import torch from torch.utils.data import DataLoader from dataloaders.dataloader_MGSV_EC_feature import MGSV_EC_DataLoader def dataloader_MGSV_EC_train(args): MGSV_EC_trainset = MGSV_EC_DataLoader( csv_path=args.train_csv, args=args, ) train_sampler = torch.utils.data.distributed.DistributedSampler(MGSV_EC_trainset, num_replicas=args.world_size, rank=args.rank) dataloader = DataLoader( MGSV_EC_trainset, batch_size=args.batch_size_train // args.gpu_num, num_workers=args.num_workers, shuffle=(train_sampler is None), sampler=train_sampler, drop_last=True, pin_memory=True, ) return dataloader, len(MGSV_EC_trainset), train_sampler def dataloader_MGSV_EC_val(args): MGSV_EC_valset = MGSV_EC_DataLoader( csv_path=args.val_csv, args=args, ) val_sampler = torch.utils.data.distributed.DistributedSampler(MGSV_EC_valset, num_replicas=args.world_size, rank=args.rank) dataloader = DataLoader( MGSV_EC_valset, batch_size=args.batch_size_val // args.gpu_num, num_workers=args.num_workers, shuffle=(val_sampler is None), sampler=val_sampler, drop_last=False, ) return dataloader, len(MGSV_EC_valset), val_sampler def dataloader_MGSV_EC_test(args): MGSV_EC_testset = MGSV_EC_DataLoader( csv_path=args.val_csv, args=args, ) test_sampler = torch.utils.data.distributed.DistributedSampler(MGSV_EC_testset, num_replicas=args.world_size, rank=args.rank) dataloader = DataLoader( MGSV_EC_testset, batch_size=args.batch_size_val // args.gpu_num, num_workers=args.num_workers, shuffle=(test_sampler is None), sampler=test_sampler, drop_last=False, ) return dataloader, len(MGSV_EC_testset), test_sampler DATALOADER_DICT = {} DATALOADER_DICT["kuai50k_uni"] = { "train": dataloader_MGSV_EC_train, "val": dataloader_MGSV_EC_val, "test": dataloader_MGSV_EC_test } # DATALOADER_DICT["kuai50k_vmr"] = { # "train": dataloader_MGSV_EC_train, # "val": dataloader_MGSV_EC_val, # "test": dataloader_MGSV_EC_test # } # DATALOADER_DICT["kuai50k_mr"] = { # "train": dataloader_MGSV_EC_train, # "val": dataloader_MGSV_EC_val, # "test": dataloader_MGSV_EC_test # }