train.py

import os
import data
import evals
import losses
import models
import myCallbacks
import tensorflow as tf
from tensorflow import keras

# import multiprocessing as mp

# if mp.get_start_method() != "forkserver":
#     mp.set_start_method("forkserver", force=True)

gpus = tf.config.experimental.list_physical_devices("GPU")
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
# strategy = tf.distribute.MirroredStrategy()
# strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")


class Train:
    def __init__(
        self,
        data_path,
        save_path,
        eval_paths=[],
        basic_model=None,
        model=None,
        compile=True,
        output_weight_decay=1,  # L2 regularizer for output layer, 0 for None, >=1 for value in basic_model, (0, 1) for specific value
        custom_objects={},
        pretrained=None,  # If reloat weights from another h5 file
        batch_size=128,
        lr_base=0.001,
        lr_decay=0.05,  # for cosine it's m_mul, or it's decay_rate for exponential or constant
        lr_decay_steps=0,  # <=1 for Exponential, (1, 500) for Cosine decay on epoch, >= 500 for Cosine decay on batch, list for Constant
        lr_min=1e-6,
        lr_warmup_steps=0,
        eval_freq=1,
        random_status=0,
        random_cutout_mask_area=0.0,  # ratio of randomly cutout bottom 2/5 area, regarding as ignoring mask area
        image_per_class=0,  # For triplet, image_per_class will be `4` if it's `< 4`
        samples_per_mining=0,  # **Not working well**. Set a value > 0 will use offline_triplet_mining dataset
        mixup_alpha=0,  # mixup alpha, value in (0, 1] to enable
        partial_fc_split=0,  # **Not working well**. Set a int number like `2`, will build model and dataset with total classes split in parts.
        teacher_model_interf=None,  # Teacher model to generate embedding data, used for distilling training.
        sam_rho=0,
        vpl_start_iters=-1,  # Enable by setting value > 0, like 8000. https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Variational_Prototype_Learning_for_Deep_Face_Recognition_CVPR_2021_paper.pdf
        vpl_allowed_delta=200,
        steps_per_execution=None,  # steps_per_execution for model.compile, default to None for not using
    ):
        from inspect import getmembers, isfunction, isclass

        custom_objects.update(dict([ii for ii in getmembers(losses) if isfunction(ii[1]) or isclass(ii[1])]))
        custom_objects.update({"NormDense": models.NormDense})

        self.model, self.basic_model, self.save_path, self.inited_from_model, self.sam_rho, self.pretrained = None, None, save_path, False, sam_rho, pretrained
        self.steps_per_execution, self.vpl_start_iters, self.vpl_allowed_delta = steps_per_execution, vpl_start_iters, vpl_allowed_delta
        if model is None and basic_model is None:
            model = os.path.join("checkpoints", save_path)
            print(">>>> Try reload from:", model)

        if isinstance(model, str):
            if model.endswith(".h5") and os.path.exists(model):
                print(">>>> Load model from h5 file: %s..." % model)
                with keras.utils.custom_object_scope(custom_objects):
                    self.model = keras.models.load_model(model, compile=compile, custom_objects=custom_objects)
                embedding_layer = basic_model if basic_model is not None else self.__search_embedding_layer__(self.model)
                self.basic_model = keras.models.Model(self.model.inputs[0], self.model.layers[embedding_layer].output)
                # self.model.summary()
        elif isinstance(model, keras.models.Model):
            self.model = model
            embedding_layer = basic_model if basic_model is not None else self.__search_embedding_layer__(self.model)
            self.basic_model = keras.models.Model(self.model.inputs[0], self.model.layers[embedding_layer].output)
            self.inited_from_model = True
            print(">>>> Specified model structure, output layer will keep from changing")
        elif isinstance(basic_model, str):
            if basic_model.endswith(".h5") and os.path.exists(basic_model):
                print(">>>> Load basic_model from h5 file: %s..." % basic_model)
                with keras.utils.custom_object_scope(custom_objects):
                    self.basic_model = keras.models.load_model(basic_model, compile=compile, custom_objects=custom_objects)
        elif isinstance(basic_model, keras.models.Model):
            self.basic_model = basic_model

