--- a
+++ b/custom_moco_bolts.py
@@ -0,0 +1,603 @@
+import pytorch_lightning as pl
+from pl_bolts.models.self_supervised import MocoV2
+from pl_bolts.optimizers.lars_scheduling import LARSWrapper
+from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
+from torch.optim import Adam
+import torch
+import re
+import pdb
+from argparse import ArgumentParser
+from typing import Union
+from warnings import warn
+import torch.nn.functional as F
+from torch import nn
+from pl_bolts.metrics import precision_at_k  # , mean
+from clinical_ts.create_logger import create_logger
+from models.resnet_simclr import ResNetSimCLR
+import re
+
+import time
+
+import yaml
+import logging
+import pickle
+import os
+from clinical_ts.simclr_dataset_wrapper import SimCLRDataSetWrapper
+from clinical_ts.create_logger import create_logger
+import pickle
+from pytorch_lightning import Trainer, seed_everything
+
+from torch import nn
+from pytorch_lightning.loggers import TensorBoardLogger
+from pl_bolts.models.self_supervised.evaluator import Flatten
+import pdb
+logger = create_logger(__name__)
+method="moco"
+
+def _accuracy(zis, zjs, batch_size):
+    with torch.no_grad():
+        representations = torch.cat([zjs, zis], dim=0)
+        similarity_matrix = torch.mm(
+            representations, representations.t().contiguous())
+        corrected_similarity_matrix = similarity_matrix - \
+            torch.eye(2*batch_size).type_as(similarity_matrix)
+        pred_similarities, pred_indices = torch.max(
+            corrected_similarity_matrix[:batch_size], dim=1)
+        correct_indices = torch.arange(batch_size)+batch_size
+        correct_preds = (
+            pred_indices == correct_indices.type_as(pred_indices)).sum()
+    return correct_preds.float()/batch_size
+
+
+def mean(res, key1, key2=None):
+    if key2 is not None:
+        return torch.stack([x[key1][key2] for x in res]).mean()
+    return torch.stack([x[key1] for x in res if type(x) == dict and key1 in x.keys()]).mean()
+
+# utils
+
+
+@torch.no_grad()
+def concat_all_gather(tensor):
+    """
+    Performs all_gather operation on the provided tensors.
+    *** Warning ***: torch.distributed.all_gather has no gradient.
+    """
+    tensors_gather = [torch.ones_like(tensor)
+                      for _ in range(torch.distributed.get_world_size())]
+    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
+
+    output = torch.cat(tensors_gather, dim=0)
+    return output
+
+
+class CustomMoCo(pl.LightningModule):
+
+    def __init__(self,
+                 base_encoder,
+                 emb_dim: int = 128,
+                 num_negatives: int = 65536,
+                 encoder_momentum: float = 0.999,
+                 softmax_temperature: float = 0.07,
+                 learning_rate: float = 0.03,
+                 momentum: float = 0.9,
+                 weight_decay: float = 1e-6,
+                 datamodule: pl.LightningDataModule = None,
+                 data_dir: str = './',
+                 batch_size: int = 256,
+                 use_mlp: bool = False,
+                 num_workers: int = 8,
+                 config=None,
+                 transformations=None,
+                 warmup_epochs=10,
+                 *args, **kwargs):
+
+        super(CustomMoCo, self).__init__()
+        self.base_encoder = base_encoder
+        self.emb_dim = emb_dim
+        self.num_negatives = num_negatives
+        self.encoder_momentum = encoder_momentum
+        self.softmax_temperature = softmax_temperature
+        self.learning_rate = learning_rate
+        self.momentum = momentum
+        self.weight_decay = weight_decay
+        self.datamodule = datamodule
+        self.data_dir = data_dir
+        self.batch_size = batch_size
+        self.use_mlp = use_mlp
+        self.num_workers = num_workers
+        self.warmup_epochs = warmup_epochs
+        self.config = config
+        self.transformations = transformations
+        self.epoch = 0
+        # create the encoders
+        # num_classes is the output fc dimension
+        self.encoder_q, self.encoder_k = self.init_encoders(base_encoder)
+
+        if use_mlp:  # hack: brute-force replacement
+            dim_mlp = self.encoder_q.fc.weight.shape[1]
+            self.encoder_q.fc = nn.Sequential(nn.Linear(dim_mlp, dim_mlp), nn.ReLU(), self.encoder_q.fc)
