = DictContainer(parameters)
logging.config.dictConfig(parameters.get('parameters'))
if (parameters.get('colored', False) and
'console' in parameters.get_path('parameters.handlers')):
try:
# Check if coloredlogs is available
import coloredlogs
coloredlogs.install(
level=parameters.get_path('parameters.handlers.console.level'),
fmt=parameters.get_path('parameters.formatters')[
parameters.get_path('parameters.handlers.console.formatter')
].get('format')
)
except ImportError:
pass
# Function to handle uncaught expections
def handle_exception(exc_type, exc_value, exc_traceback):
if issubclass(exc_type, KeyboardInterrupt):
sys.__excepthook__(exc_type, exc_value, exc_traceback)
return
logger.error('Uncaught exception', exc_info=(exc_type, exc_value, exc_traceback))
sys.excepthook = handle_exception
def submission_authors(self,
authors,
check_email=True,
check_affiliation=True,
check_affiliation_abbreviation=True,
check_affiliation_department=True):
"""Check submission authors
Parameters
----------
authors : list of dict
List of authors dicts.
check_email : bool
Check that author email exists.
check_affiliation : bool
Check author affiliation.
check_affiliation_abbreviation : bool
Check that affiliation abbreviation exists.
check_affiliation_department : bool
Check that affiliation has department defined.
Returns
-------
self
"""
if not isinstance(authors, list):
self.error_log.append(
u'Authors not given in list format for the submission')
for author in authors:
author = DictContainer(author)
if author.get('lastname') is None:
self.error_log.append(
u'No lastname given for author ({last_name:s}, {first_name:s})'
.format(last_name=author['lastname'],
first_name=author['firstname']))
if author.get('firstname') is None:
self.error_log.append(
u'No firstname given for author ({last_name:s}, {first_name:s})'
.format(last_name=author['lastname'],
first_name=author['firstname']))
if check_email:
if author.get('email') is None:
self.error_log.append(
u'No email given for author ({last_name:s}, {first_name:s})'
.format(last_name=author['lastname'],
first_name=author['firstname']))
if check_affiliation:
if author.get('affiliation') is None:
self.error_log.append(
u'No affiliation given for author ({last_name:s}, {first_name:s})'
.format(last_name=author['lastname'],
first_name=author['firstname']))
else:
if isinstance(author.get('affiliation'), list):
for a in author.get('affiliation'):
affiliation = ', '.join(
filter(None, list(a.values())))
if check_affiliation_abbreviation:
if a.get('abbreviation') is None:
self.error_log.append(
u'No abbreviation given ({last_name:s}, {first_name:s}, {affiliation:s})'
.format(last_name=author['lastname'],
first_name=author['firstname'],
affiliation=affiliation))
if check_affiliation_department:
if a.get('department') is None:
self.error_log.append(
u'No department given ({last_name:s}, {first_name:s}, {affiliation:s})'
.format(last_name=author['lastname'],
first_name=author['firstname'],
affiliation=affiliation))
if a.get('institute') is None:
self.error_log.append(
u'No institute given ({last_name:s}, {first_name:s}, {affiliation:s})'
.format(last_name=author['lastname'],
first_name=author['firstname'],
affiliation=affiliation))
if a.get('location') is None:
self.error_log.append(
u'No location given ({last_name:s}, {first_name:s}, {affiliation:s})'
.format(last_name=author['lastname'],
first_name=author['firstname'],
affiliation=affiliation))
else:
affiliation = ', '.join(
filter(None, list(author['affiliation'].values())))
if check_affiliation_abbreviation:
if author.get_path(
'affiliation.abbreviation') is None:
self.error_log.append(
u'No abbreviation given ({last_name:s}, {first_name:s}, {affiliation:s})'
.format(last_name=author['lastname'],
first_name=author['firstname'],
affiliation=affiliation))
if check_affiliation_department:
if author.get_path(
'affiliation.department') is None:
self.error_log.append(
u'No department given ({last_name:s}, {first_name:s}, {affiliation:s})'
.format(last_name=author['lastname'],
first_name=author['firstname'],
affiliation=affiliation))
if author.get_path('affiliation.institute') is None:
self.error_log.append(
u'No institute given ({last_name:s}, {first_name:s}, {affiliation:s})'
.format(last_name=author['lastname'],
first_name=author['firstname'],
affiliation=affiliation))
if author.get_path('affiliation.location') is None:
self.error_log.append(
u'No location given ({last_name:s}, {first_name:s})'
.format(last_name=author['lastname'],
first_name=author['firstname'],
affiliation=affiliation))
return self
def model_summary_string(keras_model, mode='keras', show_parameters=True, display=False):
"""Model summary in a formatted string, similar to Keras model summary function.
