def train_agent_models(config_path: str):
usr_dir = paths.USR_PATH
a = build_agent_from_config(config_path)
for skill_config in a.skill_configs:
model_config = skill_config['model']
model_name = model_config['name']
if issubclass(REGISTRY[model_name], Trainable):
reader_config = skill_config['dataset_reader']
reader = from_params(REGISTRY[reader_config['name']], {})
data = reader.read(reader_config.get('data_path', usr_dir))
dataset_config = skill_config['dataset']
dataset_name = dataset_config['name']
dataset = from_params(REGISTRY[dataset_name],
dataset_config,
data=data)
model = from_params(REGISTRY[model_name], model_config)
model.train(dataset)
else:
print('Model {} is not an instance of Trainable, skip training.'.
format(model_name),
file=sys.stderr)
def train_model_from_config(config_path: str, mode='train'):
usr_dir = paths.USR_PATH
config = read_json(config_path)
reader_config = config['dataset_reader']
# NOTE: Why there are no params for dataset reader? Because doesn't have __init__()
reader = from_params(REGISTRY[reader_config['name']], {})
data = reader.read(reader_config.get('data_path', usr_dir))
dataset_config = config['dataset']
dataset_name = dataset_config['name']
dataset = from_params(REGISTRY[dataset_name], dataset_config, data=data)
vocabs = {}
if 'vocabs' in config:
for vocab_param_name, vocab_config in config['vocabs'].items():
vocab_name = vocab_config['name']
v = from_params(REGISTRY[vocab_name], vocab_config, mode=mode)
v.train(dataset.iter_all('train'))
vocabs[vocab_param_name] = v
model_config = config['model']
model_name = model_config['name']
model = from_params(REGISTRY[model_name],
model_config,
vocabs=vocabs,
mode=mode)
model.train(dataset)
def train_model_from_config(config_path: str, mode='train'):
usr_dir = paths.USR_PATH
config = read_json(config_path)
reader_config = config['dataset_reader']
# NOTE: Why there are no params for dataset reader? Because doesn't have __init__()
reader = from_params(REGISTRY[reader_config['name']], {})
data = reader.read(reader_config.get('data_path', usr_dir))
dataset_config = config['dataset']
dataset_name = dataset_config['name']
dataset = from_params(REGISTRY[dataset_name], dataset_config, data=data)
vocabs = {}
if 'vocabs' in config:
for vocab_param_name, vocab_config in config['vocabs'].items():
vocab_name = vocab_config['name']
v = from_params(REGISTRY[vocab_name], vocab_config, mode=mode)
v.train(dataset.iter_all('train'))
vocabs[vocab_param_name] = v
model_config = config['model']
model_name = model_config['name']
model = from_params(REGISTRY[model_name], model_config, vocabs=vocabs, mode=mode)
model.train(dataset)
def build_model_from_config(config,
mode='infer',
load_trained=False,
as_component=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'),
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, 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(component, c_in, c_out, 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
def build_model_from_config(config, mode='infer'):
model_config = config['model']
model_name = model_config['name']
vocabs = {}
if 'vocabs' in config:
for vocab_param_name, vocab_config in config['vocabs'].items():
vocab_name = vocab_config['name']
v = from_params(REGISTRY[vocab_name], vocab_config, mode=mode)
vocabs[vocab_param_name] = v
model = from_params(REGISTRY[model_name], model_config, vocabs=vocabs, mode=mode)
model.reset()
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 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 interact_agent(config_path: str) -> None:
"""Start interaction with the agent described in corresponding configuration file."""
a = build_agent_from_config(config_path)
commutator = from_params(a.commutator_config)
models = [build_model_from_config(sk) for sk in a.skill_configs]
while True:
# get input from user
context = input(':: ')
# check for exit command
if context == 'exit' or context == 'stop' or context == 'quit' or context == 'q':
return
predictions = []
for model in models:
predictions.append(
{model.__class__.__name__: model.infer(context, )})
idx, name, pred = commutator.infer(predictions, )
print('>>', pred)
a.history.append({
'context': context,
"predictions": predictions,
"winner": {
"idx": idx,
"model": name,
"prediction": pred
}
})
log.debug("Current history: {}".format(a.history))
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 get_iterator_from_config(config: dict, data: dict):
"""Create iterator (from config) for specified data."""
iterator_config = config['dataset_iterator']
iterator: Union[DataLearningIterator,
DataFittingIterator] = from_params(iterator_config,
data=data)
return iterator
def read(self, data_path, tasks: Dict[str, Dict[str, str]]):
"""Creates dataset readers for tasks and returns what task dataset readers `read()` methods return.
