def __init__(self, metadata):
"""
Args:
metadata: an AutoDLMetadata object. Its definition can be found in
AutoDL_ingestion_program/dataset.py
"""
self.done_training = False
self.metadata = metadata
self.domain = "speech"
test_metadata_filename = self.metadata.get_dataset_name().replace('train', 'test') + '/metadata.textproto'
self.test_num = [int(line.split(':')[1]) for line in open(test_metadata_filename, 'r').readlines()[:3] if 'sample_count' in line][0]
# DomainModel = meta_domain_2_model(self.domain)
self.domain_metadata = get_domain_metadata(metadata, self.domain)
self.domain_metadata["test_num"] = self.test_num
self.class_num = self.domain_metadata["class_num"]
self.train_num = self.domain_metadata["train_num"]
logger.info("Note:domain={}, domain_metadata is {}".format(self.domain, self.domain_metadata))
# self.domain_model = DomainModel(self.domain_metadata)
self.domain_model = ModelExecutor(self.domain_metadata)
self.ensemble_val_record_list = list()
self.ensemble_val_nauc_list = list()
self.cur_cls_name = None
self.cur_train_his_report = dict()
self.g_predevel_space = list()
self.g_train_loss_list = list()
as_timer("model_speech_init")
def offline_fit(self,
train_examples_x: np.ndarray,
train_examples_y: np.ndarray,
fit_params: dict = None):
# for single-label
if fit_params.get("if_multilabel") is False:
train_examples_y = ohe2cat(train_examples_y)
self.model.fit(train_examples_x, train_examples_y)
self.label_map = self.model.classes_
# for multi-labels.
else:
if self.ml_mode == 1:
for cls in range(self.class_num):
cls_y = train_examples_y[:, cls]
# self.logReg_pipeline.fit(train_examples_x, cls_y)
self.ml_models[cls].fit(train_examples_x, cls_y)
elif self.ml_mode == 2:
self.ml_model.fit(train_examples_x, train_examples_y)
elif self.ml_mode == 3:
for cls in range(self.class_num):
cls_y = train_examples_y[:, cls]
self.logReg_pipeline.fit(train_examples_x, cls_y)
else:
error("Error: wrong ml_mode={}".format(self.ml_mode))
as_timer("lr_liblinear_fit_{}".format(len(train_examples_x)))
def test(self, dataset, remaining_time_budget=None):
"""Test method of domain-specific model."""
cur_y_pred = self.domain_model.test_pipeline(dataset)
self.cur_train_his_report["pred_probas"] = cur_y_pred
if self.cur_cls_name == CLS_TR34 and self.domain_model.tr34_cls_train_pip_run >= 8:
loss_godown_rate = self.domain_model.decision_maker.ensemble_learner.get_loss_godown_rate(self.g_train_loss_list, EVAL_TLOSS_TAIL_SIZE)
if loss_godown_rate >= EVAL_TLOSS_GODOWN_RATE_THRES:
self.domain_model.decision_maker.ensemble_learner.add_eval_pred_item(self.cur_train_his_report)
if self.domain_model.tr34_cls_train_pip_run >= 15:
pred_rule = {
"t_loss": 5,
# "t_acc": 1
}
pred_ensemble = self.domain_model.decision_maker.ensemble_learner.softvoting_ensemble_preds(pred_rule)
else:
pred_ensemble = cur_y_pred
# if pred ensemble.
# real_y_pred =
self.done_training = False
as_timer("test_start")
# return cur_y_pred
return pred_ensemble
def online_fit(self, train_examples_x: np.ndarray, train_examples_y: np.ndarray, fit_params:dict):
self.trn_gen = Tr34DataGenerator(train_examples_x, train_examples_y, **self.tr34_cls_params)
# self.first_r_train_x = self.train_x
cur_train_len = len(train_examples_x)
self.first_r_data_generator = self.trn_gen
cur_epoch = self.decide_epoch_curround(fit_params.get("first_epoch", 14), fit_params.get("left_epoch", 1))
early_stopping = TerminateOnBaseline(monitor="acc", baseline=0.999)
cur_fit_history = self.model.fit_generator(
self.first_r_data_generator,
# steps_per_epoch=int(cur_train_len // self.tr34_cls_params["batch_size"] // 2),
steps_per_epoch=self.decide_stepperepoch_curround(cur_train_len),
validation_data=fit_params.get("valid_data"), #todo: put in.
