def __init__(self, chainer_config: dict, *, batch_size: int = -1,
metrics: Iterable[Union[str, dict]] = ('accuracy',),
evaluation_targets: Iterable[str] = ('valid', 'test'),
show_examples: bool = False,
tensorboard_log_dir: Optional[Union[str, Path]] = None,
max_test_batches: int = -1,
**kwargs) -> None:
if kwargs:
log.info(f'{self.__class__.__name__} got additional init parameters {list(kwargs)} that will be ignored:')
self.chainer_config = chainer_config
self._chainer = Chainer(chainer_config['in'], chainer_config['out'], chainer_config.get('in_y'))
self.batch_size = batch_size
self.metrics = parse_metrics(metrics, self._chainer.in_y, self._chainer.out_params)
self.evaluation_targets = tuple(evaluation_targets)
self.show_examples = show_examples
self.max_test_batches = None if max_test_batches < 0 else max_test_batches
self.tensorboard_log_dir: Optional[Path] = tensorboard_log_dir
if tensorboard_log_dir is not None:
try:
# noinspection PyPackageRequirements
# noinspection PyUnresolvedReferences
import tensorflow
except ImportError:
log.warning('TensorFlow could not be imported, so tensorboard log directory'
f'`{self.tensorboard_log_dir}` will be ignored')
self.tensorboard_log_dir = None
else:
self.tensorboard_log_dir = expand_path(tensorboard_log_dir)
self._tf = tensorflow
self._built = False
self._saved = False
self._loaded = False
def fit_chainer(config: dict, iterator: Union[DataLearningIterator,
DataFittingIterator]):
chainer_config: dict = config['chainer']
chainer = Chainer(chainer_config['in'], chainer_config['out'],
chainer_config.get('in_y'))
for component_config in chainer_config['pipe']:
component = from_params(component_config, mode='train')
if 'fit_on' in component_config:
component: Estimator
preprocessed = chainer(*iterator.get_instances('train'),
to_return=component_config['fit_on'])
if len(component_config['fit_on']) == 1:
preprocessed = [preprocessed]
else:
preprocessed = zip(*preprocessed)
component.fit(*preprocessed)
component.save()
if 'fit_on_batch' in component_config:
component: Estimator
component.fit_batches(iterator, config['train']['batch_size'])
component.save()
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
chainer.append(component, c_in, c_out, in_y, main)
return chainer
def fit_chainer(config: dict, iterator: Union[DataLearningIterator, DataFittingIterator]) -> Chainer:
"""Fit and return the chainer described in corresponding configuration dictionary."""
chainer_config: dict = config['chainer']
chainer = Chainer(chainer_config['in'], chainer_config['out'], chainer_config.get('in_y'))
for component_config in chainer_config['pipe']:
component = from_params(component_config, mode='train')
if 'fit_on' in component_config:
component: Estimator
preprocessed = chainer(*iterator.get_instances('train'), to_return=component_config['fit_on'])
if len(component_config['fit_on']) == 1:
preprocessed = [preprocessed]
else:
preprocessed = zip(*preprocessed)
component.fit(*preprocessed)
component.save()
if 'fit_on_batch' in component_config:
component: Estimator
component.fit_batches(iterator, config['train']['batch_size'])
component.save()
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
chainer.append(component, c_in, c_out, in_y, main)
return chainer
def build_model_from_config(config,
mode='infer',
load_trained=False,
as_component=False):
set_deeppavlov_root(config)
model_config = config['chainer']
model = Chainer(model_config['in'],
model_config['out'],
model_config.get('in_y'),
as_component=as_component)
for component_config in model_config['pipe']:
if load_trained and ('fit_on' in component_config
or 'in_y' in component_config):
try:
component_config['load_path'] = component_config['save_path']
except KeyError:
log.warning(
'No "save_path" parameter for the {} component, so "load_path" will not be renewed'
.format(
component_config.get(
'name', component_config.get('ref', 'UNKNOWN'))))
component = from_params(component_config, mode=mode)
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
model.append(component, c_in, c_out, in_y, main)
return model
def build_model_from_config(config: [str, Path, dict], mode: str = 'infer', load_trained: bool = False) -> Chainer:
"""Build and return the model described in corresponding configuration file."""