+            self.encoder_k.fc = nn.Sequential(nn.Linear(dim_mlp, dim_mlp), nn.ReLU(), self.encoder_k.fc)
+
+        for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
+            param_k.data.copy_(param_q.data)  # initialize
+            param_k.requires_grad = False  # not update by gradient
+
+        # create the queue
+        self.register_buffer("queue", torch.randn(emb_dim, num_negatives))
+        self.queue = nn.functional.normalize(self.queue, dim=0)
+
+        self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long))
+        # self.warmup_epochs = config["warm_up"]
+
+    def exclude_from_wt_decay(self, named_params, weight_decay, skip_list=['bias', 'bn']):
+        params = []
+        excluded_params = []
+
+        for name, param in named_params:
+            if not param.requires_grad:
+                continue
+            elif any(layer_name in name for layer_name in skip_list):
+                excluded_params.append(param)
+            else:
+                params.append(param)
+
+        return [
+            {'params': params, 'weight_decay': weight_decay},
+            {'params': excluded_params, 'weight_decay': 0.}
+        ]
+
+    def setup(self, stage):
+        global_batch_size = self.trainer.world_size * self.batch_size
+        self.train_iters_per_epoch = self.datamodule.num_samples // global_batch_size
+
+
+    def configure_optimizers(self):
+        # global_batch_size = self.trainer.world_size * self.batch_size
+        # self.train_iters_per_epoch = self.datamodule.num_samples // global_batch_size
+        # # TRICK 1 (Use lars + filter weights)
+        # # exclude certain parameters
+        # parameters = self.exclude_from_wt_decay(
+        #     self.named_parameters(),
+        #     weight_decay=self.weight_decay
+        # )
+
+        # optimizer = LARSWrapper(Adam(parameters, lr=self.learning_rate))
+
+        # # Trick 2 (after each step)
+        # self.warmup_epochs = self.warmup_epochs * self.train_iters_per_epoch
+        # max_epochs = self.trainer.max_epochs * self.train_iters_per_epoch
+
+        # linear_warmup_cosine_decay = LinearWarmupCosineAnnealingLR(
+        #     optimizer,
+        #     warmup_epochs=self.warmup_epochs,
+        #     max_epochs=max_epochs,
+        #     warmup_start_lr=0,
+        #     eta_min=0
+        # )
+
+        # scheduler = {
+        #     'scheduler': linear_warmup_cosine_decay,
+        #     'interval': 'step',
+        #     'frequency': 1
+        # }
+
+        # self.scheduler = linear_warmup_cosine_decay
+
+        logger.debug("configure_optimizers")
+        optimizer = torch.optim.Adam(self.parameters(
+        ), self.learning_rate, weight_decay=self.weight_decay)
+        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=self.config["epochs"], eta_min=0,
+                                                                    last_epoch=-1)
+        return [optimizer], [self.scheduler]
+
+    def init_encoders(self, base_encoder):
+        """
+        Override to add your own encoders
+        """
+
+        encoder_q = base_encoder()
+        encoder_k = base_encoder()
+
+        return encoder_q, encoder_k
+
+    def forward(self, img_q, img_k):
+        """
+        Input:
+            im_q: a batch of query images
+            im_k: a batch of key images
+        Output:
+            logits, targets
+        """
+
+        # ugly fix
+        img_q = img_q.type_as(self.encoder_q.features[0][0].weight.data)
+        img_k = img_k.type_as(self.encoder_q.features[0][0].weight.data)
+
+        # compute query features
+        q = self.encoder_q(img_q)[1]  # queries: NxC
+        q = nn.functional.normalize(q, dim=1)
+
+        # compute key features
+        with torch.no_grad():  # no gradient to keys
+            self._momentum_update_key_encoder()  # update the key encoder
+
+            # shuffle for making use of BN
+            if self.use_ddp or self.use_ddp2:
+                img_k, idx_unshuffle = self._batch_shuffle_ddp(img_k)
+
+            k = self.encoder_k(img_k)[1]  # keys: NxC
+            k = nn.functional.normalize(k, dim=1)
+
+            # undo shuffle
+            if self.use_ddp or self.use_ddp2:
+                k = self._batch_unshuffle_ddp(k, idx_unshuffle)
