def load_vocabs(self, save_dir, filename='vocabs.json'):
save_dir = get_resource(save_dir)
vocabs = SerializableDict()
vocabs.load_json(os.path.join(save_dir, filename))
for key, value in vocabs.items():
vocab = VocabTF()
vocab.copy_from(value)
setattr(self.transform, key, vocab)
class KerasComponent(Component, ABC):
def __init__(self, transform: Transform) -> None:
super().__init__()
self.model: Optional[tf.keras.Model] = None
self.config = SerializableDict()
self.transform = transform
# share config with transform for convenience, so we don't need to pass args around
if self.transform.config:
for k, v in self.transform.config.items():
self.config[k] = v
self.transform.config = self.config
def evaluate(self, input_path: str, save_dir=None, output=False, batch_size=128, logger: logging.Logger = None,
callbacks: List[tf.keras.callbacks.Callback] = None, warm_up=True, verbose=True, **kwargs):
input_path = get_resource(input_path)
file_prefix, ext = os.path.splitext(input_path)
name = os.path.basename(file_prefix)
if not name:
name = 'evaluate'
if save_dir and not logger:
logger = init_logger(name=name, root_dir=save_dir, level=logging.INFO if verbose else logging.WARN,
mode='w')
tst_data = self.transform.file_to_dataset(input_path, batch_size=batch_size)
samples = self.num_samples_in(tst_data)
num_batches = math.ceil(samples / batch_size)
if warm_up:
self.model.predict_on_batch(tst_data.take(1))
if output:
assert save_dir, 'Must pass save_dir in order to output'
if isinstance(output, bool):
output = os.path.join(save_dir, name) + '.predict' + ext
elif isinstance(output, str):
output = output
else:
raise RuntimeError('output ({}) must be of type bool or str'.format(repr(output)))
timer = Timer()
eval_outputs = self.evaluate_dataset(tst_data, callbacks, output, num_batches, **kwargs)
loss, score, output = eval_outputs[0], eval_outputs[1], eval_outputs[2]
delta_time = timer.stop()
speed = samples / delta_time.delta_seconds
if logger:
f1: IOBES_F1_TF = None
for metric in self.model.metrics:
if isinstance(metric, IOBES_F1_TF):
f1 = metric
break
extra_report = ''
if f1:
overall, by_type, extra_report = f1.state.result(full=True, verbose=False)
extra_report = ' \n' + extra_report
logger.info('Evaluation results for {} - '
'loss: {:.4f} - {} - speed: {:.2f} sample/sec{}'
.format(name + ext, loss,
format_scores(score) if isinstance(score, dict) else format_metrics(self.model.metrics),
speed, extra_report))
if output:
logger.info('Saving output to {}'.format(output))
with open(output, 'w', encoding='utf-8') as out:
self.evaluate_output(tst_data, out, num_batches, self.model.metrics)
return (loss, score, speed) + eval_outputs[3:]
def num_samples_in(self, dataset):
return size_of_dataset(dataset)
def evaluate_dataset(self, tst_data, callbacks, output, num_batches, **kwargs):
loss, score = self.model.evaluate(tst_data, callbacks=callbacks, steps=num_batches)
return loss, score, output
def evaluate_output(self, tst_data, out, num_batches, metrics: List[tf.keras.metrics.Metric]):
# out.write('x\ty_true\ty_pred\n')
for metric in metrics:
metric.reset_states()
for idx, batch in enumerate(tst_data):
outputs = self.model.predict_on_batch(batch[0])
for metric in metrics:
metric(batch[1], outputs, outputs._keras_mask if hasattr(outputs, '_keras_mask') else None)
self.evaluate_output_to_file(batch, outputs, out)
print('\r{}/{} {}'.format(idx + 1, num_batches, format_metrics(metrics)), end='')
print()
def evaluate_output_to_file(self, batch, outputs, out):
for x, y_gold, y_pred in zip(self.transform.X_to_inputs(batch[0]),
self.transform.Y_to_outputs(batch[1], gold=True),
self.transform.Y_to_outputs(outputs, gold=False)):
out.write(self.transform.input_truth_output_to_str(x, y_gold, y_pred))
def save_meta(self, save_dir, filename='meta.json', **kwargs):
self.meta['create_time']: now_datetime()
self.meta.update(kwargs)
save_json(self.meta, os.path.join(save_dir, filename))
