在python中使用 torchvision.transforms 将train_dataset中的图像大小调整为32x32
时间: 2024-09-20 22:12:55 浏览: 27
在PyTorch中,`torchvision.transforms`提供了一系列预定义的数据转换工具,用于对图像进行预处理。如果你想将`train_dataset`中的图像大小调整为32x32,你可以使用`ToTensor()`函数将图像转换为张量,并结合`Resize()`或`Compose()`来调整尺寸。这里是一个示例:
```python
from torchvision import transforms
# 创建一个变换器,包含大小调整到32x32的操作
transform = transforms.Compose([
transforms.Resize((32, 32)), # 或者 transforms.CenterCrop(32)
transforms.ToTensor(),
])
# 应用这个变换器到train_dataset
train_dataset.transformed = transform(train_dataset) # 假设train_dataset有一个transform属性接收变换
# 现在train_dataset里的图像都是32x32了
```
这里的`Resize()`会按比例缩放图片保持宽高比,如果想要裁剪图片然后填充至指定尺寸,可以用`CenterCrop()`。
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修改一下这段代码在pycharm中的实现,import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)
修改后的代码如下所示:
```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score
data_ = pd.read_csv(r"C:\Users\zhangjinyue\Desktop\rice.csv", header=None)
data_ = np.array(data_).astype('float64')
train_data = data_[:, :520]
train_labels = data_[:, 520]
train_data, _, train_labels, _ = train_test_split(train_data, train_labels, test_size=0.33333)
train_data = torch.Tensor(train_data)
train_labels = torch.LongTensor(train_labels)
train_data = train_data.reshape(-1, 1, 20, 26)
start_epoch = 1
num_epoch = 1
BATCH_SIZE = 70
Ir = 0.001
classes = ('0', '1', '2', '3', '4', '5')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
best_acc = 0.0
train_dataset = data.TensorDataset(train_data, train_labels)
test_dataset = data.TensorDataset(train_data, train_labels)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
```
请注意,我做了以下修改:
1. 移除了不需要的导入语句。
2. 修复了变量名拼写错误。
3. 移除了重复的代码行。
4. 修正了 `torch.utils.data.DataLoader` 的拼写错误。
5. 修正了数据集分割时的变量名错误。
请根据你的实际需求进一步调整代码。
这段python代码什么意思:import torch import torchvision import torchvision.transforms as transforms import PIL train_transform = transforms.Compose( [transforms.RandomHorizontalFlip(p=0.5), transforms.RandomAffine(degrees=(-5, 5), translate=(0.1, 0.1), scale=(0.9, 1.1), interpolation=PIL.Image.BILINEAR), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) test_transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transform) train_set, val_set = torch.utils.data.random_split(dataset, [40000, 10000]) train_loader = torch.utils.data.DataLoader(train_set, batch_size=128, shuffle=True, num_workers=8) val_loader = torch.utils.data.DataLoader(val_set, batch_size=128, shuffle=False, num_workers=8) test_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform) test_loader = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False, num_workers=8) classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
这段 Python 代码是用 PyTorch 和 torchvision 库来处理 CIFAR-10 数据集的。具体来说,它的作用是:
1. 导入所需的库和模块。这里包括了 PyTorch 和 torchvision 库,以及数据预处理模块 transforms 和 PIL(Python Imaging Library)库。
2. 定义了两个数据集的预处理方式。其中,train_transform 用于训练集数据的预处理,包括随机水平翻转、随机仿射变换、转化为张量并进行归一化;test_transform 用于测试集数据的预处理,只包括转化为张量并进行归一化。
3. 加载 CIFAR-10 数据集,并进行数据集划分。这里使用了 torchvision.datasets.CIFAR10() 函数来加载 CIFAR-10 数据集,root 参数指定数据集存放的根目录,train 参数指定是否加载训练集数据,download 参数指定是否下载数据集,transform 参数指定对数据进行的转换。接着使用 torch.utils.data.random_split() 函数将数据集划分为训练集和验证集,划分的比例为 4:1。
4. 定义了数据加载器。这里使用了 torch.utils.data.DataLoader() 函数来定义数据加载器,分别对训练集、验证集和测试集进行定义。其中,batch_size 参数指定批量大小,shuffle 参数指定是否打乱数据集顺序,num_workers 参数指定加载数据的线程数。
5. 定义了 CIFAR-10 数据集中的类别。这里定义了 10 个类别,分别是飞机、汽车、鸟、猫、鹿、狗、青蛙、马、船和卡车。