EEG-DINO / utils.py
EEG-DINO
11cc6a7 verified over 1 year ago
28 kB
# --------------------------------------------------------
# EEG-DINO: Learning EEG Foundation Models via Hierarchical Self-Distillation
# Based on BEiT-v2, timm, DeiT, DINO v2, LaBraM and CBraMod code bases
# https://github.com/microsoft/unilm/tree/master/beitv2
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit/
# https://github.com/facebookresearch/dinov2
# https://github.com/935963004/LaBraM
# https://github.com/wjq-learning/CBraMod
# ---------------------------------------------------------

import io
import os
import math
import time
import json
import glob
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict

from pathlib import Path
import argparse

import torch
import torch.distributed as dist
from torch import inf

from tensorboardX import SummaryWriter
import pickle
from scipy.signal import resample
from pyhealth.metrics import binary_metrics_fn, multiclass_metrics_fn

def bool_flag(s):
    """
    Parse boolean arguments from the command line.
    """
    FALSY_STRINGS = {"off", "false", "0"}
    TRUTHY_STRINGS = {"on", "true", "1"}
    if s.lower() in FALSY_STRINGS:
        return False
    elif s.lower() in TRUTHY_STRINGS:
        return True
    else:
        raise argparse.ArgumentTypeError("invalid value for a boolean flag")

def get_model(model):
    if isinstance(model, torch.nn.DataParallel) \
      or isinstance(model, torch.nn.parallel.DistributedDataParallel):
        return model.module
    else:
        return model
            
class SmoothedValue(object):
    """Track a series of values and provide access to smoothed values over a
    window or the global series average.
    """

    def __init__(self, window_size=20, fmt=None):
        if fmt is None:
            fmt = "{median:.4f} ({global_avg:.4f})"
        self.deque = deque(maxlen=window_size)
        self.total = 0.0
        self.count = 0
        self.fmt = fmt

    def update(self, value, n=1):
        self.deque.append(value)
        self.count += n
        self.total += value * n

    def synchronize_between_processes(self):
        """
        Warning: does not synchronize the deque!
        """
        if not is_dist_avail_and_initialized():
            return
        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
        dist.barrier()
        dist.all_reduce(t)
        t = t.tolist()
        self.count = int(t[0])
        self.total = t[1]

    @property
    def median(self):
        d = torch.tensor(list(self.deque))
        return d.median().item()

    @property
    def avg(self):
        d = torch.tensor(list(self.deque), dtype=torch.float32)
        return d.mean().item()

    @property
    def global_avg(self):
        return self.total / self.count

    @property
    def max(self):
        return max(self.deque)

    @property
    def value(self):
        return self.deque[-1]

    def __str__(self):
        return self.fmt.format(
            median=self.median,
            avg=self.avg,
            global_avg=self.global_avg,
            max=self.max,
            value=self.value)


class MetricLogger(object):
    def __init__(self, delimiter="\t"):
        self.meters = defaultdict(SmoothedValue)
        self.delimiter = delimiter

    def update(self, **kwargs):
        for k, v in kwargs.items():
            if v is None:
                continue
            if isinstance(v, torch.Tensor):
                v = v.item()
            assert isinstance(v, (float, int))
            self.meters[k].update(v)

    def __getattr__(self, attr):
        if attr in self.meters:
            return self.meters[attr]
        if attr in self.__dict__:
            return self.__dict__[attr]
        raise AttributeError("'{}' object has no attribute '{}'".format(
            type(self).__name__, attr))

    def __str__(self):
        loss_str = []
        for name, meter in self.meters.items():
            loss_str.append(
                "{}: {}".format(name, str(meter))
            )
        return self.delimiter.join(loss_str)

    def synchronize_between_processes(self):
        for meter in self.meters.values():
            meter.synchronize_between_processes()