def load_meta(self, save_dir, filename='meta.json'):
save_dir = get_resource(save_dir)
metapath = os.path.join(save_dir, filename)
if os.path.isfile(metapath):
self.meta.update(load_json(metapath))
def save_config(self, save_dir, filename='config.json'):
self.config.save_json(os.path.join(save_dir, filename))
def load_config(self, save_dir, filename='config.json'):
save_dir = get_resource(save_dir)
self.config.load_json(os.path.join(save_dir, filename))
def save_weights(self, save_dir, filename='model.h5'):
self.model.save_weights(os.path.join(save_dir, filename))
def load_weights(self, save_dir, filename='model.h5', **kwargs):
assert self.model.built or self.model.weights, 'You must call self.model.built() in build_model() ' \
'in order to load it'
save_dir = get_resource(save_dir)
self.model.load_weights(os.path.join(save_dir, filename))
def save_vocabs(self, save_dir, filename='vocabs.json'):
vocabs = SerializableDict()
for key, value in vars(self.transform).items():
if isinstance(value, VocabTF):
vocabs[key] = value.to_dict()
vocabs.save_json(os.path.join(save_dir, filename))
def load_vocabs(self, save_dir, filename='vocabs.json'):
save_dir = get_resource(save_dir)
vocabs = SerializableDict()
vocabs.load_json(os.path.join(save_dir, filename))
for key, value in vocabs.items():
vocab = VocabTF()
vocab.copy_from(value)
setattr(self.transform, key, vocab)
def load_transform(self, save_dir) -> Transform:
"""
Try to load transform only. This method might fail due to the fact it avoids building the model.
If it do fail, then you have to use `load` which might be too heavy but that's the best we can do.
:param save_dir: The path to load.
"""
save_dir = get_resource(save_dir)
self.load_config(save_dir)
self.load_vocabs(save_dir)
self.transform.build_config()
self.transform.lock_vocabs()
return self.transform
def save(self, save_dir: str, **kwargs):
self.save_config(save_dir)
self.save_vocabs(save_dir)
self.save_weights(save_dir)
def load(self, save_dir: str, logger=edparser.utils.log_util.logger, **kwargs):
self.meta['load_path'] = save_dir
save_dir = get_resource(save_dir)
self.load_config(save_dir)
self.load_vocabs(save_dir)
self.build(**merge_dict(self.config, training=False, logger=logger, **kwargs, overwrite=True, inplace=True))
self.load_weights(save_dir, **kwargs)
self.load_meta(save_dir)
@property
def input_shape(self) -> List:
return self.transform.output_shapes[0]
def build(self, logger, **kwargs):
self.transform.build_config()
self.model = self.build_model(**merge_dict(self.config, training=kwargs.get('training', None),
loss=kwargs.get('loss', None)))
self.transform.lock_vocabs()
optimizer = self.build_optimizer(**self.config)
loss = self.build_loss(
**self.config if 'loss' in self.config else dict(list(self.config.items()) + [('loss', None)]))
# allow for different
metrics = self.build_metrics(**merge_dict(self.config, metrics=kwargs.get('metrics', 'accuracy'),
logger=logger, overwrite=True))
if not isinstance(metrics, list):
if isinstance(metrics, tf.keras.metrics.Metric):
metrics = [metrics]
if not self.model.built:
sample_inputs = self.sample_data
if sample_inputs is not None:
self.model(sample_inputs, training=kwargs.get('training', None))
else:
if len(self.transform.output_shapes[0]) == 1 and self.transform.output_shapes[0][0] is None:
x_shape = self.transform.output_shapes[0]
else:
x_shape = list(self.transform.output_shapes[0])
for i, shape in enumerate(x_shape):
x_shape[i] = [None] + shape # batch + X.shape
self.model.build(input_shape=x_shape)
self.compile_model(optimizer, loss, metrics)
return self.model, optimizer, loss, metrics
def compile_model(self, optimizer, loss, metrics):
self.model.compile(optimizer=optimizer, loss=loss, metrics=metrics, run_eagerly=self.config.run_eagerly)
def build_optimizer(self, optimizer, **kwargs):
if isinstance(optimizer, (str, dict)):
custom_objects = {'AdamWeightDecay': AdamWeightDecay}
