co_classes.py

"""
Custom datasets from both in-memory and out-of-memory data
"""

import torch.utils.data as data

from PIL import Image
import os
import os.path

IMG_EXTENSIONS = [
    '.jpg', '.JPG', '.jpeg', '.JPEG',
    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]


def is_image_file(filename):
    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)


def find_classes(dir):
    classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))]
    classes.sort()
    class_to_idx = {classes[i]: i for i in range(len(classes))}
    return classes, class_to_idx


def make_dataset(dir, class_to_idx):
    images = []
    for target in os.listdir(dir):
        d = os.path.join(dir, target)
        if not os.path.isdir(d):
            continue

        for root, _, fnames in sorted(os.walk(d)):
            for fname in fnames:
                if is_image_file(fname):
                    path = os.path.join(root, fname)
                    item = (path, class_to_idx[target])
                    images.append(item)

    return images

def default_loader(path):
    return Image.open(path).convert('RGB')

class FolderDataset(data.Dataset):

    def __init__(self, 
                 root, 
                 transform=None, 
                 target_transform=None,
                 co_transform=None, 
                 loader=default_loader):
        classes, class_to_idx = find_classes(root)
        imgs = make_dataset(root, class_to_idx)
        if len(imgs) == 0:
            raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n"
                               "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))

        self.root = root
        self.imgs = imgs
        self.classes = classes
        self.class_to_idx = class_to_idx
        self.transform = transform
        self.target_transform = target_transform
        self.co_transform = co_transform
        self.loader = loader

    def __getitem__(self, index):
        path, target = self.imgs[index]
        img = self.loader(os.path.join(self.root, path))
        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None:
            target = self.target_transform(target)
        if self.co_transform is not None:
            img, target = self.co_transform(img, target)

        return img, target

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

class TensorDataset(data.Dataset):

    def __init__(self, 
                 input_tensor, 
                 target_tensor, 
                 transform=None,
                 target_transform=None, 
                 co_transform=None):
        assert input_tensor.size(0) == target_tensor.size(0)
        self.input_tensor = input_tensor
        self.target_tensor = target_tensor
        self.transform = transform
        self.target_transform = target_transform
        self.co_transform = co_transform

    def __getitem__(self, index):
        img, target = self.input_tensor[index], self.target_tensor[index]
        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None:
            target = self.target_transform(target)
        if self.co_transform is not None:
            img, target = self.co_transform(img, target)

        return img, target
    
    def __len__(self):
        return self.input_tensor.size(0)