<reponame>NiteshBharadwaj/ignoringhumanpose
import os
from typing import Optional, Callable, Tuple, Any, List
import torchvision.datasets as datasets
from torchvision.datasets.folder import default_loader


class ImageList(datasets.VisionDataset):
    """A generic Dataset class for domain adaptation in image classification

    Parameters:
        - **root** (str): Root directory of dataset
        - **classes** (List[str]): The names of all the classes
        - **data_list_file** (str): File to read the image list from.
        - **transform** (callable, optional): A function/transform that  takes in an PIL image \
            and returns a transformed version. E.g, ``transforms.RandomCrop``.
        - **target_transform** (callable, optional): A function/transform that takes in the target and transforms it.

    .. note:: In `data_list_file`, each line 2 values in the following format.
        ::
            source_dir/dog_xxx.png 0
            source_dir/cat_123.png 1
            target_dir/dog_xxy.png 0
            target_dir/cat_nsdf3.png 1

        The first value is the relative path of an image, and the second value is the label of the corresponding image.
        If your data_list_file has different formats, please over-ride `parse_data_file`.
    """
    def __init__(self,
                 root: str,
                 classes: List[str],
                 data_list_file: str,
                 domain_idx: int,
                 transform: Optional[Callable] = None,
                 target_transform: Optional[Callable] = None):
        super().__init__(root,
                         transform=transform,
                         target_transform=target_transform)
        self.data = self.parse_data_file(data_list_file)
        self.classes = classes
        self.class_to_idx = {
            cls: idx
            for idx, clss in enumerate(self.classes) for cls in clss
        }
        self.loader = default_loader
        self.domain_idx = domain_idx

    def __getitem__(self, index: int) -> Tuple[Any, int]:
        """
        Parameters:
            - **index** (int): Index
            - **return** (tuple): (image, target) where target is index of the target class.
        """
        path, target = self.data[index]
        img = self.loader(path)
        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None and target is not None:
            target = self.target_transform(target)

        return img, target, self.domain_idx

    def __len__(self) -> int:
        return len(self.data)

    def parse_data_file(self, file_name: str) -> List[Tuple[str, int]]:
        """Parse file to data list

        Parameters:
            - **file_name** (str): The path of data file
            - **return** (list): List of (image path, class_index) tuples
        """
        with open(file_name, "r") as f:
            data_list = []
            for line in f.readlines():
                path, target = line.split()
                if not os.path.isabs(path):
                    path = os.path.join(self.root, path)
                target = int(target)
                data_list.append((path, target))
        return data_list

    @property
    def num_classes(self) -> int:
        """Number of classes"""
        return len(self.classes)

import os
from typing import Optional, Callable, Tuple, Any, List
import torchvision.datasets as datasets
from torchvision.datasets.folder import default_loader


class ImageList(datasets.VisionDataset):
    """A generic Dataset class for domain adaptation in image classification

    Parameters:
        - **root** (str): Root directory of dataset
        - **classes** (List[str]): The names of all the classes
        - **data_list_file** (str): File to read the image list from.
        - **transform** (callable, optional): A function/transform that  takes in an PIL image \
            and returns a transformed version. E.g, ``transforms.RandomCrop``.
        - **target_transform** (callable, optional): A function/transform that takes in the target and transforms it.

    .. note:: In `data_list_file`, each line 2 values in the following format.
        ::
            source_dir/dog_xxx.png 0
            source_dir/cat_123.png 1
            target_dir/dog_xxy.png 0
            target_dir/cat_nsdf3.png 1

        The first value is the relative path of an image, and the second value is the label of the corresponding image.
        If your data_list_file has different formats, please over-ride `parse_data_file`.
    """
    def __init__(self,
                 root: str,
                 classes: List[str],
                 data_list_file: str,
                 domain_idx: int,
                 transform: Optional[Callable] = None,
                 target_transform: Optional[Callable] = None):
        super().__init__(root,
                         transform=transform,
                         target_transform=target_transform)
        self.data = self.parse_data_file(data_list_file)
        self.classes = classes
        self.class_to_idx = {
            cls: idx
            for idx, clss in enumerate(self.classes) for cls in clss
        }
        self.loader = default_loader
        self.domain_idx = domain_idx

    def __getitem__(self, index: int) -> Tuple[Any, int]:
        """
        Parameters:
            - **index** (int): Index
            - **return** (tuple): (image, target) where target is index of the target class.
        """
        path, target = self.data[index]
        img = self.loader(path)
        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None and target is not None:
            target = self.target_transform(target)

        return img, target, self.domain_idx

    def __len__(self) -> int:
        return len(self.data)

    def parse_data_file(self, file_name: str) -> List[Tuple[str, int]]:
        """Parse file to data list

        Parameters:
            - **file_name** (str): The path of data file
            - **return** (list): List of (image path, class_index) tuples
        """
        with open(file_name, "r") as f:
            data_list = []
            for line in f.readlines():
                path, target = line.split()
                if not os.path.isabs(path):
                    path = os.path.join(self.root, path)
                target = int(target)
                data_list.append((path, target))
        return data_list

    @property
    def num_classes(self) -> int:
        """Number of classes"""
        return len(self.classes)
A generic Dataset class for domain adaptation in image classification      Parameters:         - **root** (str): Root directory of dataset         - **classes** (List[str]): The names of all the classes         - **data_list_file** (str): File to read the image list from.         - **transform** (callable, optional): A function/transform that  takes in an PIL image \             and returns a transformed version. E.g, ``transforms.RandomCrop``.         - **target_transform** (callable, optional): A function/transform that takes in the target and transforms it.      .. note:: In `data_list_file`, each line 2 values in the following format.         ::             source_dir/dog_xxx.png 0             source_dir/cat_123.png 1             target_dir/dog_xxy.png 0             target_dir/cat_nsdf3.png 1          The first value is the relative path of an image, and the second value is the label of the corresponding image.         If your data_list_file has different formats, please over-ride `parse_data_file`. Parameters:             - **index** (int): Index             - **return** (tuple): (image, target) where target is index of the target class. Parse file to data list          Parameters:             - **file_name** (str): The path of data file             - **return** (list): List of (image path, class_index) tuples Number of classes