if isinstance(config, (str, Path)):
config = read_json(config)
set_deeppavlov_root(config)
import_packages(config.get('metadata', {}).get('imports', []))
model_config = config['chainer']
model = Chainer(model_config['in'], model_config['out'], model_config.get('in_y'))
for component_config in model_config['pipe']:
if load_trained and ('fit_on' in component_config or 'in_y' in component_config):
try:
component_config['load_path'] = component_config['save_path']
except KeyError:
log.warning('No "save_path" parameter for the {} component, so "load_path" will not be renewed'
.format(component_config.get('name', component_config.get('ref', 'UNKNOWN'))))
component = from_params(component_config, mode=mode)
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
model.append(component, c_in, c_out, in_y, main)
return model
def build_model_from_config(config: [str, Path, dict], mode: str = 'infer', load_trained: bool = False,
as_component: bool = False) -> Chainer:
"""Build and return the model described in corresponding configuration file."""
if isinstance(config, (str, Path)):
config = read_json(config)
set_deeppavlov_root(config)
import_packages(config.get('metadata', {}).get('imports', []))
model_config = config['chainer']
model = Chainer(model_config['in'], model_config['out'], model_config.get('in_y'), as_component=as_component)
for component_config in model_config['pipe']:
if load_trained and ('fit_on' in component_config or 'in_y' in component_config):
try:
component_config['load_path'] = component_config['save_path']
except KeyError:
log.warning('No "save_path" parameter for the {} component, so "load_path" will not be renewed'
.format(component_config.get('name', component_config.get('ref', 'UNKNOWN'))))
component = from_params(component_config, mode=mode)
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
model.append(component, c_in, c_out, in_y, main)
return model
def fit_chainer(config: dict, iterator: BasicDatasetIterator) -> Chainer:
chainer_config: dict = config['chainer']
chainer = Chainer(chainer_config['in'], chainer_config['out'],
chainer_config.get('in_y'))
for component_config in chainer_config['pipe']:
component = from_params(component_config, vocabs=[], mode='train')
if 'fit_on' in component_config:
component: Estimator
preprocessed = chainer(*iterator.iter_all('train'),
to_return=component_config['fit_on'])
if len(component_config['fit_on']) == 1:
preprocessed = [preprocessed]
else:
preprocessed = zip(*preprocessed)
component.fit(*preprocessed)
component.save()
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
chainer.append(c_in, c_out, component, in_y, main)
return chainer
def fit_chainer(config: dict, iterator: Union[DataLearningIterator, DataFittingIterator]) -> Chainer:
"""Fit and return the chainer described in corresponding configuration dictionary."""
chainer_config: dict = config['chainer']
chainer = Chainer(chainer_config['in'], chainer_config['out'], chainer_config.get('in_y'))
for component_config in chainer_config['pipe']:
component = from_params(component_config, mode='train')
if 'fit_on' in component_config:
component: Estimator
targets = component_config['fit_on']
if isinstance(targets, str):
targets = [targets]
preprocessed = chainer.compute(*iterator.get_instances('train'), targets=targets)
if len(component_config['fit_on']) == 1:
preprocessed = [preprocessed]
component.fit(*preprocessed)
component.save()
if 'fit_on_batch' in component_config:
component: Estimator
component.fit_batches(iterator, config['train']['batch_size'])
component.save()
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
chainer.append(component, c_in, c_out, in_y, main)
return chainer
def build_model(config: Union[str, Path, dict],
mode: str = 'infer',
load_trained: bool = False,
download: bool = False,
serialized: Optional[bytes] = None) -> Chainer:
"""Build and return the model described in corresponding configuration file."""