optimizer: tf.keras.optimizers.Optimizer = tf.keras.utils.deserialize_keras_object(optimizer,
module_objects=tf.keras.optimizers,
custom_objects=custom_objects)
self.config.optimizer = tf.keras.utils.serialize_keras_object(optimizer)
return optimizer
def build_loss(self, loss, **kwargs):
if not loss:
loss = tf.keras.losses.SparseCategoricalCrossentropy(
reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
from_logits=True)
elif isinstance(loss, (str, dict)):
loss = tf.keras.utils.deserialize_keras_object(loss, module_objects=tf.keras.losses)
if isinstance(loss, tf.keras.losses.Loss):
self.config.loss = tf.keras.utils.serialize_keras_object(loss)
return loss
def build_transform(self, **kwargs):
return self.transform
def build_vocab(self, trn_data, logger):
train_examples = self.transform.fit(trn_data, **self.config)
self.transform.summarize_vocabs(logger)
return train_examples
def build_metrics(self, metrics, logger: logging.Logger, **kwargs):
metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
return [metric]
@abstractmethod
def build_model(self, **kwargs) -> tf.keras.Model:
pass
def _capture_config(self, config: Dict,
exclude=(
'trn_data', 'dev_data', 'save_dir', 'kwargs', 'self', 'logger', 'verbose',
'dev_batch_size', '__class__')):
"""
Save arguments to config
Parameters
----------
config
`locals()`
exclude
"""
if 'kwargs' in config:
config.update(config['kwargs'])
config = dict(
(key, tf.keras.utils.serialize_keras_object(value)) if hasattr(value, 'get_config') else (key, value) for
key, value in config.items())
for key in exclude:
config.pop(key, None)
self.config.update(config)
def fit(self, trn_data, dev_data, save_dir, batch_size, epochs, run_eagerly=False, logger=None, verbose=True,
**kwargs):
self._capture_config(locals())
self.transform = self.build_transform(**self.config)
if not save_dir:
save_dir = tempdir_human()
if not logger:
logger = init_logger(name='train', root_dir=save_dir, level=logging.INFO if verbose else logging.WARN)
logger.info('Hyperparameter:\n' + self.config.to_json())
num_examples = self.build_vocab(trn_data, logger)
# assert num_examples, 'You forgot to return the number of training examples in your build_vocab'
logger.info('Building...')
train_steps_per_epoch = math.ceil(num_examples / batch_size) if num_examples else None
self.config.train_steps = train_steps_per_epoch * epochs if num_examples else None
model, optimizer, loss, metrics = self.build(**merge_dict(self.config, logger=logger, training=True))
logger.info('Model built:\n' + summary_of_model(self.model))
self.save_config(save_dir)
self.save_vocabs(save_dir)
self.save_meta(save_dir)
trn_data = self.build_train_dataset(trn_data, batch_size, num_examples)
dev_data = self.build_valid_dataset(dev_data, batch_size)
callbacks = self.build_callbacks(save_dir, logger, **self.config)
# need to know #batches, otherwise progbar crashes
dev_steps = math.ceil(self.num_samples_in(dev_data) / batch_size)
checkpoint = get_callback_by_class(callbacks, tf.keras.callbacks.ModelCheckpoint)
timer = Timer()
history = None
try:
history = self.train_loop(**merge_dict(self.config, trn_data=trn_data, dev_data=dev_data, epochs=epochs,
num_examples=num_examples,
train_steps_per_epoch=train_steps_per_epoch, dev_steps=dev_steps,
callbacks=callbacks, logger=logger, model=model, optimizer=optimizer,
loss=loss,
metrics=metrics, overwrite=True))
except KeyboardInterrupt:
print()
if not checkpoint or checkpoint.best in (np.Inf, -np.Inf):
self.save_weights(save_dir)
logger.info('Aborted with model saved')
else:
logger.info(f'Aborted with model saved with best {checkpoint.monitor} = {checkpoint.best:.4f}')
if not history:
# noinspection PyTypeChecker
history: tf.keras.callbacks.History = get_callback_by_class(callbacks, tf.keras.callbacks.History)
delta_time = timer.stop()
best_epoch_ago = 0
if history and hasattr(history, 'epoch'):
trained_epoch = len(history.epoch)
logger.info('Trained {} epochs in {}, each epoch takes {}'.