        if self.basic_model == None:
            print(
                "Initialize model by:\n"
                "| basic_model                                                     | model           |\n"
                "| --------------------------------------------------------------- | --------------- |\n"
                "| model structure                                                 | None            |\n"
                "| basic model .h5 file                                            | None            |\n"
                "| None for 'embedding' layer or layer index of basic model output | model .h5 file  |\n"
                "| None for 'embedding' layer or layer index of basic model output | model structure |\n"
                "| None                                                            | None            |\n"
                "* Both None for reload model from 'checkpoints/{}'\n".format(save_path)
            )
            return

        input_shape = self.basic_model.input_shape[1:]
        if input_shape[0] is None or input_shape[1] is None:
            input_shape = (112, 112, 3)
        self.input_shape = input_shape

        self.softmax, self.arcface, self.arcface_partial, self.triplet = "softmax", "arcface", "arcface_partial", "triplet"
        self.center, self.distill = "center", "distill"
        if output_weight_decay >= 1:
            l2_weight_decay = 0
            for ii in self.basic_model.layers:
                if hasattr(ii, "kernel_regularizer") and isinstance(ii.kernel_regularizer, keras.regularizers.L2):
                    l2_weight_decay = ii.kernel_regularizer.l2
                    break
            print(">>>> L2 regularizer value from basic_model:", l2_weight_decay)
            output_weight_decay *= l2_weight_decay * 2
        self.output_weight_decay = output_weight_decay

        self.batch_size, self.batch_size_per_replica = batch_size, batch_size
        if tf.distribute.has_strategy():
            strategy = tf.distribute.get_strategy()
            self.batch_size = batch_size * strategy.num_replicas_in_sync
            print(">>>> num_replicas_in_sync: %d, batch_size: %d" % (strategy.num_replicas_in_sync, self.batch_size))
            self.data_options = tf.data.Options()
            self.data_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA

        my_evals = [evals.eval_callback(self.basic_model, ii, batch_size=self.batch_size_per_replica, eval_freq=eval_freq) for ii in eval_paths]
        if len(my_evals) != 0:
            my_evals[-1].save_model = os.path.splitext(save_path)[0]
        self.my_history, self.model_checkpoint, self.lr_scheduler, self.gently_stop = myCallbacks.basic_callbacks(
            save_path,
            my_evals,
            lr=lr_base,
            lr_decay=lr_decay,
            lr_min=lr_min,
            lr_decay_steps=lr_decay_steps,
            lr_warmup_steps=lr_warmup_steps,
        )
        self.gently_stop = None  # may not working for windows
        self.my_evals, self.custom_callbacks = my_evals, []
        self.metrics = ["accuracy"]
        self.default_optimizer = "adam"

        self.data_path, self.random_status, self.image_per_class, self.mixup_alpha = data_path, random_status, image_per_class, mixup_alpha
        self.random_cutout_mask_area, self.partial_fc_split, self.samples_per_mining = random_cutout_mask_area, partial_fc_split, samples_per_mining
        self.train_ds, self.steps_per_epoch, self.classes, self.is_triplet_dataset = None, None, 0, False
        self.teacher_model_interf, self.is_distill_ds = teacher_model_interf, False
        self.distill_emb_map_layer = None

    def __search_embedding_layer__(self, model):
        for ii in range(1, 6):
            if model.layers[-ii].name == "embedding":
                return -ii

    def __init_dataset__(self, type, emb_loss_names):
        init_as_triplet = self.triplet in emb_loss_names or type == self.triplet
        is_offline_triplet = self.samples_per_mining > 0
        if self.train_ds is not None and init_as_triplet == self.is_triplet_dataset and not self.is_distill_ds and not is_offline_triplet:
            return