    def add_meter(self, name, meter):
        self.meters[name] = meter

    def log_every(self, iterable, print_freq, header=None):
        i = 0
        if not header:
            header = ''
        start_time = time.time()
        end = time.time()
        iter_time = SmoothedValue(fmt='{avg:.4f}')
        data_time = SmoothedValue(fmt='{avg:.4f}')
        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
        log_msg = [
            header,
            '[{0' + space_fmt + '}/{1}]',
            'eta: {eta}',
            '{meters}',
            'time: {time}',
            'data: {data}'
        ]
        if torch.cuda.is_available():
            log_msg.append('max mem: {memory:.0f}')
        log_msg = self.delimiter.join(log_msg)
        MB = 1024.0 * 1024.0
        for obj in iterable:
            data_time.update(time.time() - end)
            yield obj
            iter_time.update(time.time() - end)
            if i % print_freq == 0 or i == len(iterable) - 1:
                eta_seconds = iter_time.global_avg * (len(iterable) - i)
                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
                if torch.cuda.is_available():
                    print(log_msg.format(
                        i, len(iterable), eta=eta_string,
                        meters=str(self),
                        time=str(iter_time), data=str(data_time),
                        memory=torch.cuda.max_memory_allocated() / MB))
                else:
                    print(log_msg.format(
                        i, len(iterable), eta=eta_string,
                        meters=str(self),
                        time=str(iter_time), data=str(data_time)))
            i += 1
            end = time.time()
        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        print('{} Total time: {} ({:.4f} s / it)'.format(
            header, total_time_str, total_time / len(iterable)))


class TensorboardLogger(object):
    def __init__(self, log_dir):
        self.writer = SummaryWriter(logdir=log_dir)
        self.step = 0

    def set_step(self, step=None):
        if step is not None:
            self.step = step
        else:
            self.step += 1

    def update(self, head='scalar', step=None, **kwargs):
        for k, v in kwargs.items():
            if v is None:
                continue
            if isinstance(v, torch.Tensor):
                v = v.item()
            assert isinstance(v, (float, int))
            self.writer.add_scalar(head + "/" + k, v, self.step if step is None else step)
    
    def update_image(self, head='images', step=None, **kwargs):
        for k, v in kwargs.items():
            if v is None:
                continue
            self.writer.add_image(head + "/" + k, v, self.step if step is None else step)
            
    def flush(self):
        self.writer.flush()

def setup_for_distributed(is_master):
    """
    This function disables printing when not in master process
    """
    import builtins as __builtin__
    builtin_print = __builtin__.print

    def print(*args, **kwargs):
        force = kwargs.pop('force', False)
        if is_master or force:
            builtin_print(*args, **kwargs)

    __builtin__.print = print


def is_dist_avail_and_initialized():
    if not dist.is_available():
        return False
    if not dist.is_initialized():
        return False
    return True


def get_world_size():
    if not is_dist_avail_and_initialized():
        return 1
    return dist.get_world_size()


def get_rank():
    if not is_dist_avail_and_initialized():
        return 0
    return dist.get_rank()


def is_main_process():
    return get_rank() == 0


def save_on_master(*args, **kwargs):
    if is_main_process():
        torch.save(*args, **kwargs)

def all_reduce(tensor, op=dist.ReduceOp.SUM, async_op=False):
    world_size = get_world_size()

    if world_size == 1:
        return tensor
    dist.all_reduce(tensor, op=op, async_op=async_op)

    return tensor

def all_gather_batch(tensors):
    """
    Performs all_gather operation on the provided tensors.
    """
    # Queue the gathered tensors
    world_size = get_world_size()
    # There is no need for reduction in the single-proc case
    if world_size == 1:
        return tensors
    tensor_list = []
    output_tensor = []
    for tensor in tensors:
        tensor_all = [torch.ones_like(tensor) for _ in range(world_size)]
        dist.all_gather(
            tensor_all,
            tensor,
            async_op=False  # performance opt
        )

        tensor_list.append(tensor_all)

    for tensor_all in tensor_list:
        output_tensor.append(torch.cat(tensor_all, dim=0))
    return output_tensor

class GatherLayer(torch.autograd.Function):
    """
    Gather tensors from all workers with support for backward propagation:
    This implementation does not cut the gradients as torch.distributed.all_gather does.
    """

    @staticmethod
    def forward(ctx, x):
        output = [torch.zeros_like(x) for _ in range(dist.get_world_size())]
        dist.all_gather(output, x)
        return tuple(output)