config = parse_config(config)
if serialized:
serialized: list = pickle.loads(serialized)
if download:
deep_download(config)
import_packages(config.get('metadata', {}).get('imports', []))
model_config = config['chainer']
model = Chainer(model_config['in'], model_config['out'],
model_config.get('in_y'))
for component_config in model_config['pipe']:
if load_trained and ('fit_on' in component_config
or 'in_y' in component_config):
try:
component_config['load_path'] = component_config['save_path']
except KeyError:
log.warning(
'No "save_path" parameter for the {} component, so "load_path" will not be renewed'
.format(
component_config.get(
'class_name',
component_config.get('ref', 'UNKNOWN'))))
if serialized and 'in' in component_config:
component_serialized = serialized.pop(0)
else:
component_serialized = None
component = from_params(component_config,
mode=mode,
serialized=component_serialized)
if 'id' in component_config:
model._components_dict[component_config['id']] = component
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
model.append(component, c_in, c_out, in_y, main)
return model
def _test_model(
model: Chainer,
metrics_functions: List[Metric],
iterator: DataLearningIterator,
batch_size=-1,
data_type='valid',
start_time: float = None,
show_examples=False) -> Dict[str, Union[int, OrderedDict, str]]:
if start_time is None:
start_time = time.time()
expected_outputs = list(set().union(model.out_params,
*[m.inputs
for m in metrics_functions]))
outputs = {out: [] for out in expected_outputs}
examples = 0
for x, y_true in iterator.gen_batches(batch_size, data_type,
shuffle=False):
examples += len(x)
y_predicted = list(
model.compute(list(x), list(y_true), targets=expected_outputs))
if len(expected_outputs) == 1:
y_predicted = [y_predicted]
for out, val in zip(outputs.values(), y_predicted):
out += list(val)
metrics = [(m.name, m.fn(*[outputs[i] for i in m.inputs]))
for m in metrics_functions]
report = {
'eval_examples_count':
examples,
'metrics':
prettify_metrics(metrics),
'time_spent':
str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5)))
}
if show_examples:
try:
report['examples'] = [{
'x': x_item,
'y_predicted': y_predicted_item,
'y_true': y_true_item
} for x_item, y_predicted_item, y_true_item in zip(
x, {k: outputs[k]
for k in model.out_params}, y_true)]
except NameError:
log.warning(
f'Could not log examples for {data_type}, assuming it\'s empty'
)
return report
def build_model(config: Union[str, Path, dict], mode: str = 'infer',
load_trained: bool = False, download: bool = False,
serialized: Optional[bytes] = None) -> Chainer:
"""Build and return the model described in corresponding configuration file."""
config = parse_config(config)
if serialized:
serialized: list = pickle.loads(serialized)
if download:
deep_download(config)
import_packages(config.get('metadata', {}).get('imports', []))
model_config = config['chainer']
model = Chainer(model_config['in'], model_config['out'], model_config.get('in_y'))
for component_config in model_config['pipe']:
if load_trained and ('fit_on' in component_config or 'in_y' in component_config):
try:
component_config['load_path'] = component_config['save_path']
except KeyError:
log.warning('No "save_path" parameter for the {} component, so "load_path" will not be renewed'
.format(component_config.get('class_name', component_config.get('ref', 'UNKNOWN'))))
if serialized and 'in' in component_config:
component_serialized = serialized.pop(0)
else:
component_serialized = None
component = from_params(component_config, mode=mode, serialized=component_serialized)
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
model.append(component, c_in, c_out, in_y, main)
return model
def build_model_from_config(config, mode='infer', load_trained=False):
set_deeppavlov_root(config)
if 'chainer' in config:
model_config = config['chainer']
model = Chainer(model_config['in'], model_config['out'], model_config.get('in_y'))
for component_config in model_config['pipe']:
if load_trained and ('fit_on' in component_config or 'in_y' in component_config):
try:
component_config['load_path'] = component_config['save_path']
except KeyError:
log.warning('No "save_path" parameter for the {} component, so "load_path" will not be renewed'
.format(component_config.get('name', component_config.get('ref', 'UNKNOWN'))))
component = from_params(component_config, vocabs=[], mode=mode)
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
model.append(c_in, c_out, component, in_y, main)
return model
model_config = config['model']
if load_trained:
try:
model_config['load_path'] = model_config['save_path']
except KeyError:
log.warning('No "save_path" parameter for the model, so "load_path" will not be renewed')
vocabs = {}
if 'vocabs' in config:
for vocab_param_name, vocab_config in config['vocabs'].items():
v = from_params(vocab_config, mode=mode)
vocabs[vocab_param_name] = v
model = from_params(model_config, vocabs=vocabs, mode=mode)
model.reset()
return model
class FitTrainer:
"""
Trainer class for fitting and evaluating :class:`Estimators <deeppavlov.core.models.estimator.Estimator>`
Args:
chainer_config: ``"chainer"`` block of a configuration file
batch_size: batch_size to use for partial fitting (if available) and evaluation,
the whole dataset is used if ``batch_size`` is negative or zero (default is ``-1``)
metrics: iterable of metrics where each metric can be a registered metric name or a dict of ``name`` and
``inputs`` where ``name`` is a registered metric name and ``inputs`` is a collection of parameter names
from chainer’s inner memory that will be passed to the metric function;
default value for ``inputs`` parameter is a concatenation of chainer’s ``in_y`` and ``out`` fields
(default is ``('accuracy',)``)
evaluation_targets: data types on which to evaluate trained pipeline (default is ``('valid', 'test')``)
show_examples: a flag used to print inputs, expected outputs and predicted outputs for the last batch
in evaluation logs (default is ``False``)
tensorboard_log_dir: path to a directory where tensorboard logs can be stored, ignored if None
(default is ``None``)
max_test_batches: maximum batches count for pipeline testing and evaluation, ignored if negative
(default is ``-1``)
**kwargs: additional parameters whose names will be logged but otherwise ignored
"""
def __init__(self, chainer_config: dict, *, batch_size: int = -1,
metrics: Iterable[Union[str, dict]] = ('accuracy',),
evaluation_targets: Iterable[str] = ('valid', 'test'),
show_examples: bool = False,
tensorboard_log_dir: Optional[Union[str, Path]] = None,
max_test_batches: int = -1,
**kwargs) -> None:
if kwargs:
log.info(f'{self.__class__.__name__} got additional init parameters {list(kwargs)} that will be ignored:')
self.chainer_config = chainer_config
self._chainer = Chainer(chainer_config['in'], chainer_config['out'], chainer_config.get('in_y'))
self.batch_size = batch_size
self.metrics = parse_metrics(metrics, self._chainer.in_y, self._chainer.out_params)
self.evaluation_targets = tuple(evaluation_targets)
self.show_examples = show_examples
self.max_test_batches = None if max_test_batches < 0 else max_test_batches
self.tensorboard_log_dir: Optional[Path] = tensorboard_log_dir
if tensorboard_log_dir is not None:
try:
# noinspection PyPackageRequirements
# noinspection PyUnresolvedReferences
import tensorflow
except ImportError:
log.warning('TensorFlow could not be imported, so tensorboard log directory'
f'`{self.tensorboard_log_dir}` will be ignored')
self.tensorboard_log_dir = None
else:
self.tensorboard_log_dir = expand_path(tensorboard_log_dir)
self._tf = tensorflow
self._built = False
self._saved = False
self._loaded = False
def fit_chainer(self, iterator: Union[DataFittingIterator, DataLearningIterator]) -> None:
"""
Build the pipeline :class:`~deeppavlov.core.common.chainer.Chainer` and successively fit
:class:`Estimator <deeppavlov.core.models.estimator.Estimator>` components using a provided data iterator
"""
if self._built:
raise RuntimeError('Cannot fit already built chainer')
for component_index, component_config in enumerate(self.chainer_config['pipe'], 1):
component = from_params(component_config, mode='train')
if 'fit_on' in component_config:
component: Estimator
targets = component_config['fit_on']
if isinstance(targets, str):
targets = [targets]
if self.batch_size > 0 and callable(getattr(component, 'partial_fit', None)):
writer = None
for i, (x, y) in enumerate(iterator.gen_batches(self.batch_size, shuffle=False)):
preprocessed = self._chainer.compute(x, y, targets=targets)
# noinspection PyUnresolvedReferences
result = component.partial_fit(*preprocessed)
if result is not None and self.tensorboard_log_dir is not None:
if writer is None:
writer = self._tf.summary.FileWriter(str(self.tensorboard_log_dir /
f'partial_fit_{component_index}_log'))
for name, score in result.items():
summary = self._tf.Summary()
summary.value.add(tag='partial_fit/' + name, simple_value=score)
writer.add_summary(summary, i)
writer.flush()
else:
preprocessed = self._chainer.compute(*iterator.get_instances(), targets=targets)
if len(targets) == 1:
preprocessed = [preprocessed]
result: Optional[Dict[str, Iterable[float]]] = component.fit(*preprocessed)
if result is not None and self.tensorboard_log_dir is not None:
writer = self._tf.summary.FileWriter(str(self.tensorboard_log_dir /
f'fit_log_{component_index}'))
for name, scores in result.items():
for i, score in enumerate(scores):
summary = self._tf.Summary()
summary.value.add(tag='fit/' + name, simple_value=score)
writer.add_summary(summary, i)
writer.flush()
component.save()
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
self._chainer.append(component, c_in, c_out, in_y, main)
self._built = True
def _load(self) -> None:
if not self._loaded:
self._chainer.destroy()
self._chainer = build_model({'chainer': self.chainer_config}, load_trained=self._saved)
self._loaded = True
def get_chainer(self) -> Chainer:
"""Returns a :class:`~deeppavlov.core.common.chainer.Chainer` built from ``self.chainer_config`` for inference"""
self._load()
return self._chainer
def train(self, iterator: Union[DataFittingIterator, DataLearningIterator]) -> None:
"""Calls :meth:`~fit_chainer` with provided data iterator as an argument"""
self.fit_chainer(iterator)
self._saved = True
def test(self, data: Iterable[Tuple[Collection[Any], Collection[Any]]],
metrics: Optional[Collection[Metric]] = None, *,
start_time: Optional[float] = None, show_examples: Optional[bool] = None) -> dict:
"""
Calculate metrics and return reports on provided data for currently stored
:class:`~deeppavlov.core.common.chainer.Chainer`
Args:
data: iterable of batches of inputs and expected outputs
metrics: collection of metrics namedtuples containing names for report, metric functions
and their inputs names (if omitted, ``self.metrics`` is used)
start_time: start time for test report
show_examples: a flag used to return inputs, expected outputs and predicted outputs for the last batch
in a result report (if omitted, ``self.show_examples`` is used)
Returns:
a report dict containing calculated metrics, spent time value, examples count in tested data
and maybe examples
"""
if start_time is None:
start_time = time.time()
if show_examples is None:
show_examples = self.show_examples
if metrics is None:
metrics = self.metrics
expected_outputs = list(set().union(self._chainer.out_params, *[m.inputs for m in metrics]))
outputs = {out: [] for out in expected_outputs}
examples = 0
data = islice(data, self.max_test_batches)
for x, y_true in data:
examples += len(x)
y_predicted = list(self._chainer.compute(list(x), list(y_true), targets=expected_outputs))
if len(expected_outputs) == 1:
y_predicted = [y_predicted]
for out, val in zip(outputs.values(), y_predicted):
out += list(val)
if examples == 0:
log.warning('Got empty data iterable for scoring')
return {'eval_examples_count': 0, 'metrics': None, 'time_spent': str(datetime.timedelta(seconds=0))}
# metrics_values = [(m.name, m.fn(*[outputs[i] for i in m.inputs])) for m in metrics]
metrics_values = []
for metric in metrics:
value = metric.fn(*[outputs[i] for i in metric.inputs])
metrics_values.append((metric.alias, value))
report = {
'eval_examples_count': examples,
'metrics': prettify_metrics(metrics_values),
'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5)))
}
if show_examples:
y_predicted = zip(*[y_predicted_group
for out_name, y_predicted_group in zip(expected_outputs, y_predicted)
if out_name in self._chainer.out_params])
if len(self._chainer.out_params) == 1:
y_predicted = [y_predicted_item[0] for y_predicted_item in y_predicted]
report['examples'] = [{
'x': x_item,
'y_predicted': y_predicted_item,
'y_true': y_true_item
} for x_item, y_predicted_item, y_true_item in zip(x, y_predicted, y_true)]
return report
def evaluate(self, iterator: DataLearningIterator, evaluation_targets: Optional[Iterable[str]] = None, *,
print_reports: bool = True) -> Dict[str, dict]:
"""
Run :meth:`test` on multiple data types using provided data iterator
Args:
iterator: :class:`~deeppavlov.core.data.data_learning_iterator.DataLearningIterator` used for evaluation
evaluation_targets: iterable of data types to evaluate on
print_reports: a flag used to print evaluation reports as json lines
Returns:
a dictionary with data types as keys and evaluation reports as values
"""
self._load()
if evaluation_targets is None:
evaluation_targets = self.evaluation_targets
res = {}
for data_type in evaluation_targets:
data_gen = iterator.gen_batches(self.batch_size, data_type=data_type, shuffle=False)
report = self.test(data_gen)
res[data_type] = report
if print_reports:
print(json.dumps({data_type: report}, ensure_ascii=False, cls=NumpyArrayEncoder))
return res
def fit_chainer(config: dict, iterator: Union[DataLearningIterator, DataFittingIterator]) -> Chainer:
"""Fit and return the chainer described in corresponding configuration dictionary."""