        dataset_params = {
            "data_path": self.data_path,
            "batch_size": self.batch_size,
            "img_shape": self.input_shape,
            "random_status": self.random_status,
            "random_cutout_mask_area": self.random_cutout_mask_area,
            "image_per_class": self.image_per_class,
            "mixup_alpha": self.mixup_alpha,
            "teacher_model_interf": self.teacher_model_interf,
        }

        if is_offline_triplet:
            print(">>>> Init offline triplet dataset...")
            aa = data.Triplet_dataset_offline(basic_model=self.basic_model, samples_per_mining=self.samples_per_mining, **dataset_params)
            self.train_ds, self.steps_per_epoch = aa.ds, aa.steps_per_epoch
            self.is_triplet_dataset = False
        elif init_as_triplet:
            print(">>>> Init triplet dataset...")
            if isinstance(self.data_path, str) and self.data_path.endswith(".tfrecord"):
                print(">>>> Combining tfrecord dataset with triplet is NOT recommended.")
                self.train_ds, self.steps_per_epoch = data.prepare_distill_dataset_tfrecord(**dataset_params)
            else:
                aa = data.Triplet_dataset(**dataset_params)
                self.train_ds, self.steps_per_epoch = aa.ds, aa.steps_per_epoch
            self.is_triplet_dataset = True
        else:
            print(">>>> Init softmax dataset...")
            if isinstance(self.data_path, str) and self.data_path.endswith(".tfrecord") and "*" in self.data_path:
                print(">>>> data_path is in format like `*.tfrecord`, regarding it as a tfrecord dataset")
                self.train_ds, self.steps_per_epoch = data.prepare_dataset(**dataset_params)
            elif isinstance(self.data_path, str) and self.data_path.endswith(".tfrecord"):
                self.train_ds, self.steps_per_epoch = data.prepare_distill_dataset_tfrecord(**dataset_params)
            else:
                self.train_ds, self.steps_per_epoch = data.prepare_dataset(**dataset_params, partial_fc_split=self.partial_fc_split)
            self.is_triplet_dataset = False
        if self.train_ds is None:
            return

        if tf.distribute.has_strategy():
            self.train_ds = self.train_ds.with_options(self.data_options)

        label_spec = self.train_ds.element_spec[-1]
        if isinstance(label_spec, tuple):
            # dataset with embedding values
            self.is_distill_ds = True
            self.teacher_emb_size = label_spec[0].shape[-1]
            self.classes = label_spec[1].shape[-1]
            if type == self.distill:
                # Loss is distill type: [label * n, embedding]
                self.train_ds = self.train_ds.map(lambda xx, yy: (xx, yy[1:] * len(emb_loss_names) + yy[:1]))
            elif (self.distill in emb_loss_names and len(emb_loss_names) != 1) or (self.distill not in emb_loss_names and len(emb_loss_names) != 0):
                # Will attach distill loss as embedding loss, and there are other embedding losses: [embedding, label * n]
                label_data_len = len(emb_loss_names) if self.distill in emb_loss_names else len(emb_loss_names) + 1
                self.train_ds = self.train_ds.map(lambda xx, yy: (xx, yy[:1] + yy[1:] * label_data_len))
        else:
            self.is_distill_ds = False
            self.classes = label_spec.shape[-1]

    def __init_optimizer__(self, optimizer):
        if optimizer == None:
            if self.model != None and self.model.optimizer != None:
                # Model loaded from .h5 file already compiled
                # Saving may meet Error: OSError: Unable to create link (name already exists)
                self.optimizer = self.model.optimizer
                compiled_opt = self.optimizer.inner_optimizer if isinstance(self.optimizer, keras.mixed_precision.LossScaleOptimizer) else self.optimizer
                print(">>>> Reuse optimizer from previoue model:", compiled_opt.__class__.__name__)
                # if isinstance(self.model.optimizer, keras.mixed_precision.LossScaleOptimizer):
                #     inner_optimizer_pre = self.model.optimizer.inner_optimizer
                #     inner_optimizer = inner_optimizer_pre.__class__(**inner_optimizer_pre.get_config())
                #     # self.optimizer = keras.mixed_precision.LossScaleOptimizer(inner_optimizer)
                #     self.optimizer = inner_optimizer
                # else:
                #     self.optimizer = self.model.optimizer.__class__(**self.model.optimizer.get_config())
            else:
                print(">>>> Use default optimizer:", self.default_optimizer)
                self.optimizer = self.default_optimizer
        else:
            print(">>>> Use specified optimizer:", optimizer)
            self.optimizer = optimizer