+
+        # compute logits
+        # Einstein sum is more intuitive
+        # positive logits: Nx1
+
+        l_pos = torch.einsum('nc,nc->n', [q, k]).unsqueeze(-1)
+        # negative logits: NxK
+        l_neg = torch.einsum('nc,ck->nk', [q, self.queue.clone().detach()])
+
+        # logits: Nx(1+K)
+        logits = torch.cat([l_pos, l_neg], dim=1)
+
+        # apply temperature
+        logits /= self.softmax_temperature
+
+        # labels: positive key indicators
+        labels = torch.zeros(logits.shape[0], dtype=torch.long)
+        labels = labels.type_as(logits)
+
+        # dequeue and enqueue
+        self._dequeue_and_enqueue(k)
+
+        return logits, labels
+
+    def training_step(self, batch, batch_idx):
+        (img_1, _), (img_2, _) = batch
+        output, target = self(img_q=img_1.float(), img_k=img_2.float())
+        loss = F.cross_entropy(output.float(), target.long())
+        acc = precision_at_k(output, target, top_k=(1,))[0]
+
+        log = {
+            'train_loss': loss,
+            'train_acc': acc
+        }
+        return {'loss': loss, 'log': log, 'progress_bar': log}
+
+    def validation_step(self, batch, batch_idx, dataloader_idx):
+        if dataloader_idx != 0:
+            return {}
+
+        (img_1, _), (img_2, _) = batch
+
+        output, target = self(img_q=img_1, img_k=img_2)
+        loss = F.cross_entropy(output, target.long())
+
+        acc = precision_at_k(output, target, top_k=(1,))[0]
+        results = {
+            'val_loss': loss,
+            'val_acc': acc
+        }
+        return results
+
+    def training_epoch_end(self, outputs):
+        train_loss = mean(outputs, 'log', 'train_loss')
+        train_acc = mean(outputs, 'log', 'train_acc')
+
+        log = {
+            'train/train_loss': train_loss,
+            'train/train_acc': train_acc
+        }
+        return {'train_loss': train_loss, 'log': log, 'progress_bar': log}
+
+    def validation_epoch_end(self, outputs):
+        # outputs[0] because we are using multiple datasets!
+        val_loss = mean(outputs[0], 'val_loss')
+        val_acc = mean(outputs[0], 'val_acc')
+
+        log = {
+            'val/val_loss': val_loss,
+            'val/val_acc': val_acc
+        }
+        return {'val_loss': val_loss, 'log': log, 'progress_bar': log}
+
+    def on_train_start(self):
+        # log configuration
+        config_str = re.sub(r"[,\}\{]", "<br/>", str(self.config))
+        config_str = re.sub(r"[\[\]\']", "", config_str)
+        transformation_str = re.sub(r"[\}]", "<br/>", str(["<br>" + str(
+            t) + ":<br/>" + str(t.get_params()) for t in self.transformations]))
+        transformation_str = re.sub(r"[,\"\{\'\[\]]", "", transformation_str)
+        self.logger.experiment.add_text(
+            "configuration", str(config_str), global_step=0)
+        self.logger.experiment.add_text("transformations", str(
+            transformation_str), global_step=0)
+        self.epoch = 0
+
+    def on_epoch_end(self):
+        # import pdb
+        # pdb.set_trace()
+        self.logger.experiment.add_scalar('cosine_lr_decay', self.scheduler.get_lr()[
+            0], global_step=self.epoch)
+        self.epoch += 1
+        if self.epoch >= 10:
+            self.scheduler.step()
+
+    @torch.no_grad()
+    def _dequeue_and_enqueue(self, keys):
+        # gather keys before updating queue
+        if self.use_ddp or self.use_ddp2:
+            keys = concat_all_gather(keys)
+
+        batch_size = keys.shape[0]
+        # import pdb
+        # pdb.set_trace()
+        ptr = int(self.queue_ptr)
+
+        # replace the keys at ptr (dequeue and enqueue)
+        remainder = self.queue[:, ptr:ptr + batch_size].shape[1]
+        if remainder < batch_size:
+            self.queue[:, -remainder:] = keys.T[:, :remainder]
+            self.queue[:, :batch_size-remainder] = keys.T[:, remainder:]
+            ptr = batch_size-remainder
+        else:
+            self.queue[:, ptr:ptr + batch_size] = keys.T
+            ptr = (ptr + batch_size) % self.num_negatives  # move pointer
+
+        self.queue_ptr[0] = ptr
+
+    @torch.no_grad()
+    def _batch_shuffle_ddp(self, x):  # pragma: no-cover
+        """
+        Batch shuffle, for making use of BatchNorm.
