class RandomCrop(object):

这是一个Python类，名为RandomCrop，可能是用于数据增强（data augmentation）的一个操作。具体而言，这个类可能会对输入的图像进行随机裁剪（random crop）操作，以便增加训练数据的多样性。在机器学习中，数据增强是一种常用的技术，它可以通过对原始数据进行一系列随机变换，从而生成更多的训练数据，提高模型的鲁棒性和泛化能力。

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class Trainer(object): def __init__(self, net, per_num=20, start_num=0, end_num=10, save_path="./model/Lwf", epoch=50, lr=0.0005, batch_size=128): self.lr = lr self.epoch = epoch self.batch_size = batch_size self.strat_num = start_num self.end_num = end_num self.class_num = end_num - start_num self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.save_path = save_path self.main_net_path = save_path + "/LwF_" + str(start_num) + ".pth" transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.RandomRotation(10), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) trainset = Cifar100Split(start_num=start_num, end_num=end_num, train=True, transform=transform_train) testset = Cifar100Split(start_num=start_num, end_num=end_num, train=False, transform=transform_test) test_all = Cifar100Split(start_num=0, end_num=end_num, train=False, transform=transform_test) self.train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0) self.test_loader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0) self.test_loader_all = DataLoader(test_all, batch_size=batch_size, shuffle=False, num_workers=0)

这段代码是一个PyTorch中的类Trainer的初始化函数。在初始化时，它接受一些参数，包括网络模型net、每个类别的训练样本数per_num、起始类别编号start_num、结束类别编号end_num、保存路径save_path、训练轮数epoch、学习率lr、批量大小batch_size等。此外，该类还定义了一些图像预处理的操作，包括随机裁剪、随机翻转、随机旋转等，并对训练集和测试集进行了划分和加载。该类的作用是训练深度神经网络模型以实现对图像数据的分类任务。

随机对比度调整 随机裁剪pytorch代码

随机对比度调整：

import torch
import torchvision.transforms.functional as F

class RandomContrast(object):
    def __init__(self, lower=0.5, upper=1.5):
        self.lower = lower
        self.upper = upper

    def __call__(self, x):
        contrast_factor = torch.FloatTensor(1).uniform_(self.lower, self.upper)
        return F.adjust_contrast(x, contrast_factor)

随机裁剪：

import random
import torchvision.transforms.functional as F

class RandomCrop(object):
    def __init__(self, size):
        self.size = size

    def __call__(self, x):
        w, h = x.size
        th, tw = self.size
        if w == tw and h == th:
            return x
        i = random.randint(0, h - th)
        j = random.randint(0, w - tw)
        return F.crop(x, i, j, th, tw)

使用方式：

transform = transforms.Compose([
    RandomCrop(224),
    RandomContrast(),
    transforms.ToTensor(),
    transforms.Normalize(mean, std)
])

dataset = datasets.ImageFolder(root='path/to/data', transform=transform)