officehome

OfficeHome

Bases: BaseDownloadableDataset

A custom train/test split of OfficeHomeFull.

Extends BaseDownloadableDataset, so the dataset can be downloaded by setting download=True when initializing.

Source code in pytorch_adapt\datasets\officehome.py

class OfficeHome(BaseDownloadableDataset):
    """
    A custom train/test split of [OfficeHomeFull][pytorch_adapt.datasets.OfficeHomeFull].

    Extends [BaseDownloadableDataset][pytorch_adapt.datasets.BaseDownloadableDataset],
    so the dataset can be downloaded by setting ```download=True``` when
    initializing.
    """

    url = "https://cornell.box.com/shared/static/xwsbubtcr8flqfuds5f6okqbr3z0w82t"
    filename = "officehome_resized.tar.gz"
    md5 = "52d6039512434aa561d66de9c10828c3"

    def __init__(self, root: str, domain: str, train: bool, transform=None, **kwargs):
        """
        Arguments:
            root: The dataset must be located at ```<root>/officehome```
            domain: One of ```"art", "clipart", "product", "real"```.
            train: Whether or not to use the training set.
            transform: The image transform applied to each sample.
        """
        self.train = check_train(train)
        super().__init__(root=root, domain=domain, **kwargs)
        self.transform = transform

    def set_paths_and_labels(self, root):
        name = "train" if self.train else "test"
        labels_file = os.path.join(root, "officehome", f"{self.domain}_{name}.txt")
        img_dir = os.path.join(root, "officehome")

        with open(labels_file) as f:
            content = [line.rstrip().split(" ") for line in f]
        self.img_paths = [os.path.join(img_dir, x[0]) for x in content]
        check_img_paths(img_dir, self.img_paths, self.domain)
        check_length(
            self,
            {
                "art": {"train": 1941, "test": 486}[name],
                "clipart": {"train": 3492, "test": 873}[name],
                "product": {"train": 3551, "test": 888}[name],
                "real": {"train": 3485, "test": 872}[name],
            }[self.domain],
        )
        self.labels = [int(x[1]) for x in content]

__init__(root, domain, train, transform=None, **kwargs)

Parameters:

Name	Type	Description	Default
root	str	The dataset must be located at <root>/officehome	required
domain	str	One of "art", "clipart", "product", "real".	required
train	bool	Whether or not to use the training set.	required
transform		The image transform applied to each sample.	None

Source code in pytorch_adapt\datasets\officehome.py

def __init__(self, root: str, domain: str, train: bool, transform=None, **kwargs):
    """
    Arguments:
        root: The dataset must be located at ```<root>/officehome```
        domain: One of ```"art", "clipart", "product", "real"```.
        train: Whether or not to use the training set.
        transform: The image transform applied to each sample.
    """
    self.train = check_train(train)
    super().__init__(root=root, domain=domain, **kwargs)
    self.transform = transform

OfficeHomeFull

Bases: BaseDataset

A dataset consisting of 65 classes in 4 domains: art, clipart, product, and real.

Source code in pytorch_adapt\datasets\officehome.py

class OfficeHomeFull(BaseDataset):
    """
    A dataset consisting of 65 classes in 4 domains:
    art, clipart, product, and real.
    """

    def __init__(self, root: str, domain: str, transform):
        """
        Arguments:
            root: The dataset must be located at ```<root>/officehome```
            domain: One of ```"art", "clipart", "product", "real"```.
            transform: The image transform applied to each sample.
        """
        super().__init__(domain=domain)
        self.transform = transform
        self.dataset = torch_datasets.ImageFolder(
            os.path.join(root, "officehome", domain),
            transform=self.transform,
        )
        check_length(
            self, {"art": 2427, "clipart": 4365, "product": 4439, "real": 4357}[domain]
        )

    def __len__(self):
        return len(self.dataset)

    def __getitem__(self, idx):
        return self.dataset[idx]

__init__(root, domain, transform)

Parameters:

Name	Type	Description	Default
root	str	The dataset must be located at <root>/officehome	required
domain	str	One of "art", "clipart", "product", "real".	required
transform		The image transform applied to each sample.	required

Source code in pytorch_adapt\datasets\officehome.py

def __init__(self, root: str, domain: str, transform):
    """
    Arguments:
        root: The dataset must be located at ```<root>/officehome```
        domain: One of ```"art", "clipart", "product", "real"```.
        transform: The image transform applied to each sample.
    """
    super().__init__(domain=domain)
    self.transform = transform
    self.dataset = torch_datasets.ImageFolder(
        os.path.join(root, "officehome", domain),
        transform=self.transform,
    )
    check_length(
        self, {"art": 2427, "clipart": 4365, "product": 4439, "real": 4357}[domain]
    )