Parameters
----------
keras_model : keras model
Keras model
mode : str
Summary mode ['extended', 'keras']. In case 'keras', standard Keras summary is returned.
Default value keras
show_parameters : bool
Show model parameter count and input / output shapes
Default value True
display : bool
Display summary immediately, otherwise return string
Default value False
Returns
-------
str
Model summary
"""
if is_jupyter():
ui = FancyHTMLStringifier()
html_mode = True
else:
ui = FancyStringifier()
html_mode = False
output = ''
output += ui.line('Model summary') + '\n'
if mode == 'extended' or mode == 'extended_wide':
layer_name_map = {
'BatchNormalization': 'BatchNorm',
}
layer_type_html_tags = {
'InputLayer': '<span class="label label-default">{0:s}</span>',
'Dense': '<span class="label label-primary">{0:s}</span>',
'TimeDistributed': '<span class="label label-primary">{0:s}</span>',
'BatchNorm': '<span class="label label-default">{0:s}</span>',
'Activation': '<span class="label label-default">{0:s}</span>',
'Dropout': '<span class="label label-default">{0:s}</span>',
'Flatten': '<span class="label label-success">{0:s}</span>',
'Reshape': '<span class="label label-success">{0:s}</span>',
'Permute': '<span class="label label-success">{0:s}</span>',
'Conv1D': '<span class="label label-warning">{0:s}</span>',
'Conv2D': '<span class="label label-warning">{0:s}</span>',
'MaxPooling1D': '<span class="label label-success">{0:s}</span>',
'MaxPooling2D': '<span class="label label-success">{0:s}</span>',
'MaxPooling3D': '<span class="label label-success">{0:s}</span>',
'AveragePooling1D': '<span class="label label-success">{0:s}</span>',
'AveragePooling2D': '<span class="label label-success">{0:s}</span>',
'AveragePooling3D': '<span class="label label-success">{0:s}</span>',
'GlobalMaxPooling1D': '<span class="label label-success">{0:s}</span>',
'GlobalMaxPooling2D': '<span class="label label-success">{0:s}</span>',
'GlobalMaxPooling3D': '<span class="label label-success">{0:s}</span>',
'GlobalAveragePooling1D': '<span class="label label-success">{0:s}</span>',
'GlobalAveragePooling2D': '<span class="label label-success">{0:s}</span>',
'GlobalAveragePooling3D': '<span class="label label-success">{0:s}</span>',
'RNN': '<span class="label label-danger">{0:s}</span>',
'SimpleRNN': '<span class="label label-danger">{0:s}</span>',
'GRU': '<span class="label label-danger">{0:s}</span>',
'CuDNNGRU': '<span class="label label-danger">{0:s}</span>',
'LSTM': '<span class="label label-danger">{0:s}</span>',
'CuDNNLSTM': '<span class="label label-danger">{0:s}</span>',
'Bidirectional': '<span class="label label-danger">{0:s}</span>'
}
from tensorflow import keras
from distutils.version import LooseVersion
import tensorflow.keras.backend as keras_backend
table_data = {
'layer_type': [],
'output': [],
'parameter_count': [],
'name': [],
'connected_to': [],
'activation': [],
'initialization': []
}
row_separators = []
prev_name = None
for layer_id, layer in enumerate(keras_model.layers):
connections = []
if LooseVersion(keras.__version__) >= LooseVersion('2.1.3'):
for node_index, node in enumerate(layer._inbound_nodes):
for i in range(len(node.inbound_layers)):
inbound_layer = node.inbound_layers[i].name
inbound_node_index = node.node_indices[i]
inbound_tensor_index = node.tensor_indices[i]
connections.append(
inbound_layer + '[' + str(inbound_node_index) + '][' + str(inbound_tensor_index) + ']'
)
else:
for node_index, node in enumerate(layer.inbound_nodes):
for i in range(len(node.inbound_layers)):
inbound_layer = node.inbound_layers[i].name
inbound_node_index = node.node_indices[i]
inbound_tensor_index = node.tensor_indices[i]
connections.append(
inbound_layer + '[' + str(inbound_node_index) + '][' + str(inbound_tensor_index) + ']'
)
config = DictContainer(layer.get_config())
layer_name = layer.__class__.__name__
if layer_name in layer_name_map:
layer_name = layer_name_map[layer_name]
if html_mode and layer_name in layer_type_html_tags:
layer_name = layer_type_html_tags[layer_name].format(layer_name)
if config.get_path('kernel_initializer.class_name') == 'VarianceScaling':
init = str(config.get_path('kernel_initializer.config.distribution', '---'))
