Image Datasets

Image datasets

Plots

make_grid

 make_grid (images, size=64)

Given a list of PIL images, stack them together into a line for easy viewing

show_images

 show_images (x:torch.Tensor, ncols:int=8)

Given a batch of images x, make a grid and convert to PIL

ImageDataset Mixin

ImagePlotMixin

 ImagePlotMixin ()

Mixin class for image datasets providing visualization of (image, label) samples

# ImagePlotMixin.plot(test, 0)
# ImagePlotMixin.plot_grid(test, 2,2)
# ImagePlotMixin.plot(test, 0, int2label = {0:'zero', 1:'one', 2:'two', 3:'three', 4:'four', 5:'five', 6:'six', 7:'seven', 8:'eight', 9:'nine'})
# ImagePlotMixin.plot(test, 0, int2label = test.hf_ds.features['label'].int2str)
# ImagePlotMixin.plot_grid(test, 2,2, int2label = test.hf_ds.features['label'].int2str)

Image Dataset

ImageDataset

 ImageDataset (name:str='mnist', *args,
               data_dir:Optional[str]='../data/image', split='train', tran
               sforms:Optional[torchvision.transforms.v2._container.Compos
               e]=Compose(    ToTensor()), streaming:bool=False,
               exclude_grey_scale=False, verification_mode='no_checks',
               from_image_folder=False, from_disk=False)

Image dataset

Image normalization

if both train and validation splits are available normalize both. else just train

normalize_image_datasets

 normalize_image_datasets (name, data_dir='../data/image',
                           splits=['train', 'validation'])

mean, std = normalize_image_datasets('slegroux/tiny-imagenet-200-clean')
print(f"mean:{mean}, std: {std}")

Image DataModule

ImageDataModule

 ImageDataModule (name:str, *args, data_dir:Optional[str]='~/Data/', trans
                  forms:Union[torchvision.transforms.v2._container.Compose
                  ,Callable,NoneType]=Compose(       ToTensor()
                  Normalize(mean=[0.1307], std=[0.3081], inplace=False) ),
                  train_val_split:List[float]=[0.8, 0.2],
                  batch_size:int=64, num_workers:int=0,
                  pin_memory:bool=False, persistent_workers:bool=False,
                  **kwargs)

Mixin class for image datasets providing visualization of (image, label) samples

	Type	Default	Details
name	str		name of dataset from hugging face
args
data_dir	Optional	~/Data/
transforms	Union	Compose( ToTensor() Normalize(mean=[0.1307], std=[0.3081], inplace=False) )
train_val_split	List	[0.8, 0.2]
batch_size	int	64
num_workers	int	0
pin_memory	bool	False
persistent_workers	bool	False
kwargs

Usage

dm = ImageDataModule(
    'frgfm/imagenette','160px',
    transforms=transforms.Compose([transforms.ToTensor(),transforms.Resize((128, 128))]),
    data_dir='../data/image',
    train_val_split=[0.8, 0.2],
    batch_size = 16,
    num_workers = 0, # main process
    pin_memory= False,
    persistent_workers=False,
    exclude_grey_scale = True
)

# download or reference data from dir
dm.prepare_data()

# define train, eval, test subsets
dm.setup()
print(f" num_classes: {dm.num_classes}, labels: {dm.label_names}, img shape: {dm.train_ds[0][0].shape}")
# show data
dm.show(1)
dm.show_grid(3,3)

