Shortcuts

Source code for tllib.vision.datasets.regression.image_regression

"""
@author: Junguang Jiang
@contact: JiangJunguang1123@outlook.com
"""
import os
from typing import Optional, Callable, Tuple, Any, List, Sequence
import torchvision.datasets as datasets
from torchvision.datasets.folder import default_loader
import numpy as np


[docs]class ImageRegression(datasets.VisionDataset): """A generic Dataset class for domain adaptation in image regression Args: root (str): Root directory of dataset factors (sequence[str]): Factors selected. Default: ('scale', 'position x', 'position y'). 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, :class:`torchvision.transforms.RandomCrop`. target_transform (callable, optional): A function/transform that takes in the target and transforms it. .. note:: In `data_list_file`, each line has `1+len(factors)` values in the following format. :: source_dir/dog_xxx.png x11, x12, ... source_dir/cat_123.png x21, x22, ... target_dir/dog_xxy.png x31, x32, ... target_dir/cat_nsdf3.png x41, x42, ... The first value is the relative path of an image, and the rest values are the ground truth of the corresponding factors. If your data_list_file has different formats, please over-ride :meth:`ImageRegression.parse_data_file`. """ def __init__(self, root: str, factors: Sequence[str], data_list_file: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None): super().__init__(root, transform=transform, target_transform=target_transform) self.samples = self.parse_data_file(data_list_file) self.factors = factors self.loader = default_loader self.data_list_file = data_list_file def __getitem__(self, index: int) -> Tuple[Any, Tuple[float]]: """ Args: index (int): Index Returns: (image, target) where target is a numpy float array. """ path, target = self.samples[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 def __len__(self) -> int: return len(self.samples)
[docs] def parse_data_file(self, file_name: str) -> List[Tuple[str, Any]]: """Parse file to data list Args: file_name (str): The path of data file Returns: List of (image path, (factors)) tuples """ with open(file_name, "r") as f: data_list = [] for line in f.readlines(): data = line.split() path = str(data[0]) target = np.array([float(d) for d in data[1:]], dtype=np.float) if not os.path.isabs(path): path = os.path.join(self.root, path) data_list.append((path, target)) return data_list
@property def num_factors(self) -> int: return len(self.factors)

Docs

Access comprehensive documentation for Transfer Learning Library

View Docs

Tutorials

Get started for Transfer Learning Library

Get Started

Paper List

Get started for transfer learning

View Resources