Shortcuts

Debug Datasets

DummyDataset

class pl_bolts.datasets.dummy_dataset.DummyDataset(*shapes, num_samples=10000)[source]

Bases: Generic[torch.utils.data.dataset.T_co]

Generate a dummy dataset.

Example

>>> from pl_bolts.datasets import DummyDataset
>>> from torch.utils.data import DataLoader
>>> # mnist dims
>>> ds = DummyDataset((1, 28, 28), (1, ))
>>> dl = DataLoader(ds, batch_size=7)
>>> # get first batch
>>> batch = next(iter(dl))
>>> x, y = batch
>>> x.size()
torch.Size([7, 1, 28, 28])
>>> y.size()
torch.Size([7, 1])
Parameters
  • *shapes – list of shapes

  • num_samples (int) – how many samples to use in this dataset

DummyDetectionDataset

class pl_bolts.datasets.dummy_dataset.DummyDetectionDataset(img_shape=(3, 256, 256), num_boxes=1, num_classes=2, num_samples=10000)[source]

Bases: Generic[torch.utils.data.dataset.T_co]

Generate a dummy dataset for object detection.

Example

>>> from pl_bolts.datasets import DummyDetectionDataset
>>> from torch.utils.data import DataLoader
>>> ds = DummyDetectionDataset()
>>> dl = DataLoader(ds, batch_size=7)
>>> # get first batch
>>> batch = next(iter(dl))
>>> x,y = batch
>>> x.size()
torch.Size([7, 3, 256, 256])
>>> y['boxes'].size()
torch.Size([7, 1, 4])
>>> y['labels'].size()
torch.Size([7, 1])
Parameters
  • *shapes – list of shapes

  • num_samples (int) – how many samples to use in this dataset

RandomDataset

class pl_bolts.datasets.dummy_dataset.RandomDataset(size, num_samples=250)[source]

Bases: Generic[torch.utils.data.dataset.T_co]

Generate a dummy dataset.

Example

>>> from pl_bolts.datasets import RandomDataset
>>> from torch.utils.data import DataLoader
>>> ds = RandomDataset(10)
>>> dl = DataLoader(ds, batch_size=7)
>>> batch = next(iter(dl))
>>> len(batch),len(batch[0])
(7, 10)
Parameters
  • size (int) – tuple

  • num_samples (int) – number of samples

RandomDictDataset

class pl_bolts.datasets.dummy_dataset.RandomDictDataset(size, num_samples=250)[source]

Bases: Generic[torch.utils.data.dataset.T_co]

Generate a dummy dataset with a dict structure.

Example

>>> from pl_bolts.datasets import RandomDictDataset
>>> from torch.utils.data import DataLoader
>>> ds = RandomDictDataset(10)
>>> dl = DataLoader(ds, batch_size=7)
>>> batch = next(iter(dl))
>>> len(batch['a']),len(batch['a'][0])
(7, 10)
>>> len(batch['b']),len(batch['b'][0])
(7, 10)
Parameters
  • size (int) – integer representing the length of a feature_vector

  • num_samples (int) – number of samples

RandomDictStringDataset

class pl_bolts.datasets.dummy_dataset.RandomDictStringDataset(size, num_samples=250)[source]

Bases: Generic[torch.utils.data.dataset.T_co]

Generate a dummy dataset with in dict structure with strings as indexes.

Example

>>> from pl_bolts.datasets import RandomDictStringDataset
>>> from torch.utils.data import DataLoader
>>> ds = RandomDictStringDataset(10)
>>> dl = DataLoader(ds, batch_size=7)
>>> batch = next(iter(dl))
>>> batch['id']
['0', '1', '2', '3', '4', '5', '6']
>>> len(batch['x'])
7
Parameters
  • size (int) – tuple

  • num_samples (int) – number of samples

Read the Docs v: 0.6.0.post1
Versions
latest
stable
0.6.0.post1
0.6.0
0.5.0
0.4.0
0.3.4
0.3.3
0.3.2
0.3.1
0.3.0
0.2.5
0.2.4
0.2.3
0.2.2
0.2.1
0.2.0
0.1.1
docs-build-rtd
0.1.0
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.