elif config.get_path('kernel_initializer.class_name') == 'RandomUniform':
init = 'uniform'
else:
init = '-'
name_parts = layer.name.split('_')
if prev_name != name_parts[0]:
row_separators.append(layer_id)
prev_name = name_parts[0]
table_data['layer_type'].append(layer_name)
table_data['output'].append(str(layer.output_shape))
table_data['parameter_count'].append(str(layer.count_params()))
table_data['name'].append(layer.name)
table_data['connected_to'].append(str(connections[0]) if len(connections) > 0 else '-')
table_data['activation'].append(str(config.get('activation', '-')))
table_data['initialization'].append(init)
trainable_count = int(
numpy.sum([keras_backend.count_params(p) for p in set(keras_model.trainable_weights)])
)
non_trainable_count = int(
numpy.sum([keras_backend.count_params(p) for p in set(keras_model.non_trainable_weights)])
)
# Show row separators only if they are useful
if len(row_separators) == len(keras_model.layers):
row_separators = None
if mode == 'extended':
output += ui.table(
cell_data=[table_data['name'], table_data['layer_type'], table_data['output'], table_data['parameter_count']],
column_headers=['Layer name', 'Layer type', 'Output shape', 'Parameters'],
column_types=['str30', 'str20', 'str25', 'str20'],
column_separators=[1, 2],
row_separators=row_separators,
indent=4
)
elif mode == 'extended_wide':
output += ui.table(
cell_data=[table_data['name'], table_data['layer_type'], table_data['output'], table_data['parameter_count'],
table_data['activation'], table_data['initialization']],
column_headers=['Layer name', 'Layer type', 'Output shape', 'Parameters', 'Act.', 'Init.'],
column_types=['str30', 'str20', 'str25', 'str20', 'str15', 'str15'],
column_separators=[1, 2, 3],
row_separators=row_separators,
indent=4
)
if show_parameters:
output += ui.line('') + '\n'
output += ui.line('Parameters', indent=4) + '\n'
output += ui.data(indent=6, field='Total', value=trainable_count + non_trainable_count) + '\n'
output += ui.data(indent=6, field='Trainable', value=trainable_count) + '\n'
output += ui.data(indent=6, field='Non-Trainable', value=non_trainable_count) + '\n'
else:
output_buffer = []
keras_model.summary(print_fn=output_buffer.append)
for line in output_buffer:
if is_jupyter():
output += ui.line('<code>'+line+'</code>', indent=4) + '\n'
else:
output += ui.line(line, indent=4) + '\n'
model_config = keras_model.get_config()
if show_parameters:
output += ui.line('') + '\n'
output += ui.line('Input', indent=4) + '\n'
output += ui.data(indent=6, field='Shape', value=keras_model.input_shape) + '\n'
output += ui.line('Output', indent=4) + '\n'
output += ui.data(indent=6, field='Shape', value=keras_model.output_shape) + '\n'
if isinstance(model_config, dict) and 'layers' in model_config:
output += ui.data(
indent=6,
field='Activation',
value=model_config['layers'][-1]['config'].get('activation')
) + '\n'
elif isinstance(model_config, list):
output += ui.data(
indent=6,
field='Activation',
value=model_config[-1].get('config', {}).get('activation')
) + '\n'
if display:
if is_jupyter():
from IPython.core.display import display, HTML
display(HTML(output))
else:
print(output)
else:
return output
def create_sequential_model(model_parameter_list, input_shape=None, output_shape=None, constants=None, return_functional=False):
"""Create sequential Keras model
Example parameters::
model_parameter_list = [
{
'class_name': 'Dense',
'config': {
'units': 'CONSTANT_B',
'kernel_initializer': 'uniform',
'activation': 'relu'
}
},
{
'class_name': 'Dropout',
'config': {
'rate': 0.2
}
},
{
'class_name': 'Dense',
'config': {
'units': 'CONSTANT_A' * 2,
'kernel_initializer': 'uniform',
'activation': 'relu'
}
},
{
'class_name': 'Dropout',
'config': {
'rate': 0.2
}
},
{
'class_name': 'Dense',
'config': {
'units': 'CLASS_COUNT',
'kernel_initializer': 'uniform',
'activation': 'softmax'
}
}
]
constants = {
'CONSTANT_A': 50,
'CONSTANT_B': 100
}
Parameters
----------
model_parameter_list : dict or DictContainer
Model parameters
input_shape : int
Size of the input layer
Default value None
output_shape : int
Size of the output layer
Default value None
constants : dict or DictContainer
Constants used in the model_parameter definitions.
