sampler#
Module Contents#
Samples elements from a given list of indices, always in the same order once initialized. |
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Partition dataset according to |
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Get data sample indices given client id from data file with dict. |
- class SubsetSampler(indices, shuffle=False)#
Bases:
torch.utils.data.Sampler
Samples elements from a given list of indices, always in the same order once initialized.
It is a
Sampler
used inDataloader
, that each partition will be fixed once initialized.- Parameters
- __iter__(self)#
- __len__(self)#
- class RawPartitionSampler(dataset, client_id, num_replicas=None)#
Bases:
torch.utils.data.Sampler
Partition dataset according to
num_replicas
.Every client get a equal shard of dataset.
- Parameters
dataset (torch.utils.data.Dataset) –
client_id (int) –
num_replicas (int, optional) – Number of data replications. Default
None
means total number of client processes.
- __iter__(self)#
- __len__(self)#
- class DictFileSampler(dict_file, client_id)#
Bases:
torch.utils.data.Sampler
Get data sample indices given client id from data file with dict.
- __iter__(self)#
- __len__(self)#