    @staticmethod
    def backward(ctx, *grads):
        all_gradients = torch.stack(grads)
        dist.all_reduce(all_gradients)
        return all_gradients[dist.get_rank()]


def all_gather_batch_with_grad(tensors):
    """
    Performs all_gather operation on the provided tensors.
    Graph remains connected for backward grad computation.
    """
    # Queue the gathered tensors
    world_size = get_world_size()
    # There is no need for reduction in the single-proc case
    if world_size == 1:
        return tensors
    tensor_list = []
    output_tensor = []

    for tensor in tensors:
        tensor_all = GatherLayer.apply(tensor)
        tensor_list.append(tensor_all)

    for tensor_all in tensor_list:
        output_tensor.append(torch.cat(tensor_all, dim=0))
    return output_tensor

def _get_rank_env():
    if "RANK" in os.environ:
        return int(os.environ["RANK"])
    else:
        return int(os.environ['OMPI_COMM_WORLD_RANK'])


def _get_local_rank_env():
    if "LOCAL_RANK" in os.environ:
        return int(os.environ["LOCAL_RANK"])
    else:
        return int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])


def _get_world_size_env():
    if "WORLD_SIZE" in os.environ:
        return int(os.environ["WORLD_SIZE"])
    else:
        return int(os.environ['OMPI_COMM_WORLD_SIZE'])


def init_distributed_mode(args):
    if args.dist_on_itp:
        args.rank = _get_rank_env()
        args.world_size = _get_world_size_env()  # int(os.environ['OMPI_COMM_WORLD_SIZE'])
        args.gpu = _get_local_rank_env()
        args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
        os.environ['LOCAL_RANK'] = str(args.gpu)
        os.environ['RANK'] = str(args.rank)
        os.environ['WORLD_SIZE'] = str(args.world_size)
        # ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"]
    elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
        args.rank = int(os.environ["RANK"])
        args.world_size = int(os.environ['WORLD_SIZE'])
        args.gpu = int(os.environ['LOCAL_RANK'])
    elif 'SLURM_PROCID' in os.environ:
        args.rank = int(os.environ['SLURM_PROCID'])
        args.gpu = args.rank % torch.cuda.device_count()
    else:
        print('Not using distributed mode')
        args.distributed = False
        return

    args.distributed = True

    torch.cuda.set_device(args.gpu)
    args.dist_backend = 'nccl'
    print('| distributed init (rank {}): {}, gpu {}'.format(
        args.rank, args.dist_url, args.gpu), flush=True)
    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                         world_size=args.world_size, rank=args.rank)
    torch.distributed.barrier()
    setup_for_distributed(args.rank == 0)


def load_state_dict(model, state_dict, prefix='', ignore_missing="relative_position_index"):
    missing_keys = []
    unexpected_keys = []
    error_msgs = []
    # copy state_dict so _load_from_state_dict can modify it
    metadata = getattr(state_dict, '_metadata', None)
    state_dict = state_dict.copy()
    if metadata is not None:
        state_dict._metadata = metadata

    def load(module, prefix=''):
        local_metadata = {} if metadata is None else metadata.get(
            prefix[:-1], {})
        module._load_from_state_dict(
            state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
        for name, child in module._modules.items():
            if child is not None:
                load(child, prefix + name + '.')

    load(model, prefix=prefix)

    warn_missing_keys = []
    ignore_missing_keys = []
    for key in missing_keys:
        keep_flag = True
        for ignore_key in ignore_missing.split('|'):
            if ignore_key in key:
                keep_flag = False
                break
        if keep_flag:
            warn_missing_keys.append(key)
        else:
            ignore_missing_keys.append(key)

    missing_keys = warn_missing_keys

    if len(missing_keys) > 0:
        print("Weights of {} not initialized from pretrained model: {}".format(
            model.__class__.__name__, missing_keys))
    if len(unexpected_keys) > 0:
        print("Weights from pretrained model not used in {}: {}".format(
            model.__class__.__name__, unexpected_keys))
    if len(ignore_missing_keys) > 0:
        print("Ignored weights of {} not initialized from pretrained model: {}".format(
            model.__class__.__name__, ignore_missing_keys))
    if len(error_msgs) > 0:
        print('\n'.join(error_msgs))