Default value None
return_functional : bool
Convert sequential model into function model.
Default value False
Returns
-------
Keras model
"""
from tensorflow.keras.models import Sequential
keras_model = Sequential()
tuple_fields = [
'input_shape',
'kernel_size',
'pool_size',
'dims',
'target_shape',
'strides'
]
# Get constants for model
if constants is None:
constants = {}
if 'INPUT_SHAPE' not in constants and input_shape is not None:
constants['INPUT_SHAPE'] = input_shape
if 'OUTPUT_SHAPE' not in constants and output_shape is not None:
constants['OUTPUT_SHAPE'] = output_shape
if 'CLASS_COUNT' not in constants:
constants['CLASS_COUNT'] = output_shape
if 'FEATURE_VECTOR_LENGTH' not in constants:
constants['FEATURE_VECTOR_LENGTH'] = input_shape
def logger():
logger_instance = logging.getLogger(__name__)
if not logger_instance.handlers:
setup_logging()
return logger_instance
def process_field(value, constants_dict):
math_eval = SimpleMathStringEvaluator()
if isinstance(value, str):
sub_fields = value.split()
if len(sub_fields) > 1:
# Inject constants to math formula
for subfield_id, subfield in enumerate(sub_fields):
if subfield in constants_dict:
sub_fields[subfield_id] = str(constants_dict[subfield])
value = ''.join(sub_fields)
else:
# Inject constants
if value in constants_dict:
value = str(constants_dict[value])
return math_eval.eval(value)
elif isinstance(value, list):
processed_value_list = []
for item_id, item in enumerate(value):
processed_value_list.append(
process_field(
value=item,
constants_dict=constants_dict
)
)
return processed_value_list
else:
return value
# Inject constant into constants with equations
for field in list(constants.keys()):
constants[field] = process_field(
value=constants[field],
constants_dict=constants
)
# Setup layers
for layer_id, layer_setup in enumerate(model_parameter_list):
# Get layer parameters
layer_setup = DictContainer(layer_setup)
if 'config' not in layer_setup:
layer_setup['config'] = {}
# Get layer class
try:
layer_class = getattr(
importlib.import_module('tensorflow.keras.layers'),
layer_setup['class_name']
)
except AttributeError:
message = 'Invalid Keras layer type [{type}].'.format(
type=layer_setup['class_name']
)
logger().exception(message)
raise AttributeError(message)
# Inject constants
for config_field in list(layer_setup['config'].keys()):
layer_setup['config'][config_field] = process_field(
value=layer_setup['config'][config_field],
constants_dict=constants
)
# Convert lists into tuples
for field in tuple_fields:
if field in layer_setup['config'] and isinstance(layer_setup['config'][field], list):
layer_setup['config'][field] = tuple(layer_setup['config'][field])
# Inject input shape for Input layer if not given
if layer_id == 0 and layer_setup.get_path('config.input_shape') is None and input_shape is not None:
# Set input layer dimension for the first layer if not set
layer_setup['config']['input_shape'] = (input_shape,)
if 'wrapper' in layer_setup:
# Get layer wrapper class
try:
wrapper_class = getattr(
importlib.import_module("tensorflow.keras.layers"),
layer_setup['wrapper']
)
except AttributeError:
message = 'Invalid Keras layer wrapper type [{type}].'.format(
type=layer_setup['wrapper']
)
logger().exception(message)
raise AttributeError(message)
wrapper_parameters = layer_setup.get('config_wrapper', {})
if layer_setup.get('config'):
keras_model.add(
wrapper_class(layer_class(**dict(layer_setup.get('config'))), **dict(wrapper_parameters)))
else:
keras_model.add(wrapper_class(layer_class(), **dict(wrapper_parameters)))
else:
if layer_setup.get('config'):
keras_model.add(layer_class(**dict(layer_setup.get('config'))))
else:
keras_model.add(layer_class())
if return_functional:
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
input_layer = Input(batch_shape=keras_model.layers[0].input_shape)
prev_layer = input_layer
for layer in keras_model.layers:
prev_layer = layer(prev_layer)
keras_model = Model(
inputs=[input_layer],
outputs=[prev_layer]
)
return keras_model
def test_log():
with dcase_util.utils.DisableLogger():
DictContainer(filename='test.yaml').log()
def get_audio_info(filename, logger=None):
"""Get information about audio file without opening it.
