basicsr.data.__init__

basicsr.data.__init__.build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None)[source]

Build dataloader.

Parameters:

  • dataset (torch.utils.data.Dataset) – Dataset.

  • dataset_opt (dict) – Dataset options. It contains the following keys: phase (str): ‘train’ or ‘val’. num_worker_per_gpu (int): Number of workers for each GPU. batch_size_per_gpu (int): Training batch size for each GPU.

  • num_gpu (int) – Number of GPUs. Used only in the train phase. Default: 1.

  • dist (bool) – Whether in distributed training. Used only in the train phase. Default: False.

  • sampler (torch.utils.data.sampler) – Data sampler. Default: None.

  • seed (int | None) – Seed. Default: None

basicsr.data.__init__.build_dataset(dataset_opt)[source]

Build dataset from options.

Parameters:

dataset_opt (dict) – Configuration for dataset. It must contain: name (str): Dataset name. type (str): Dataset type.