class torch.utils.data.DataLoader(
dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
num_workers=0, collate_fn=None, pin_memory=False, drop_last=False,
timeout=0, worker_init_fn=None, multiprocessing_context=None, generator=None, *,
prefetch_factor=2, persistent_workers=False)
For more information, see torch.utils.data.DataLoader.
Datasets in MindSpore do not require a loader.
PyTorch: The dataset, sampler object and batching, shuffling, parallel parameters need to be passed to the DataLoader, to implement parallel data iteration with sampling, batching and shuffling.
MindSpore: The sampler object and batching, shuffling, parallel parameters can be passed to the dataset object while construction, or defined by the class methods of dataset. Therefore, the dataset itself will have the functions of sampling, batching, shuffling and parallel data iteration without a loader.
import torch
import mindspore.dataset as ds
import numpy as np
np.random.seed(0)
data = np.random.random((6, 2, 2))
# The following implements data iteration with PyTorch.
dataset = torch.utils.data.TensorDataset(torch.Tensor(data))
sampler = torch.utils.data.SequentialSampler(dataset)
loader = torch.utils.data.DataLoader(dataset, batch_size=3)
print(next(iter(loader)))
# Out:
# [tensor([[[0.5488, 0.7152],
# [0.6028, 0.5449]],
#
# [[0.4237, 0.6459],
# [0.4376, 0.8918]],
#
# [[0.9637, 0.3834],
# [0.7917, 0.5289]]])]
# The following implements data iteration with MindSpore.
sampler = ds.SequentialSampler()
dataset = ds.NumpySlicesDataset(data, sampler=sampler)
dataset = dataset.batch(batch_size=3)
iterator = dataset.create_dict_iterator()
print(next(iter(iterator)))
# Out:
# {'column_0': Tensor(shape=[3, 2, 2], dtype=Float64, value=
# [[[ 5.48813504e-01, 7.15189366e-01],
# [ 6.02763376e-01, 5.44883183e-01]],
# [[ 4.23654799e-01, 6.45894113e-01],
# [ 4.37587211e-01, 8.91773001e-01]],
# [[ 9.63662761e-01, 3.83441519e-01],
# [ 7.91725038e-01, 5.28894920e-01]]])}
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