DataLoader in PyTorch

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DataLoader() can get the zero or more tensors of the zero or more elements flexibly manipulated from the dataset of the 0D or more D tensor of zero or more elements as shown below:

*Memos:

• The 1st argument is dataset(Required-Type:Dataset or tensor of int, float, complex or bool). *The 0D or more D tensor of zero or more elements can be used.
• The 2nd argument is batch_size(Optional-Default:1-Type:int): *Memos:
  • It must be 1 <= x.
  • None can be set.
• The 3rd argument is shuffle(Optional-Default:False-Type:bool). *If it's True, dataset is randomly shuffled.
• The 4th argument is sampler(Optional-Default:None-Type:Sampler or Iterable(tuple or list of int or bool, etc)). *If it's not None, shuffle must be False.
• The 5th argument is batch_sampler(Optional-Default:None-Type:Sampler or Iterable(tuple or list of int or bool, etc)): *Memos:
  • It must be more than 1D.
  • If it's not None, batch_sampler must be 1, shuffle must be False or None, sampler must be None and drop_last must be False.
• The 6th argument is num_workers(Optional-Default:0-Type:int). *It must be 0 <= x.
• The 7th argument is collate_fn(Optional-Default:None-Type:function).
• The 8th argument is pin_memory(Optional-Default:False-Type:bool).
• The 9th argument is drop_last(Optional-Default:False-Type:bool). *If it's True, the last incomplete batch is dropped(removed).
• The 10th argument is timeout(Optional-Default:0-Type:int or float): *Memos:
  • It must be 0 <= x.
  • 0 disables it.
• The 11th argument is worker_init_fn(Optional-Default:None-Type:function).
• The 12th argument is multiprocessing_context(Optional-Default:None-Type:str or multiprocessing.context.BaseContext): *Memos:
  • spawn can be set for str.
  • If it's None, the default multiprocessing context of your operating system will be used.
• The 13th argument is generator(Optional-Default:None-Type:torch.Generator). *Only cpu can be set for Generator.
• There is prefetch_factor argument(Optional-Type:function): *Memos:
  • It must 0 <= x.
  • Its default depends on num_workers: *Memos:
  • If num_workers = 0, its default is None.
  • If num_workers > 0, its default is 2).
  • If num_workers = 0, its must be None.
  • prefetch_factor= must be used.
• There is persistent_workers argument(Optional-Default:False-Type:bool). *persistent_workers= must be used.
• There is pin_memory_device argument(Optional-Default:''-Type:str). *pin_memory_device= must be used.
• dl.shuffle don't work.

import torch
from torch.utils.data import DataLoader

my_tensor = torch.tensor([8, -3, 0, 1, 5, -2, -1, 4])

dl = DataLoader(dataset=my_tensor)

list(dl)
# [tensor([8]),
#  tensor([-3]),
#  tensor([0]),
#  tensor([1]),
#  tensor([5]),
#  tensor([-2]),
#  tensor([-1]),
#  tensor([4])]

dl
# <torch.utils.data.dataloader.DataLoader at 0x1ae4386d150>

dl.dataset
# tensor([8, -3, 0, 1, 5, -2, -1, 4])

dl.batch_size
# 1

dl.sampler
# <torch.utils.data.sampler.SequentialSampler at 0x1ae432cff50>

dl.batch_sampler
# <torch.utils.data.sampler.BatchSampler at 0x1ae424532d0>

dl.num_workers
# 0

dl.collate_fn
# <function torch.utils.data._utils.collate.default_collate(batch)>

dl.pin_memory
# False

dl.drop_last
# False

dl.timeout
# 0

dl.worker_init_fn
# None

dl.multiprocessing_context
# None

dl.generator
# None

dl.prefetch_factor
# None

dl.persistent_workers
# False

dl.pin_memory_device
# ''

dl = DataLoader(dataset=my_tensor, 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=None,
         persistent_workers=False, pin_memory_device='')
list(dl)
# [tensor([8]),
#  tensor([-3]),
#  tensor([0]),
#  tensor([1]),
#  tensor([5]),
#  tensor([-2]),
#  tensor([-1]),
#  tensor([4])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=1,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([-2]),
#  tensor([5]),
#  tensor([8]),
#  tensor([4]),
#  tensor([-1]),
#  tensor([1]),
#  tensor([-3]),
#  tensor([0])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=2,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([-2, 5]),
#  tensor([8, 4]),
#  tensor([-1, 1]),
#  tensor([-3, 0])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=3,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([-2, 5, 8]),
#  tensor([4, -1, 1])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=4,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([-2, 5, 8, 4]),
#  tensor([-1, 1, -3, 0])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=5,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([-2, 5, 8, 4, -1])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=6,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([-2, 5, 8, 4, -1, 1])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=7,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([-2, 5, 8, 4, -1, 1, -3])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=8,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([-2, 5, 8, 4, -1, 1, -3, 0])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=9,
                shuffle=True, drop_last=True)
list(dl)
# []

my_tensor = torch.tensor([[8, -3, 0, 1],
                          [5, -2, -1, 4]])
dl = DataLoader(dataset=my_tensor)

list(dl)
# [tensor([[8, -3, 0, 1]]),
#  tensor([[5, -2, -1, 4]])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=1,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([[5, -2, -1, 4]]),
#  tensor([[8, -3, 0, 1]])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=2,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([[5, -2, -1, 4],
#          [8, -3, 0, 1]])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=3,
                shuffle=True, drop_last=True)
list(dl)
# []

my_tensor = torch.tensor([[[8, -3], [0, 1]],
                          [[5, -2], [-1, 4]]])
dl = DataLoader(dataset=my_tensor)

list(dl)
# [tensor([[[8, -3], [0, 1]]]),
#  tensor([[[5, -2], [-1, 4]]])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=1,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([[[5, -2], [-1, 4]]]),
#  tensor([[[8, -3], [0, 1]]])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=2,
                shuffle=True, drop_last=True)
list(dl)
# [tensor([[[5, -2], [-1, 4]],
#          [[8, -3], [0, 1]]])]

torch.manual_seed(47)

dl = DataLoader(dataset=my_tensor, batch_size=3,
                shuffle=True, drop_last=True)
list(dl)
# []

my_tensor = torch.tensor([[[8., -3.], [0., 1.]],
                          [[5., -2.], [-1., 4.]]])
dl = DataLoader(dataset=my_tensor)

list(dl)
# [tensor([[[8., -3.], [0., 1.]]]),
#  tensor([[[5., -2.], [-1., 4.]]])]

my_tensor = torch.tensor([[[8.+0.j, -3.+0.j], [0.+0.j, 1.+0.j]],
                          [[5.+0.j, -2.+0.j], [-1.+0.j, 4.+0.j]]])
dl = DataLoader(dataset=my_tensor)

list(dl)
# [tensor([[[8.+0.j, -3.+0.j], [0.+0.j, 1.+0.j]]]),
#  tensor([[[5.+0.j, -2.+0.j], [-1.+0.j, 4.+0.j]]])]

my_tensor = torch.tensor([[[True, False], [True, False]],
                          [[False, True], [False, True]]])
dl = DataLoader(dataset=my_tensor)

list(dl)
# [tensor([[[True, False], [True, False]]]),
#  tensor([[[False, True], [False, True]]])]