Datasets¶
Collection of useful datasets
Debugging¶
Use these datasets to debug
DummyDataset¶
-
class
pl_bolts.datasets.dummy_dataset.
DummyDataset
(*shapes, num_samples=10000)[source] Bases:
torch.utils.data.
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])
DummyDetectionDataset¶
-
class
pl_bolts.datasets.dummy_dataset.
DummyDetectionDataset
(img_shape=(3, 256, 256), num_boxes=1, num_classes=2, num_samples=10000)[source] Bases:
torch.utils.data.
Generate a dummy dataset for detection.
Example
>>> from pl_bolts.datasets import DummyDetectionDataset >>> from torch.utils.data import DataLoader >>> ds = DummyDetectionDataset() >>> dl = DataLoader(ds, batch_size=7)
RandomDataset¶
-
class
pl_bolts.datasets.dummy_dataset.
RandomDataset
(size, num_samples=250)[source] Bases:
torch.utils.data.
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)
RandomDictDataset¶
-
class
pl_bolts.datasets.dummy_dataset.
RandomDictDataset
(size, num_samples=250)[source] Bases:
torch.utils.data.
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)
RandomDictStringDataset¶
-
class
pl_bolts.datasets.dummy_dataset.
RandomDictStringDataset
(size, num_samples=250)[source] Bases:
torch.utils.data.
Generate a dummy dataset with strings.
Example
>>> from pl_bolts.datasets import RandomDictStringDataset >>> from torch.utils.data import DataLoader >>> ds = RandomDictStringDataset(10) >>> dl = DataLoader(ds, batch_size=7)