Args:
data_path: can be anything since it is not used. `data_path` is present because it is
required in train.py script.
tasks: dictionary which keys are task names and values are dictionaries with `DatasetReader`
subclasses specs. `DatasetReader` specs are provided in the same format as "dataset_reader"
in the model config except for "class_name" field which has to be named "reader_class_name".
```json
"tasks": {
"query_prediction": {
"reader_class_name": "basic_classification_reader",
"x": "Question",
"y": "Class",
"data_path": "{DOWNLOADS_PATH}/query_prediction"
}
}
```
Returns:
dictionary which keys are task names and values are what task readers `read()` methods returned.
"""
data = {}
for task_name, reader_params in tasks.items():
reader_params = copy.deepcopy(reader_params)
tasks[task_name] = from_params(
{"class_name": reader_params['reader_class_name']})
del reader_params['reader_class_name']
reader_params['data_path'] = Path(
reader_params['data_path']).expanduser()
data[task_name] = tasks[task_name].read(**reader_params)
return data
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 predict_with_model(config_path):
config = read_json(config_path)
set_deeppavlov_root(config)
reader_config = config['dataset_reader']
reader = get_model(reader_config['name'])()
data_path = expand_path(reader_config.get('data_path', ''))
read_params = {k: v for k, v in reader_config.items() if k not in ['name', 'data_path']}
data = reader.read(data_path, **read_params)
iterator_config = config['dataset_iterator']
iterator: MorphoTaggerDatasetIterator =\
from_params(iterator_config, data=data)
model = build_model_from_config(config, load_trained=True)
answers = [None] * len(iterator.test)
batch_size = config['predict'].get("batch_size", -1)
for indexes, (x, _) in iterator.gen_batches(
batch_size=batch_size, data_type="test", shuffle=False, return_indexes=True):
y = model(x)
for i, elem in zip(indexes, y):
answers[i] = elem
outfile = config['predict'].get("outfile")
if outfile is not None:
outfile = Path(outfile)
if not outfile.exists():
outfile.parent.mkdir(parents=True, exist_ok=True)
with open(outfile, "w", encoding="utf8") as fout:
for elem in answers:
fout.write(elem + "\n")
return answers
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 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 train_agent_models(config_path: str):
usr_dir = paths.USR_PATH
a = build_agent_from_config(config_path)
for skill_config in a.skill_configs:
model_config = skill_config['model']
model_name = model_config['name']
if issubclass(REGISTRY[model_name], Trainable):
reader_config = skill_config['dataset_reader']
reader = from_params(REGISTRY[reader_config['name']], {})
data = reader.read(reader_config.get('data_path', usr_dir))
dataset_config = skill_config['dataset']
dataset_name = dataset_config['name']
dataset = from_params(REGISTRY[dataset_name], dataset_config, data=data)
model = from_params(REGISTRY[model_name], model_config)
model.train(dataset)
else:
print('Model {} is not an instance of Trainable, skip training.'.format(model_name),
file=sys.stderr)
def predict_with_model(config_path: [Path, str]) -> List[Optional[List[str]]]:
"""Returns predictions of morphotagging model given in config :config_path:.
Args:
config_path: a path to config
Returns:
a list of morphological analyses for each sentence. Each analysis is either a list of tags
or a list of full CONLL-U descriptions.