chainer_config: dict = config['chainer']
chainer = Chainer(chainer_config['in'], chainer_config['out'], chainer_config.get('in_y'))
for component_config in chainer_config['pipe']:
component = from_params(component_config, mode='train')
if ('fit_on' in component_config) and \
(not callable(getattr(component, 'partial_fit', None))):
component: Estimator
targets = component_config['fit_on']
if isinstance(targets, str):
targets = [targets]
preprocessed = chainer.compute(*iterator.get_instances('train'), targets=targets)
if len(component_config['fit_on']) == 1:
preprocessed = [preprocessed]
result = component.fit(*preprocessed)
if result is not None and config['train'].get('tensorboard_log_dir') is not None:
import tensorflow as tf
tb_log_dir = expand_path(config['train']['tensorboard_log_dir'])
writer = tf.summary.FileWriter(str(tb_log_dir / 'fit_log'))
for name, scores in result.items():
for i, score in enumerate(scores):
summ = tf.Summary()
summ.value.add(tag='fit/' + name, simple_value=score)
writer.add_summary(summ, i)
writer.flush()
component.save()
if 'fit_on_batch' in component_config:
log.warning('`fit_on_batch` is deprecated and will be removed in future versions.'
' Please use `fit_on` instead.')
if ('fit_on_batch' in component_config) or \
(('fit_on' in component_config) and
callable(getattr(component, 'partial_fit', None))):
component: Estimator
targets = component_config.get('fit_on', component_config['fit_on_batch'])
if isinstance(targets, str):
targets = [targets]
for i, data in enumerate(iterator.gen_batches(config['train']['batch_size'], shuffle=False)):
preprocessed = chainer.compute(*data, targets=targets)
if len(targets) == 1:
preprocessed = [preprocessed]
result = component.partial_fit(*preprocessed)
if result is not None and config['train'].get('tensorboard_log_dir') is not None:
if i == 0:
import tensorflow as tf
tb_log_dir = expand_path(config['train']['tensorboard_log_dir'])
writer = tf.summary.FileWriter(str(tb_log_dir / 'fit_batches_log'))
for name, score in result.items():
summ = tf.Summary()
summ.value.add(tag='fit_batches/' + name, simple_value=score)
writer.add_summary(summ, i)
writer.flush()
component.save()
if 'in' in component_config:
c_in = component_config['in']
c_out = component_config['out']
in_y = component_config.get('in_y', None)
main = component_config.get('main', False)
chainer.append(component, c_in, c_out, in_y, main)
return chainer
def _train_batches(model: Chainer, iterator: DataLearningIterator, train_config: dict,
metrics_functions: List[Metric], *, start_epoch_num: Optional[int] = None) -> NNModel:
default_train_config = {
'epochs': 0,
'start_epoch_num': 0,
'max_batches': 0,
'batch_size': 1,
'metric_optimization': 'maximize',
'validation_patience': 5,
'val_every_n_epochs': 0,
'val_every_n_batches': 0,
'log_every_n_batches': 0,
'log_every_n_epochs': 0,
'validate_best': True,
'test_best': True,
'tensorboard_log_dir': None,
}
train_config = dict(default_train_config, **train_config)
if 'train_metrics' in train_config:
train_metrics_functions = _parse_metrics(train_config['train_metrics'], model.in_y, model.out_params)
else:
train_metrics_functions = metrics_functions
expected_outputs = list(set().union(model.out_params, *[m.inputs for m in train_metrics_functions]))
if train_config['metric_optimization'] == 'maximize':
def improved(score, best):
return score > best
best = float('-inf')
elif train_config['metric_optimization'] == 'minimize':
def improved(score, best):
return score < best
best = float('inf')
else:
raise ConfigError('metric_optimization has to be one of {}'.format(['maximize', 'minimize']))
i = 0
epochs = start_epoch_num if start_epoch_num is not None else train_config['start_epoch_num']
examples = 0
saved = False
patience = 0
log_on = train_config['log_every_n_batches'] > 0 or train_config['log_every_n_epochs'] > 0
outputs = {key: [] for key in expected_outputs}
losses = []
start_time = time.time()
break_flag = False
if train_config['tensorboard_log_dir'] is not None:
import tensorflow as tf