def get_grad_norm(parameters, norm_type=2):
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    parameters = list(filter(lambda p: p.grad is not None, parameters))
    norm_type = float(norm_type)
    total_norm = 0
    for p in parameters:
        param_norm = p.grad.data.norm(norm_type)
        total_norm += param_norm.item() ** norm_type
    total_norm = total_norm ** (1. / norm_type)
    return total_norm

class NativeScalerWithGradNormCount:
    state_dict_key = "amp_scaler"

    def __init__(self):
        self._scaler = torch.cuda.amp.GradScaler()

    def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True, layer_names=None):
        self._scaler.scale(loss).backward(create_graph=create_graph)
        if update_grad:
            if clip_grad is not None:
                assert parameters is not None
                self._scaler.unscale_(optimizer)  # unscale the gradients of optimizer's assigned params in-place
                norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
            else:
                self._scaler.unscale_(optimizer)
                norm = get_grad_norm_(parameters, layer_names=layer_names)
            self._scaler.step(optimizer)
            self._scaler.update()
        else:
            norm = None
        return norm

    def state_dict(self):
        return self._scaler.state_dict()

    def load_state_dict(self, state_dict): 
        self._scaler.load_state_dict(state_dict)


def get_grad_norm_(parameters, norm_type: float = 2.0, layer_names=None) -> torch.Tensor:
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    
    parameters = [p for p in parameters if p.grad is not None]
        
    norm_type = float(norm_type)
    if len(parameters) == 0:
        return torch.tensor(0.)
    device = parameters[0].grad.device
    
    if norm_type == inf:
        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
    else:
        # total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
        layer_norm = torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters])
        total_norm = torch.norm(layer_norm, norm_type)
        # print(layer_norm.max(dim=0))
        
        if layer_names is not None:
            if torch.isnan(total_norm) or torch.isinf(total_norm) or total_norm > 1.0:
                value_top, name_top = torch.topk(layer_norm, k=5)
                print(f"Top norm value: {value_top}")
                print(f"Top norm name: {[layer_names[i][7:] for i in name_top.tolist()]}")
        
    return total_norm


def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0,
                     start_warmup_value=0, warmup_steps=-1):
    warmup_schedule = np.array([])
    warmup_iters = warmup_epochs * niter_per_ep
    if warmup_steps > 0:
        warmup_iters = warmup_steps
    print("Set warmup steps = %d" % warmup_iters)
    if warmup_epochs > 0:
        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)

    iters = np.arange(epochs * niter_per_ep - warmup_iters)
    schedule = np.array(
        [final_value + 0.5 * (base_value - final_value) * (1 + math.cos(math.pi * i / (len(iters)))) for i in iters])

    schedule = np.concatenate((warmup_schedule, schedule))

    assert len(schedule) == epochs * niter_per_ep
    return schedule


def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_disc=None, save_ckpt_freq=1):
    output_dir = Path(args.output_dir)
    epoch_name = str(epoch)

    if not getattr(args, 'enable_deepspeed', False):
        checkpoint_paths = [output_dir / 'checkpoint.pth']
        if epoch == 'best':
            checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name),]
        elif (epoch + 1) % save_ckpt_freq == 0:
            checkpoint_paths.append(output_dir / ('checkpoint-%s.pth' % epoch_name))

        for checkpoint_path in checkpoint_paths:
            to_save = {
                'model': model_without_ddp.state_dict(),
                'optimizer': optimizer.state_dict(),
                'epoch': epoch,
                # 'scaler': loss_scaler.state_dict(),
                'args': args,
            }
            if loss_scaler is not None:
                to_save['scaler'] = loss_scaler.state_dict()

            if model_ema is not None:
                to_save['model_ema'] = get_state_dict(model_ema)
                
            if optimizer_disc is not None:
                to_save['optimizer_disc'] = optimizer_disc.state_dict()

            save_on_master(to_save, checkpoint_path)
    else:
        client_state = {'epoch': epoch}
        if model_ema is not None:
            client_state['model_ema'] = get_state_dict(model_ema)
        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)           