Parameters
----------
filename : str
filename
logger : Logger class
Logger class
Default value None
Returns
-------
DictContainer
Dict with audio file information
"""
from dcase_util.utils.files import FileFormat
from dcase_util.files import File
from dcase_util.containers import DictContainer
if logger is None:
logger = logging.getLogger(__name__)
file = File(filename=filename,
valid_formats=[
FileFormat.WAV, FileFormat.FLAC, FileFormat.OGG,
FileFormat.MP3, FileFormat.M4A, FileFormat.MP4,
FileFormat.WEBM
])
if not file.exists():
# File does not exists
message = '{name}: File does not exists [{filename}] '.format(
name=__name__, filename=filename)
logger.exception(message)
raise IOError(message)
file.detect_file_format()
if file.format is None:
# Unknown format
message = '{name}: File format cannot be detected for file [{filename}] '.format(
name=__name__, filename=filename)
logger.exception(message)
raise IOError(message)
info = DictContainer({
'filename': file.filename,
'bytes': file.bytes,
'format': file.format
})
if file.format == FileFormat.WAV:
import soundfile
wav_info = soundfile.info(file=file.filename)
info['fs'] = wav_info.samplerate
info['channels'] = wav_info.channels
info['duration_sec'] = wav_info.duration
info['duration_ms'] = (wav_info.frames /
float(wav_info.samplerate)) * 1000
info['duration_samples'] = wav_info.frames
info['subtype'] = {
'name': wav_info.subtype,
'info': wav_info.subtype_info
}
# Map sub type to bit depth
if info['subtype'] == 'PCM_16':
info['bit_depth'] = 16
elif info['subtype'] == 'PCM_24':
info['bit_depth'] = 24
elif info['subtype'] == 'PCM_32':
info['bit_depth'] = 32
elif file.format in [
FileFormat.FLAC, FileFormat.OGG, FileFormat.MP3, FileFormat.M4A,
FileFormat.MP4, FileFormat.WEBM
]:
# Use ffprobe to get file info from other formats
import subprocess
import json
import shlex
cmd = "ffprobe -v quiet -print_format json -show_streams"
args = shlex.split(cmd)
args.append(file.filename)
# Run command line command, fetch and parse json output
try:
output = subprocess.check_output(args).decode('utf-8')
except OSError:
# Error while running the command
message = '{name}: It seems that ffmpeg (ffprobe) is not installed.'.format(
name=__name__, filename=filename)
logger.exception(message)
raise IOError(message)
ffmpeg_meta = json.loads(output)
for stream in ffmpeg_meta['streams']:
if stream['codec_type'] == 'audio':
# Fetch audio info from first audio stream
info['fs'] = int(stream['sample_rate'])
# Get duration
if 'duration' not in stream:
info['duration_sec'] = None
elif is_float(stream['duration']):
info['duration_sec'] = float(stream['duration'])
else:
info['duration_sec'] = None
# Get bit rate
if 'bit_rate' not in stream:
info['bit_rate'] = None
elif is_int(stream['bit_rate']):
info['bit_rate'] = int(stream['bit_rate'])
else:
info['bit_rate'] = None
# Get codec info
info['codec'] = {}
if 'codec_name' in stream:
info['codec']['name'] = stream['codec_name']
if 'codec_long_name' in stream:
info['codec']['name_long'] = stream['codec_long_name']
if 'codec_type' in stream:
info['codec']['type'] = stream['codec_type']
break
return info
def test_wrong_path():
with dcase_util.utils.DisableLogger():
DictContainer(data).get_path(path=9)
def prepare(self):
"""Prepare dataset for the usage.
