def get_data_loaders(self): data_dir = self.args.data_dir self.train_dataset = PickleDataset( os.path.join(data_dir, f'{self.args.train_set}.pkl'), os.path.join(data_dir, self.args.train_index_file), segment_size=self.config.segment_size) self.val_dataset = PickleDataset( os.path.join(data_dir, f'{self.args.val_set}.pkl'), os.path.join(data_dir, self.args.val_index_file), segment_size=self.config.segment_size) self.train_loader = get_data_loader(self.train_dataset, batch_size=self.config.batch_size, shuffle=self.config.shuffle, num_workers=4, drop_last=False) self.val_loader = get_data_loader(self.val_dataset, batch_size=self.config.batch_size, shuffle=self.config.shuffle, num_workers=4, drop_last=False) self.train_iter = infinite_iter(self.train_loader) return
def get_data_loaders(self): data_dir = self.args.data_dir self.gpu_num = torch.cuda.device_count() if torch.cuda.is_available( ) else 1 self.train_dataset = PickleDataset( os.path.join(data_dir, f'{self.args.train_set}.pkl'), os.path.join(data_dir, self.args.train_index_file), segment_size=self.config['data_loader']['segment_size']) self.train_loader = get_data_loader( self.train_dataset, frame_size=self.config['data_loader']['frame_size'], batch_size=self.config['data_loader']['batch_size'] * self.gpu_num, num_workers=0, shuffle=self.config['data_loader']['shuffle'], drop_last=False) self.train_iter = infinite_iter(self.train_loader) if self.args.use_eval_set: self.eval_dataset = PickleDataset( os.path.join(data_dir, f'{self.args.eval_set}.pkl'), os.path.join(data_dir, self.args.eval_index_file), segment_size=self.config['data_loader']['segment_size']) self.eval_loader = get_data_loader( self.eval_dataset, frame_size=self.config['data_loader']['frame_size'], batch_size=self.config['data_loader']['batch_size'] * self.gpu_num, shuffle=self.config['data_loader']['shuffle'], num_workers=0, drop_last=False) self.eval_iter = infinite_iter(self.eval_loader) if self.args.use_test_set: self.test_dataset = PickleDataset( os.path.join(data_dir, f'{self.args.test_set}.pkl'), os.path.join(data_dir, self.args.test_index_file), segment_size=self.config['data_loader']['segment_size']) self.test_loader = get_data_loader( self.test_dataset, frame_size=self.config['data_loader']['frame_size'], batch_size=self.config['data_loader']['batch_size'], shuffle=False, num_workers=0, drop_last=False) self.test_iter = infinite_iter(self.test_loader) return
def get_data_loaders(self): data_dir = self.args.data_dir self.test_dataset = PickleDataset( os.path.join(data_dir, f'{self.args.test_set}.pkl'), os.path.join(data_dir, self.args.test_index_file), segment_size=self.config['data_loader']['segment_size']) self.test_loader = get_data_loader( self.test_dataset, frame_size=self.config['data_loader']['frame_size'], batch_size=self.config['data_loader']['batch_size'], shuffle=False, drop_last=False)
def get_data_loaders(self): data_dir = self.args.data_dir self.train_dataset = PickleDataset( os.path.join(data_dir, f'{self.args.train_set}.pkl'), os.path.join(data_dir, self.args.train_index_file), segment_size=self.config['data_loader']['segment_size']) self.train_loader = get_data_loader( self.train_dataset, frame_size=self.config['data_loader']['frame_size'], batch_size=self.config['data_loader']['batch_size'], shuffle=self.config['data_loader']['shuffle'], num_workers=4, drop_last=False) self.train_iter = infinite_iter(self.train_loader) return
def get_data_loaders(self): data_dir = self.args.data_dir self.train_dataset = PickleDataset( os.path.join(data_dir, f"{self.args.train_set}.pkl"), os.path.join(data_dir, self.args.train_index_file), segment_size=self.config["data_loader"]["segment_size"], ) self.train_loader = get_data_loader( self.train_dataset, frame_size=self.config["data_loader"]["frame_size"], batch_size=self.config["data_loader"]["batch_size"], shuffle=self.config["data_loader"]["shuffle"], num_workers=0, drop_last=False, ) self.train_iter = infinite_iter(self.train_loader) return