如圖所示卜高，做多類別分類戈泼，每個文件夾代表一個類別，所有圖像均為NIFTI格式眼滤，如何加載進 MONAI 進行訓練巴席？

在這之前，我們來看看 MONAI dataset 加載方法：

MONAI dataset 的數(shù)據(jù)(image, label)輸入有兩種形式诅需，一種是 array(數(shù)組)漾唉， 一種是dict(字典)。

簡單區(qū)分一下

以 array 形式加載數(shù)據(jù)

images = [
        "IXI314-IOP-0889-T1.nii.gz",
        "IXI249-Guys-1072-T1.nii.gz",
        "IXI609-HH-2600-T1.nii.gz",
        "IXI173-HH-1590-T1.nii.gz",
        "IXI020-Guys-0700-T1.nii.gz",
    ]

labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)

train_ds = ImageDataset(image_files=images, labels=labels, transform=train_transforms)
train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2, pin_memory=torch.cuda.is_available())

從代碼里很容易看到诱担，images 和 labels 都是 array, 直接作為 ImageDataset 的參數(shù)就行毡证。

以 dict 形式加載數(shù)據(jù)

images = [
        "IXI314-IOP-0889-T1.nii.gz",
        "IXI249-Guys-1072-T1.nii.gz",
        "IXI609-HH-2600-T1.nii.gz",
        "IXI173-HH-1590-T1.nii.gz",
        "IXI020-Guys-0700-T1.nii.gz",
    ]

labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)

train_files = [{"img": img, "label": label} for img, label in zip(images, labels)]
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, pin_memory=torch.cuda.is_available())

這里 images 和 labels 都是 array, 只不過最后會把他們打包成一個字典，使得每個樣本的 image和label相對應(yīng)起來蔫仙。然后傳給 Dataset料睛。

所以，回到最初的問題摇邦，不管用array形式還是dict形式恤煞，我們都需要構(gòu)建一個 images/labels, 其中images里面是每個image的地址，如果是分類問題施籍，labels是每個圖像的類別居扒， 如果是分割問題，則是ground truth的地址丑慎。

進一步的問題是：如何給文件夾的每個圖像定義label喜喂？
當然，這在torchvision中竿裂，有一個函數(shù)可以輕松搞定玉吁！

但是！他的缺點是不可以加載后綴為gz的文件腻异，但是醫(yī)學圖像大部分都是三維圖像进副，后綴為nii.gz，怎么辦悔常？影斑？？

我們可以借鑒他的思路机打，自己寫一個支持 .gz文件的不就好了矫户。

說干就干

第一種：直接修改源代碼

查看源碼，它不支持 gz的主要原因是它指定了后綴為下面這些??

IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')

因為不包含gz姐帚，所以不支持吏垮。

源碼在torchvision/datasets/folder.py

那一種簡單粗暴地方法就是直接修改 IMG_EXTENSIONS障涯，在后面加一個 '.gz',就可以使用了。

使用案例：

from torchvision.datasets import ImageFolder
data_root = '/dataset'
dataset = ImageFolder(root=data_root)
classes = dataset.classes  # 獲得類別名稱（文件夾的名字）
class_to_idx = dataset.class_to_idx # 獲得類別對應(yīng)的索引或標簽
images_labels = dataset.imgs
images = [tup[0] for tup in images_labels] # array
labels = [tup[1] for tup in images_labels] # array

# for dict
train_files = [{'image': tup[0], 'label': tup[1]} for tup in images_labels] # dict

然后就可以傳到上述兩種dataset了膳汪，完美解決????

