pytorch一致数据增强

（2024.1.14）本文的例程有个错误：segmentation label 的 resize interpolation 应只能用 nearest 模式，否则可能会出现错误 label（如 3、5 二类插出 4 类，但那一片本来没有 4 类的 pixels），而本文例程用了默认的 bilinear。改进版 MultiCompose 见后作 [5]，通过支持各输入同用、独用 augmentation 修正此错误。

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分割任务对 image 做（某些）transform 时，要对 label（segmentation mask）也做对应的 transform，如 Resize、RandomRotation 等。如果对 image、label 分别用 transform 处理一遍，则涉及随机操作的可能不一致，如 RandomRotation 将 image 转了 a 度、却将 label 转了 b 度。

MONAI 有个 ArrayDataset 实现了这功能，思路是每次 transform 前都重置一次 random seed 先。对 monai 订制 transform 的方法不熟，torchvision.transforms 的订制接口比较简单，考虑基于 pytorch 实现。要改两个东西：

• 扩展 torchvison.transforms.Compose，使之支持多个输入（image、label）；
• 一个 wrapper，扩展 transform，使之支持多输入。

思路也是重置 random seed，参考 [1-4]。

Code

• to_multi：将处理单幅图的 transform 扩展成可处理多幅；
• MultiCompose：扩展 torchvision.transforms.Compose，可输入多幅图。内部调用 to_multi 扩展传入的 transforms。

import random, os
import numpy as np
import torch

def seed_everything(seed=42):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = True

def to_multi(trfm):
    """wrap a transform to extend to multiple input with synchronised random seed
    Input:
        trfm: transformation function/object (custom or from torchvision.transforms)
    Output:
        _multi_transform: function
    """
    # numpy.random.seed range error:
    #   ValueError: Seed must be between 0 and 2**32 - 1
    min_seed = 0 # - 0x8000_0000_0000_0000
    max_seed = min(2**32 - 1, 0xffff_ffff_ffff_ffff)
    def _multi_transform(*images):
        """images: [C, H, W]"""
        if len(images) == 1:
            return trfm(images[0])
        _seed = random.randint(min_seed, max_seed)
        res = []
        for img in images:
            seed_everything(_seed)
            res.append(trfm(img))
        return tuple(res)

    return _multi_transform


class MultiCompose:
    """Extension of torchvision.transforms.Compose that accepts multiple input.
    Usage is the same as torchvision.transforms.Compose. This class will wrap input
    transforms with `to_multi` to support simultaneous multiple transformation.
    This can be useful when simultaneously transforming images & segmentation masks.
    """
    def __init__(self, transforms):
        """transforms should be wrapped by `to_multi`"""
        self.transforms = [to_multi(t) for t in transforms]

    def __call__(self, *images):
        for t in self.transforms:
            images = t(*images)
        return images

test

测试一致性，用到预处理过的 verse’19 数据集、一些工具函数、一个订制 transform：

• verse’19 数据集及预处理见 iTomxy/data/verse；
• digit_sort_key：数据文件排序用；
• get_palette、color_seg、blend_seg：可视化用；
• MyDataset：看其中 __getitem__ 的 transform 用法，即同时传入 image 和 label；
• ResizeZoomPad：一个订制的 transform；

import os, os.path as osp, random
from glob import glob
import numpy as np
from PIL import Image
import torch
import torchvision.transforms as transforms
import torchvision.transforms.functional as F


def digit_sort_key(s, num_pattern=re.compile('([0-9]+)')):
    """natural sort，数据排序用"""
    return [int(text) for text in num_pattern.split(s) if text.isdigit()]


def get_palette(n_classes, pil_format=True):
    """创建调色盘，可视化用"""
    n = n_classes
    palette = [0] * (n * 3)
    for j in range(0, n):
        lab = j
        palette[j * 3 + 0] = 0
        palette[j * 3 + 1] = 0
        palette[j * 3 + 2] = 0
        i = 0
        while lab:
            palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
            palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
            palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
            i += 1
            lab >>= 3

    if pil_format:
        return palette

    res = []
    for i in range(0, len(palette), 3):
        res.append(tuple(palette[i: i+3]))
    return res


def color_seg(label, n_classes=0):
    """segmentation mask 上色，可视化用"""
    if n_classes < 1:
        n_classes = math.ceil(np.max(label)) + 1
    label_rgb = Image.fromarray(label.astype(np.int32)).convert("L")
    label_rgb.putpalette(get_palette(n_classes))
    return label_rgb.convert("RGB")


def blend_seg(image, label, n_classes=0, alpha=0.7, rescale=False, transparent_bg=True, save_file=""):
    """融合 image 和其 segmentation mask，可视化用"""
    if rescale:
        denom = image.max() - image.min()
        if 0 != denom:
            image = (image - image.min()) / denom * 255
        image = np.clip(image, 0, 255).astype(np.uint8)
    img_pil = Image.fromarray(image).convert("RGB")
    lab_pil = color_seg(label, n_classes)
    blended_image = Image.blend(img_pil, lab_pil, alpha)
    if transparent_bg:
        blended_image = Image.fromarray(np.where(
            (0 == label)[:, :, np.newaxis],
            np.asarray(img_pil),
            np.asarray(blended_image)
        ))
    if save_file:
        blended_image.save(save_file)
    return blended_image