+        *** Only support DistributedDataParallel (DDP) model. ***
+        """
+        # gather from all gpus
+        batch_size_this = x.shape[0]
+        x_gather = concat_all_gather(x)
+        batch_size_all = x_gather.shape[0]
+
+        num_gpus = batch_size_all // batch_size_this
+
+        # random shuffle index
+        idx_shuffle = torch.randperm(batch_size_all).cuda()
+
+        # broadcast to all gpus
+        torch.distributed.broadcast(idx_shuffle, src=0)
+
+        # index for restoring
+        idx_unshuffle = torch.argsort(idx_shuffle)
+
+        # shuffled index for this gpu
+        gpu_idx = torch.distributed.get_rank()
+        idx_this = idx_shuffle.view(num_gpus, -1)[gpu_idx]
+
+        return x_gather[idx_this], idx_unshuffle
+
+    @torch.no_grad()
+    def _batch_unshuffle_ddp(self, x, idx_unshuffle):  # pragma: no-cover
+        """
+        Undo batch shuffle.
+        *** Only support DistributedDataParallel (DDP) model. ***
+        """
+        # gather from all gpus
+        batch_size_this = x.shape[0]
+        x_gather = concat_all_gather(x)
+        batch_size_all = x_gather.shape[0]
+
+        num_gpus = batch_size_all // batch_size_this
+
+        # restored index for this gpu
+        gpu_idx = torch.distributed.get_rank()
+        idx_this = idx_unshuffle.view(num_gpus, -1)[gpu_idx]
+
+        return x_gather[idx_this]
+
+    @torch.no_grad()
+    def _momentum_update_key_encoder(self):
+        """
+        Momentum update of the key encoder
+        """
+        for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
+            em = self.encoder_momentum
+            param_k.data = param_k.data * em + param_q.data * (1. - em)
+
+    def type(self):
+        return self.encoder_k.features[0][0].weight.type()
+
+    def get_representations(self, x):
+        return self.encoder_q.features(x)
+
+    def get_model(self):
+        return self.encoder_q
+        
+    def get_device(self):
+        return self.encoder_k.features[0][0].weight.device
+
+def parse_args(parent_parser):
+    parser = ArgumentParser(parents=[parent_parser], add_help=False)
+    parser.add_argument('-t', '--trafos', nargs='+', help='add transformation to data augmentation pipeline',
+                        default=["GaussianNoise", "ChannelResize", "RandomResizedCrop"])
+    # GaussianNoise
+    parser.add_argument(
+            '--gaussian_scale', help='std param for gaussian noise transformation', default=0.005, type=float)
+    # RandomResizedCrop
+    parser.add_argument('--rr_crop_ratio_range',
+                            help='ratio range for random resized crop transformation', default=[0.5, 1.0], type=float)
+    parser.add_argument(
+            '--output_size', help='output size for random resized crop transformation', default=250, type=int)
+    # DynamicTimeWarp
+    parser.add_argument(
+            '--warps', help='number of warps for dynamic time warp transformation', default=3, type=int)
+    parser.add_argument(
+            '--radius', help='radius of warps of dynamic time warp transformation', default=10, type=int)
+    # TimeWarp
+    parser.add_argument(
+            '--epsilon', help='epsilon param for time warp', default=10, type=float)
+    # ChannelResize
+    parser.add_argument('--magnitude_range', nargs='+',
+                            help='range for scale param for ChannelResize transformation', default=[0.5, 2], type=float)
+    # Downsample
+    parser.add_argument(
+            '--downsample_ratio', help='downsample ratio for Downsample transformation', default=0.2, type=float)
+    # TimeOut
+    parser.add_argument('--to_crop_ratio_range', nargs='+',
+                            help='ratio range for timeout transformation', default=[0.2, 0.4], type=float)
+    # resume training
+    parser.add_argument('--resume', action='store_true')
+    parser.add_argument(
+            '--gpus', help='number of gpus to use; use cpu if gpu=0', type=int, default=1)
+    parser.add_argument(
+            '--num_nodes', default=1, help='number of cluster nodes', type=int)
+    parser.add_argument(
+            '--distributed_backend', help='sets backend type')