Returns
-------
self
"""
if not self.meta_container.exists():
scene_label = 'home'
dcase_cross_val_data = ListDictContainer(filename=os.path.join(
self.local_path, 'chime_home',
'development_chunks_refined_crossval_dcase2016.csv')).load(
fields=['id', 'filename', 'set_id'])
audio_files = {}
for item in dcase_cross_val_data:
audio_filename = os.path.join(
'chime_home', 'chunks',
item['filename'] + self.sample_mode + '.wav')
annotation_filename = os.path.join('chime_home', 'chunks',
item['filename'] + '.csv')
if audio_filename not in audio_files:
audio_files[audio_filename] = {
'audio': audio_filename,
'meta': annotation_filename
}
meta_data = MetaDataContainer()
for audio_filename, data in iteritems(audio_files):
current_meta_data = DictContainer(filename=os.path.join(
self.local_path, data['meta'])).load()
tags = []
for i, tag in enumerate(current_meta_data['majorityvote']):
if tag != 'S' and tag != 'U':
tags.append(self.tagcode_to_taglabel(tag))
name = os.path.split(audio_filename)[1]
segment_name = name[0:name.find('_chunk')]
chunk_name = name[name.find('_chunk') + 1:].split('.')[0]
item = MetaDataItem({
'filename': audio_filename,
'scene_label': scene_label,
'tags': ';'.join(tags) + ';',
'identifier': segment_name
})
self.process_meta_item(item=item, absolute_path=False)
meta_data.append(item)
# Save meta
meta_data.save(filename=self.meta_file)
# Load meta and cross validation
self.load()
all_folds_found = True
for fold in range(1, self.crossvalidation_folds + 1):
train_filename = self.evaluation_setup_filename(setup_part='train',
fold=fold)
test_filename = self.evaluation_setup_filename(setup_part='test',
fold=fold)
eval_filename = self.evaluation_setup_filename(
setup_part='evaluate', fold=fold)
if not os.path.isfile(train_filename):
all_folds_found = False
if not os.path.isfile(test_filename):
all_folds_found = False
if not os.path.isfile(eval_filename):
all_folds_found = False
if not all_folds_found:
Path().makedirs(path=self.evaluation_setup_path)
dcase_crossval = {
1: [],
2: [],
3: [],
4: [],
5: [],
}
dcase_cross_val_data = ListDictContainer(filename=os.path.join(
self.local_path, 'chime_home',
'development_chunks_refined_crossval_dcase2016.csv')).load(
fields=['id', 'filename', 'set_id'])
for item in dcase_cross_val_data:
dcase_crossval[int(item['set_id']) + 1].append(
self.relative_to_absolute_path(
os.path.join(
'chime_home', 'chunks',
item['filename'] + self.sample_mode + '.wav')))
for fold in range(1, self.crossvalidation_folds + 1):
# Collect training and testing files
train_files = []
for f in range(1, self.crossvalidation_folds + 1):
if f is not fold:
train_files += dcase_crossval[f]
test_files = dcase_crossval[fold]
# Create meta containers and save them
# Train
train_filename = self.evaluation_setup_filename(
setup_part='train', fold=fold)
train_meta = MetaDataContainer(filename=train_filename)
for filename in train_files:
item = self.file_meta(filename)[0]
self.process_meta_item(item=item, absolute_path=False)
train_meta.append(item)
train_meta.save()
# Test
test_filename = self.evaluation_setup_filename(
setup_part='test', fold=fold)
test_meta = MetaDataContainer(filename=test_filename)
for filename in test_files:
item = MetaDataItem(
{'filename': self.absolute_to_relative_path(filename)})
test_meta.append(item)
test_meta.save()
# Evaluate
eval_filename = self.evaluation_setup_filename(
setup_part='evaluate', fold=fold)
eval_meta = MetaDataContainer(filename=eval_filename)
for filename in test_files:
item = self.file_meta(filename)[0]
self.process_meta_item(item=item, absolute_path=False)
eval_meta.append(item)
eval_meta.save()
# Load meta and cross validation
self.load()
return self
def test_load_not_found():
with dcase_util.utils.DisableLogger():
DictContainer().load(filename=os.path.join(tempfile.gettempdir(), 'wrong.cpickle'))
def prepare(self):
"""Prepare dataset for the usage.
