def make(train):
root = get_resource(SIGHAN2005)
train = os.path.join(root, train.split('#')[-1])
if not os.path.isfile(train):
full = train.replace('_90.txt', '.utf8')
logger.info(f'Splitting {full} into training set and valid set with 9:1 proportion')
valid = train.replace('90.txt', '10.txt')
split_file(full, train=0.9, valid=0.1, test=0, names={'train': train, 'valid': valid})
assert os.path.isfile(train), f'Failed to make {train}'
assert os.path.isfile(valid), f'Failed to make {valid}'
logger.info(f'Successfully made {train} {valid}')
def to(self, devices=Union[str, List[int]], logger=None):
if isinstance(devices, str):
devices = cuda_devices(devices)
if devices:
if logger:
logger.info(f'Using GPUs: {devices}')
self.model = self.model.to(devices[0])
if len(devices) > 1 and not isdebugging():
self.model = nn.DataParallel(self.model, device_ids=devices)
else:
if logger:
logger.info('Using CPU')
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)
def summarize_vocabs(self, logger=None, header='Vocab summary:'):
output = header + '\n'
vocabs = {}
for key, value in vars(self).items():
if isinstance(value, VocabTF):
vocabs[key] = value
# tag vocab comes last usually
for key, value in sorted(vocabs.items(), key=lambda kv: len(kv[1]), reverse=True):
output += f'{key}' + value.summary(verbose=False) + '\n'
output = output.strip()
if logger:
logger.info(output)
else:
print(output)
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 convert_to_stanford_dependency_330(src, dst):
logger.info(
f'Converting {os.path.basename(src)} to {os.path.basename(dst)} using Stanford Parser Version 3.3.0. '
f'It might take a while...')
sp_home = 'https://nlp.stanford.edu/software/stanford-parser-full-2013-11-12.zip'
sp_home = get_resource(sp_home)
# jar_path = get_resource(f'{sp_home}#stanford-parser.jar')
code, out, err = get_exitcode_stdout_stderr(
f'java -cp {sp_home}/* edu.stanford.nlp.trees.EnglishGrammaticalStructure -basic -keepPunct -conllx '
f'-treeFile {src}')
with open(dst, 'w') as f:
f.write(out)
if code:
raise RuntimeError(
f'Conversion failed with code {code} for {src}. The err message is:\n {err}'
f'Do you have java installed? Do you have enough memory?')
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 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