tb_log_dir = expand_path(train_config['tensorboard_log_dir'])
tb_train_writer = tf.summary.FileWriter(str(tb_log_dir / 'train_log'))
tb_valid_writer = tf.summary.FileWriter(str(tb_log_dir / 'valid_log'))
# validate first (important if model is pre-trained)
if train_config['val_every_n_epochs'] > 0 or train_config['val_every_n_batches'] > 0:
report = _test_model(model, metrics_functions, iterator,
train_config['batch_size'], 'valid', start_time, train_config['show_examples'])
report['epochs_done'] = epochs
report['batches_seen'] = i
report['train_examples_seen'] = examples
metrics = list(report['metrics'].items())
m_name, score = metrics[0]
if improved(score, best):
patience = 0
log.info('New best {} of {}'.format(m_name, score))
best = score
log.info('Saving model')
model.save()
saved = True
else:
patience += 1
log.info('Did not improve on the {} of {}'.format(m_name, best))
report['impatience'] = patience
if train_config['validation_patience'] > 0:
report['patience_limit'] = train_config['validation_patience']
model.process_event(event_name='after_validation', data=report)
report = {'valid': report}
print(json.dumps(report, ensure_ascii=False))
try:
while True:
for x, y_true in iterator.gen_batches(train_config['batch_size']):
if log_on and len(train_metrics_functions) > 0:
y_predicted = list(model.compute(list(x), list(y_true), targets=expected_outputs))
if len(expected_outputs) == 1:
y_predicted = [y_predicted]
for out, val in zip(outputs.values(), y_predicted):
out += list(val)
result = model.train_on_batch(x, y_true)
if not isinstance(result, dict):
result = {'loss': result} if result is not None else {}
if 'loss' in result:
losses.append(result['loss'])
i += 1
examples += len(x)
if train_config['log_every_n_batches'] > 0 and i % train_config['log_every_n_batches'] == 0:
metrics = [(m.name, m.fn(*[outputs[i] for i in m.inputs])) for m in train_metrics_functions]
report = {
'epochs_done': epochs,
'batches_seen': i,
'examples_seen': examples,
'metrics': prettify_metrics(metrics),
'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5)))
}
default_report_keys = list(report.keys())
report.update(result)
if train_config['show_examples']:
try:
y_predicted = zip(*[y_predicted_group
for out_name, y_predicted_group in zip(expected_outputs, y_predicted)
if out_name in model.out_params])
if len(model.out_params) == 1:
y_predicted = [y_predicted_item[0] for y_predicted_item in y_predicted]
report['examples'] = [{
'x': x_item,
'y_predicted': y_predicted_item,
'y_true': y_true_item
} for x_item, y_predicted_item, y_true_item
in zip(x, y_predicted, y_true)]
except NameError:
log.warning('Could not log examples as y_predicted is not defined')
if losses:
report['loss'] = sum(losses)/len(losses)
losses = []
model.process_event(event_name='after_train_log', data=report)
if train_config['tensorboard_log_dir'] is not None:
summ = tf.Summary()
for name, score in metrics:
summ.value.add(tag='every_n_batches/' + name, simple_value=score)
for name, score in report.items():
if name not in default_report_keys:
summ.value.add(tag='every_n_batches/' + name, simple_value=score)
tb_train_writer.add_summary(summ, i)
tb_train_writer.flush()
report = {'train': report}
print(json.dumps(report, ensure_ascii=False))
for out in outputs.values():
out.clear()
if train_config['val_every_n_batches'] > 0 and i % train_config['val_every_n_batches'] == 0:
report = _test_model(model, metrics_functions, iterator,
train_config['batch_size'], 'valid', start_time, train_config['show_examples'])
report['epochs_done'] = epochs
report['batches_seen'] = i
report['train_examples_seen'] = examples
metrics = list(report['metrics'].items())
if train_config['tensorboard_log_dir'] is not None:
summ = tf.Summary()
for name, score in metrics:
summ.value.add(tag='every_n_batches/' + name, simple_value=score)
tb_valid_writer.add_summary(summ, i)
tb_valid_writer.flush()
m_name, score = metrics[0]
if improved(score, best):
patience = 0
log.info('New best {} of {}'.format(m_name, score))
best = score
