class YOLOLayer(nn.Module):FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor但是pycharm提示torch.cuda下面没有FloatTensor？

在 PyCharm 中，当你输入 `torch.cuda.FloatTensor` 时，可能会出现提示 `torch.cuda` 下没有 `FloatTensor` 的情况。这是因为在较新的 PyTorch 版本中，`FloatTensor` 类已被弃用，取而代之的是 `torch.cuda.FloatTensor`。

解决此问题的一种方法是将 `FloatTensor` 替换为 `torch.cuda.FloatTensor`，以匹配当前版本的 PyTorch。你可以这样修改代码：

import torch

cuda = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor

# 使用 FloatTensor 进行后续的操作

这样修改后，代码应该就不会再提示错误了。记得确保你已正确安装了 PyTorch 和 CUDA，并根据你的环境设置合适的 CUDA 编译器版本。

相关问题

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：

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。

代码为import torch import torch.nn as nn class STAE(nn.Module): def __init__(self): super(STAE, self).__init__() self.c1 = nn.Sequential( nn.Conv1d(1, 64, kernel_size=3, stride=1, padding=1), nn.BatchNorm1d(64), nn.MaxPool1d(kernel_size=2, stride=2), nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1), nn.BatchNorm1d(128), nn.MaxPool1d(kernel_size=2, stride=2), nn.Conv1d(128, 128, kernel_size=3, stride=1, padding=1), nn.BatchNorm1d(128), nn.MaxPool1d(kernel_size=2, stride=2), nn.Conv1d(128, 256, kernel_size=3, stride=1, padding=0), nn.BatchNorm1d(256), nn.MaxPool1d(kernel_size=2, stride=2), nn.Conv1d(256, 256, kernel_size=3, stride=1, padding=1), nn.BatchNorm1d(256), nn.MaxPool1d(kernel_size=2, stride=2), )#31*256 长*通道数（原先1000*1） self.r1 = nn.Sequential( nn.LSTM(input_size=30, hidden_size=64, batch_first=True), ) self.l1 = nn.Linear(64, 1) def forward(self,x): x=self.c1(x) x=self.r1(x) return x if __name__ == '__main__': a=torch.ones(1,1,1000) net=STAE() output=net(a) print(torch.Size(a))。代码出错Traceback (most recent call last): File "D:\Program Files\JetBrains\PyCharm 2023.1\PycharmProject\test\STAE.py", line 38, in <module> print(torch.Size(a)) TypeError: torch.Size() takes an iterable of 'int' (item 0 is 'Tensor')。请问如何修改

你需要修改代码中的print(torch.Size(a))这行代码，将其修改为print(a.size())即可。因为torch.Size()函数需要传入一个整数类型的可迭代对象，而a是一个Tensor类型的对象，所以会抛出TypeError异常。而a.size()函数可以直接返回a的形状信息，是一个torch.Size类型的对象，可以直接打印输出。