/Users/slegroux/miniforge3/envs/nimrod/lib/python3.11/site-packages/torchvision/transforms/v2/_deprecated.py:42: UserWarning: The transform `ToTensor()` is deprecated and will be removed in a future release. Instead, please use `v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`.Output is equivalent up to float precision.
  warnings.warn(
[18:42:31] INFO - Init ImageDataModule for frgfm/imagenette
/Users/slegroux/miniforge3/envs/nimrod/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:209: Attribute 'transforms' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['transforms'])`.
[18:42:33] INFO - loading dataset frgfm/imagenette with args ('160px',) from split train
[18:42:34] WARNING - filtering out grey scale images
[18:42:37] INFO - loading dataset frgfm/imagenette with args ('160px',) from split validation
[18:42:38] WARNING - filtering out grey scale images
[18:42:38] INFO - split train into train/val [0.8, 0.2]
[18:42:38] INFO - train: 7437 val: 1859, test: 3856

 num_classes: 10, labels: ['tench', 'English springer', 'cassette player', 'chain saw', 'church', 'French horn', 'garbage truck', 'gas pump', 'golf ball', 'parachute'], img shape: torch.Size([3, 128, 128])

dm.hparams.batch_size = 32
print(dm.hparams)

xb, yb = next(iter(dm.train_dataloader()))
# print(xb.shape)
dm.show_batch(xb)

"batch_size":         32
"data_dir":           ../data/image
"exclude_grey_scale": True
"name":               frgfm/imagenette
"num_workers":        0
"persistent_workers": False
"pin_memory":         False
"train_val_split":    [0.8, 0.2]
"transforms":         Compose(
      ToTensor()
      Resize(size=[128, 128], interpolation=InterpolationMode.BILINEAR, antialias=True)
)

/var/folders/b5/v9y3kpzs29g41d99xvrdp3yr0000gn/T/ipykernel_78438/575801441.py:163: DeprecationWarning: __array__ implementation doesn't accept a copy keyword, so passing copy=False failed. __array__ must implement 'dtype' and 'copy' keyword arguments. To learn more, see the migration guide https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword
  grid_im = Image.fromarray(np.array(grid_im).astype(np.uint8))

# dm.label_names

['tench',
 'English springer',
 'cassette player',
 'chain saw',
 'church',
 'French horn',
 'garbage truck',
 'gas pump',
 'golf ball',
 'parachute']

# access data batches via dataloader
test_dl = dm.test_dataloader()
# X,Y = next(iter(test_dl))
# print("X dim(B,C,W,H): ", X.shape, "Y: dim(B)", Y.shape)

Config

# cfg = OmegaConf.load("../config/image/data/mnist.yaml")
# print(cfg.datamodule)
# dm = instantiate(cfg.datamodule)
# dm.prepare_data()
# dm.setup()
# test_dl = dm.test_dataloader()
# len(dm.test_ds), len(dm.train_ds), len(dm.val_ds)
cfg = OmegaConf.load('../config/data/image/fashion_mnist.yaml')
dm = instantiate(cfg)
dm.prepare_data()
dm.setup()
print(f"num_classes: {dm.num_classes}, batch_size: {dm.batch_size}")
print(f"labels: {dm.label_names}")
x, y = dm.test_ds[0]
print(f"X: {x.shape}, Y: {y}")
dm.show(1)
dm.show_grid(3,3)

[17:28:55] INFO - Init ImageDataModule for fashion_mnist
[17:29:08] INFO - split train into train/val [0.8, 0.2]
[17:29:08] INFO - train: 48000 val: 12000, test: 10000

num_classes: 10, batch_size: 128
labels: ['T - shirt / top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
X: torch.Size([1, 32, 32]), Y: 9

cfg = OmegaConf.load('../config/data/image/smithsonian_butterflies.yaml')
dm = instantiate(cfg)
dm.prepare_data()
dm.setup()
print(f"num_classes: {dm.num_classes}, batch_size: {dm.batch_size}")
print(f"labels: {dm.label_names}")
x, y = dm.test_ds[0]
print(f"X: {x.shape}, Y: {y}")
x,y = next(iter(dm.train_dataloader()))
print(f"X: {x.shape}, Y: {y.shape}")
dm.show(1)