+    parser.add_argument('--batch_size', type=int)
+    parser.add_argument('--epochs', type=int)
+    parser.add_argument('--debug', action='store_true')
+    parser.add_argument('--warm_up', default=1, type=int)
+    parser.add_argument('--precision', type=int)
+    parser.add_argument('--datasets', dest="target_folders",
+                            nargs='+', help='used datasets for pretraining')
+    parser.add_argument('--log_dir', default="./experiment_logs")
+    parser.add_argument('--checkpoint_path', default="")
+    parser.add_argument(
+            '--percentage', help='determines how much of the dataset shall be used during the pretraining', type=float, default=1.0)
+    parser.add_argument('--lr', type=float, help="learning rate")
+    parser.add_argument('--out_dim', type=int, help="output dimension of model")
+    parser.add_argument('--filter_cinc', default=False, action="store_true", help="only valid if cinc is selected: filter out the ptb data")
+    parser.add_argument('--base_model')
+    parser.add_argument('--widen',type=int, help="use wide xresnet1d50")
+    parser.add_argument('--run_callbacks', default=False, action="store_true", help="run callbacks which asses linear evaluaton and finetuning metrics during pretraining")
+
+    return parser
+
+def init_logger(config):
+    level = logging.INFO
+
+    if config['debug']:
+        level = logging.DEBUG
+
+    # remove all handlers to change basic configuration
+    for handler in logging.root.handlers[:]:
+        logging.root.removeHandler(handler)
+    if not os.path.isdir(config['log_dir']):
+        os.mkdir(config['log_dir'])
+    logging.basicConfig(filename=os.path.join(config['log_dir'], 'info.log'), level=level,
+                        format='%(asctime)s %(name)s:%(lineno)s %(levelname)s:  %(message)s  ')
+    return logging.getLogger(__name__)
+
+def pretrain_routine(args):
+    t_params = {"gaussian_scale": args.gaussian_scale, "rr_crop_ratio_range": args.rr_crop_ratio_range, "output_size": args.output_size, "warps": args.warps, "radius": args.radius,
+                "epsilon": args.epsilon, "magnitude_range": args.magnitude_range, "downsample_ratio": args.downsample_ratio, "to_crop_ratio_range": args.to_crop_ratio_range,
+                "bw_cmax":0.1, "em_cmax":0.5, "pl_cmax":0.2, "bs_cmax":1}
+    transformations = args.trafos
+    checkpoint_config = os.path.join("checkpoints", "bolts_config.yaml")
+    config_file = checkpoint_config if args.resume and os.path.isfile(
+        checkpoint_config) else "bolts_config.yaml"
+    config = yaml.load(open(config_file, "r"), Loader=yaml.FullLoader)
+    args_dict = vars(args)
+    for key in set(config.keys()).union(set(args_dict.keys())):
+        config[key] = config[key] if (key not in args_dict.keys() or key in args_dict.keys(
+        ) and key in config.keys() and args_dict[key] is None) else args_dict[key]
+    if args.target_folders is not None:
+        config["dataset"]["target_folders"] = args.target_folders
+    config["dataset"]["percentage"] = args.percentage if args.percentage is not None else config["dataset"]["percentage"]
+    config["dataset"]["filter_cinc"] = args.filter_cinc if args.filter_cinc is not None else config["dataset"]["filter_cinc"]
+    config["model"]["base_model"] = args.base_model if args.base_model is not None else config["model"]["base_model"]
+    config["model"]["widen"] = args.widen if args.widen is not None else config["model"]["widen"]
+    if args.out_dim is not None:
+        config["model"]["out_dim"] = args.out_dim
+    init_logger(config)
+    dataset = SimCLRDataSetWrapper(
+        config['batch_size'], **config['dataset'], transformations=transformations, t_params=t_params)
+    for i, t in enumerate(dataset.transformations):
+        logger.info(str(i) + ". Transformation: " +
+                    str(t) + ": " + str(t.get_params()))
+    date = time.asctime()
+    label_to_num_classes = {"label_all": 71, "label_diag": 44, "label_form": 19,
+                            "label_rhythm": 12, "label_diag_subclass": 23, "label_diag_superclass": 5}
+    ptb_num_classes = label_to_num_classes[config["eval_dataset"]
+                                           ["ptb_xl_label"]]