"""
config = parse_config(config_path)
reader_config = config['dataset_reader']
reader = get_model(reader_config['class_name'])()
data_path = expand_path(reader_config.get('data_path', ''))
read_params = {
k: v
for k, v in reader_config.items()
if k not in ['class_name', 'data_path']
}
data: Dict = reader.read(data_path, **read_params)
iterator_config = config['dataset_iterator']
iterator: MorphoTaggerDatasetIterator = from_params(iterator_config,
data=data)
model = build_model(config, load_trained=True)
answers = [None] * len(iterator.test)
batch_size = config['predict'].get("batch_size", -1)
for indexes, (x, _) in iterator.gen_batches(batch_size=batch_size,
data_type="test",
shuffle=False,
return_indexes=True):
y = model(x)
for i, elem in zip(indexes, y):
answers[i] = elem
outfile = config['predict'].get("outfile")
if outfile is not None:
outfile = Path(outfile)
if not outfile.exists():
outfile.parent.mkdir(parents=True, exist_ok=True)
with open(outfile, "w", encoding="utf8") as fout:
for elem in answers:
fout.write(elem + "\n")
return answers
def __init__(self, data: dict, tasks: dict):
self.task_iterators = {}
for task_name, task_iterator_params in tasks.items():
task_iterator_params = copy.deepcopy(task_iterator_params)
task_iterator_params['class_name'] = task_iterator_params['iterator_class_name']
del task_iterator_params['iterator_class_name']
self.task_iterators[task_name] = from_params(task_iterator_params, data=data[task_name])
self.train = self._extract_data_type('train')
self.valid = self._extract_data_type('valid')
self.test = self._extract_data_type('test')
self.data = {
'train': self.train,
'valid': self.valid,
'test': self.test,
'all': self._unite_dataset_parts(self.train, self.valid, self.test)
}
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 predict_with_model(config_path: [Path, str]) -> List[Optional[List[str]]]:
"""Returns predictions of morphotagging model given in config :config_path:.
Args:
config_path: a path to config
Returns:
a list of morphological analyses for each sentence. Each analysis is either a list of tags
or a list of full CONLL-U descriptions.
"""
config = parse_config(config_path)
reader_config = config['dataset_reader']
reader = get_model(reader_config['class_name'])()
data_path = expand_path(reader_config.get('data_path', ''))
read_params = {k: v for k, v in reader_config.items() if k not in ['class_name', 'data_path']}
data: Dict = reader.read(data_path, **read_params)
iterator_config = config['dataset_iterator']
iterator: MorphoTaggerDatasetIterator = from_params(iterator_config, data=data)
model = build_model(config, load_trained=True)
answers = [None] * len(iterator.test)
batch_size = config['predict'].get("batch_size", -1)
for indexes, (x, _) in iterator.gen_batches(
batch_size=batch_size, data_type="test", shuffle=False, return_indexes=True):
y = model(x)
for i, elem in zip(indexes, y):
answers[i] = elem
outfile = config['predict'].get("outfile")
if outfile is not None:
outfile = Path(outfile)
if not outfile.exists():
outfile.parent.mkdir(parents=True, exist_ok=True)
with open(outfile, "w", encoding="utf8") as fout:
for elem in answers:
fout.write(elem + "\n")
return answers
def interact_agent(config_path: str) -> None:
"""Start interaction with the agent described in corresponding configuration file."""