def auto_load_model(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_disc=None):
    output_dir = Path(args.output_dir)
    
    if not getattr(args, 'enable_deepspeed', False):
        # torch.amp
        if args.auto_resume and len(args.resume) == 0:
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint.pth'))
            if len(all_checkpoints) > 0:
                args.resume = os.path.join(output_dir, 'checkpoint.pth')
            else:
                all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*.pth'))
                latest_ckpt = -1
                for ckpt in all_checkpoints:
                    t = ckpt.split('-')[-1].split('.')[0]
                    if t.isdigit():
                        latest_ckpt = max(int(t), latest_ckpt)
                if latest_ckpt >= 0:
                    args.resume = os.path.join(output_dir, 'checkpoint-%d.pth' % latest_ckpt)
            print("Auto resume checkpoint: %s" % args.resume)

        if args.resume:
            if args.resume.startswith('https'):
                checkpoint = torch.hub.load_state_dict_from_url(
                    args.resume, map_location='cpu', check_hash=True)
            else:
                checkpoint = torch.load(args.resume, map_location='cpu', weights_only=False)
            model_without_ddp.load_state_dict(checkpoint['model']) # strict: bool=True, , strict=False
            print("Resume checkpoint %s" % args.resume)
            if 'optimizer' in checkpoint and 'epoch' in checkpoint:
                optimizer.load_state_dict(checkpoint['optimizer'])
                print(f"Resume checkpoint at epoch {checkpoint['epoch']}")
                args.start_epoch = 1#checkpoint['epoch'] + 1
                if 'scaler' in checkpoint:
                    loss_scaler.load_state_dict(checkpoint['scaler'])
                print("With optim & sched!")
            if 'optimizer_disc' in checkpoint:
                optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
    else:
        # deepspeed, only support '--auto_resume'.
        if args.auto_resume:
            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*'))
            latest_ckpt = -1
            for ckpt in all_checkpoints:
                t = ckpt.split('-')[-1].split('.')[0]
                if t.isdigit():
                    latest_ckpt = max(int(t), latest_ckpt)
            if latest_ckpt >= 0:
                args.resume = os.path.join(output_dir, 'checkpoint-%d' % latest_ckpt)
                print("Auto resume checkpoint: %d" % latest_ckpt)
                _, client_states = model.load_checkpoint(args.output_dir, tag='checkpoint-%d' % latest_ckpt)
                args.start_epoch = client_states['epoch'] + 1

def create_ds_config(args):
    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    with open(os.path.join(args.output_dir, "latest"), mode="w") as f:
        pass

    args.deepspeed_config = os.path.join(args.output_dir, "deepspeed_config.json")
    with open(args.deepspeed_config, mode="w") as writer:
        ds_config = {
            "train_batch_size": args.batch_size * args.update_freq * get_world_size(),
            "train_micro_batch_size_per_gpu": args.batch_size,
            "steps_per_print": 1000,
            "optimizer": {
                "type": "Adam",
                "adam_w_mode": True,
                "params": {
                    "lr": args.lr,
                    "weight_decay": args.weight_decay,
                    "bias_correction": True,
                    "betas": [
                        0.9,
                        0.999
                    ],
                    "eps": 1e-8
                }
            },
            "fp16": {
                "enabled": True,
                "loss_scale": 0,
                "initial_scale_power": 7,
                "loss_scale_window": 128
            }
        }

        writer.write(json.dumps(ds_config, indent=2))

class TUABLoader(torch.utils.data.Dataset):
    def __init__(self, root, files, sampling_rate=200):
        self.root = root
        self.files = files
        self.default_rate = 200
        self.sampling_rate = sampling_rate

    def __len__(self):
        return len(self.files)

    def __getitem__(self, index):
        sample = pickle.load(open(os.path.join(self.root, self.files[index]), "rb"))
        X = sample["X"]
        if self.sampling_rate != self.default_rate:
            X = resample(X, 10 * self.sampling_rate, axis=-1)
        Y = sample["y"]
        X = torch.FloatTensor(X)
        return X, Y
    