        try:
            import tensorflow_addons as tfa
        except:
            pass
        else:
            compiled_opt = self.optimizer.inner_optimizer if isinstance(self.optimizer, keras.mixed_precision.LossScaleOptimizer) else self.optimizer
            if isinstance(compiled_opt, tfa.optimizers.weight_decay_optimizers.DecoupledWeightDecayExtension):
                print(">>>> Append weight decay callback...")
                lr_base, wd_base = self.optimizer.lr.numpy(), self.optimizer.weight_decay.numpy()
                wd_callback = myCallbacks.OptimizerWeightDecay(lr_base, wd_base, is_lr_on_batch=self.is_lr_on_batch)
                self.callbacks.append(wd_callback)  # should be after lr_scheduler

    def __init_model__(self, type, loss_top_k=1, header_append_norm=False):
        inputs = self.basic_model.inputs[0]
        embedding = self.basic_model.outputs[0]
        is_multi_output = lambda mm: len(mm.outputs) != 1 or isinstance(mm.layers[-1], keras.layers.Concatenate)
        if self.model != None and is_multi_output(self.model):
            output_layer = min(len(self.basic_model.layers), len(self.model.layers) - 1)
            self.model = keras.models.Model(inputs, self.model.layers[output_layer].output)

        if self.output_weight_decay != 0:
            print(">>>> Add L2 regularizer to model output layer, output_weight_decay = %f" % self.output_weight_decay)
            output_kernel_regularizer = keras.regularizers.L2(self.output_weight_decay / 2)
        else:
            output_kernel_regularizer = None

        model_output_layer_name = None if self.model is None else self.model.output_names[-1]
        # arcface_not_match = self.model.layers[-1].append_norm != header_append_norm or self.partial_fc_split != self.model.layers[-1].partial_fc_split
        if type == self.softmax and model_output_layer_name != self.softmax:
            print(">>>> Add softmax layer...")
            softmax_logits = keras.layers.Dense(self.classes, use_bias=False, name=self.softmax + "_logits", kernel_regularizer=output_kernel_regularizer)
            if self.model != None and "_embedding" not in self.model.output_names[-1]:
                softmax_logits.build(embedding.shape)
                weight_cur = softmax_logits.get_weights()
                weight_pre = self.model.layers[-1].get_weights()
                if len(weight_cur) == len(weight_pre) and weight_cur[0].shape == weight_pre[0].shape:
                    print(">>>> Reload previous %s weight..." % (self.model.output_names[-1]))
                    softmax_logits.set_weights(weight_pre)
            logits = softmax_logits(embedding)
            output_fp32 = keras.layers.Activation("softmax", dtype="float32", name=self.softmax)(logits)
            self.model = keras.models.Model(inputs, output_fp32)
        elif type == self.arcface and (model_output_layer_name != self.arcface or self.model.layers[-1].append_norm != header_append_norm):
            vpl_start_iters = self.vpl_start_iters * self.steps_per_epoch if self.vpl_start_iters < 50 else self.vpl_start_iters
            vpl_kwargs = {"vpl_lambda": 0.15, "start_iters": vpl_start_iters, "allowed_delta": self.vpl_allowed_delta}
            arc_kwargs = {"loss_top_k": loss_top_k, "append_norm": header_append_norm, "partial_fc_split": self.partial_fc_split, "name": self.arcface}
            print(">>>> Add arcface layer, arc_kwargs={}, vpl_kwargs={}...".format(arc_kwargs, vpl_kwargs))
            if vpl_start_iters > 0:
                batch_size = self.batch_size_per_replica
                arcface_logits = models.NormDenseVPL(batch_size, self.classes, output_kernel_regularizer, **arc_kwargs, **vpl_kwargs, dtype="float32")
            else:
                arcface_logits = models.NormDense(self.classes, output_kernel_regularizer, **arc_kwargs, dtype="float32")

