def avg_checkpints(hparams: Hparams):
logger = logging.getLogger(__name__)
(model,) = build_model(hparams, return_losses=False, return_metrics=False, return_optimizer=False)
logger.info(f"Average checkpoints from {hparams.prefix_or_checkpints}")
average_checkpoints(model, hparams.prefix_or_checkpints, hparams.num_last_checkpoints, hparams.ckpt_weights)
evaluation(hparams, model=model)
logger.info(f"Save model in {hparams.get_model_filename()}")
model.save_weights(hparams.get_model_filename(), save_format="tf")
def evaluation(hparams: Hparams,
checkpoints=None,
model=None,
test_dataset=None):
"""Evaluate the model and build report according to different task.
:param model:
:param test_dataset:
:param hparams:
:return:
"""
logger.info("Start Evaluate.")
output_hparams = deepcopy(hparams.dataset.outputs)
if test_dataset is None:
test_dataset = next(
load_dataset(hparams,
ret_train=False,
ret_dev=False,
ret_info=False))[0]
if model is None:
# build model
(model, ) = build_model(hparams,
return_losses=False,
return_metrics=False,
return_optimizer=False)
# predict using default model saved
if checkpoints is None:
# load weights
if not os.path.exists(hparams.get_model_filename() + ".index"):
logger.warning(
f"Model from {hparams.get_model_filename()} is not exists, load nothing!"
)
else:
logger.info(
f"Load model weights from {hparams.get_model_filename()}")
model.load_weights(hparams.get_model_filename())
# prediction
# print(model.evaluate(test_dataset))
for inputs, outputs in tqdm(test_dataset):
model_outputs = model.predict(inputs)
if not isinstance(model_outputs, (tuple, list)):
model_outputs = (model_outputs, )
for idx, one_output_hparam in enumerate(output_hparams):
if "ground_truth" not in one_output_hparam:
one_output_hparam["ground_truth"] = []
if "predictions" not in one_output_hparam:
one_output_hparam['predictions'] = []
prediction_output = tf.nn.softmax(model_outputs[idx], -1)
tmp_name = one_output_hparam.name
tmp_type = one_output_hparam.type
tmp_ground_truth = outputs[tmp_name]
if tmp_type in [CLASSLABEL, LIST_OF_CLASSLABEL, LIST_OF_INT]:
if tmp_type in [LIST_OF_INT]:
tmp_tg = tf.argmax(tmp_ground_truth, -1)
else:
tmp_tg = tmp_ground_truth
if one_output_hparam.task == NER: # [[sent1], [sent2]]
one_output_hparam.ground_truth.extend(
tmp_tg.numpy().tolist())
tmp_predictions = tf.argmax(prediction_output,
-1).numpy().tolist()
one_output_hparam.predictions.extend(tmp_predictions)
else: # [1, 0, 1, ...]
one_output_hparam.ground_truth.extend(
tmp_tg.numpy().reshape(-1).tolist())
tmp_predictions = tf.argmax(
prediction_output,
-1).numpy().reshape(-1).tolist()
one_output_hparam.predictions.extend(tmp_predictions)
elif isinstance(checkpoints, (tuple, list)):
# predict using multi checkpints from k-fold cross validation.
for i, ckpt in enumerate(checkpoints):
if not os.path.exists(ckpt + ".index"):
logger.warning(
f"Model from {ckpt} is not exists, load nothing!")
continue
else:
logger.info(f"Load model weights from {ckpt}")
model.load_weights(ckpt)
for j, (inputs, outputs) in tqdm(enumerate(test_dataset)):
model_outputs = model.predict(inputs)
if not isinstance(model_outputs, (tuple, list)):
model_outputs = (model_outputs, )
for idx, one_output_hparam in enumerate(output_hparams):
prediction_output = tf.nn.softmax(model_outputs[idx], -1)
if i == 0:
if "ground_truth" not in one_output_hparam:
one_output_hparam["ground_truth"] = []
if "predictions" not in one_output_hparam:
one_output_hparam['predictions'] = []
one_output_hparam['tmp_preds'] = []
one_output_hparam['tmp_preds'].append(
prediction_output)
tmp_name = one_output_hparam.name
tmp_type = one_output_hparam.type
tmp_ground_truth = outputs[tmp_name]
if tmp_type in [
CLASSLABEL, LIST_OF_CLASSLABEL, LIST_OF_INT
]:
if tmp_type in [LIST_OF_INT]:
tmp_tg = tf.argmax(tmp_ground_truth, -1)
else:
tmp_tg = tmp_ground_truth
if one_output_hparam.task == NER: # [[sent1], [sent2]]
one_output_hparam.ground_truth.extend(
tmp_tg.numpy().tolist())
else: # [1, 0, 1, ...]