[17:40:22] INFO - Init ImageDataModule for huggan/smithsonian_butterflies_subset
Repo card metadata block was not found. Setting CardData to empty.
[17:40:22] WARNING - Repo card metadata block was not found. Setting CardData to empty.
Repo card metadata block was not found. Setting CardData to empty.
[17:40:24] WARNING - Repo card metadata block was not found. Setting CardData to empty.
[17:40:29] WARNING - split train into train/val/test [0.8, 0.2] 
[17:40:29] INFO - train: 800 val: 200, test: 200
[17:40:29] INFO - split train into train/val [0.8, 0.2]
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8029231..1.0000005].
[17:40:29] WARNING - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.8029231..1.0000005].

num_classes: 45, batch_size: 128
labels: ['Animalia, Arthropoda, Insecta, Lepidoptera, Pyralidae, Epipaschiinae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Geometridae, Larentiinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae, Nymphalinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae, Satyrinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Saturniidae, Saturniinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae, Limenitidinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Papilionidae, Papilioninae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Pieridae, Dismorphiinae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Arctiidae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Lycaenidae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Papilionidae, Parnassiinae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Pyralidae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Pieridae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae, Heliconinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Arctiidae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Noctuidae, Erebinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae, Morphinae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Pieridae, Coliadinae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Pieridae, Pierinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Papilionidae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Geometridae, Ennominae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Pieridae, Dismorphiinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Sphingidae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Lasiocampidae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae, Danainae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Pieridae, Pierinae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Pieridae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Nymphalidae, Nymphalinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Saturniidae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Papilionidae, Papilioninae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Geometridae, Oenochrominae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Glossata, Gelechiidae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Pieridae, Coliadinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Lycaenidae, Lycaeninae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Uraniidae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae, Heliconiinae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Nymphalidae, Charaxinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Pterygota, Holometabola, Lepidoptera, Nymphalidae, Danainae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Geometridae, Sterrhinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Tortricidae', 'Animalia, Arthropoda, Insecta, Lepidoptera, Geometridae, Geometrinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae, Biblidinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Nymphalidae, Charaxinae', 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Lycaenidae, Polyommatinae']
X: torch.Size([3, 128, 128]), Y: 7
X: torch.Size([128, 3, 128, 128]), Y: torch.Size([128])

Image Super Resolution Dataset

ImageSuperResDataset

 ImageSuperResDataset (name:str='fashion_mnist',
                       data_dir:str='../data/image', split='train', transf
                       orm_x:Optional[torchvision.transforms.v2._container
                       .Compose]=Compose(       ToTensor()       Compose(
                       Resize(size=[32, 32],
                       interpolation=InterpolationMode.BILINEAR,
                       antialias=True)         Resize(size=[64, 64],
                       interpolation=InterpolationMode.BILINEAR,
                       antialias=True)   ) ), transform_y:Optional[torchvi
                       sion.transforms.v2._container.Compose]=Compose(
                       ToTensor()))

Image dataset

ds = ImageSuperResDataset(
    'slegroux/tiny-imagenet-200-clean',
    data_dir='../data/image',
    split='test'
    )

[20:53:06] INFO - loading dataset slegroux/tiny-imagenet-200-clean with args () from split test
[20:53:06] INFO - loading dataset slegroux/tiny-imagenet-200-clean from split test
Overwrite dataset info from restored data version if exists.
[20:53:07] INFO - Overwrite dataset info from restored data version if exists.
Loading Dataset info from ../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[20:53:07] INFO - Loading Dataset info from ../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
Found cached dataset tiny-imagenet-200-clean (/user/s/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2)
[20:53:07] INFO - Found cached dataset tiny-imagenet-200-clean (/user/s/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2)
Loading Dataset info from /user/s/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[20:53:07] INFO - Loading Dataset info from /user/s/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2

idx = torch.randint(0, len(ds), ())
x,y = ds[idx]
print(x.shape, y.shape)
fig, ax = plt.subplots(1,2)
fig.suptitle('Super resolution')
fig.tight_layout()
ax[0].imshow(x.permute(1,2,0).squeeze())
ax[0].set_title('low res')
ax[1].imshow(y.permute(1,2,0).squeeze())
ax[1].set_title('high res')

torch.Size([3, 64, 64])
torch.Size([3, 64, 64])