            if self.model != None and "_embedding" not in self.model.output_names[-1]:
                arcface_logits.build(embedding.shape)
                weight_cur = arcface_logits.get_weights()
                weight_pre = self.model.layers[-1].get_weights()
                if len(weight_cur) == len(weight_pre) and weight_cur[0].shape == weight_pre[0].shape:
                    print(">>>> Reload previous %s weight..." % (self.model.output_names[-1]))
                    arcface_logits.set_weights(weight_pre)
            output_fp32 = arcface_logits(embedding)
            # output_fp32 = keras.layers.Activation('linear', dtype='float32', name=self.arcface)(output_fp32)
            self.model = keras.models.Model(inputs, output_fp32)
        elif type in [self.triplet, self.center, self.distill]:
            self.model = self.basic_model
            self.model.output_names[0] = type + "_embedding"
        else:
            print(">>>> Will NOT change model output layer.")

        if self.pretrained is not None:
            if self.model is None:
                self.basic_model.load_weights(self.pretrained)
            else:
                self.model.load_weights(self.pretrained)
            self.pretrained = None

    def __add_emb_output_to_model__(self, emb_type, emb_loss, emb_loss_weight):
        nns = self.model.output_names
        emb_shape = self.basic_model.output_shape[-1]
        if emb_type == self.distill and self.teacher_emb_size != emb_shape:
            print(">>>> Add a dense layer to map embedding: student %d --> teacher %d" % (emb_shape, self.teacher_emb_size))
            embedding = self.basic_model.outputs[0]
            if self.distill_emb_map_layer is None:
                self.distill_emb_map_layer = keras.layers.Dense(self.teacher_emb_size, use_bias=False, name="distill_map", dtype="float32")
            emb_map_output = self.distill_emb_map_layer(embedding)
            self.model = keras.models.Model(self.model.inputs[0], [emb_map_output] + self.model.outputs)
        else:
            self.model = keras.models.Model(self.model.inputs[0], self.basic_model.outputs + self.model.outputs)

        self.model.output_names[0] = emb_type + "_embedding"
        for id, nn in enumerate(nns):
            self.model.output_names[id + 1] = nn
        self.cur_loss = [emb_loss, *self.cur_loss]
        self.loss_weights.update({self.model.output_names[0]: emb_loss_weight})

    def __init_type_by_loss__(self, loss):
        print(">>>> Init type by loss function name...")
        if isinstance(loss, str):
            return self.softmax

        if loss.__class__.__name__ == "function":
            ss = loss.__name__.lower()
            if self.softmax in ss:
                return self.softmax
            if self.arcface in ss:
                return self.arcface
            if self.triplet in ss:
                return self.triplet
            if self.distill in ss:
                return self.distill
        else:
            ss = loss.__class__.__name__.lower()
            if isinstance(loss, losses.TripletLossWapper) or self.triplet in ss:
                return self.triplet
            if isinstance(loss, losses.CenterLoss) or self.center in ss:
                return self.center
            if isinstance(loss, losses.ArcfaceLoss) or self.arcface in ss:
                return self.arcface
            if isinstance(loss, losses.ArcfaceLossSimple) or isinstance(loss, losses.AdaCosLoss):
                return self.arcface
            if isinstance(loss, losses.DistillKLDivergenceLoss):
                return self.arcface  # Use NormDense layer
            if self.softmax in ss:
                return self.softmax
        return self.softmax