epochs=cur_epoch,
max_queue_size=10,
callbacks=self.callbacks + [early_stopping],
use_multiprocessing=False,
workers=1,
verbose=ThinRes34Config.VERBOSE,
)
self.round_idx += 1
as_timer("TR34Cls_r{}_fit".format(self.round_idx))
# get fit history
cur_train_loss = round(cur_fit_history.history.get("loss")[-1], 6)
cur_train_acc = round(cur_fit_history.history.get("acc")[-1], 6)
cur_lr = cur_fit_history.history.get("lr")[-1]
cur_fit_history_report = {
"t_loss": cur_train_loss,
"t_acc": cur_train_acc
}
return cur_fit_history_report
def online_fit(self, train_examples_x: np.ndarray,
train_examples_y: np.ndarray, fit_params: dict):
self.cur_train_num = len(train_examples_x)
cur_training_generator = MixupGenerator(
train_examples_x,
train_examples_y,
batch_size=self.train_batch_size,
datagen=self.img_freqmasking_datagen)()
# update
self._fit_params_decision(fit_params)
self.model.fit_generator(
cur_training_generator,
steps_per_epoch=self.fit_params_res["steps_per_epoch"],
epochs=self.fit_params_res["epochs"],
initial_epoch=self.fit_params_res["initial_epoch"],
shuffle=True,
verbose=1,
)
self.n_iter += 5
as_timer("CNNCls_fit_{}_{}_{}_{}".format(
self.cur_train_num,
self.fit_params_res["initial_epoch"],
self.fit_params_res["epochs"],
self.fit_params_res["steps_per_epoch"],
))
def init(self, class_num: int, init_params: dict):
"""
:param class_num:
:param init_params:
- n_mels
- pretrain_path
:return:
"""
self.class_num = class_num
self.clf_name = "cnn_pret"
self.n_mels = init_params.get("n_mels") # 64, fixed, as pretrained.
# self.model = self._load_pretrained_model(input_shape=(self.n_mels, self.n_mels, 1), n_classes=self.class_num)
self.model = cnn_load_pretrained_model(input_shape=(self.n_mels,
self.n_mels, 1),
n_classes=self.class_num)
info("Backbone classifier={} is init, class_num={}, init_params={}".
format(self.clf_name, self.class_num, init_params))
as_timer("clf_{}_init".format(self.clf_name))
self.train_batch_size = init_params.get("train_batch_size")
self.predict_batch_size = init_params.get("predict_batch_size")
self.n_iter = 0
# option:
self.img_freqmasking_datagen = ImageDataGenerator(
preprocessing_function=DNpAugPreprocessor.frequency_masking)
def predict_proba(self, test_examples: np.ndarray,
predict_prob_params: dict) -> np.ndarray:
cur_test_generator = TTAGenerator(test_examples,
batch_size=self.predict_batch_size)()
test_size = len(test_examples)
pred_probs = self.model.predict_generator(
cur_test_generator,
steps=int(np.ceil(test_size / self.predict_batch_size)))
as_timer("CNNCls_testpred")
return pred_probs
def eval_val(self, val_examples_x, val_examples_y):
valid_generator = TTAGenerator(val_examples_x,
batch_size=self.predict_batch_size)()
valid_size = len(val_examples_x)
valid_probas = self.model.predict_generator(
valid_generator,
steps=int(np.ceil(valid_size / self.predict_batch_size)))
# val_auc = ATEvaluator.autodl_auc(val_examples_y, valid_probas)
val_auc = ATEvaluator.skl_auc_macro(val_examples_y, valid_probas)
as_timer("CNNCls_evalval_{}_{}".format(valid_size, val_auc))
return val_auc
def predict_proba(self, test_examples: np.ndarray, predict_prob_params: dict=None) -> np.ndarray:
K.set_learning_phase(0)
if self.good_to_predict():
y_pred = self.model.predict(test_examples, batch_size=self.tr34_mconfig.PRED_SIZE)
as_timer("TR34Cls_Test")
self.test_idx += 1
self.last_y_pred = y_pred
self.last_y_pred_round = self.round_idx
return y_pred
else:
return self.last_y_pred
def offline_fit(self,
train_examples_x: np.ndarray,
train_examples_y: np.ndarray,
fit_params: dict = None):
if fit_params.get("if_multilabel") is False:
train_examples_y = ohe2cat(train_examples_y)
self.model.fit(train_examples_x, train_examples_y)
self.label_map = self.model.classes_
else:
self.ml_model.fit(train_examples_x, train_examples_y)
as_timer("lr_sag_fit_{}".format(len(train_examples_x)))
def predict_proba(self,
test_examples: np.ndarray,
predict_prob_params: dict = None) -> np.ndarray:
if predict_prob_params.get("if_multilabel") is True:
return self.predict_proba_multilabel(test_examples)
else:
raw_pred_probas = self.model.predict_proba(test_examples)
if len(self.label_map) < self.class_num:
rebuilt_pred_proba = self.rebuild_prob_res(
self.label_map, raw_pred_probas)
as_timer("lr_liblinaer_pred_proba_{}".format(
len(test_examples)))
return rebuilt_pred_proba
else:
return raw_pred_probas
def init_kapre_melspectrogram_extractor(self):
self.kapre_melspectrogram_extractor = self.make_melspectrogram_extractor(
(1, self.kape_params.get("CROP_SEC") *
self.kape_params.get("SAMPLING_RATE")))
if KAPRE_FMAKER_WARMUP:
warmup_size = 10
warmup_x = [
np.array([np.random.uniform() for i in range(48000)],
dtype=np.float32) for j in range(warmup_size)
]
# warmup_x_mel = extract_features(warmup_x)
warmup_x_mel = self.make_features(warmup_x,
feats_maker_params={
"len_sample": 5,
"sr": 16000
})
info("Kpare_featmaker warmup.")
as_timer("Kpare_featmaker_warmup")
def train(self, dataset, remaining_time_budget=None):
"""Train method of domain-specific model."""
logger.info("Note: speech_train_process model.py starts train")
as_timer("train_start")
if IF_TRAIN_BREAK_CONDITION:
while True:
self.cur_train_his_report = self.domain_model.train_pipeline(dataset)
self.cur_cls_name = self.cur_train_his_report.get("cls_name")
cur_val_nauc = self.cur_train_his_report["val_nauc"]
self.ensemble_val_record_list.append([self.cur_cls_name, cur_val_nauc])
self.ensemble_val_nauc_list.append(cur_val_nauc)
if cur_val_nauc == -1 or cur_val_nauc > self.get_accept_nauc():
info("Decision=Yes, cur_cls_name={}, cur_val_nauc={}, his_top_nauc={}".format(self.cur_cls_name, cur_val_nauc, max(self.ensemble_val_nauc_list)))
break
else:
info("Decision=No, cur_cls_name={}, cur_val_nauc={}, his_top_nauc={}".format(self.cur_cls_name, cur_val_nauc, max(self.ensemble_val_nauc_list)))
else:
self.cur_train_his_report = self.domain_model.train_pipeline(dataset)
self.cur_cls_name = self.cur_train_his_report.get("cls_name")
cur_t_loss = self.cur_train_his_report.get("t_loss")
if cur_t_loss is None:
self.g_train_loss_list.append(100000)
else:
self.g_train_loss_list.append(cur_t_loss)
info("train_his_report={}".format(self.cur_train_his_report))
cur_val_nauc = self.cur_train_his_report["val_nauc"]
self.ensemble_val_record_list.append([self.cur_cls_name, cur_val_nauc])
self.ensemble_val_nauc_list.append(cur_val_nauc)
as_timer("speech_model_basic_train")
def predict_proba_2(self, test_examples: np.ndarray, predict_prob_params: dict=None) -> np.ndarray:
K.set_learning_phase(0)
y_pred = self.model.predict(test_examples, batch_size=self.tr34_mconfig.PRED_SIZE)
as_timer("TR34Cls_Test")
self.test_idx += 1
return y_pred
def init(self, class_num: int, init_params: dict=None):
self.class_num = class_num
self.model, self.callbacks = self.tr34_model_init(class_num)
self.choose_round_spec_len()
as_timer("TR34Cls_init")
