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 = size_of_dataset(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()
loss, score, output = self.evaluate_dataset(tst_data, callbacks,
output, num_batches)
delta_time = timer.stop()
speed = samples / delta_time.delta_seconds
if logger:
f1: IOBES_F1 = None
for metric in self.model.metrics:
if isinstance(metric, IOBES_F1):
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
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(size_of_dataset(dev_data) / batch_size)
checkpoint = get_callback_by_class(callbacks,
tf.keras.callbacks.ModelCheckpoint)
timer = Timer()
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}'
)
# 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 saved with best '
f'{checkpoint.monitor} = {checkpoint.best:.4f} '
f'saved {best_epoch_ago} epochs ago')
self.load_weights(save_dir) # restore best model
return history