    def __init_emb_losses__(self, embLossTypes=None, embLossWeights=1):
        emb_loss_names, emb_loss_weights = {}, {}
        if embLossTypes is not None:
            embLossTypes = embLossTypes if isinstance(embLossTypes, list) else [embLossTypes]
            for id, ee in enumerate(embLossTypes):
                emb_loss_name = ee.lower() if isinstance(ee, str) else ee.__name__.lower()
                emb_loss_weight = float(embLossWeights[id] if isinstance(embLossWeights, list) else embLossWeights)
                if "centerloss" in emb_loss_name:
                    emb_loss_names[self.center] = losses.CenterLoss if isinstance(ee, str) else ee
                    emb_loss_weights[self.center] = emb_loss_weight
                elif "triplet" in emb_loss_name:
                    emb_loss_names[self.triplet] = losses.BatchHardTripletLoss if isinstance(ee, str) else ee
                    emb_loss_weights[self.triplet] = emb_loss_weight
                elif "distill" in emb_loss_name:
                    emb_loss_names[self.distill] = losses.distiller_loss_cosine if ee == None or isinstance(ee, str) else ee
                    emb_loss_weights[self.distill] = emb_loss_weight
        return emb_loss_names, emb_loss_weights

    def __basic_train__(self, epochs, initial_epoch=0):
        self.model.compile(
            optimizer=self.optimizer, loss=self.cur_loss, metrics=self.metrics, loss_weights=self.loss_weights, steps_per_execution=self.steps_per_execution
        )
        cur_optimizer = self.model.optimizer
        if not hasattr(cur_optimizer, "_variables") and hasattr(cur_optimizer, "_optimizer") and hasattr(cur_optimizer._optimizer, "_variables"):
            # Bypassing TF 2.11 error AttributeError: 'LossScaleOptimizerV3' object has no attribute '_variables'
            # setattr(self.model.optimizer, "_variables", self.model.optimizer._optimizer._variables)
            setattr(self.model.optimizer, "variables", self.model.optimizer._optimizer.variables)
        self.model.fit(
            self.train_ds,
            epochs=epochs,
            verbose=1,
            callbacks=self.callbacks,
            initial_epoch=initial_epoch,
            steps_per_epoch=self.steps_per_epoch,
            # steps_per_epoch=0,
            use_multiprocessing=True,
            workers=4,
        )

    def reset_dataset(self, data_path=None):
        self.train_ds = None
        if data_path != None:
            self.data_path = data_path

    def train_single_scheduler(
        self,
        epoch,
        loss=None,
        initial_epoch=0,
        lossWeight=1,
        optimizer=None,
        bottleneckOnly=False,
        lossTopK=1,
        type=None,
        embLossTypes=None,
        embLossWeights=1,
        tripletAlpha=0.35,
    ):
        emb_loss_names, emb_loss_weights = self.__init_emb_losses__(embLossTypes, embLossWeights)

        if loss is None:
            if self.model.built:
                loss = self.model.loss[0]
            else:
                return

        if type is None and not self.inited_from_model:
            type = self.__init_type_by_loss__(loss)
        print(">>>> Train %s..." % type)
        self.__init_dataset__(type, emb_loss_names)
        if self.train_ds is None:
            print(">>>> [Error]: train_ds is None.")
            if self.model is not None:
                self.model.stop_training = True
            return
        if self.is_distill_ds == False and type == self.distill:
            print(">>>> [Error]: Dataset doesn't contain embedding data.")
            if self.model is not None:
                self.model.stop_training = True
            return

        self.is_lr_on_batch = isinstance(self.lr_scheduler, myCallbacks.CosineLrScheduler)
        if self.is_lr_on_batch:
            self.lr_scheduler.steps_per_epoch = self.steps_per_epoch

        basic_callbacks = [ii for ii in [self.my_history, self.model_checkpoint, self.lr_scheduler] if ii is not None]
        self.callbacks = self.my_evals + self.custom_callbacks + basic_callbacks
        # self.basic_model.trainable = True
        self.__init_optimizer__(optimizer)
        if not self.inited_from_model:
            header_append_norm = isinstance(loss, losses.MagFaceLoss) or isinstance(loss, losses.AdaFaceLoss)
            self.__init_model__(type, lossTopK, header_append_norm)