Returns
-------
self
"""
if not self.meta_container.exists():
scene_label = 'home'
evaluation_chunks = ListDictContainer(
filename=os.path.join(self.local_path, 'chime_home',
'evaluation_chunks_refined.csv')).load(
fields=['id', 'filename', 'set_id'])
audio_files = {}
for item in dcase_cross_val_data:
audio_filename = os.path.join(
'chime_home', 'chunks',
item['filename'] + self.sample_mode + '.wav')
annotation_filename = os.path.join('chime_home', 'chunks',
item['filename'] + '.csv')
if audio_filename not in audio_files:
audio_files[audio_filename] = {
'audio': audio_filename,
'meta': annotation_filename
}
meta_data = MetaDataContainer()
for audio_filename, data in iteritems(audio_files):
current_meta_data = DictContainer(filename=os.path.join(
self.local_path, data['meta'])).load()
tags = []
for i, tag in enumerate(current_meta_data['majorityvote']):
if tag != 'S' and tag != 'U':
tags.append(self.tagcode_to_taglabel(tag))
name = os.path.split(audio_filename)[1]
segment_name = name[0:name.find('_chunk')]
chunk_name = name[name.find('_chunk') + 1:].split('.')[0]
item = MetaDataItem({
'filename': audio_filename,
'scene_label': scene_label,
'tags': ';'.join(tags) + ';',
'identifier': segment_name
})
self.process_meta_item(item=item, absolute_path=False)
meta_data.append(item)
# Save meta
meta_data.save(filename=self.meta_file)
# Load meta and cross validation
self.load()
all_folds_found = True
train_filename = self.evaluation_setup_filename(setup_part='train')
test_filename = self.evaluation_setup_filename(setup_part='test')
eval_filename = self.evaluation_setup_filename(setup_part='evaluate')
if not os.path.isfile(train_filename):
all_folds_found = False
if not os.path.isfile(test_filename):
all_folds_found = False
if not os.path.isfile(eval_filename):
all_folds_found = False
if not all_folds_found:
Path().makedirs(path=self.evaluation_setup_path)
# Train
train_filename = self.evaluation_setup_filename(setup_part='train')
train_meta = MetaDataContainer(filename=train_filename)
for filename in self.train_files():
train_meta.append(self.file_meta(filename)[0])
train_meta.save()
# Test
test_filename = self.evaluation_setup_filename(setup_part='test')
test_meta = MetaDataContainer(filename=test_filename)
for filename in self.test_files():
test_meta.append(
MetaDataItem(
{'filename':
self.absolute_to_relative_path(filename)}))
test_meta.save()
# Evaluate
eval_filename = self.evaluation_setup_filename(
setup_part='evaluate')
eval_meta = MetaDataContainer(filename=eval_filename)
for filename in self.test_files():
eval_meta.append(self.file_meta(filename)[0])
eval_meta.save()
# Load meta and cross validation
self.load()
return self
def test_container():
data_container = DictContainer(data)
nose.tools.eq_(data_container.get_path(path='level1.field1'), 1)
nose.tools.eq_(data_container.get_path(path='level1.level2a.field2'), 2)
nose.tools.eq_(data_container.get_path(path='level1.level2b.field3'), 3)
nose.tools.eq_(data_container.get_path(path='level1.level2a.level3a.field1'), 1)
nose.tools.eq_(data_container.get_path(path='level1.level2a.level3a'), {'field1': 1, 'field2': 2, 'field3': 3})
nose.tools.eq_(data_container.get_path(path='level1.level2c.*.field1'), [1, 1])
nose.tools.eq_(data_container.get_path(path=['level1', 'field1']), 1)
nose.tools.eq_(data_container.get_path(path=['level1', 'level2a', 'field2']), 2)
nose.tools.eq_(data_container.get_hash(), '23ffcb8de3af794547779197397ab987')