one_output_hparam.ground_truth.extend(
tmp_tg.numpy().reshape(-1).tolist())
else:
one_output_hparam['tmp_preds'][j] += prediction_output
for idx, one_output_hparam in enumerate(output_hparams):
prediction_output = one_output_hparam['tmp_preds'][idx]
tmp_type = one_output_hparam.type
if tmp_type in [CLASSLABEL, LIST_OF_CLASSLABEL, LIST_OF_INT]:
if one_output_hparam.task == NER: # [[sent1], [sent2]]
tmp_predictions = tf.argmax(prediction_output,
-1).numpy().tolist()
one_output_hparam.predictions.extend(tmp_predictions)
else: # [1, 0, 1, ...]
tmp_predictions = tf.argmax(
prediction_output, -1).numpy().reshape(-1).tolist()
one_output_hparam.predictions.extend(tmp_predictions)
# save reports
report_folder = hparams.get_report_dir()
# evaluation, TODO more reports
for one_output_hparam in output_hparams:
ground_truth = one_output_hparam.ground_truth
predictions = one_output_hparam.predictions
if one_output_hparam.type in [
CLASSLABEL, LIST_OF_CLASSLABEL, LIST_OF_INT
]:
# some filename
cur_report_folder = os.path.join(
report_folder,
f'{one_output_hparam.name}_{one_output_hparam.type.lower()}')
if not os.path.exists(cur_report_folder):
os.makedirs(cur_report_folder)
if one_output_hparam.task == NER:
labels = one_output_hparam.labels
# confusion matrix
cm = ConfusionMatrix(_2d_to_1d_list(ground_truth),
_2d_to_1d_list(predictions), labels)
# ner evaluation
labels = list(
set([
itm[2:] for itm in labels
if itm.startswith("B-") or itm.startswith("I-")
]))
ner_eval = NEREvaluator(
_id_to_label(ground_truth, one_output_hparam.labels),
_id_to_label(predictions, one_output_hparam.labels),
labels)
ner_results, ner_results_agg = ner_eval.evaluate()
save_json(os.path.join(cur_report_folder, "ner_results.json"),
ner_results)
save_json(
os.path.join(cur_report_folder, "ner_results_agg.json"),
ner_results_agg)
else:
cm = ConfusionMatrix(ground_truth, predictions,
one_output_hparam.labels)
# print some reports
print_boxed(f"{one_output_hparam.name} Evaluation")
cms = cm.confusion_matrix_visual()
if len(cm.label2idx) < 10:
print(cms)
# save reports to files
with open(
os.path.join(cur_report_folder,
"confusion_matrix.txt"), 'w') as f:
f.write(cms)
print()
print(json.dumps(cm.stats(), indent=4))
save_json(os.path.join(cur_report_folder, "stats.json"),
cm.stats())
save_json(os.path.join(cur_report_folder, 'per_class_stats.json'),
cm.per_class_stats())
# save reports to hparams
hparams['performance'] = Hparams()
hparams.performance["stats"] = cm.stats()
hparams.performance["per_class_stats"] = cm.per_class_stats()
logger.info(
f"Save {one_output_hparam.name} reports in {cur_report_folder}"
)
else:
logger.warning(
f"{one_output_hparam.name}'s evaluation has not be implemented."
)
def k_fold_experiment(hparams: Hparams):
"""
k_fold training
:param hparams:
:return:
"""
logger = logging.getLogger(__name__)
if hparams.use_mixed_float16:
logger.info("Use auto mixed policy")
# tf.keras.mixed_precision.experimental.set_policy('mixed_float16')
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
strategy = tf.distribute.MirroredStrategy(
devices=[f"/gpu:{id}" for id in hparams.gpus])
# build dataset
model_saved_dirs = []
for idx, (train_dataset, dev_dataset,
dataset_info) in enumerate(load_dataset(hparams,
ret_test=False)):
logger.info(f"Start {idx}th-fold training")
with strategy.scope():
# build model
model, (losses,
loss_weights), metrics, optimizer = build_model(hparams)
# build callbacks
callbacks = build_callbacks(hparams.training.callbacks)
# compile
model.compile(optimizer=optimizer,
loss=losses,
metrics=metrics,
loss_weights=loss_weights)
# fit
if hparams.training.do_eval:
validation_data = dev_dataset
validation_steps = hparams.training.validation_steps
else:
logger.info("Do not evaluate.")