        # loss_weights
        self.cur_loss, self.loss_weights = [loss], {ii: lossWeight for ii in self.model.output_names}
        if self.center in emb_loss_names and type != self.center:
            loss_class = emb_loss_names[self.center]
            print(">>>> Attach center loss:", loss_class.__name__)
            emb_shape = self.basic_model.output_shape[-1]
            initial_file = os.path.splitext(self.save_path)[0] + "_centers.npy"
            center_loss = loss_class(self.classes, emb_shape=emb_shape, initial_file=initial_file)
            self.callbacks.append(center_loss.save_centers_callback)
            self.__add_emb_output_to_model__(self.center, center_loss, emb_loss_weights[self.center])

        if self.triplet in emb_loss_names and type != self.triplet:
            loss_class = emb_loss_names[self.triplet]
            print(">>>> Attach triplet loss: %s, alpha = %f..." % (loss_class.__name__, tripletAlpha))
            triplet_loss = loss_class(alpha=tripletAlpha)
            self.__add_emb_output_to_model__(self.triplet, triplet_loss, emb_loss_weights[self.triplet])

        if self.is_distill_ds and type != self.distill:
            distill_loss = emb_loss_names.get(self.distill, losses.distiller_loss_cosine)
            print(">>>> Attach disill loss:", distill_loss.__name__)
            self.__add_emb_output_to_model__(self.distill, distill_loss, emb_loss_weights.get(self.distill, 1))

        print(">>>> loss_weights:", self.loss_weights)
        self.metrics = {ii: None if "embedding" in ii else "accuracy" for ii in self.model.output_names}
        # self.callbacks.append(keras.callbacks.TerminateOnNaN())
        self.callbacks.append(myCallbacks.ExitOnNaN())  # Exit directly avoiding further saving
        # self.callbacks.append(keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, logs=None: keras.backend.clear_session()))
        # self.callbacks.append(keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, logs=None: self.basic_model.save("aa_epoch{}.h5".format(epoch))))

        if self.vpl_start_iters > 0:  # VPL mode, needs the actual batch_size
            loss.build(self.batch_size_per_replica)
            self.callbacks.append(myCallbacks.VPLUpdateQueue())

        if self.gently_stop:
            self.callbacks.append(self.gently_stop)

        if bottleneckOnly:
            print(">>>> Train bottleneckOnly...")
            self.basic_model.trainable = False
            self.callbacks = self.callbacks[len(self.my_evals) :]  # Exclude evaluation callbacks
            self.__basic_train__(epoch, initial_epoch=0)
            self.basic_model.trainable = True
        else:
            self.__basic_train__(initial_epoch + epoch, initial_epoch=initial_epoch)

        print(">>>> Train %s DONE!!! epochs = %s, model.stop_training = %s" % (type, self.model.history.epoch, self.model.stop_training))
        print(">>>> My history:")
        self.my_history.print_hist()
        latest_save_path = os.path.join("checkpoints", os.path.splitext(self.save_path)[0] + "_basic_model_latest.h5")
        print(">>>> Saving latest basic model to:", latest_save_path)
        self.basic_model.save(latest_save_path)

    def train(self, train_schedule, initial_epoch=0):
        train_schedule = [train_schedule] if isinstance(train_schedule, dict) else train_schedule
        for sch in train_schedule:
            for ii in ["centerloss", "triplet", "distill"]:
                if ii in sch:
                    sch.setdefault("embLossTypes", []).append(ii)
                    sch.setdefault("embLossWeights", []).append(sch.pop(ii))
            if "alpha" in sch:
                sch["tripletAlpha"] = sch.pop("alpha")

            self.train_single_scheduler(**sch, initial_epoch=initial_epoch)
            initial_epoch += 0 if sch.get("bottleneckOnly", False) else sch["epoch"]

            if self.model is None or self.model.stop_training == True:
                print(">>>> But it's an early stop, break...")
                break
        return initial_epoch