nose.tools.eq_(data_container.get_hash_for_path(dotted_path='level1.level2c'), 'a084001c6e49eef233a95f8996d1183c')
data_container.merge(override={
'level1': {
'field1': 10,
'field2': 20,
'field3': 30,
'level2a': {
'field1': 10,
'field2': 20,
'field3': 30,
'level3a': {
'field1': 10,
'field2': 20,
'field3': 30,
},
'level3b': {
'field1': 10,
'field2': 20,
'field3': 30,
},
}
}
})
nose.tools.eq_(data_container.get_path(path='level1.field1'), 10)
nose.tools.eq_(data_container.get_path(path='level1.level2a.field2'), 20)
nose.tools.eq_(data_container.get_path(path='level1.level2b.field3'), 3)
data_container.set_path(path='level1.field1', new_value=100)
nose.tools.eq_(data_container.get_path(path='level1.field1'), 100)
data_container.set_path(path='level1.level2c.*.field1', new_value=100)
nose.tools.eq_(data_container.get_path(path='level1.level2c.*.field1'), [100, 100])
nose.tools.eq_(data_container.get_hash(), '0adb9bf0f7f579e8b297b7186b0570da')
data_container['_hash'] = 'test'
nose.tools.eq_(data_container.get_hash(), '0adb9bf0f7f579e8b297b7186b0570da')
data_container.set_path(path=['level1', 'field2'], new_value=100)
nose.tools.eq_(data_container.get_path(path='level1.field2'), 100)
data_container = DictContainer(data)
nose.tools.eq_(data_container.get_leaf_path_list(),
['level1.field1',
'level1.field2',
'level1.field3',
'level1.level2a.field1',
'level1.level2a.field2',
'level1.level2a.field3',
'level1.level2a.level3a.field1',
'level1.level2a.level3a.field2',
'level1.level2a.level3a.field3',
'level1.level2a.level3b.field1',
'level1.level2a.level3b.field2',
'level1.level2a.level3b.field3',
'level1.level2b.field1',
'level1.level2b.field2',
'level1.level2b.field3',
'level1.level2b.level3.field1',
'level1.level2b.level3.field2',
'level1.level2b.level3.field3',
'level1.level2c.level3a.field1',
'level1.level2c.level3a.field2',
'level1.level2c.level3a.field3',
'level1.level2c.level3b.field1',
'level1.level2c.level3b.field2',
'level1.level2c.level3b.field3'])
nose.tools.eq_(data_container.get_leaf_path_list(target_field='field1'),
['level1.field1',
'level1.level2a.field1',
'level1.level2a.level3a.field1',
'level1.level2a.level3b.field1',
'level1.level2b.field1',
'level1.level2b.level3.field1',
'level1.level2c.level3a.field1',
'level1.level2c.level3b.field1'])
nose.tools.eq_(data_container.get_leaf_path_list(target_field_startswith='field'),
['level1.field1',
'level1.field2',
'level1.field3',
'level1.level2a.field1',
'level1.level2a.field2',
'level1.level2a.field3',
'level1.level2a.level3a.field1',
'level1.level2a.level3a.field2',
'level1.level2a.level3a.field3',
'level1.level2a.level3b.field1',
'level1.level2a.level3b.field2',
'level1.level2a.level3b.field3',
'level1.level2b.field1',
'level1.level2b.field2',
'level1.level2b.field3',
'level1.level2b.level3.field1',
'level1.level2b.level3.field2',
'level1.level2b.level3.field3',
'level1.level2c.level3a.field1',
'level1.level2c.level3a.field2',
'level1.level2c.level3a.field3',
'level1.level2c.level3b.field1',
'level1.level2c.level3b.field2',
'level1.level2c.level3b.field3'])
nose.tools.eq_(data_container.get_leaf_path_list(target_field_endswith='d1'),
['level1.field1',
'level1.level2a.field1',
'level1.level2a.level3a.field1',
'level1.level2a.level3b.field1',
'level1.level2b.field1',
'level1.level2b.level3.field1',
'level1.level2c.level3a.field1',
'level1.level2c.level3b.field1'])
def __init__(self, *args, **kwargs):
# Run DictContainer init
DictContainer.__init__(self, *args, **kwargs)
super(OneToOneMappingContainer, self).__init__(*args, **kwargs)