Source code for common.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)