a = build_agent_from_config(config_path)
commutator = from_params(a.commutator_config)
models = [build_model_from_config(sk) for sk in a.skill_configs]
while True:
# get input from user
context = input(':: ')
# check for exit command
if context == 'exit' or context == 'stop' or context == 'quit' or context == 'q':
return
predictions = []
for model in models:
predictions.append({model.__class__.__name__: model.infer(context, )})
idx, name, pred = commutator.infer(predictions, )
print('>>', pred)
a.history.append({'context': context, "predictions": predictions,
"winner": {"idx": idx, "model": name, "prediction": pred}})
log.debug("Current history: {}".format(a.history))
def interact_agent(config_path):
a = build_agent_from_config(config_path)
commutator_name = a.commutator_config['name']
commutator = from_params(REGISTRY[commutator_name], a.commutator_config)
models = [build_model_from_config(sk) for sk in a.skill_configs]
while True:
# get input from user
context = input(':: ')
# check for exit command
if context == 'exit' or context == 'stop' or context == 'quit' or context == 'q':
return
predictions = []
for model in models:
predictions.append({model.__class__.__name__: model.infer(context)})
idx, name, pred = commutator.infer(predictions, a.history)
print('>>', pred)
a.history.append({'context': context, "predictions": predictions,
"winner": {"idx": idx, "model": name, "prediction": pred}})
print("Current history: {}".format(a.history))
def build_model_from_config(config,
mode='infer',
load_trained=False,
as_component=False):
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 train_model_from_config(config_path: str) -> None:
config = read_json(config_path)
set_deeppavlov_root(config)
dataset_config = config.get('dataset', None)
if dataset_config:
config.pop('dataset')
ds_type = dataset_config['type']
if ds_type == 'classification':
reader = {'name': 'basic_classification_reader'}
iterator = {'name': 'basic_classification_iterator'}
config['dataset_reader'] = {**dataset_config, **reader}
config['dataset_iterator'] = {**dataset_config, **iterator}
else:
raise Exception("Unsupported dataset type: {}".format(ds_type))
reader_config = config['dataset_reader']
reader = get_model(reader_config['name'])()
data_path = expand_path(reader_config.get('data_path', ''))
kwargs = {
k: v
for k, v in reader_config.items() if k not in ['name', 'data_path']
}
data = reader.read(data_path, **kwargs)
iterator_config = config['dataset_iterator']
iterator: BasicDatasetIterator = from_params(iterator_config, data=data)
if 'chainer' in config:
model = fit_chainer(config, iterator)
else:
vocabs = config.get('vocabs', {})
for vocab_param_name, vocab_config in vocabs.items():
v: Estimator = from_params(vocab_config, mode='train')
vocabs[vocab_param_name] = _fit(v, iterator)
model_config = config['model']
model = from_params(model_config, vocabs=vocabs, mode='train')
train_config = {
'metrics': ['accuracy'],
'validate_best': True,
'test_best': True
}
try:
train_config.update(config['train'])
except KeyError:
log.warning('Train config is missing. Populating with default values')
metrics_functions = list(
zip(train_config['metrics'],
get_metrics_by_names(train_config['metrics'])))
if callable(getattr(model, 'train_on_batch', None)):
_train_batches(model, iterator, train_config, metrics_functions)
elif callable(getattr(model, 'fit', None)):
_fit(model, iterator, train_config)
elif not isinstance(model, Chainer):
log.warning('Nothing to train')
if train_config['validate_best'] or train_config['test_best']:
# 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')
model = build_model_from_config(config, load_trained=True)
log.info('Testing the best saved model')
if train_config['validate_best']:
report = {
'valid':
_test_model(model, metrics_functions, iterator,
train_config.get('batch_size', -1), 'valid')
}
print(json.dumps(report, ensure_ascii=False))
if train_config['test_best']:
report = {
'test':
_test_model(model, metrics_functions, iterator,
train_config.get('batch_size', -1), 'test')
}
print(json.dumps(report, ensure_ascii=False))
},
"tokenizer": {
"name": "stream_spacy_tokenizer",
"lowercase": False
},
"tracker": {
"name": "featurized_tracker",
"slot_names": ["pricerange", "this", "area", "food", "name"]
},
"main": True,
"debug": False
}
mode_train = {"mode": "train"}
tracker = from_params(bot_dict["tracker"], "train")
tokenizer = from_params(bot_dict["tokenizer"], "train")
network_parameters = bot_dict["network_parameters"]
template_path = bot_dict["template_path"]
save_path = bot_dict["save_path"]
load_path = bot_dict["load_path"]
template_type = bot_dict["template_type"]
