train_data = datasets.ImageFolder(training_dir, transform=transform) test_data = datasets.ImageFolder(validation_dir, transform=transform) batch_size = 48 train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=True) print() print('Size of Training dataset: ', (len(train_loader.dataset))) print('Size of Testing dataset: ', (len(test_loader.dataset))) print()

这段代码定义了训练和测试数据集，并设置了batch_size为48。通过datasets.ImageFolder函数读取图片，并通过transform参数对图片进行预处理（如resize、归一化等）。接着，通过torch.utils.data.DataLoader函数将数据集转换成可迭代的数据加载器，用于训练和测试模型。最后，输出训练集和测试集的大小。

相关问题

帮我把这段代码从tensorflow框架改成pytorch框架： import tensorflow as tf import os import numpy as np import matplotlib.pyplot as plt os.environ["CUDA_VISIBLE_DEVICES"] = "0" base_dir = 'E:/direction/datasetsall/' train_dir = os.path.join(base_dir, 'train_img/') validation_dir = os.path.join(base_dir, 'val_img/') train_cats_dir = os.path.join(train_dir, 'down') train_dogs_dir = os.path.join(train_dir, 'up') validation_cats_dir = os.path.join(validation_dir, 'down') validation_dogs_dir = os.path.join(validation_dir, 'up') batch_size = 64 epochs = 50 IMG_HEIGHT = 128 IMG_WIDTH = 128 num_cats_tr = len(os.listdir(train_cats_dir)) num_dogs_tr = len(os.listdir(train_dogs_dir)) num_cats_val = len(os.listdir(validation_cats_dir)) num_dogs_val = len(os.listdir(validation_dogs_dir)) total_train = num_cats_tr + num_dogs_tr total_val = num_cats_val + num_dogs_val train_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) validation_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size, directory=train_dir, shuffle=True, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size, directory=validation_dir, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') sample_training_images, _ = next(train_data_gen) model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(2, activation='softmax') ]) model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=['accuracy']) model.summary() history = model.fit_generator( train_data_gen, steps_per_epoch=total_train // batch_size, epochs=epochs, validation_data=val_data_gen, validation_steps=total_val // batch_size ) # 可视化训练结果 acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs_range = range(epochs) model.save("./model/timo_classification_128_maxPool2D_dense256.h5")

import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torchvision import datasets, transforms import os BATCH_SIZE = 64 EPOCHS = 50 IMG_HEIGHT = 128 IMG_WIDTH = 128 train_transforms = transforms.Compose([ transforms.Resize((IMG_HEIGHT,IMG_WIDTH)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])]) test_transforms = transforms.Compose([ transforms.Resize((IMG_HEIGHT,IMG_WIDTH)), transforms.ToTensor(), transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])]) base_dir = 'E:/direction/datasetsall/' train_dir = os.path.join(base_dir, 'train_img/') validation_dir = os.path.join(base_dir, 'val_img/') train_cats_dir = os.path.join(train_dir, 'down') train_dogs_dir = os.path.join(train_dir, 'up') validation_cats_dir = os.path.join(validation_dir, 'down') validation_dogs_dir = os.path.join(validation_dir, 'up') train_dataset = datasets.ImageFolder(train_dir, transform=train_transforms) train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True) test_dataset = datasets.ImageFolder(validation_dir, transform=test_transforms) test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = nn.Sequential( nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Flatten(), nn.Linear(64 * (IMG_HEIGHT // 8) * (IMG_WIDTH // 8), 256), nn.ReLU(), nn.Linear(256, 2), nn.Softmax(dim=1) ) model.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) for epoch in range(EPOCHS): train_loss = 0.0 train_acc = 0.0 model.train() for images, labels in train_loader: images = images.to(device) labels = labels.to(device) optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() train_loss += loss.item() * images.size(0) _, preds = torch.max(outputs, 1) train_acc += torch.sum(preds == labels.data) train_loss = train_loss / len(train_loader.dataset) train_acc = train_acc / len(train_loader.dataset) print('Epoch: {} \tTraining Loss: {:.6f} \tTraining Accuracy: {:.6f}'.format(epoch+1, train_loss,train_acc)) with torch.no_grad(): test_loss = 0.0 test_acc = 0.0 model.eval() for images, labels in test_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) loss = criterion(outputs, labels) test_loss += loss.item() * images.size(0) _, preds = torch.max(outputs, 1) test_acc += torch.sum(preds == labels.data) test_loss = test_loss / len(test_loader.dataset) test_acc = test_acc / len(test_loader.dataset) print('Test Loss: {:.6f} \tTest Accuracy: {:.6f}'.format(test_loss,test_acc))

我文件夹里有5个子文件夹。是全部数据集。如何结合ImageFolder重写Datasets类

首先，为了将5个子文件夹作为数据集处理，并结合`torchvision.datasets.ImageFolder`的功能，你需要创建一个自定义的`Datasets`类。`ImageFolder`是一个现成的工具，它已经实现了按照目录结构自动识别图片并将其划分为训练、验证和测试集等功能。

下面是如何基于`ImageFolder`重写`Datasets`类的一个简化示例：

from torch.utils.data import Dataset
from torchvision.datasets import ImageFolder
from torchvision import transforms

class CustomImageDataset(Dataset):
    def __init__(self, root_dir, train=True, transform=None):
        # 将根目录改为包含5个子文件夹的路径
        self.root_dir = root_dir
        self.image_folder = ImageFolder(root=self.root_dir, transform=transform)
        
        # 如果train参数未指定，默认假设所有子文件夹都是训练数据
        if train is True:
            self.subfolders = ['subfolder1', 'subfolder2', 'subfolder3', 'subfolder4', 'subfolder5']
        else:
            raise ValueError("Please specify whether it's for training or testing, as only one subfolder should be used for validation/test")

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

    def __getitem__(self, idx):
        path, target = self.image_folder[idx]
        return {'image': path, 'label': target}

# 使用自定义数据集
train_dataset = CustomImageDataset(root_dir='your_directory_path', train=True, transform=transforms.ToTensor())

在这个例子中，`CustomImageDataset`类继承了`Dataset`，并且在初始化时指定了根目录。你可以根据需要调整`subfolders`列表以指定训练、验证或测试的数据子文件夹。`__getitem__`方法返回的是每张图像及其对应的标签。

请注意，由于没有具体说明是用于训练还是评估，这个示例默认所有子文件夹都用于训练。如果你想要区分训练集和验证集，可以在构造函数中添加额外条件，并从单个子文件夹中加载验证数据。