Continuation of #858 for #839 Cifar10 revision

=1.7.0

@@ -0,0 +1,4 @@
+Collecting package metadata (current_repodata.json): ...working... done
+Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.
+Collecting package metadata (repodata.json): ...working... done
+Solving environment: ...working... failed with initial frozen solve. Retrying with flexible solve.

pl_bolts/datasets/cifar10_dataset.py

@@ -5,6 +5,7 @@
 
 import torch
 from torch import Tensor
+from torchvision.datasets import CIFAR10
 
 from pl_bolts.datasets import LightDataset
 from pl_bolts.datasets.utils import safe_extract_tarfile
@@ -19,7 +20,7 @@
 
 
 @under_review()
-class CIFAR10(LightDataset):
+class IndependentCIFAR10(LightDataset):
     """Customized `CIFAR10 <http://www.cs.toronto.edu/~kriz/cifar.html>`_ dataset for testing Pytorch Lightning
     without the torchvision dependency.
 

tests/datamodules/test_dataloader.py

@@ -6,7 +6,7 @@
 
 
 def test_async_dataloader(datadir):
-    ds = CIFAR10(data_dir=datadir)
+    ds = CIFAR10(root=datadir)
 
     if torch.cuda.device_count() > 0:  # Can only run this test with a GPU
         device = torch.device("cuda", 0)

tests/datamodules/test_datamodules.py

@@ -20,7 +20,7 @@
 
 
 def test_dev_datasets(datadir):
-    ds = CIFAR10(data_dir=datadir)
+    ds = CIFAR10(root=datadir)
     for _ in ds:
         pass
 

tests/datasets/test_datasets.py

@@ -15,6 +15,7 @@
     RandomDictDataset,
     RandomDictStringDataset,
 )
+from pl_bolts.datasets.cifar10_dataset import CIFAR10
 from pl_bolts.datasets.dummy_dataset import DummyDetectionDataset
 from pl_bolts.datasets.sr_mnist_dataset import SRMNIST
 from pl_bolts.utils import _PIL_AVAILABLE
@@ -147,6 +148,25 @@ def test_sr_datasets(datadir, scale_factor):
     assert torch.allclose(lr_image.max(), torch.tensor(1.0), atol=atol)
 
 
+def test_cifar10_datasets(datadir):
+    transform = transform_lib.Compose(
+        [transform_lib.ToTensor(), transform_lib.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+    )
+    dl = DataLoader(CIFAR10(root=datadir, download=True, transform=transform))
+    hr_image, lr_image = next(iter(dl))
+    print("==============================", lr_image.size())
+
+    hr_image_size = 32
+    assert hr_image.size() == torch.Size([1, 3, hr_image_size, hr_image_size])
+    assert lr_image.size() == torch.Size([1])
+
+    atol = 0.3
+    assert torch.allclose(hr_image.min(), torch.tensor(-1.0), atol=atol)
+    assert torch.allclose(hr_image.max(), torch.tensor(1.0), atol=atol)
+    assert torch.greater_equal(lr_image.min(), torch.tensor(0))
+    assert torch.less_equal(lr_image.max(), torch.tensor(9))
+
+
 def test_binary_mnist_dataset(datadir):
     """Check BinaryMNIST image and target dimensions and value range."""
     dl = DataLoader(BinaryMNIST(root=datadir, download=True, transform=transform_lib.ToTensor()))