Text(0.5, 1.0, 'high res')

Image SuperRes DataModule

ImageSuperResDataModule

 ImageSuperResDataModule (name:str='slegroux/tiny-imagenet-200-clean',
                          data_dir:str='../data/image', transform_x:Option
                          al[torchvision.transforms.v2._container.Compose]
                          =Sequential(   (0): ToTensor()   (1):
                          Sequential(     (0): Resize(size=[32, 32],
                          interpolation=InterpolationMode.BILINEAR,
                          antialias=True)     (1): Resize(size=[64, 64],
                          interpolation=InterpolationMode.BILINEAR,
                          antialias=True)   ) ), transform_y:Optional[torc
                          hvision.transforms.v2._container.Compose]=Sequen
                          tial(   (0): ToTensor() ),
                          train_val_split:Optional[List[float]]=[0.8,
                          0.2], batch_size:int=64, num_workers:int=0,
                          pin_memory:bool=False,
                          persistent_workers:bool=False)

Mixin class for image datasets providing visualization of (image, label) samples

dm = ImageSuperResDataModule(
    'fashion_mnist',
    data_dir='../data/image',
    transform_x=transforms.Compose([transforms.ToTensor(), transforms.Resize(32)]),
    transform_y=transforms.Compose([transforms.ToTensor(), transforms.Resize(32)]),
)

/user/s/slegroux/miniconda3/envs/nimrod/lib/python3.11/site-packages/torchvision/transforms/v2/_deprecated.py:42: UserWarning: The transform `ToTensor()` is deprecated and will be removed in a future release. Instead, please use `v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`.Output is equivalent up to float precision.
  warnings.warn(
[21:24:09] INFO - Init ImageSuperResDataModule for fashion_mnist
/user/s/slegroux/miniconda3/envs/nimrod/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:209: Attribute 'transform_x' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['transform_x'])`.
/user/s/slegroux/miniconda3/envs/nimrod/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:209: Attribute 'transform_y' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['transform_y'])`.
[21:24:09] INFO - Init ImageDataModule for fashion_mnist
/user/s/slegroux/miniconda3/envs/nimrod/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:209: Attribute 'transforms' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['transforms'])`.

dm.prepare_data()
dm.setup()

[21:09:03] INFO - loading dataset fashion_mnist with args () from split train
[21:09:03] INFO - loading dataset fashion_mnist from split train
Overwrite dataset info from restored data version if exists.
[21:09:05] INFO - Overwrite dataset info from restored data version if exists.
Loading Dataset info from ../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
[21:09:05] INFO - Loading Dataset info from ../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
Found cached dataset fashion_mnist (/user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2)
[21:09:05] INFO - Found cached dataset fashion_mnist (/user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2)
Loading Dataset info from /user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
[21:09:05] INFO - Loading Dataset info from /user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
[21:09:09] INFO - loading dataset fashion_mnist with args () from split test
[21:09:09] INFO - loading dataset fashion_mnist from split test
Overwrite dataset info from restored data version if exists.
[21:09:11] INFO - Overwrite dataset info from restored data version if exists.
Loading Dataset info from ../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
[21:09:11] INFO - Loading Dataset info from ../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
Found cached dataset fashion_mnist (/user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2)
[21:09:11] INFO - Found cached dataset fashion_mnist (/user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2)
Loading Dataset info from /user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
[21:09:11] INFO - Loading Dataset info from /user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
[21:09:13] INFO - loading dataset fashion_mnist with args () from split test
[21:09:13] INFO - loading dataset fashion_mnist from split test
Overwrite dataset info from restored data version if exists.
[21:09:15] INFO - Overwrite dataset info from restored data version if exists.
Loading Dataset info from ../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
[21:09:15] INFO - Loading Dataset info from ../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
Found cached dataset fashion_mnist (/user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2)
[21:09:15] INFO - Found cached dataset fashion_mnist (/user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2)
Loading Dataset info from /user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
[21:09:15] INFO - Loading Dataset info from /user/s/slegroux/Projects/nimrod/nbs/../data/image/fashion_mnist/fashion_mnist/0.0.0/531be5e2ccc9dba0c201ad3ae567a4f3d16ecdd2
[21:09:15] WARNING - same dataset for validation and test

