问题遇到的现象和发生背景
想问下为什么我添加了cuda后它还是跑不起来，提示我好像运算的时候同时用了cpu和gpu所以报错
问题相关代码，请勿粘贴截图
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 22 10:26:33 2022
@author: Blucoris Liang
"""
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
EPOCH=1
BATCH_SIZE=40
LR=0.001
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
'C:\\Users\\19544\\.spyder-py3\\leapGestRecog\\00',
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_loader = Data.DataLoader(
train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
test_loader = Data.DataLoader(
datasets.ImageFolder(
'C:\\Users\\19544\\.spyder-py3\\leapGestRecog\\03',
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=BATCH_SIZE, shuffle=False,)
# 数据集长度
train_data_size = len(train_dataset)
print('训练集的长度为:{}'.format(train_data_size))
model = models.resnet18(pretrained=True)
################################
if torch.cuda.is_available(): #
model = models.resnet18(pretrained=True).cuda() #
################################
model.fc = torch.nn.Linear(in_features=512, out_features=10, bias=True)
fc_params = list(map(id, model.fc.parameters())) # map函数是将fc.parameters()的id返回并组成一个列表
base_params = filter(lambda p: id(p) not in fc_params, model.parameters()) # filter函数是将model.parameters()中地址不在fc.parameters的id中的滤出来
optimizer = torch.optim.SGD([ {'params': base_params}, {'params': model.fc.parameters(), 'lr': LR * 100}], lr=LR,)
loss_func=nn.CrossEntropyLoss()
################################
if torch.cuda.is_available(): #
loss_func = loss_func.cuda() #
################################
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
def accuracy(output, target, topk=(1,)):
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
train_losses = AverageMeter('TrainLoss', ':.4e')
train_top1 = AverageMeter('TrainAccuracy', ':6.2f')
test_losses = AverageMeter('TestLoss', ':.4e')
test_top1 = AverageMeter('TestAccuracy', ':6.2f')
for epoch in range(EPOCH):
model.train()
for i,(images,target) in enumerate(train_loader):
################################
if torch.cuda.is_available(): #
images = images.cuda() #
target = target.cuda() #
################################
output=model(images)
loss= loss_func(output,target)
acc1, = accuracy(output, target, topk=(1,))
train_losses.update(loss.item(), images.size(0))
train_top1.update(acc1[0], images.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('Epoch[{}/{}],TrainLoss:{}, TrainAccuracy:{}'.format(epoch,EPOCH,train_losses.val, train_top1.val))
model.eval()
with torch.no_grad():
for i,(images,target) in enumerate(test_loader):
################################
if torch.cuda.is_available(): #
images = images.cuda() #
target = target.cuda() #
################################
output=model(images)
loss= loss_func(output,target)
acc1, = accuracy(output, target, topk=(1,))
test_losses.update(loss.item(), images.size(0))
test_top1.update(acc1[0], images.size(0))
print('TestLoss:{}, TestAccuracy:{}'.format(test_losses.avg, test_top1.avg))
import visdom
# 新建一个连接客户端
# 指定env = u'test1', 默认端口为8097，host是localhost
vis = visdom.Visdom(env = u'test1')
vis.line(X = train_top1.val , Y = train_losses.val)
运行结果及报错内容
runfile('C:/Users/19544/.spyder-py3/成功对手势识别用resnet进行了第一次训练.py', wdir='C:/Users/19544/.spyder-py3')
训练集的长度为:2000
Traceback (most recent call last):
File "C:\Users\19544\.spyder-py3\成功对手势识别用resnet进行了第一次训练.py", line 126, in <module>
output=model(images)
File "D:\ANACONDA\envs\MyEnv\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "D:\ANACONDA\envs\MyEnv\lib\site-packages\torchvision\models\resnet.py", line 283, in forward
return self._forward_impl(x)
File "D:\ANACONDA\envs\MyEnv\lib\site-packages\torchvision\models\resnet.py", line 278, in _forward_impl
x = self.fc(x)
File "D:\ANACONDA\envs\MyEnv\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "D:\ANACONDA\envs\MyEnv\lib\site-packages\torch\nn\modules\linear.py", line 103, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument mat1 in method wrapper_addmm)
我的解答思路和尝试过的方法
我尝试过在几处补上了cuda项，可是还是没有成功
我想要达到的结果
希望用cuda跑起来。