但是這種方法對源代碼造成了破壞唯蝶，不易移植，雖然簡單粗暴遗嗽，但是不推薦Ｕ澄摇！

我們可以根據(jù)他的思路自己寫一個

第二種：構(gòu)建自己的ImageFolder

構(gòu)建思路：

• step 1 獲取文件夾名稱作為classes痹换，并給它標簽征字。

def find_classes(directory: str):
    """Finds the class folders in a dataset.
    """
    classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
    if not classes:
        raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")

    class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
    return classes, class_to_idx

[圖片上傳失敗...(image-e2d7b2-1663059142560)]

• step 2 遍歷文件夾，賦予每個圖像標簽
  在這一步中娇豫，我們會檢查每個圖像的后綴匙姜。

img_label_dict = []
imgs = []
labels = []
for target_class in sorted(class_to_idx.keys()):
  class_index = class_to_idx[target_class] 
  target_dir = os.path.join(directory, target_class)
  if not os.path.isdir(target_dir):
    continue
  for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):
      for fname in sorted(fnames):
        if is_valid_file(fname): # 判斷后綴是否有效
            path = os.path.join(root, fname)
            item = {'img': path, 'label': class_index}
            img_label_dict.append(item)
            imgs.append(path)
            labels.append(class_index)

這是關(guān)鍵代碼，不全冯痢。

最后貼上完整代碼

import os
from typing import Any, Callable, cast, Dict, List, Optional, Tuple


# 從 data 根目錄自動獲取不同的類別文件夾氮昧，并自動給文件夾標簽
def find_classes(directory: str):
    """Finds the class folders in a dataset.
    """
    classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())
    if not classes:
        raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")

    class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
    return classes, class_to_idx


# 檢查 file 的后綴是不是在允許的擴展中
def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:
    """Checks if a file is an allowed extension.

    Args:
        filename (string): path to a file
        extensions (tuple of strings): extensions to consider (lowercase)

    Returns:
        bool: True if the filename ends with one of given extensions
    """
    return filename.lower().endswith(extensions)


# 從根目錄中獲取 圖像的類別，以及自動為類別設(shè)置類標簽浦楣，返回【圖像-標簽對袖肥， 類別名， 類別對應(yīng)的索引等】
def make_dataset(
    directory: str,
    class_to_idx: Optional[Dict[str, int]] = None,
    extensions: Optional[Tuple[str, ...]] = None,
    is_valid_file: Optional[Callable[[str], bool]] = None,
) -> List[Tuple[str, int]]:
    """Generates a list of samples of a form (path_to_sample, class).
    """
    directory = os.path.expanduser(directory)

    if class_to_idx is None:
        classes, class_to_idx = find_classes(directory)
    elif not class_to_idx:
        raise ValueError("'class_to_index' must have at least one entry to collect any samples.")

    both_none = extensions is None and is_valid_file is None
    both_something = extensions is not None and is_valid_file is not None
    if both_none or both_something:
        raise ValueError("Both extensions and is_valid_file cannot be None or not None at the same time")

    if extensions is not None:

        def is_valid_file(x: str) -> bool:
            return has_file_allowed_extension(x, cast(Tuple[str, ...], extensions))

    is_valid_file = cast(Callable[[str], bool], is_valid_file)

    img_label_dict = []
    imgs = []
    labels = []
    available_classes = set()
    for target_class in sorted(class_to_idx.keys()):
        class_index = class_to_idx[target_class]
        target_dir = os.path.join(directory, target_class)
        if not os.path.isdir(target_dir):
            continue
        for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):
            for fname in sorted(fnames):
                if is_valid_file(fname):
                    path = os.path.join(root, fname)
                    item = {'img': path, 'label': class_index}
                    img_label_dict.append(item)
                    imgs.append(path)
                    labels.append(class_index)

                    if target_class not in available_classes:
                        available_classes.add(target_class)

    empty_classes = set(class_to_idx.keys()) - available_classes
    if empty_classes:
        msg = f"Found no valid file for the classes {', '.join(sorted(empty_classes))}. "
        if extensions is not None:
            msg += f"Supported extensions are: {', '.join(extensions)}"
        raise FileNotFoundError(msg)

    return img_label_dict, imgs, labels, classes, class_to_idx


if __name__ == '__main__':
    data_root = 'dataset'
    # classes, class_to_idx = find_classes(data_root)
    # 允許的擴展名
    extensions = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp', '.gz')
    img_label_dict, imgs, labels, classes, class_to_idx= make_dataset(data_root, extensions=extensions)

完結(jié)~