pyanomaly/datatools/dataclass/datasets_factory.py
def _produce_train_dataset(self):
        """
        The method to produce the dataset used for the training process.
        Args:
            None
        Returns:
            train_dataset_dict(OrderedDict): The dataset dictionary contains the torch.data.Dataloader object used for training process
            For example:
            {
                'video_keys': 'all',
                'video_datasets': {
                    'all': torch.data.Dataset_object
                }
            }
        """
        train_dataset_dict = OrderedDict()
        train_dataset = self.ingredient(self.dataset_params.train.data_path, clip_length=self.dataset_params.train.clip_length, 
                                        sampled_clip_length=self.dataset_params.train.sampled_clip_length, 
                                        frame_step=self.dataset_params.train.frame_step,clip_step=self.dataset_params.train.clip_step, is_training=True,
                                        transforms=self.aug_dict['train_aug'], cfg=self.cfg)
        train_dataset_dict['video_keys'] = ['all']
        train_dataset_dict['video_datasets'] = OrderedDict()
        train_dataset_dict['video_datasets']['all'] = train_dataset
        return train_dataset_dict

        The method to produce the dataset used for the training process.
        Args:
            None
        Returns:
            train_dataset_dict(OrderedDict): The dataset dictionary contains the torch.data.Dataloader object used for training process
            For example:
            {
                'video_keys': 'all',
                'video_datasets': {
                    'all': torch.data.Dataset_object
                }
            }
        
The method to produce the dataset used for the training process.
The method to produce the dataset used for the training process .
def _produce_train_dataset(self):
        train_dataset_dict = OrderedDict()
        train_dataset = self.ingredient(self.dataset_params.train.data_path, clip_length=self.dataset_params.train.clip_length, 
                                        sampled_clip_length=self.dataset_params.train.sampled_clip_length, 
                                        frame_step=self.dataset_params.train.frame_step,clip_step=self.dataset_params.train.clip_step, is_training=True,
                                        transforms=self.aug_dict['train_aug'], cfg=self.cfg)
        train_dataset_dict['video_keys'] = ['all']
        train_dataset_dict['video_datasets'] = OrderedDict()
        train_dataset_dict['video_datasets']['all'] = train_dataset
        return train_dataset_dict
def _produce_train_dataset ( self ) : train_dataset_dict = OrderedDict ( ) train_dataset = self . ingredient ( self . dataset_params . train . data_path , clip_length = self . dataset_params . train . clip_length , sampled_clip_length = self . dataset_params . train . sampled_clip_length , frame_step = self . dataset_params . train . frame_step , clip_step = self . dataset_params . train . clip_step , is_training = True , transforms = self . aug_dict [ 'train_aug' ] , cfg = self . cfg ) train_dataset_dict [ 'video_keys' ] = [ 'all' ] train_dataset_dict [ 'video_datasets' ] = OrderedDict ( ) train_dataset_dict [ 'video_datasets' ] [ 'all' ] = train_dataset return train_dataset_dict
The method to produce the dataset used for the training process.
The method to produce the dataset used for the training process .
"""
        The method to produce the dataset used for the training process.
        Args:
            None
        Returns:
            train_dataset_dict(OrderedDict): The dataset dictionary contains the torch.data.Dataloader object used for training process
            For example:
            {
                'video_keys': 'all',
                'video_datasets': {
                    'all': torch.data.Dataset_object
                }
            }
        """
self