# 'str' object has no attribute 'items'
word_vocab = from_params(bot_dict["word_vocab"], "train")
bow_embedder = from_params(bot_dict["bow_embedder"], "train")
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_evaluate_model_from_config(config: [str, Path, dict], to_train: bool = True, to_validate: bool = True) -> None:
"""Make training and evaluation of 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', []))
dataset_config = config.get('dataset', None)
if dataset_config:
config.pop('dataset')
ds_type = dataset_config['type']
if ds_type == 'classification':
reader = {'name': 'basic_classification_reader'}
iterator = {'name': 'basic_classification_iterator'}
config['dataset_reader'] = {**dataset_config, **reader}
config['dataset_iterator'] = {**dataset_config, **iterator}
else:
raise Exception("Unsupported dataset type: {}".format(ds_type))
data = []
reader_config = config.get('dataset_reader', None)
if reader_config:
reader_config = config['dataset_reader']
if 'class' in reader_config:
c = reader_config.pop('class')
try:
module_name, cls_name = c.split(':')
reader = getattr(importlib.import_module(module_name), cls_name)()
except ValueError:
e = ConfigError('Expected class description in a `module.submodules:ClassName` form, but got `{}`'
.format(c))
log.exception(e)
raise e
else:
reader = get_model(reader_config.pop('name'))()
data_path = reader_config.pop('data_path', '')
if isinstance(data_path, list):
data_path = [expand_path(x) for x in data_path]
else:
data_path = expand_path(data_path)
data = reader.read(data_path, **reader_config)
else:
log.warning("No dataset reader is provided in the JSON config.")
iterator_config = config['dataset_iterator']
iterator: Union[DataLearningIterator, DataFittingIterator] = from_params(iterator_config,
data=data)
train_config = {
'metrics': ['accuracy'],
'validate_best': to_validate,
'test_best': True,
'show_examples': False
}
try:
train_config.update(config['train'])
except KeyError:
log.warning('Train config is missing. Populating with default values')
metrics_functions = list(zip(train_config['metrics'], get_metrics_by_names(train_config['metrics'])))
if to_train:
model = fit_chainer(config, iterator)
if callable(getattr(model, 'train_on_batch', None)):
_train_batches(model, iterator, train_config, metrics_functions)
elif callable(getattr(model, 'fit_batches', None)):
_fit_batches(model, iterator, train_config)
elif callable(getattr(model, 'fit', None)):
_fit(model, iterator, train_config)
elif not isinstance(model, Chainer):
log.warning('Nothing to train')
if train_config['validate_best'] or train_config['test_best']:
# 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')
model = build_model_from_config(config, load_trained=True)
log.info('Testing the best saved model')
if train_config['validate_best']:
report = {
'valid': _test_model(model, metrics_functions, iterator,
train_config.get('batch_size', -1), 'valid',
show_examples=train_config['show_examples'])
}
print(json.dumps(report, ensure_ascii=False))
if train_config['test_best']:
report = {
'test': _test_model(model, metrics_functions, iterator,
train_config.get('batch_size', -1), 'test',
show_examples=train_config['show_examples'])
}
print(json.dumps(report, ensure_ascii=False))
def train_evaluate_model_from_config(config: [str, Path, dict],
to_train=True,
to_validate=True) -> None:
if isinstance(config, (str, Path)):
config = read_json(config)
set_deeppavlov_root(config)
import_packages(config.get('metadata', {}).get('imports', []))
dataset_config = config.get('dataset', None)
if dataset_config:
config.pop('dataset')
ds_type = dataset_config['type']
if ds_type == 'classification':
reader = {'name': 'basic_classification_reader'}
iterator = {'name': 'basic_classification_iterator'}
config['dataset_reader'] = {**dataset_config, **reader}
config['dataset_iterator'] = {**dataset_config, **iterator}
else:
raise Exception("Unsupported dataset type: {}".format(ds_type))
data = []
reader_config = config.get('dataset_reader', None)
if reader_config:
reader_config = config['dataset_reader']
if 'class' in reader_config:
c = reader_config.pop('class')
try:
module_name, cls_name = c.split(':')
reader = getattr(importlib.import_module(module_name),
cls_name)()
except ValueError:
e = ConfigError(
'Expected class description in a `module.submodules:ClassName` form, but got `{}`'
.format(c))
log.exception(e)
raise e
else:
reader = get_model(reader_config.pop('name'))()
data_path = expand_path(reader_config.pop('data_path', ''))
data = reader.read(data_path, **reader_config)
else:
log.warning("No dataset reader is provided in the JSON config.")