class MyDataset(torch.utils.data.Dataset):
    """订制 dataset，看 __getitem__ 处 transform 的调法"""
    def __init__(self, image_list, label_list, transform=None):
        assert len(image_list) == len(label_list)
        self.image_list = image_list
        self.label_list = label_list
        self.transform = transform
    def __len__(self):
        return len(self.image_list)
    def __getitem__(self, index):
        img = np.load(self.image_list[index]) # [h, w]
        lab = np.load(self.label_list[index])
        img = torch.from_numpy(img).unsqueeze(0).float() # -> [c=1, h, w]
        lab = torch.from_numpy(lab).unsqueeze(0).int()
        if self.transform is not None:
            img, lab = self.transform(img, lab) # 同时传入 image、label
        return img, lab


class ResizeZoomPad:
    """订制 resize"""
    def __init__(self, size, interpolation="bilinear"):
        if isinstance(size, int):
            assert size > 0
            self.size = [size, size]
        elif isinstance(size, (tuple, list)):
            assert len(size) == 2 and size[0] > 0 and size[1] > 0
            self.size = size

        if isinstance(interpolation, str):
            assert interpolation.lower() in {"nearest", "bilinear", "bicubic", "box", "hamming", "lanczos"}
            interpolation = {
                "nearest": F.InterpolationMode.NEAREST,
                "bilinear": F.InterpolationMode.BILINEAR,
                "bicubic": F.InterpolationMode.BICUBIC,
                "box": F.InterpolationMode.BOX,
                "hamming": F.InterpolationMode.HAMMING,
                "lanczos": F.InterpolationMode.LANCZOS
            }[interpolation.lower()]
        self.interpolation = interpolation

    def __call__(self, image):
        """image: [C, H, W]"""
        scale_h, scale_w = float(self.size[0]) / image.size(1), float(self.size[1]) / image.size(2)
        scale = min(scale_h, scale_w)
        tmp_size = [ # clipping to ensure size
            min(int(image.size(1) * scale), self.size[0]),
            min(int(image.size(2) * scale), self.size[1])
        ]
        image = F.resize(image, tmp_size, self.interpolation)
        assert image.size(1) <= self.size[0] and image.size(2) <= self.size[1]
        pad_h, pad_w = self.size[0] - image.size(1), self.size[1] - image.size(2)
        if pad_h > 0 or pad_w > 0:
            pad_left, pad_right = pad_w // 2, (pad_w + 1) // 2
            pad_top, pad_bottom = pad_h // 2, (pad_h + 1) // 2
            image = F.pad(image, (pad_left, pad_top, pad_right, pad_bottom))
        return image


# 读数据文件
data_path = os.path.expanduser("~/data/verse/processed-verse19-npy-horizontal")
train_images, train_labels, val_images, val_labels = [], [], [], []
for d in os.listdir(osp.join(data_path, "training")):
    if d.endswith("_ct"):
        img_p = osp.join(data_path, "training", d)
        lab_p = osp.join(data_path, "training", d[:-3]+"_seg-vert_msk")
        assert osp.isdir(lab_p)
        train_labels.extend(glob(os.path.join(lab_p, "*.npy")))
        train_images.extend(glob(os.path.join(img_p, "*.npy")))
for d in os.listdir(osp.join(data_path, "validation")):
    if d.endswith("_ct"):
        img_p = osp.join(data_path, "validation", d)
        lab_p = osp.join(data_path, "validation", d[:-3]+"_seg-vert_msk")
        assert osp.isdir(lab_p)
        val_labels.extend(glob(os.path.join(lab_p, "*.npy")))
        val_images.extend(glob(os.path.join(img_p, "*.npy")))

# 数据文件名排序
train_images = sorted(train_images, key=lambda f: digit_sort_key(os.path.basename(f)))
train_labels = sorted(train_labels, key=lambda f: digit_sort_key(os.path.basename(f)))
val_images = sorted(val_images, key=lambda f: digit_sort_key(os.path.basename(f)))
val_labels = sorted(val_labels, key=lambda f: digit_sort_key(os.path.basename(f)))

# transform
# 用 MultiCompose，其内部调用 to_multi 将 transforms wrap 成支持多输入的
train_trans = MultiCompose([
    ResizeZoomPad((224, 256)),
    transforms.RandomRotation(30),
])

# 测试：读数据，可试化 image 和 label
check_ds = MyDataset(train_images, train_labels, train_trans)
check_loader = torch.utils.data.DataLoader(check_ds, batch_size=10, shuffle=True)
for images, labels in check_loader:
    print(images.size(), labels.size())
    for i in range(images.size(0)):
        # print(i, end='\r')
        img = images[i][0].numpy()
        lab = labels[i][0].numpy()
        print(np.unique(lab))
        img = (255 * (img - img.min()) / (img.max() - img.min())).astype(np.uint8)
        seg_img = blend_seg(img, lab)
        img = np.asarray(Image.fromarray(img).convert("RGB"))
        lab = np.asarray(color_seg(lab))
        comb = np.concatenate([img, lab, seg_img], axis=1)
        Image.fromarray(comb).save(f"test-dataset-{i}.png")
    break

效果：

可见，image 和 label 转了同一个随机角度。

Limits

有些 augmentations 是只对 image 做而不对 label 做的，如 ColorJitter，这里没有考虑怎么处理。

References

1. How to Set Random Seeds in PyTorch and Tensorflow
2. ihoromi4/seed_everything.py
3. Reproducibility
4. What is the max seed you can set up?
5. pytorch一致数据增强—独用增强