format(trained_epoch, delta_time, delta_time / trained_epoch if trained_epoch else delta_time))
io_util.save_json(history.history, io_util.path_join(save_dir, 'history.json'), cls=io_util.NumpyEncoder)
monitor_history: List = history.history.get(checkpoint.monitor, None)
if monitor_history:
best_epoch_ago = len(monitor_history) - monitor_history.index(checkpoint.best)
if checkpoint and monitor_history and checkpoint.best != monitor_history[-1]:
logger.info(f'Restored the best model with '
f'{checkpoint.monitor} = {checkpoint.best:.4f} '
f'saved {best_epoch_ago} epochs ago')
self.load_weights(save_dir) # restore best model
return history
def train_loop(self, trn_data, dev_data, epochs, num_examples, train_steps_per_epoch, dev_steps, model, optimizer,
loss, metrics, callbacks,
logger, **kwargs):
history = self.model.fit(trn_data, epochs=epochs, steps_per_epoch=train_steps_per_epoch,
validation_data=dev_data,
callbacks=callbacks,
validation_steps=dev_steps,
) # type:tf.keras.callbacks.History
return history
def build_valid_dataset(self, dev_data, batch_size):
dev_data = self.transform.file_to_dataset(dev_data, batch_size=batch_size, shuffle=False)
return dev_data
def build_train_dataset(self, trn_data, batch_size, num_examples):
trn_data = self.transform.file_to_dataset(trn_data, batch_size=batch_size,
shuffle=True,
repeat=-1 if self.config.train_steps else None)
return trn_data
def build_callbacks(self, save_dir, logger, **kwargs):
metrics = kwargs.get('metrics', 'accuracy')
if isinstance(metrics, (list, tuple)):
metrics = metrics[-1]
monitor = f'val_{metrics}'
checkpoint = tf.keras.callbacks.ModelCheckpoint(
os.path.join(save_dir, 'model.h5'),
# verbose=1,
monitor=monitor, save_best_only=True,
mode='max',
save_weights_only=True)
logger.debug(f'Monitor {checkpoint.monitor} for checkpoint')
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=io_util.makedirs(io_util.path_join(save_dir, 'logs')))
csv_logger = FineCSVLogger(os.path.join(save_dir, 'train.log'), separator=' | ', append=True)
callbacks = [checkpoint, tensorboard_callback, csv_logger]
lr_decay_per_epoch = self.config.get('lr_decay_per_epoch', None)
if lr_decay_per_epoch:
learning_rate = self.model.optimizer.get_config().get('learning_rate', None)
if not learning_rate:
logger.warning('Learning rate decay not supported for optimizer={}'.format(repr(self.model.optimizer)))
else:
logger.debug(f'Created LearningRateScheduler with lr_decay_per_epoch={lr_decay_per_epoch}')
callbacks.append(tf.keras.callbacks.LearningRateScheduler(
lambda epoch: learning_rate / (1 + lr_decay_per_epoch * epoch)))
anneal_factor = self.config.get('anneal_factor', None)
if anneal_factor:
anneal_monitor = self.config.get('anneal_monitor', None)
if not anneal_monitor:
anneal_monitor = 'val_loss'
self.config['anneal_monitor'] = anneal_monitor
anneal_monitor_mode = self.config.get('anneal_monitor_mode', None)
if not anneal_monitor_mode:
anneal_monitor_mode = 'min' if 'loss' in anneal_monitor else 'max'
self.config['anneal_monitor_mode'] = anneal_monitor_mode
callbacks.append(tf.keras.callbacks.ReduceLROnPlateau(factor=anneal_factor,
monitor=anneal_monitor,
mode=anneal_monitor_mode,
patience=self.config.get('anneal_patience', 10)))
early_stopping_patience = self.config.get('early_stopping_patience', None)
