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DRAFT: 54 dataset #59

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DRAFT: 54 dataset #59

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efbc317
Add labels to dataset object
gpitteri Jun 25, 2024
bb46c75
Merge branch 'main' into 54_dataset
giorgiapitteri Jul 2, 2024
5ddd683
device set to cpu by default
giorgiapitteri Jul 2, 2024
38f962d
minor changes
giorgiapitteri Jul 12, 2024
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64 changes: 41 additions & 23 deletions src/explib/datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,9 @@ class DequantizedDataset(torch.utils.data.Dataset):
def __init__(
self,
dataset: T.Union[os.PathLike, torch.utils.data.Dataset, np.ndarray],
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Should we also specify here that dataset can be a torch.Tensor as well?

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Would it be better to have a dataset object like a dictionary with images/labels? Otherwise I would renamed this "images" instead of dataset since we now have the labels object as well.

labels: T.Union[np.ndarray, torch.Tensor],
num_bits: int = 8,
device: torch.device = None,
device: torch.device = "cpu",
):
if isinstance(dataset, torch.utils.data.Dataset) or isinstance(
dataset, np.ndarray
Expand All @@ -31,8 +32,11 @@ def __init__(
else:
self.dataset = pd.read_csv(dataset).values

#
self.dataset = self.dataset.to(device)
if not isinstance(labels, torch.Tensor):
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If dataset is a torch.utils.data.Dataset we don't need labels right? It automatically has a getitem method that returns the data and the label. So I would need to add a check here.

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I think The baseclass getitem method is going to fail if the dataset is only a np array or a torch.Tensor. I understand there is the correct implementation in the derived class but maybe here we would need to put a check or raise an error?

labels = torch.Tensor(labels)
self.labels = labels.to(device)

self.num_bits = num_bits
self.num_levels = 2**num_bits
self.transform = transforms.Compose(
Expand All @@ -43,9 +47,9 @@ def __init__(
)

def __getitem__(self, index: int):
x, y = self.dataset[index]
x = self.dataset[index]
x = Tensor(self.transform(x))
return x, y
return x, self.labels[index]

def __len__(self):
return len(self.dataset)
Expand Down Expand Up @@ -241,7 +245,8 @@ def __init__(
dataloc: os.PathLike = None,
train: bool = True,
label: T.Optional[int] = None,
scale: bool = False
scale: bool = False,
device: torch.device = "cpu"
):
rel_path = (
"FashionMNIST/raw/train-images-idx3-ubyte"
Expand All @@ -256,21 +261,25 @@ def __init__(
if scale:
dataset = dataset[:, ::3, ::3]
dataset = dataset.reshape(dataset.shape[0], -1)
if label is not None:
rel_path = (

rel_path = (
"FashionMNIST/raw/train-labels-idx1-ubyte"
if train
else "FashionMNIST/raw/t10k-labels-idx1-ubyte"
)
path = os.path.join(dataloc, rel_path)
labels = idx2numpy.convert_from_file(path)
path = os.path.join(dataloc, rel_path)
labels = idx2numpy.convert_from_file(path)

if label is not None:
dataset = dataset[labels == label]
super().__init__(dataset, num_bits=8)
labels = labels[labels == label]

super().__init__(dataset, torch.Tensor(labels), num_bits=8, device=device)

def __getitem__(self, index: int):
x = Tensor(self.dataset[index].copy())
x = self.transform(x)
return x, 0
return x, self.labels[index]


class FashionMnistSplit(DataSplit):
Expand All @@ -279,19 +288,21 @@ def __init__(
dataloc: os.PathLike = None,
val_split: float = 0.1,
label: T.Optional[int] = None,
device: torch.device = "cpu"
):
self.label = label
if dataloc is None:
dataloc = os.path.join(os.getcwd(), "data")
self.dataloc = dataloc
self.train = FashionMnistDequantized(self.dataloc, train=True, label=label)
self.train = FashionMnistDequantized(self.dataloc, train=True, label=label, device=device)
shuffle = torch.randperm(len(self.train))
self.val = torch.utils.data.Subset(
self.train, shuffle[: int(len(self.train) * val_split)]
)
self.train = torch.utils.data.Subset(
self.train, shuffle[int(len(self.train) * val_split) :]
)
self.test = FashionMnistDequantized(self.dataloc, train=False, label=label)
self.test = FashionMnistDequantized(self.dataloc, train=False, label=label, device=device)

def get_train(self) -> torch.utils.data.Dataset:
return self.train
Expand All @@ -312,7 +323,7 @@ def __init__(
digit: T.Optional[int] = None,
flatten=True,
scale: bool = False,
device: torch.device = None
device: torch.device = "cpu"
):
if train:
rel_path = "MNIST/raw/train-images-idx3-ubyte"
Expand All @@ -323,27 +334,32 @@ def __init__(
MNIST(dataloc, train=train, download=True)

dataset = idx2numpy.convert_from_file(path)

if scale:
dataset = dataset[:, ::3, ::3]
if flatten:
dataset = dataset.reshape(dataset.shape[0], -1)

if train:
rel_path = "MNIST/raw/train-labels-idx1-ubyte"
else:
rel_path = "MNIST/raw/t10k-labels-idx1-ubyte"
path = os.path.join(dataloc, rel_path)
labels = idx2numpy.convert_from_file(path)

if digit is not None:
if train:
rel_path = "MNIST/raw/train-labels-idx1-ubyte"
else:
rel_path = "MNIST/raw/t10k-labels-idx1-ubyte"
path = os.path.join(dataloc, rel_path)
labels = idx2numpy.convert_from_file(path)
dataset = dataset[labels == digit]
super().__init__(torch.Tensor(dataset), num_bits=8, device=device)
labels = labels[labels == digit]

super().__init__(torch.Tensor(dataset), torch.Tensor(labels), num_bits=8, device=device)

def __getitem__(self, index: int):
if not isinstance(self.dataset, torch.Tensor):
x = Tensor(self.dataset[index].copy())
else:
x = self.dataset[index]
x = self.transform(x)
return x, 0
return x, self.labels[index]

class MnistSplit(DataSplit):
def __init__(
Expand All @@ -352,8 +368,9 @@ def __init__(
val_split: float = 0.1,
digit: T.Optional[int] = None,
scale: bool = False,
device: torch.device = None
device: torch.device = "cpu"
):
self.digit = digit
if dataloc is None:
dataloc = os.path.join(os.getcwd(), "data")
self.dataloc = dataloc
Expand Down Expand Up @@ -393,3 +410,4 @@ def __init__(
if not os.path.exists(path):
CIFAR10(dataloc, train=train, download=True)


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