class TUEVLoader(torch.utils.data.Dataset):
    def __init__(self, root, files, sampling_rate=200):
        self.root = root
        self.files = files
        self.default_rate = 200
        self.sampling_rate = sampling_rate

    def __len__(self):
        return len(self.files)

    def __getitem__(self, index):
        sample = pickle.load(open(os.path.join(self.root, self.files[index]), "rb"))
        X = sample["signal"]
        
        if self.sampling_rate != self.default_rate:
            X = resample(X, 5 * self.sampling_rate, axis=-1)
        Y = int(sample["label"][0] - 1)
        X = torch.FloatTensor(X)
        return X, Y

class SEEDVLoader(torch.utils.data.Dataset):
    def __init__(self, root, files, sampling_rate=200):
        self.root = root
        self.files = files
        self.default_rate = 200
        self.sampling_rate = sampling_rate

    def __len__(self):
        return len(self.files)

    def __getitem__(self, index):
        sample = pickle.load(open(os.path.join(self.root, self.files[index]), "rb"))
        X = sample["X"]
        if self.sampling_rate != self.default_rate:
            X = resample(X, self.sampling_rate, axis=-1)
        Y = int(sample["y"])
        X = torch.FloatTensor(X)
        return X, Y

def prepare_TUEV_dataset(root):
    # set random seed
    seed = 8250
    np.random.seed(seed)

    train_files = os.listdir(os.path.join(root, "processed_train"))
    val_files = os.listdir(os.path.join(root, "processed_eval"))
    test_files = os.listdir(os.path.join(root, "processed_test"))

    # prepare training and test data loader
    train_dataset = TUEVLoader(
        os.path.join(
            root, "processed_train"), train_files
    )
    test_dataset = TUEVLoader(
        os.path.join(
            root, "processed_test"), test_files
    )
    val_dataset = TUEVLoader(
        os.path.join(
            root, "processed_eval"), val_files
    )
    print(len(train_files), len(val_files), len(test_files))
    return train_dataset, test_dataset, val_dataset


def prepare_TUAB_dataset(root):
    # set random seed
    seed = 12345
    np.random.seed(seed)

    train_files = os.listdir(os.path.join(root, "train"))
    np.random.shuffle(train_files)
    val_files = os.listdir(os.path.join(root, "val"))
    test_files = os.listdir(os.path.join(root, "test"))

    print(len(train_files), len(val_files), len(test_files))

    # prepare training and test data loader
    train_dataset = TUABLoader(os.path.join(root, "train"), train_files)
    test_dataset = TUABLoader(os.path.join(root, "test"), test_files)
    val_dataset = TUABLoader(os.path.join(root, "val"), val_files)
    print(len(train_files), len(val_files), len(test_files))
    return train_dataset, test_dataset, val_dataset

def prepare_SEEDV_dataset(root):
    # set random seed
    seed = 8250
    np.random.seed(seed)

    train_files = os.listdir(os.path.join(root, "train"))
    np.random.shuffle(train_files)
    val_files = os.listdir(os.path.join(root, "val"))
    test_files = os.listdir(os.path.join(root, "test"))

    print(len(train_files), len(val_files), len(test_files))

    # prepare training and test data loader
    train_dataset = SEEDVLoader(os.path.join(root, "train"), train_files)
    test_dataset = SEEDVLoader(os.path.join(root, "test"), test_files)
    val_dataset = SEEDVLoader(os.path.join(root, "val"), val_files)
    print(len(train_files), len(val_files), len(test_files))
    return train_dataset, test_dataset, val_dataset

def get_metrics(output, target, metrics, is_binary, threshold=0.5):
    if is_binary:
        if 'roc_auc' not in metrics or sum(target) * (len(target) - sum(target)) != 0:  # to prevent all 0 or all 1 and raise the AUROC error
            results = binary_metrics_fn(
                target,
                output,
                metrics=metrics,
                threshold=threshold,
            )
        else:
            results = {
                "accuracy": 0.0,
                "balanced_accuracy": 0.0,
                "pr_auc": 0.0,
                "roc_auc": 0.0,
            }
    else:
        results = multiclass_metrics_fn(
            target, output, metrics=metrics
        )
    return results