log.info('Saving model')
model.save()
saved = True
else:
patience += 1
log.info('Did not improve on the {} of {}'.format(m_name, best))
report['impatience'] = patience
if train_config['validation_patience'] > 0:
report['patience_limit'] = train_config['validation_patience']
model.process_event(event_name='after_validation', data=report)
report = {'valid': report}
print(json.dumps(report, ensure_ascii=False))
if patience >= train_config['validation_patience'] > 0:
log.info('Ran out of patience')
break_flag = True
break
if i >= train_config['max_batches'] > 0:
break_flag = True
break
report = {
'epochs_done': epochs,
'batches_seen': i,
'train_examples_seen': examples,
'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5)))
}
model.process_event(event_name='after_batch', data=report)
if break_flag:
break
epochs += 1
report = {
'epochs_done': epochs,
'batches_seen': i,
'train_examples_seen': examples,
'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5)))
}
model.process_event(event_name='after_epoch', data=report)
if train_config['log_every_n_epochs'] > 0 and epochs % train_config['log_every_n_epochs'] == 0\
and outputs:
metrics = [(m.name, m.fn(*[outputs[i] for i in m.inputs])) for m in train_metrics_functions]
report = {
'epochs_done': epochs,
'batches_seen': i,
'train_examples_seen': examples,
'metrics': prettify_metrics(metrics),
'time_spent': str(datetime.timedelta(seconds=round(time.time() - start_time + 0.5)))
}
default_report_keys = list(report.keys())
report.update(result)
if train_config['show_examples']:
try:
y_predicted = zip(*[y_predicted_group
for out_name, y_predicted_group in zip(expected_outputs, y_predicted)
if out_name in model.out_params])
if len(model.out_params) == 1:
y_predicted = [y_predicted_item[0] for y_predicted_item in y_predicted]
report['examples'] = [{
'x': x_item,
'y_predicted': y_predicted_item,
'y_true': y_true_item
} for x_item, y_predicted_item, y_true_item
in zip(x, y_predicted, y_true)]
except NameError:
log.warning('Could not log examples')
if losses:
report['loss'] = sum(losses)/len(losses)
losses = []
model.process_event(event_name='after_train_log', data=report)
if train_config['tensorboard_log_dir'] is not None:
summ = tf.Summary()
for name, score in metrics:
summ.value.add(tag='every_n_epochs/' + name, simple_value=score)
for name, score in report.items():
if name not in default_report_keys:
summ.value.add(tag='every_n_epochs/' + name, simple_value=score)
tb_train_writer.add_summary(summ, epochs)
tb_train_writer.flush()
report = {'train': report}
print(json.dumps(report, ensure_ascii=False))
for out in outputs.values():
out.clear()
if train_config['val_every_n_epochs'] > 0 and epochs % train_config['val_every_n_epochs'] == 0:
report = _test_model(model, metrics_functions, iterator,
train_config['batch_size'], 'valid', start_time, train_config['show_examples'])
report['epochs_done'] = epochs
report['batches_seen'] = i
report['train_examples_seen'] = examples
metrics = list(report['metrics'].items())
if train_config['tensorboard_log_dir'] is not None:
summ = tf.Summary()
for name, score in metrics:
summ.value.add(tag='every_n_epochs/' + name, simple_value=score)
tb_valid_writer.add_summary(summ, epochs)
tb_valid_writer.flush()
m_name, score = metrics[0]
if improved(score, best):
patience = 0
log.info('New best {} of {}'.format(m_name, score))
best = score
log.info('Saving model')
model.save()
saved = True
else:
patience += 1
log.info('Did not improve on the {} of {}'.format(m_name, best))
report['impatience'] = patience
if train_config['validation_patience'] > 0:
report['patience_limit'] = train_config['validation_patience']
model.process_event(event_name='after_validation', data=report)
report = {'valid': report}
print(json.dumps(report, ensure_ascii=False))
if patience >= train_config['validation_patience'] > 0:
log.info('Ran out of patience')
break
if epochs >= train_config['epochs'] > 0:
break
except KeyboardInterrupt:
log.info('Stopped training')
if not saved:
log.info('Saving model')
model.save()
return model