dm.show(torch.randint(0, len(dm.train_dataloader()),(1,)))

cfg = OmegaConf.load('../config/data/image/tiny_imagenet_superres.yaml')
dm = instantiate(cfg)
dm.prepare_data()
# dm.setup()
dm.show(0)

[10:59:53] INFO - Init ImageSuperResDataModule for slegroux/tiny-imagenet-200-clean
[10:59:53] INFO - Init ImageDataModule for slegroux/tiny-imagenet-200-clean
/Users/slegroux/miniforge3/envs/nimrod/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:209: Attribute 'transforms' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['transforms'])`.
[10:59:56] INFO - loading dataset slegroux/tiny-imagenet-200-clean with args () from split train
[10:59:56] INFO - loading dataset slegroux/tiny-imagenet-200-clean from split train
Overwrite dataset info from restored data version if exists.
[10:59:58] INFO - Overwrite dataset info from restored data version if exists.
Loading Dataset info from ../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[10:59:58] INFO - Loading Dataset info from ../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
Found cached dataset tiny-imagenet-200-clean (/Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2)
[10:59:58] INFO - Found cached dataset tiny-imagenet-200-clean (/Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2)
Loading Dataset info from /Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[10:59:58] INFO - Loading Dataset info from /Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[11:00:05] INFO - loading dataset slegroux/tiny-imagenet-200-clean with args () from split test
[11:00:05] INFO - loading dataset slegroux/tiny-imagenet-200-clean from split test
Overwrite dataset info from restored data version if exists.
[11:00:07] INFO - Overwrite dataset info from restored data version if exists.
Loading Dataset info from ../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[11:00:07] INFO - Loading Dataset info from ../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
Found cached dataset tiny-imagenet-200-clean (/Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2)
[11:00:07] INFO - Found cached dataset tiny-imagenet-200-clean (/Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2)
Loading Dataset info from /Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[11:00:07] INFO - Loading Dataset info from /Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[11:00:10] INFO - loading dataset slegroux/tiny-imagenet-200-clean with args () from split validation
[11:00:10] INFO - loading dataset slegroux/tiny-imagenet-200-clean from split validation
Overwrite dataset info from restored data version if exists.
[11:00:12] INFO - Overwrite dataset info from restored data version if exists.
Loading Dataset info from ../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[11:00:12] INFO - Loading Dataset info from ../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
Found cached dataset tiny-imagenet-200-clean (/Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2)
[11:00:12] INFO - Found cached dataset tiny-imagenet-200-clean (/Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2)
Loading Dataset info from /Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2
[11:00:12] INFO - Loading Dataset info from /Users/slegroux/Projects/nimrod/nbs/../data/image/slegroux___tiny-imagenet-200-clean/default/0.0.0/4b908d89fab3eb36aa8ebcd41c1996b28da7d6f2

x_mean = torch.tensor([0.4822, 0.4495, 0.3985])
x_std = torch.tensor([0.2771, 0.2690, 0.2826])

tfm_norm = transforms.Normalize(mean=x_mean, std=x_std)
tfm_denorm = transforms.Compose([transforms.Normalize(mean=[0,0,0], std=1/x_std), transforms.Normalize(mean=-x_mean, std=[1,1,1])])

x, y = dm.train_ds[0]
x, y = tfm_denorm(x), tfm_denorm(y)
fig, ax = plt.subplots(1,2, figsize=(4,4))
fig.tight_layout()
ax[0].imshow(x.permute(1,2,0).squeeze())
ax[0].set_title('low res')
ax[1].imshow(y.permute(1,2,0).squeeze())
ax[1].set_title('high res')

Text(0.5, 1.0, 'high res')