validation_data = None
validation_steps = None
model.fit(
train_dataset,
validation_data=validation_data,
epochs=hparams.training.max_epochs,
callbacks=callbacks,
steps_per_epoch=hparams.training.steps_per_epoch,
validation_steps=validation_steps,
)
# build archive dir
k_fold_dir = os.path.join(hparams.get_workspace_dir(), "k_fold",
str(idx))
if not os.path.exists(k_fold_dir):
os.makedirs(k_fold_dir)
# load best model
checkpoint_dir = os.path.join(hparams.get_workspace_dir(),
"checkpoint")
if hparams.eval_use_best and os.path.exists(checkpoint_dir):
logger.info(f"Load best model from {checkpoint_dir}")
average_checkpoints(model, checkpoint_dir)
logger.info(f"Move {checkpoint_dir, k_fold_dir}")
shutil.move(checkpoint_dir, k_fold_dir)
# save best model
logger.info(
f'Save {idx}th model in {hparams.get_model_filename()}')
model.save_weights(hparams.get_model_filename(), save_format="tf")
# eval on test dataset and make reports
evaluation(hparams)
logger.info(f"Move {hparams.get_report_dir()} to {k_fold_dir}")
shutil.move(hparams.get_report_dir(), k_fold_dir)
logger.info(f"Move {hparams.get_saved_model_dir()} to {k_fold_dir}")
cur_model_saved_dir = shutil.move(hparams.get_saved_model_dir(),
k_fold_dir)
logger.info(
f"New model saved path for {idx}th fold: {cur_model_saved_dir}")
model_saved_dirs.append(cur_model_saved_dir)
logger.info(f'{idx}th-fold experiment Finish!')
# eval on test dataset after average_checkpoints
# logger.info("Average models of all fold models.")
checkpoints = [f'{itm}/model' for itm in model_saved_dirs]
# average_checkpoints(model, checkpoints)
# logger.info(f"Save averaged model in {hparams.get_model_filename()}")
# model.save_weights(hparams.get_model_filename(), save_format="tf")
if hparams.training.do_eval:
evaluation(hparams, checkpoints=checkpoints)
logger.info('Experiment Finish!')
def experiment(hparams: Hparams):
logger = logging.getLogger(__name__)
if hparams.use_mixed_float16:
logger.info("Use auto mixed policy")
# tf.keras.mixed_precision.experimental.set_policy('mixed_float16')
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
strategy = tf.distribute.MirroredStrategy(
devices=[f"/gpu:{id}" for id in hparams.gpus])
# build dataset
train_dataset, dev_dataset, dataset_info = next(
load_dataset(hparams, ret_test=False))
with strategy.scope():
# build model
model, (losses,
loss_weights), metrics, optimizer = build_model(hparams)
# build callbacks
callbacks = build_callbacks(hparams)
# compile
model.compile(optimizer=optimizer,
loss=losses,
metrics=metrics,
loss_weights=loss_weights)
# fit
if hparams.training.do_eval:
validation_data = dev_dataset
validation_steps = hparams.training.validation_steps
else:
logger.info("Do not evaluate.")
validation_data = None
validation_steps = None
model.fit(
train_dataset,
validation_data=validation_data,
epochs=hparams.training.max_epochs,
callbacks=callbacks,
steps_per_epoch=hparams.training.steps_per_epoch,
validation_steps=validation_steps,
)
# 进行lr finder
lr_finder_call_back = [
cb for cb in callbacks if hasattr(cb, "lr_finder_plot")
]
if len(lr_finder_call_back) != 0:
logger.info(
f"Do lr finder, and save result in {hparams.get_lr_finder_jpg_file()}"
)
lr_finder_call_back[0].lr_finder_plot(hparams.get_lr_finder_jpg_file())
else:
# load best model
checkpoint_dir = os.path.join(hparams.get_workspace_dir(),
"checkpoint")
if hparams.eval_use_best and os.path.exists(checkpoint_dir):
logger.info(f"Load best model from {checkpoint_dir}")
average_checkpoints(model, checkpoint_dir)
# save best model
logger.info(f'Save model in {hparams.get_model_filename()}')
model.save_weights(hparams.get_model_filename(), save_format="tf")
# eval on test dataset and make reports
if hparams.training.do_eval:
evaluation(hparams)
logger.info('Experiment Finish!')