import torchvision
import torch
import torch.nn as nn
from torch import optim
import os
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import numpy as np
from collections import OrderedDict
import matplotlib.pyplot as plt
transformations=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
trainset=torchvision.datasets.CIFAR10(root='./CIFAR10',download=True,transform=transformations,train=True)
testset=torchvision.datasets.CIFAR10(root='./CIFAR10',download=True,transform=transformations,train=False)
trainloader=DataLoader(dataset=trainset,batch_size=4)
testloader=DataLoader(dataset=testset,batch_size=4)
inputs,labels=next(iter(trainset))
inputs.size()
resnet=torchvision.models.resnet50(pretrained=True)
if torch.cuda.is_available():
resnet=resnet.cuda()
inputs,labels=inputs.cuda(),torch.Tensor(labels).cuda()
outputs=resnet(inputs)
输出
--------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-6-904acb410fe4> in <module>()
6 inputs,labels=inputs.cuda(),torch.Tensor(labels).cuda()
7
----> 8 outputs=resnet(inputs)
5 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
344 _pair(0), self.dilation, self.groups)
345 return F.conv2d(input, weight, self.bias, self.stride,
--> 346 self.padding, self.dilation, self.groups)
347
348 def forward(self, input):
RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got 3-dimensional input of size [3, 32, 32] instead
由于某种原因，数据集是否存在问题，如果没有，我如何给出4维输入？ ResNet-50的Torchvision实现是否不能用于CIFAR-10？
我对PyTorch和神经网络一般还是陌生的。我正在尝试从CIFAR-10数据集中的torchvision实现resnet-50模型。导入torchvision导入torch导入torch.nn作为nn，从...