+    abr = {"Transpose": "Tr", "TimeOut": "TO", "DynamicTimeWarp": "DTW", "RandomResizedCrop": "RRC", "ChannelResize": "ChR", "GaussianNoise": "GN",
+           "TimeWarp": "TW", "ToTensor": "TT", "GaussianBlur": "GB", "BaselineWander": "BlW", "PowerlineNoise": "PlN", "EMNoise": "EM", "BaselineShift": "BlS"}
+    trs = re.sub(r"[,'\]\[]", "", str([abr[str(tr)] if abr[str(tr)] not in [
+                 "TT", "Tr"] else '' for tr in dataset.transformations]))
+    name = str(date) + "_" + method + "_" + str(
+        time.time_ns())[-3:] + "_" + trs[1:]
+    tb_logger = TensorBoardLogger(args.log_dir, name=name, version='')
+    config["log_dir"] = os.path.join(args.log_dir, name)
+    print(config)
+    return config, dataset, date, transformations, t_params, ptb_num_classes, tb_logger
+
+def aftertrain_routine(config, args, trainer, pl_model, datamodule, callbacks):
+    scores = {}
+    for ca in callbacks:
+        if isinstance(ca, SSLOnlineEvaluator):
+            scores[str(ca)] = {"macro": ca.best_macro}
+
+    results = {"config": config, "trafos": args.trafos, "scores": scores}
+
+    with open(os.path.join(config["log_dir"], "results.pkl"), 'wb') as handle:
+        pickle.dump(results, handle)
+
+    trainer.save_checkpoint(os.path.join(config["log_dir"], "checkpoints", "model.ckpt"))
+    with open(os.path.join(config["log_dir"], "config.txt"), "w") as text_file:
+        print(config, file=text_file)
+
+def cli_main():
+    from pytorch_lightning import Trainer
+    from online_evaluator import SSLOnlineEvaluator
+    from ecg_datamodule import ECGDataModule
+    from clinical_ts.create_logger import create_logger
+    from os.path import exists
+    
+    parser = ArgumentParser()
+    parser = parse_args(parser)
+    logger.info("parse arguments")
+    args = parser.parse_args()
+    config, dataset, date, transformations, t_params, ptb_num_classes, tb_logger = pretrain_routine(args)
+
+    # data
+    ecg_datamodule = ECGDataModule(config, transformations, t_params)
+
+    callbacks = []
+    if args.run_callbacks:
+            # callback for online linear evaluation/fine-tuning
+        linear_evaluator = SSLOnlineEvaluator(drop_p=0,
+                                          z_dim=512, num_classes=ptb_num_classes, hidden_dim=None, lin_eval_epochs=config["eval_epochs"], eval_every=config["eval_every"], mode="linear_evaluation", verbose=False)
+
+        fine_tuner = SSLOnlineEvaluator(drop_p=0,
+                                          z_dim=512, num_classes=ptb_num_classes, hidden_dim=None, lin_eval_epochs=config["eval_epochs"], eval_every=config["eval_every"], mode="fine_tuning", verbose=False)
+   
+        callbacks.append(linear_evaluator)
+        callbacks.append(fine_tuner)
+
+    # configure trainer
+    trainer = Trainer(logger=tb_logger, max_epochs=config["epochs"], gpus=args.gpus,
+                      distributed_backend=args.distributed_backend, auto_lr_find=False, num_nodes=args.num_nodes, precision=config["precision"], callbacks=callbacks)
+
+    # pytorch lightning module
+    def create_encoder(): return ResNetSimCLR(**config["model"])
+    pl_model = CustomMoCo(create_encoder, datamodule=ecg_datamodule, num_negatives=ecg_datamodule.num_samples,
+                              emb_dim=config["model"]["out_dim"], config=config, transformations=ecg_datamodule.transformations,
+                              batch_size=config["batch_size"], learning_rate=config["lr"], softmax_temperature=config["lr"],
+                              warmup_epochs=config["warm_up"], weight_decay=eval(config["weight_decay"]))
+    # load checkpoint
+    if args.checkpoint_path != "":
+        if exists(args.checkpoint_path):
+            logger.info("Retrieve checkpoint from " + args.checkpoint_path)
+            pl_model.load_from_checkpoint(args.checkpoint_path)
+        else:
+            raise("checkpoint does not exist")
+
+    # start training
+    trainer.fit(pl_model, ecg_datamodule)
+
+    aftertrain_routine(config, args, trainer, pl_model, ecg_datamodule, callbacks)
+
+if __name__ == "__main__":  
+    cli_main()
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