iterator_config = config['dataset_iterator']
iterator: Union[DataLearningIterator,
DataFittingIterator] = from_params(iterator_config,
data=data)
train_config = {
'metrics': ['accuracy'],
'validate_best': to_validate,
'test_best': True
}
try:
train_config.update(config['train'])
except KeyError:
log.warning('Train config is missing. Populating with default values')
metrics_functions = list(
zip(train_config['metrics'],
get_metrics_by_names(train_config['metrics'])))
if to_train:
model = fit_chainer(config, iterator)
if callable(getattr(model, 'train_on_batch', None)):
_train_batches(model, iterator, train_config, metrics_functions)
elif callable(getattr(model, 'fit_batches', None)):
_fit_batches(model, iterator, train_config)
elif callable(getattr(model, 'fit', None)):
_fit(model, iterator, train_config)
elif not isinstance(model, Chainer):
log.warning('Nothing to train')
if train_config['validate_best'] or train_config['test_best']:
# 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')
model = build_model_from_config(config, load_trained=True)
log.info('Testing the best saved model')
if train_config['validate_best']:
report = {
'valid':
_test_model(model, metrics_functions, iterator,
train_config.get('batch_size', -1), 'valid')
}
print(json.dumps(report, ensure_ascii=False))
if train_config['test_best']:
report = {
'test':
_test_model(model, metrics_functions, iterator,
train_config.get('batch_size', -1), 'test')
}
print(json.dumps(report, ensure_ascii=False))
def load_elmo(elmo_output_names=("word_emb",)):
config = parse_config(getattr(configs.elmo_embedder, "elmo_ru-news"))
elmo_config = config["chainer"]["pipe"][-1]
elmo_config['elmo_output_names'] = elmo_output_names
embedder = from_params(elmo_config)
return embedder
def train_model_from_config(config_path: str):
config = read_json(config_path)
set_deeppavlov_root(config)
reader_config = config['dataset_reader']
reader = get_model(reader_config['name'])()
data_path = expand_path(reader_config.get('data_path', ''))
data = reader.read(data_path)
dataset_config = config['dataset']
dataset: Dataset = from_params(dataset_config, data=data)
if 'chainer' in config:
model = fit_chainer(config, dataset)
else:
vocabs = {}
for vocab_param_name, vocab_config in config.get('vocabs', {}).items():
v: Estimator = from_params(vocab_config, mode='train')
vocabs[vocab_param_name] = _fit(v, dataset)
model_config = config['model']
model = from_params(model_config, vocabs=vocabs, mode='train')
train_config = {
'metrics': ['accuracy'],
'validate_best': True,
'test_best': True
}
try:
train_config.update(config['train'])
except KeyError:
log.warning('Train config is missing. Populating with default values')
metrics_functions = list(
zip(train_config['metrics'],
get_metrics_by_names(train_config['metrics'])))
if callable(getattr(model, 'train_on_batch', None)):
_train_batches(model, dataset, train_config, metrics_functions)
elif callable(getattr(model, 'fit', None)):
_fit(model, dataset, train_config)
elif not isinstance(model, Chainer):
log.warning('Nothing to train')
if train_config['validate_best'] or train_config['test_best']:
# 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')
model = build_model_from_config(config, load_trained=True)
log.info('Testing the best saved model')
if train_config['validate_best']:
report = {
'valid':
_test_model(model, metrics_functions, dataset,
train_config.get('batch_size', -1), 'valid')
}
print(json.dumps(report, ensure_ascii=False))
if train_config['test_best']:
report = {
'test':
_test_model(model, metrics_functions, dataset,
train_config.get('batch_size', -1), 'test')
}
print(json.dumps(report, ensure_ascii=False))