if early_stopping_patience:
callbacks.append(tf.keras.callbacks.EarlyStopping(monitor=monitor, mode='max',
verbose=1,
patience=early_stopping_patience))
return callbacks
def on_train_begin(self):
"""
Callback before the training starts
"""
pass
def predict(self, data: Any, batch_size=None, **kwargs):
assert self.model, 'Please call fit or load before predict'
if not data:
return []
data, flat = self.transform.input_to_inputs(data)
if not batch_size:
batch_size = self.config.batch_size
dataset = self.transform.inputs_to_dataset(data, batch_size=batch_size, gold=kwargs.get('gold', False),
cache=False)
results = []
num_samples = 0
data_is_list = isinstance(data, list)
for idx, batch in enumerate(dataset):
samples_in_batch = tf.shape(batch[-1] if isinstance(batch[-1], tf.Tensor) else batch[-1][0])[0]
if data_is_list:
inputs = data[num_samples:num_samples + samples_in_batch]
else:
inputs = None # if data is a generator, it's usually one-time, not able to transform into a list
for output in self.predict_batch(batch, inputs=inputs, **kwargs):
results.append(output)
num_samples += samples_in_batch
if flat:
return results[0]
return results
def predict_batch(self, batch, inputs=None, **kwargs):
X = batch[0]
Y = self.model.predict_on_batch(X)
for output in self.transform.Y_to_outputs(Y, X=X, inputs=inputs, **kwargs):
yield output
@property
def sample_data(self):
return None
@staticmethod
def from_meta(meta: dict, **kwargs):
"""
Parameters
----------
meta
kwargs
Returns
-------
KerasComponent
"""
cls = str_to_type(meta['class_path'])
obj: KerasComponent = cls()
assert 'load_path' in meta, f'{meta} doesn\'t contain load_path field'
obj.load(meta['load_path'])
return obj
def export_model_for_serving(self, export_dir=None, version=1, overwrite=False, show_hint=False):
assert self.model, 'You have to fit or load a model before exporting it'
if not export_dir:
assert 'load_path' in self.meta, 'When not specifying save_dir, load_path has to present'
export_dir = get_resource(self.meta['load_path'])
model_path = os.path.join(export_dir, str(version))
if os.path.isdir(model_path) and not overwrite:
logger.info(f'{model_path} exists, skip since overwrite = {overwrite}')
return export_dir
logger.info(f'Exporting to {export_dir} ...')
tf.saved_model.save(self.model, model_path)
logger.info(f'Successfully exported model to {export_dir}')
if show_hint:
logger.info(f'You can serve it through \n'
f'tensorflow_model_server --model_name={os.path.splitext(os.path.basename(self.meta["load_path"]))[0]} '
f'--model_base_path={export_dir} --rest_api_port=8888')
return export_dir
def serve(self, export_dir=None, grpc_port=8500, rest_api_port=0, overwrite=False, dry_run=False):
export_dir = self.export_model_for_serving(export_dir, show_hint=False, overwrite=overwrite)
if not dry_run:
del self.model # free memory
logger.info('The inputs of exported model is shown below.')
os.system(f'saved_model_cli show --all --dir {export_dir}/1')
cmd = f'nohup tensorflow_model_server --model_name={os.path.splitext(os.path.basename(self.meta["load_path"]))[0]} ' \
f'--model_base_path={export_dir} --port={grpc_port} --rest_api_port={rest_api_port} ' \
f'>serve.log 2>&1 &'
logger.info(f'Running ...\n{cmd}')
if not dry_run:
os.system(cmd)